Split (1)

train · 22.3k rows

title_s stringlengths 2 79	title_dl stringlengths 0 200	source_url stringlengths 13 64	authors listlengths 0 10	snippet_s stringlengths 0 291	text stringlengths 21 100k	date timestamp[ns]date 1926-02-14 00:00:00 2030-07-14 00:00:00	publish_date_dl stringlengths 0 10	url stringlengths 15 590	matches listlengths 1 278
The Future of Work with AI Agents - from Stanford University	The Future of Work with AI Agents - from Stanford University	https://www.linkedin.com	[ "Achyut Menon", "Ak", "Executive Search Expert", "Career Transition Coach", "I Help Nris Find Jobs In India", "Transforming Global Leadership Teams", "Shrm India", "Ethrworld Influencers Club", "Empowering", "Mentoring Startups" ]	Stanford Lab on AI and the Future of Work · Future of Work with AI Agents · More from this author · Explore topics.	The Future of Work with AI Agents - from Stanford University https://lnkd.in/geDp8Beg The Stanford Digital Economy Lab's "Future of Work" project highlights how technology, particularly AI and automation, is fundamentally reshaping the workplace, demanding a critical look at both the opportunities and challenges for workers and businesses. The research emphasizes that while innovation drives productivity, we must proactively address issues like skills gaps, job displacement, and the need for new educational frameworks to ensure equitable prosperity. Ultimately, navigating the future of work successfully requires a human-centered approach, focusing on continuous learning, adaptability, and designing technologies that augment human capabilities rather than simply replacing them. Rob Tiffany⚡️ Sean Littleton	2025-06-01T00:00:00		https://www.linkedin.com/posts/shyamvaran_future-of-work-with-ai-agents-activity-7340760640728330241-rxp6	[ { "date": "2025/06/01", "position": 34, "query": "future of work AI" } ]
Automation and the Future of Work: Scenarios and Policy ...	Automation and the Future of Work: Scenarios and Policy Options	https://www.cigionline.org	[ "Joel Blit", "Samantha St. Amand", "Joanna Wajda" ]	by J Blit · 2025 · Cited by 26 — This paper presents several possible scenarios for the future of work and draws on the Industrial Revolution to offer a historical perspective.	Driven by the exponential growth in computing power and the digitization of things, artificial intelligence and robotics are poised to transform the economy. While these technologies are likely to boost productivity and generate significant wealth, their potential impact on the labour market is concerning, with some estimates suggesting that nearly half of all existing jobs could be automated in the next two decades. What is almost certain is that these technologies will further increase inequality: workers with skills that are complementary to these new technologies will benefit, while those with skills that are substitutes will face dimming job prospects. The extent and speed of the transformation remains uncertain. This paper presents several possible scenarios for the future of work and draws on the Industrial Revolution to offer a historical perspective. It ends with a discussion of different policy options that could be deployed. Foremost, it highlights the urgent need for further international collaboration to broaden the tax base, both because tax avoidance is likely to become a bigger problem as wealth and income become increasingly concentrated and mobile and because of the likely need to expand the social safety net in the face of potentially massive and long-lasting disruptions.	2018-05-29T00:00:00	2018/05/29	https://www.cigionline.org/publications/automation-and-future-work-scenarios-and-policy-options/	[ { "date": "2025/06/01", "position": 38, "query": "future of work AI" } ]
AI Isn't Taking Jobs—It's Making Workers More Valuable	AI Isn’t Taking Jobs—It’s Making Workers More Valuable	https://theaieconomy.substack.com	[ "Ken Yeung" ]	PwC's data offers some reassurance that the AI jobpocalypse isn't inevitable. At the same time, it signals that the future of work is no longer a distant ...	IN THIS ISSUE: Unpack the AI jobs paradox—why AI isn’t replacing workers en masse (yet), but is already reshaping the workforce in profound ways. PwC’s AI jobs barometer reveals who’s thriving, who’s at risk, and why upskilling is urgent. Plus, explore Salesforce’s latest power move to lock down Slack data and what it means for the future of AI agents in the enterprise. The Prompt The AI jobpocalypse that people have been warning about? It’s not here…yet, but the technology is already reshaping the workforce. Rather than the mass elimination of employment prophesied, AI is making workers more valuable in this ever-increasing automated world. That’s according to PwC’s 2025 Global AI Jobs Barometer, released earlier this month, which draws on an analysis of nearly one billion job postings worldwide. “This research shows that the power of AI to deliver for businesses is already being realised,” Carol Stubbings, the firm’s global chief commercial officer, says in a statement. “We are only at the start of the transition. As we roll out Agentic AI at enterprise scale, we are seeing that the right combination of technology and culture can create dramatic new opportunities to reimagine how organisations work and create value.” The debate over the impact of AI on employment remains a highly contentious issue. Critics argue that the technology will lead to mass unemployment and increased income inequality. Recently, Anthropic’s CEO Dario Amodei warned that AI has the potential to eliminate “half” of all entry-level white-collar jobs and could “spike” the unemployment rate to nearly 20 percent within the next five years. Proponents, such as Nvidia CEO Jensen Huang, are pushing back, conceding that while AI will result in job displacement, it could also introduce new creative opportunities. He argues that to overcome these fears, AI development must be done “in the open.” And it’s easy to believe the AI naysayers. Just look at Klarna, Duolingo, IKEA, Salesforce, and Shopify; these are examples of companies that have chosen to eliminate jobs done by human workers in favor of AI. Some have walked back their positions, but most are still betting on AI being central to their operations. Despite the warnings, PwC’s data tells a different story: AI is creating more opportunities—for the workers who can keep up. The demand for AI-skilled employees is rising rapidly. In 2024, wages for these professionals jumped by an average of 56 percent, job postings calling for those skills increased by 38 percent, and industries most exposed to AI generated three times more revenue per worker than those least exposed. These percentages signal a fundamental shift in how value is created in the modern economy. AI should no longer be considered a productivity booster; it’s now a key differentiator between high-growth industries and those falling behind. You know how every company says that AI handles the mundane tasks and frees employees up to tackle more creative and strategic work? PwC’s research appears to prove AI is doing just that, leading to increased work growth. Humans Still Wanted, Even For Automated Roles It’s worth noting that PwC examines two different spaces: Those “most exposed” and those “least exposed” to AI. The distinction is significant because one would expect greater disruption with the former than with the latter. However, the research indicates that companies are hiring more workers in occupations exposed to AI, even in those deemed “highly automatable.” Although job postings for AI-skilled workers have increased by 38 percent, that growth lags behind the 65 percent rise seen in occupations with lower AI exposure during the same period. This seems to suggest that even roles targeted for AI automation (where AI handles the work) and augmentation (where AI assists in the job to be done) can’t be entirely human-free. The distinction between AI automation and augmentation is crucial. It reflects the reality that AI isn’t a one-size-fits-all solution. There are many tasks where humans are better suited to complete than an AI application. As AI continues to advance, the key will be finding the right balance between human and machine capabilities to maximize productivity and create value. That said, the report acknowledges that this is a complex issue. PwC admits that some jobs, such as data entry clerks and software coders, may no longer exist in their previous forms, while others could evolve into “higher value roles.” The report’s authors believe that ultimately, the critical questions society must ask itself are if: jobs are created faster than they are displaced and if people have the skills needed to adapt to this evolving job market. So what roles are companies hiring for? The research identifies that health, teaching, legal, social, and cultural professionals are on the rise, along with those in business and sales. However, there’s a decline in job openings for general and keyboard clerks, and also IT professionals. PwC’s findings appear to be matched by the recent trends report released by Bond Capital’s Mary Meeker. In sourcing data from the University of Maryland and LinkUp, she highlights that AI-related job postings in the U.S. have increased over the past seven years. Together, these reports reinforce the idea that AI’s long-term labor impact is more evolution than extinction. The AI Skills Earthquake Intensifies While the findings paint a positive picture for the workforce, they also underscore the urgent need for workers to reskill and upskill to stay competitive in the AI era. This “adapt or risk being left behind” mindset is driving the growth of AI-specific training programs from providers such as Salesforce, LinkedIn, Microsoft, and ServiceNow. As AI reshapes the nature of work, the skills traditionally associated with many roles, particularly those heavily exposed to AI, are evolving. PwC finds that the need for formal degrees is declining for these roles: Between 2019 and 2024, there’s a seven percent decrease in jobs AI augments (66 percent then vs. 59 percent now) and a nine percent drop for jobs AI automates (53 percent then vs. 44 percent now). “AI’s rapid advance is not just re-shaping industries, but fundamentally altering the workforce and the skills required,” PwC’s Global Workforce Leader, Pete Brown, asserts. “This is not a situation that employers can easily buy their way out of. Even if they can pay the premium required to attract talent with AI skills, those skills can quickly become out of date without investment in the systems to help the workforce learn.” Calls for companies to reskill their workforce are nothing new, but they remain critical as AI adoption accelerates. Still, PwC stops short of outlining which specific skills workers should focus on. That may be by design—reskilling needs may vary widely by industry and role. Yet, without clearer guidance, many employers and employees are left navigating this shift without a roadmap. As AI transforms job requirements across the board, knowing where to begin could make the difference between adaptation and obsolescence. The research also calls out the potential for AI-related gender inequality. PwC claims that in every country it analyzed, women outweighed men in AI-exposed roles, which led it to conclude that the skills pressure facing women will be higher. In other words, women are likely to be disproportionately affected by AI’s disruption of their role, but could struggle to adapt to the changing workforce. The firm cites its 2024 Workforce Radar study, which suggested that in the U.S., women lag men in adopting AI. As such, PwC deduces that women will need to work much harder to gain the necessary AI skills to remain competitive. Recommended Next Steps Now armed with this information, what should business leaders be doing with it? PwC outlines five things to do: Use AI as part of the enterprise-wide transformation Companies in AI-exposed industries need to develop a plan that capitalizes on the threefold increase in revenue generated by employees due to AI adoption. PwC urges organizations to think beyond isolated use cases and instead use AI to generate value at an enterprise-wide level. Treat AI as a growth strategy, not just an efficiency strategy AI should not be used as a reason to reduce headcount. Business leaders should instead leverage the technology to help workers create more value. How can it help identify new markets and revenue streams? Consider agentic AI as an exponential workforce multiplier AI agents are the talk of the town these days, and PwC believes they’ll not only help business leaders cut costs but can also free up workers to think better and respond faster than competitors. In other words, act like a frontier firm would behave and embrace the digital labor reality. Empower your workforce with the skills to make the most of AI Companies must determine the skills their workers need to learn and develop a plan for modernizing their workforce. Maximize AI’s transformative potential by building trust To ensure maximum value can be generated from AI, companies can’t rely on technical success. It includes responsible deployment, clear governance, and public and organizational trust. PwC emphasizes that its second annual Global AI Jobs Barometer is neither meant to convey a tech utopian nor a doomsayer viewpoint. Instead, it seeks to point out that "with intentional design…AI can empower workers, raise productivity, and increase shared prosperity.” While its findings suggest AI is more likely to augment than eliminate jobs in the near term, the ripple effects are just beginning to surface. PwC’s data offers some reassurance that the AI jobpocalypse isn’t inevitable. At the same time, it signals that the future of work is no longer a distant concept—it’s already unfolding. How businesses, workers, and policymakers respond now will shape who gains the most from this transformation. ▶️ Read PwC’s Global AI Jobs Barometer Don’t Miss out on Future Issues of ‘The AI Economy’ The AI Economy is expanding! While you've been getting weekly insights on LinkedIn, I’m gearing up to bring you even more—deep dives into AI breakthroughs, more interviews with industry leaders and entrepreneurs, and in-depth looks at the startups shaping the future. To ensure you don’t miss a thing, subscribe now on Substack, where we’ll be rolling out more frequent updates. A Closer Look Long billed as the place “where work happens,” Slack has grown into a mission-critical layer of enterprise communication. After more than ten years, it’s hard to imagine many teams functioning without it. It’s also a treasure trove of data prime for use in AI agents. However, only its parent company, Salesforce, will have the privilege of accessing it, thanks to updated terms of service prohibiting third-party developers from indexing, copying, or permanently storing Slack messages through the platform’s API. In an undated blog post noticed by The Information, Slack claims its “evolved” API strategy is intended to protect customer data ”in line with our longstanding commitments to security, privacy, and responsible processing of data.” Its ToS currently stipulates that developers may not use any Slack data to train large language models. Moreover, any data must be stored temporarily and cannot be copied, archived, or indexed. The new restrictions will undoubtedly hobble many tech firms with products that integrate with Slack. More specifically, it’s a blow to those dependent on the productivity app’s data to power their AI agents. One such company impacted is Glean. A customer email obtained by The Information notes that these changes will bar Slack data from being added to Glean’s search index, “hampering your ability to use your data with your chosen enterprise AI platform.” It could also affect AI agents from Slack partners, such as Dropbox’s Dash, Docusign’s IAM platform, and Atlassian’s Rovo. “At Salesforce, trust is our number one value, and that starts with protecting customer data. The new innovations we’ve announced, along with updates to Slack’s API Terms of Service, open the door for more intelligent, context-aware AI experiences and ensure that developers, customers, and partners can securely and responsibly interact with Slack data,” a Salesforce spokesperson tells Salesforce Ben. They go on to say, “As AI raises critical considerations around how customer data is handled, we’re committed to delivering AI and data services in a way that’s thoughtful and transparent. We’re working closely with partners and developers who share these values, ensuring customers can adopt these new capabilities with confidence and control.” While Salesforce frames the move as a security measure, it also reflects a deeper ambition: to consolidate control over enterprise data. Both can be true. This change aligns with other steps, such as its $8 billion acquisition of Informatica and its $6.5 billion deal for MuleSoft, underscoring a broader strategy to monopolize access to first-party data across the enterprise stack and creating better value for those building AI bots on its Agentforce platform. Another potential consequence of Salesforce’s action could be to soften the ground for making additional acquisitions. The enterprise tech firm has been on a spending spree lately. In addition to Informatica, it has hired the team from Moonhub and purchased the AI automation software startup Convergence. By stripping away access to Slack, Salesforce might be disrupting the business plans of startups it’s been eyeballing and then pouncing when the time is right. Slack’s API restrictions could also ripple through the broader enterprise AI landscape, particularly for Microsoft. With both Salesforce and Microsoft racing to own the enterprise AI stack, limiting developer access to Slack data may prompt some to reconsider which ecosystem offers the most open and future-proof foundation for AI innovation. Ultimately, as The Information’s Kevin McLaughlin points out, this is a reminder to users to understand who owns the data that’s created. This Week’s AI News 🏭 AI Trends and Industry Impact 🤖 AI Models and Technologies ✏️ Generative AI and Content Creation 💰 Funding and Investments ☁️ Enterprise AI Solutions ⚙️ Hardware, Robotics, and Autonomous Systems 🔬 Science and Breakthroughs 💼 Business, Marketing, Media, and Consumer Applications 🛒 Retail and Commerce ⚖️ Legal, Regulatory, and Ethical Issues 💥 Disruption, Misinformation, and Risks Share	2025-06-01T00:00:00		https://theaieconomy.substack.com/p/pwc-2025-ai-jobs-barometer-report	[ { "date": "2025/06/01", "position": 92, "query": "future of work AI" } ]
My Reflections on AI's Impact on Jobs	My Reflections on AI’s Impact on Jobs	https://www.linkedin.com	[ "Randstad Uk", "Bob Hutchins", "Phd C", "Chatgpt Ai", "Jan Weidlich", "Jean Ng", "Ai Changemaker", "Global Top Creator In Tech Ethics", "Society", "Favikon Ambassador" ]	Learn to Work With AI – Whether you're in finance, law, education, or logistics, get curious. · Reimagine Roles, Not Just Tasks – AI will force us to focus on ...	I’ve spent my career navigating change—first as a fintech founder, then in leadership roles across digital transformation, and as a coach helping others thrive in a world of exponential shifts. But few changes are as profound or urgent as what artificial intelligence (AI) is doing to the very fabric of work. Almost every week, I speak with business owners, professionals, and educators who ask the same question in different ways: “What does AI mean for my job, my team, or my industry?” There’s both excitement and anxiety in their voices—and I feel it too. So I’d like to offer my own reflections, grounded in some compelling perspectives from AI leaders like Dario Amodei (Anthropic), Sundar Pichai (Google), and the team at a16z. The Hard Truth: A Tectonic Shift for Entry-Level Jobs? Let’s start with Dario Amodei’s sobering warning. As the CEO of Anthropic, one of the most advanced AI labs today, Amodei has a front-row seat to what’s coming. He recently remarked: “AI could lead to the disappearance of half of entry-level white-collar jobs and cause 10 to 20% unemployment within the next 1 to 5 years.” Let that sink in. Amodei’s concern isn’t about the usual tech hype. He’s talking about the speed and breadth of AI capabilities—especially in areas we once considered “safe” for humans: knowledge work, writing, research, even customer support. In his words, current AI models are beginning to resemble “smart college students”, capable of performing tasks that make up the core of many junior roles. The worry isn’t just economic—it’s societal. Amodei notes that in democracies, people’s ability to contribute to the economy gives them a kind of leverage—a role, a voice. If too many people lose that leverage because AI outpaces our ability to adapt, he fears a fraying of the social contract. His suggestion? Policymakers may need to act—radically. He floated the idea of taxes on AI companies as a way to redistribute the enormous wealth these models could generate. “We should pursue the benefits of AI,” Amodei says, “but we must be honest and proactive about the harms—and fast.” I find myself resonating with that urgency. A More Hopeful View: AI as an Amplifier, Not a Replacement On the other side of the spectrum is Sundar Pichai, CEO of Google. He takes a more optimistic stance—one that I think is equally important to hear. Pichai believes AI will augment human capabilities, not replace them. He points out that even within Google, they’re using AI to help engineers—not replace them—but continue hiring: “AI enables people to be more creative and productive,” he says. “It helps people do more.” He’s particularly bullish on what AI will do for software development—removing the drudgery of boilerplate tasks so that developers can spend more time on design, architecture, and complex problem-solving. In his words, “AI lets good developers become great.” And just as Microsoft Word didn’t eliminate writers but changed the writing process, Pichai believes AI will change—not erase—creative and analytical work. His comments on education are especially relevant in Asia. Students, he observes, will increasingly use AI tools like Gemini to complete homework. This challenges us to rethink what education is really for: “The education system must shift to focus on understanding, implications, creativity—not memorisation,” he asserts. It’s a challenge we must embrace in Singapore and across Asia, where educational systems have often prized content mastery over context and creativity. Developers Are the Canary in the Coal Mine What’s happening to software developers offers a glimpse into what’s ahead for many other professionals. The team at a16z, in a recent panel, described how AI is already changing the developer workflow. Tools like GitHub Copilot and Cursor aren’t just auto-complete assistants—they’re becoming sparring partners. Developers are now: “Starting with specs, engaging in back-and-forth with the AI model, and refining both thinking and output.” That sounds familiar. In fact, it mirrors how I coach—with powerful questioning and reflection rather than prescriptive answers. In many ways, developers are learning to work with AI, not just use it. And here’s the big unlock: this paradigm opens up coding to non-coders. What the a16z team calls “vibe coding”—where ordinary people can build tools they need with AI support—is already underway. I’ve seen this in my own business: AI lets non-engineers launch MVPs, automate workflows, and ship ideas faster than ever. But it’s not all smooth sailing. As the panel notes, AI still hallucinates. It makes confident mistakes. And the chaotic, non-deterministic nature of LLMs means that even small changes in prompts can lead to radically different outputs. That’s both a superpower and a landmine. So, Where Do We Go From Here? There’s no denying it: AI is here, and it’s going to transform jobs. The real question is not whether it will—but how we choose to respond. I believe there are three imperatives: Learn to Work With AI – Whether you're in finance, law, education, or logistics, get curious. Tinker. Experiment. Learn. AI is not just for techies. Reimagine Roles, Not Just Tasks – AI will force us to focus on what’s deeply human—judgment, empathy, creativity, ethical discernment. These aren’t just buzzwords. They’re the new frontier. Push for Responsible Innovation – We need conversations—like the ones Amodei and Pichai are having—with regulators, employers, educators, and citizens. This is a shared future, not a tech-only issue. I’ll close with this: I don’t think we should fear AI. But I do think we should fear complacency. The future of work won’t be written by machines. It’ll be shaped by people—people who choose to learn, adapt, and lead through uncertainty. Let’s be those people. I lead a boutique Digitalization and AI agency focused on helping SMEs thrive in a world of exponential change. Our mission is to unlock business value through pragmatic AI solutions, process automation, and voice agents—all tailored for real-world impact. Visit circlem.io or message me to if you're ready to start building to explore how AI can work for your business. As a CliftonStrengths leadership coach, I help entrepreneurs, professionals and business leaders leverage their natural strengths to bring hope in their leadership. If you want to explore how you can be a force multiplier, let’s connect! Send me a message to start your strengths-based leadership journey today. Or take the free Clues to Talent Quiz today to discover your hidden talents. Like \| Comment \| Share	2025-06-01T00:00:00		https://www.linkedin.com/pulse/my-reflections-ais-impact-jobs-lawrence-yong-txerc	[ { "date": "2025/06/01", "position": 97, "query": "future of work AI" } ]
How to get a remote AI Engineer job? : r/learnmachinelearning	The heart of the internet	https://www.reddit.com	[]	I really want to switch to a remote AI Engineer role with a decent salary and better work environment. Could you please suggest: Which companies ...	I joined a small startup 7 months ago as a Software Engineer. During this time, I’ve worked on AI projects like RAG and other LLM-based applications using tools like LangChain, LangGraph, AWS Bedrock, and NVIDIA’s AI services. However, the salary is very low, and lately, the projects assigned to me have been completely irrelevant to my skills. On top of that, I’m being forced to work with a toxic teammate, which is affecting my mental peace. I really want to switch to a remote AI Engineer role with a decent salary and better work environment. Could you please suggest: Which companies (startups or established ones) are currently hiring for remote AI/GenAI roles? What kind of preparation or upskilling I should focus on to increase my chances? Any platforms or communities where I should actively look for such opportunities? Any guidance would be truly appreciated. Thanks in advance!	2025-06-01T00:00:00		https://www.reddit.com/r/learnmachinelearning/comments/1lxy0z1/how_to_get_a_remote_ai_engineer_job/	[ { "date": "2025/06/01", "position": 20, "query": "generative AI jobs" } ]
McKinsey Warns of AI's Impact as Entry-Level UK Jobs Plunge Post ...	McKinsey Warns of AI’s Impact as Entry-Level UK Jobs Plunge Post-ChatGPT	https://coincentral.com	[ "Newton Kitonga" ]	Research, note-taking, and document review, jobs often delegated to junior staff, are now handled more efficiently by generative AI systems.	TLDRs; Job postings in the UK have fallen 31% since 2022, with AI-exposed roles dropping even more sharply. Entry-level positions have been especially hard hit, falling nearly one-third since ChatGPT’s release. White-collar professions like consulting, programming, and design are seeing a steep hiring pullback. Sectors less touched by AI, such as real estate and education, are seeing job growth amid the disruption. The United Kingdom is witnessing a significant shift in its labor market as artificial intelligence continues to alter how companies hire and structure their teams. According to a recent analysis by McKinsey & Company, job postings across the UK fell by 31% in the three months leading up to May 2025 compared to the same period in 2022. The report emphasizes that this downturn is more pronounced in sectors and roles most vulnerable to AI integration, raising concerns about the future of traditional career pathways, especially for those just entering the workforce. Early-Career Roles Suffer Most in AI Transition McKinsey’s findings indicate that entry-level positions are taking the brunt of this disruption. Since the public launch of ChatGPT in late 2022, postings for junior roles, apprenticeships, and internships have dropped by nearly one-third. Job platforms such as Adzuna show that positions typically designed to onboard new graduates or early-career professionals have become scarcer, as companies increasingly automate tasks that were once considered stepping stones in career progression. Research, note-taking, and document review, jobs often delegated to junior staff, are now handled more efficiently by generative AI systems. Creative and Tech Sectors Pull Back Despite Growth Paradoxically, even as the tech and consulting industries report healthy business growth, they are cutting back on hiring. Roles in graphic design, programming, and management consulting have seen declines in vacancies of more than 50% over the past three years. These are precisely the fields that AI tools have rapidly transformed. For example, McKinsey’s own deployment of its internal AI platform, “Lilli,” shows how automation is replacing tasks like writing proposals and building presentations, previously done by junior consultants, as firms become more reliant on generative AI. Not all industries are retreating. Job postings in sectors with minimal AI exposure, such as education and real estate, have actually increased during the same timeframe. These sectors continue to rely heavily on human interaction, judgment, and emotional intelligence, making them less susceptible to immediate automation. This growing divergence is beginning to reshape the broader labor market, with certain skills rapidly losing value while others remain in demand. A Rethink of Workforce Strategy McKinsey’s analysis highlights a fundamental re-evaluation of workforce strategies. Companies are not just reducing headcount but reconfiguring job roles altogether, anticipating long-term productivity gains from AI adoption. As junior roles vanish or become more specialized, the traditional career ladder appears to be splintering. Experts suggest that the apprenticeship model, which has underpinned professional growth in sectors like consulting for decades, may need reinvention. With the foundation of entry-level work eroding, young professionals face a new reality, one where AI fluency and adaptability may become prerequisites, and where career development no longer follows a predictable path.	2025-07-14T00:00:00	2025/07/14	https://coincentral.com/mckinsey-warns-of-ais-impact-as-entry-level-uk-jobs-plunge-post-chatgpt/	[ { "date": "2025/06/01", "position": 80, "query": "generative AI jobs" } ]
Technology does NOT create new jobs!	Technology does NOT create new jobs!	https://daveshap.substack.com	[ "David Shapiro" ]	Wages and labor demand have been declining for decades due to automation. Automation creates new job titles, but in aggregate, it creates a net loss of jobs.	“Technology always creates new jobs!” No, it does not. While in nominal terms, yes, technology has created new jobs (cloud data center engineers didn’t exist in the 1950s!) there has not been a commensurate rise in the total number of jobs as you’d expect, proportional to population. Furthermore, there is now ample evidence that technology broadly (and automation specifically) has been eating into the demand for human labor, causing commensurate drop in wages as well as overall employment. None of these data are obscure or arcane, I have simply scraped them together from disparate sources, and they all point in the same direction; while automation might create new job categories, it absolutely does not create proportionally more jobs in absolute terms. These data have been corrected for trends such as offshoring and globalism. Even when you account for jobs lost overseas, we are still hemorrhaging jobs faster than can be explained without blaming automation. If prevailing economic theory were true, we’d expect the opposite; that secondary and ancillary jobs would be increasing, not decreasing. The near-term rise of AI agents and humanoid robots will merely continue to long-term erosion of human jobs and wages. One final thing we need to understand before looking at these charts is this; the numbers do not have to trend to 0% or $0 before the current social contract breaks down. Furthermore, these historical data do not necessarily anticipate or account for automation shocks (rapid dislocations and shocks). Our entire point here is to disabuse you of any notion that technology automatically increases aggregate demand for human labor (it does not). Each graph will serve as a header, with the subsequent paragraphs unpacking the graph. Sources for all data cited at the end. The Historical Data Labor-based business income has been steadily declining since the 1950’s Labor’s share of non-farm business income fell from the 63–65% plateau of the post-war decades to around 56–58% in the 2010s, a multi-decade nadir; globally the labor share slid from 53.9% in 2004 to 52.3% in 2024. The long-term erosion of labor’s share of non-farm business income—from a stable post-war plateau around 64% to barely 57–58% in recent decades—signals a fundamental shift in the distribution of economic value. This decline is not cyclical noise but structural dislocation: over the same period, productivity rose steadily, yet the median worker saw little to no real wage growth. In effect, the marginal returns on human effort have decoupled from overall output gains. The proceeds of economic expansion have increasingly flowed to capital—in the form of profits, rents, and intellectual property—rather than to the broad base of workers. The global data echo this trend: from 2004 to 2024, labor’s global share slipped from 53.9% to 52.3%, indicating that this is not a parochial American anomaly but a system-wide feature of advanced capitalism in the digital-automated age. The mechanisms behind this displacement are manifold but mutually reinforcing. First, capital-biased technological change—especially in automation and software—has substituted machines for routine human tasks at a pace faster than labor can reallocate to new functions. Second, globalization has expanded the effective labor pool, exerting downward pressure on wages in tradable sectors. Third, institutional supports for labor have withered: union density has collapsed, the real minimum wage has decayed, and collective bargaining has lost its teeth. Meanwhile, the rise of superstar firms and intangibles-heavy business models has amplified returns to scale without proportionally increasing headcount. In sum, the declining labor share reflects not a shrinking pie but a new rulebook for dividing it—one that no longer assumes human labor as the default engine of value capture. Since 1979 net productivity has risen ≈ 86 %, yet nonsupervisory hourly compensation is up only ≈ 32 %. Output has therefore grown roughly 2.7 × faster than typical pay. The divergence between productivity and pay since 1979 represents perhaps the most distilled expression of labor’s declining claim on economic output. According to BLS and EPI analysis, non-farm business sector productivity—measured as output per hour—has increased by approximately 86% from 1979 to 2023. Over the same period, hourly compensation for production and nonsupervisory workers rose only about 32% in real terms. This implies that productivity has grown approximately 2.7 times faster than typical worker pay. In a world where compensation tracked productivity, median earnings today would be dramatically higher; instead, the gains have been captured elsewhere—by owners of capital, high-income professionals, and technological intermediaries. This decoupling occurred for structural reasons. Until the mid-1970s, productivity and compensation moved in tandem, enforced by institutional mechanisms—strong unions, binding minimum wages, and norms of wage compression. After 1979, that compact unraveled. Labor market institutions weakened, global competition intensified, and technological change began to favor capital-augmenting innovations—robots, software, platforms—that scale without hiring. At the same time, corporate governance shifted to priorities shareholder value, flattening wage structures and offloading risks onto contract and gig workers. The result is a labor market that still generates output, but no longer treats wage growth as a necessary or even expected corollary. Automation—encompassing both physical machinery and digital software—has been the central enabler of this divergence, fundamentally altering the production function of modern firms. Since the 1980s, successive waves of technological substitution have displaced human labor in routine, codifiable tasks across manufacturing, clerical work, retail, logistics, and more recently, professional services. Capital-biased technological change, by its nature, raises output per worker without requiring a proportional increase in labor input, thereby inflating productivity statistics while suppressing aggregate wage bills. The increasing computational power of software, the scalability of cloud infrastructure, and the rise of algorithmic decision-making mean that vast swathes of value can now be generated with minimal human intervention. This not only weakens the bargaining position of remaining workers—since their marginal product is harder to distinguish from the machine’s—but also concentrates income in the hands of those who own or control the enabling technologies. In effect, automation has functioned not merely as a substitute for labor, but as an institutional bypass around it. Manufacturing employment—once the great sponge for mid-skill labour—peaked at 19.6 million jobs in 1979 and stands near 12.7 million today; its share of all non-farm jobs collapsed from 32 % in 1953 to roughly 8–9 %. Output kept rising; the head-count did not, so the missing millions represent pure labor displacement. The collapse of manufacturing employment in the United States—from a peak of 19.6 million jobs in 1979 to roughly 12.7 million today—despite continued growth in output, is among the most unambiguous illustrations of labor displacement through automation and structural change. In 1953, nearly one-third of all non-farm employment was in manufacturing; today that figure is closer to 8–9 %. This disjuncture between rising output and falling headcount reveals a deep decoupling: factories are producing more goods with fewer workers, a direct consequence of the capital-deepening process. From robotic assembly lines to just-in-time logistics and computer numerical control (CNC) machining, technological progress has rendered many mid-skill, repetitive manufacturing roles obsolete—not by offshoring them, but by eliminating their necessity altogether. The drivers of this displacement are both technical and institutional. Technically, automation in manufacturing is especially tractable: tasks tend to be routine, spatially fixed, and governed by standardized processes—all ideal conditions for robotic substitution. Moreover, digitization has enabled continuous process optimization, with sensors and software making production lines adaptive and self-correcting. Institutionally, the erosion of union power and the deregulatory turn of the 1980s and 1990s made it easier for firms to adopt labor-reducing technologies without facing meaningful resistance or needing to share the productivity gains with workers. The outcome has been a bifurcation: high-output, high-efficiency manufacturing exists, but it is capital-intensive and lean on jobs. For the broader economy, this has hollowed out the middle of the labor market, severed one of the key escalators of upward mobility for non-college workers, and intensified regional economic divergence between deindustrialized communities and knowledge-heavy metro hubs. Institutional bargaining power mirrors the pay stagnation: union density has imploded from roughly one-third of the workforce in the 1950s to about 10 % today, removing a key wage-setting counterweight. The collapse of union density in the United States—from over 33 % of the workforce in the 1950s to around 10 % today—represents not just a shift in workplace organization, but a wholesale weakening of labor’s institutional leverage. This erosion has occurred in tandem with wage stagnation, falling labor share, and rising inequality, all of which unions once counteracted through collective bargaining and standard-setting. While automation and capital intensification certainly exacerbated the decline—by rendering certain categories of manual and routine labor structurally redundant—they did not cause it in isolation. Rather, they operated within a political framework that actively disempowered organized labor. Since the late 1970s, a deliberate policy regime—commonly described under the rubric of neoliberalism—has tilted the playing field against collective bargaining. “Right to work” laws, restrictions on card check and organizing drives, aggressive employer tactics backed by weak penalties, and a judiciary increasingly skeptical of labor rights have all conspired to institutionalize labor’s precarity. In this context, automation did not merely replace workers—it replaced unionized workers under the aegis of a regulatory order designed to suppress wage floors and fragment worker solidarity. The substitution of capital for labor thus became not only an economic efficiency but also a political strategy. Without unions, the gains from productivity growth—especially when driven by machines and software—accrued almost exclusively to capital, further reinforcing the asymmetric feedback loop between technological change and wage suppression. The erosion of unions, then, is not simply a consequence of automation; it is a precondition that made automation’s wage-displacing effects politically and institutionally viable. The federal minimum wage has not kept pace with inflation or productivity. Adjusted for prices, today’s $7.25 is about 40 % below the 1968 peak and 27 % below its own 2009 value, the lowest real level in 66 years. The erosion of the federal minimum wage—now $7.25 per hour, unchanged since 2009—reflects a profound institutional failure to preserve the wage floor in line with either inflation or economic growth. Adjusted for prices, today’s minimum wage is approximately 40% below its 1968 peak in real terms and 27 % below its own inflation-adjusted value from just over a decade ago. This decline is not the result of market forces, but of legislative inaction: Congress has simply refused to raise the floor, even as the cost of living and average productivity have steadily climbed. In effect, the real minimum wage has become a shrinking guarantee—eroding its role as a safeguard against poverty and a stabilizer of low-end wages. This institutional decay intersects directly with broader dynamics of automation and capital deepening. As low-skill labor becomes more substitutable—either by machines, software, or offshored labor—the bargaining power of those at the bottom of the wage ladder is further weakened. Employers face less pressure to raise wages when technology or non-union contract labor offers a cost-effective alternative. But the erosion of the minimum wage is not just a market reaction; it is also an ideological project. Since the 1980s, economic orthodoxy has argued that wage floors distort employment and reduce efficiency. Under this logic, a stagnant or declining minimum wage became a policy choice: it enabled greater “flexibility” for capital, maintained low-cost labor pools for service sectors, and reinforced the broader disempowerment of low-wage workers. The result is an economic order in which the minimum wage no longer anchors the bottom of the labor market, but instead recedes in relevance, increasingly symbolic rather than functional. Prime-age male labor‐force participation was virtually universal in the mid-1950s (≈ 98 %) but has ratcheted downward to about 88–89 % in recent years, never recovering its previous crest after any recession. That is a ten-percentage-point loss in the cohort that should be the economy’s backbone. The decline in prime-age male labor-force participation—from approximately 98% in the mid-1950s to around 88–89% today—represents one of the most enduring and understudied structural changes in the American economy. This is not a cyclical artifact: the rate falls during recessions but never fully recovers, ratcheting downward over time across expansions. These are men aged 25–54—the demographic historically considered the backbone of the industrial workforce—now increasingly absent from formal labor markets. A ten-point drop in participation over seven decades translates into millions of missing workers, individuals who are neither employed nor actively seeking work. The implications are not merely economic but social and political: such persistent withdrawal from the labor market reshapes family structures, erodes community stability, and contributes to the broader sense of economic dislocation. The causes are complex and interlocking. Automation plays a significant role by hollowing out the demand for mid-skill, mid-wage roles—precisely the kinds of jobs that once anchored male employment in manufacturing, transport, and clerical sectors. As routine-intensive tasks are increasingly absorbed by machines or software, the opportunity structure for non-college-educated men has narrowed dramatically. Globalization and offshoring compounded this, displacing industrial work and weakening wage ladders. At the same time, institutional changes—such as the decline of unions and the stagnation of real minimum wages—have reduced the incentives and bargaining power to remain attached to marginal employment. Moreover, the rise in disability claims, especially in areas suffering from industrial decline, has provided an exit path from the labor market for many men with limited prospects. This is not always fraud or malingering; it often reflects genuine injuries or chronic conditions that render physically demanding work untenable in a labor market with little room for adaptation. Taken together, these forces constitute a structural reconfiguration: not simply fewer jobs, but fewer socially legible and economically viable roles for a growing share of prime-age men. This graph—adapted from research by economist David Autor, among others—visually dissects the cumulative job displacement in the U.S. since 1980 into two principal drivers: globalization (via trade and offshoring) and automation (via domestic productivity growth). It shows that while trade shocks, especially the China import surge post-2001, led to substantial early job losses (≈ 3–4 million by 2010), this effect plateaued as trade imbalances stabilized. In contrast, automation-driven displacement has intensified, continuing to absorb labor demand even as globalization’s direct impact flattened. The result is a total displacement figure exceeding 10 million jobs by 2025, the majority attributable not to foreign competition but to domestic capital deepening. This finding reinforces what Autor has called the problem of the “missing millions”—workers who did not merely shift to new sectors after industrial contraction, but vanished from the labor force altogether. His work demonstrates that even after controlling for offshoring and trade exposure, large swaths of mid-skill employment never rematerialized. Automation, rather than simply reallocating labor, erased the economic functions once performed by humans, especially in routine-heavy roles like assembly, clerical processing, and logistics. Unlike trade, which redistributes jobs geographically, automation shrinks the total number of human job slots. And because this displacement is continuous and endogenous—driven by technical progress and declining capital costs—it is not self-limiting. The implication is profound: policy responses that focus solely on trade adjustment assistance or re-shoring will miss the deeper, structural cause of labor displacement. The challenge is not just global competition, but technological redundancy, compounding over time and removing the economic necessity of millions of workers. Conclusion The inevitable conclusion from evaluating these data is simple: technology does not, in aggregate, “always create new jobs.” Quite the opposite. We’ve seen a long, steady, multidimensional erosion of demand for human labor and a commensurate decline in wages. While there are some cofounding variables at work, such as demographic shifts and globalization, these alone do not fully account for the “missing millions” of new jobs we’d have expected to be created. Many of these trends are hidden under normalized and nominal values, such as raw GDP growth (which you would expect GDP to decouple from labor inputs in an automated economy, exactly as we’ve seen) as well as the huge population growth, which lifts employment in absolute terms. Political forces, such as the rise of neoliberal theory, have certain “squeezed” the middle class by eroding labor union power and favoring globalization. But even that systemic shift in policy does not fully explain the erosion of wages and labor demand. Furthermore, the rise of AI and humanoid robotics will merely expand and accelerate the frontier of automation. The sphere of human-preferable jobs will continue to shrink, so far as we can tell, indefinitely. It may reach an asymptote, but that value will be much less than what we see today, and this will invariably leave many millions of people in the lurch, without any possibility for jobs. Therefore, we must create a new social contract that is more participatory. Sources • Labor-force participation (prime-age, male and total) – Current Population Survey micro-data as aggregated in BLS series LNS11300061 (men 25-54) and LNS11300060 (all 25-54); published monthly in Employment Situation – Table A-7 by the Bureau of Labor Statistics. • Manufacturing employment levels and shares – Current Employment Statistics (CES) series CEU3000000001 (manufacturing jobs) and CEU0000000001 (total non-farm payrolls), Bureau of Labor Statistics. • Labor share of non-farm business income – Bureau of Economic Analysis, National Income and Product Accounts, Table 1.14 “Gross value added of non-farm business” combined with BLS Hours & Compensation data; the integrated series is disseminated by BLS Productivity and Costs (Major Sector) release. • Productivity versus typical compensation – BLS Major Sector Productivity index for output per hour (non-farm business) and BLS average hourly compensation for production-and-nonsupervisory workers (series CES0500000008); the divergence series popularized by the Economic Policy Institute uses these two official inputs. • Real federal minimum wage – Nominal minimum from U.S. Department of Labor, Wage and Hour Division “History of Federal Minimum Wage Rates” deflated with BLS Consumer Price Index for All Urban Consumers (CPI-U). • Union membership rate – BLS annual Union Membership data, derived from CPS supplement “Characteristics of Union Members,” series LUU0204899600.	2025-06-01T00:00:00		https://daveshap.substack.com/p/labor-and-wages-have-been-declining	[ { "date": "2025/06/01", "position": 18, "query": "job automation statistics" } ]
Production automation and skill premium: a perspective of ...	Production automation and skill premium: a perspective of deepening the division of labor in enterprises	https://www.nature.com	[ "Li", "School Of Economics", "Guangzhou College Of Commerce", "Guangzhou", "Wang", "Institute Of International Economics", "Trade", "Guangdong University Of Foreign Studies", "Guangdong", "Huiping Li" ]	... data—the analysis incorporates both automation and labor specialization into a unified framework. The research evaluates not only the direct influence of ...	Benchmark regression In order to combat the impact of heteroskedasticity and autocorrelation on estimations, the regressions presented here are industry-clustered. Table 2’s columns (1), (2), and (3) delve into the baseline regression that explores the effects of production automation on wage premiums for skills. Column (1) showcases the regression’s outcome without any control variables or fixed effects included. The estimated coefficient stands at 0.0226, a figure that’s positively significant at the 1% confidence level. This translates to a 0.0226-unit increase in skill wage premiums per unit of automation. When control variables are introduced in column (2), the coefficient for production automation drops to 0.0105, yet it remains positively significant at the 1% level. Column (3), which incorporates both control variables and fixed effects, retains the same positive coefficient, suggesting that enhancing an enterprise’s level of production automation will, in turn, boost its skill wage premium. Table 2 Benchmark regression and endogenous test of the impact of production automation on skill premium. Full size table Endogenous test Direct use of enterprise production automation to analyze its impact on skill premiums may lead to endogenous issues, such as production automation affecting the labor skill premium of enterprises. Conversely, the higher the skill premium, it means that the wages of skilled workers in enterprises are much higher than those of unskilled workers, which will further stimulate skilled workers to actively update their skills and increase the use of automation equipment to improve work efficiency. This suggests a potential bidirectional link between automation in production and the skill premium, which could skew research findings if not properly addressed. To mitigate this issue, employing suitable instrumental variables for analysis is essential. In examining the academic literature on production automation, it’s clear that a range of key approaches for choosing instrumental variables are highlighted. Graetz and Michaels (2018) delve into the realm of robotics adoption across 17 nations globally from 1993 to 2017, deploying two instrumental variables in their analysis. The first variable they employ is an industry-specific “replaceability” metric, a figure that stems from comparing 1980 with 2012 U.S. job statistics. This index assesses the necessity of robotic arm usage in the said industry back in 1980. However, due to the fact that the article’s research object is a sample of developed countries, which is essentially structurally different from the use of manufacturing production automation in China, and the lack of data on the relevant industries in China, these two instrumental variables do not apply to the situation in China. In their study of the impact of robot use on industry employment in the U.S., Acemoglu and Restrepo (2020) suggest that due to the competition in manufacturing among large countries that can lead to convergence in technology and equipment, it is reasonable to select the robot installations in Germany, Japan, and South Korea as the instrumental variable. The instrumental variables selected for this study are grounded in the real-world context of China’s manufacturing sector. Covering the period from 2001 to 2014, this timeframe captures a phase of remarkable growth in China’s manufacturing competitiveness alongside escalating trade tensions with the United States. Given this backdrop, U.S. industrial robotics data serves as an appropriate instrument. This approach makes sense for three key reasons: first, the competitive dynamics between Chinese and American manufacturers create interdependence in automation investments; second, American advancements in production automation directly influence China’s adoption of related technologies; and crucially, the skill premium for Chinese workers remains largely unaffected by automation trends in the U.S. market. Therefore, the instrumental variables constructed in this paper can basically satisfy relevance and exclusivity, and to a certain extent, they are reasonable. Production automation in the U.S. is constructed in the same way as in China, and employment is benchmarked against U.S. employment by industry in 2000. The findings presented in Column (4) of Table 2 reveal the two-stage least squares (2SLS) estimates for the instrumental variables analysis. These results demonstrate a statistically significant positive relationship between production automation and labor skill premiums, providing robust evidence that automation adoption widens the skill-based wage gap within firms. This empirical validation strengthens the case for automation’s role in reshaping workforce compensation structures. Compared with the OLS estimation in column (3), the coefficient of production automation is estimated in the same direction, and the value of the coefficient is significantly increased, which indicates that the effect of production automation on skill premium is underestimated due to endogeneity problem. The Kleibergen-Paap rk LM test statistic strongly rejects the null hypothesis of under-identification at the 1% significance level, confirming that the instrumental variables are well-specified. Furthermore, the Kleibergen-Paap rk Wald F statistic comfortably exceeds the Stock-Yogo weak identification threshold at the 10% level, effectively ruling out concerns about weak instruments. These results collectively demonstrate that the chosen instrumental variables are both relevant and robust for the analysis. We recognize that the choice of U.S. industrial robotics data to construct the instrumental variables is not perfect, as industries in the U.S. and China may be subject to similar macroeconomic shocks. To ensure the robustness of the results, we refer to the idea of Yao et al. (2023) and use the industry average wage as an instrumental variable. The rising industry wage level accelerates the promotion and application of automation in China, suggesting that the average industry wage is highly positively correlated with production automation; at the same time, the average industry wage level does not directly affect the skill premium of firms, is not correlated with the original residual term, and satisfies the exclusivity requirement of the instrumental variable. The estimates presented in column (5) of Table 2 are in line with our main findings, further solidifying the robustness of our instrumental variables approach and lending additional credence to the reliability of our benchmark results. To tackle the issue of endogeneity, this study employs a dynamic panel model. Specifically, we use the two-step system GMM, incorporating a one-period lag of the skill premium to create this dynamic setup. This allows us to further confirm the impact of production automation on firms’ skill premium. Looking at column (6) of Table 2, the p values from the AR(1) and AR(2) tests suggest that the model’s residuals aren’t serially correlated, indicating a well-specified dynamic panel model. Furthermore, Sargan’s test reveals no evidence of over-identification, implying that our choice of instrumental variables is on the mark. The results presented in columns (4)–(6) of Table 2 consistently demonstrate that production automation significantly widens the firm skill premium, even after addressing endogeneity, thus bolstering the robustness of our baseline regression findings. According to the mean value of the deepening degree of division of labor among multi-level enterprises, the entire sample is divided into groups with low degree of division of labor among enterprises and high degree of division of labor among enterprises. The regression results are shown in Table 3. It can be seen that under the multi-level division of labor in enterprises, production automation significantly increases the skill premium. In firms with a high degree of specialization and a high level and position in global value chains, production automation has a stronger role in expanding the skill premium, indicating that increasing production automation will expand the skill premium in the context of deepening the division of labor in enterprises. Table 3 The effect of production automation on the skill premium in firms with different levels of division of labor. Full size table Robustness test Table 4 reports the robustness test results for five scenarios. Table 4 Robustness test. Full size table PSM-DID A multi-period double difference model (DID) is used to examine the impact of production automation on skill premiums, and the core explanatory variable, production automation, is replaced by a binary dummy variable that determines whether a company imports robots. According to the HS-8 digit tax code of products in the customs database, a total of 7253 enterprises that imported industrial robots were retrieved. After matching the last 7 digits of the enterprise name, zip code, and phone number with the industrial enterprise database, a total of 3701 enterprises remained, accounting for 51.03% of the total number of imported robot enterprises. If an enterprise imported robots in a certain year, assign a value of 1 to the enterprise in the current year and subsequent years, otherwise assign a value of 0. After the above processing, a total of 11025 observed values from imported robot enterprises are included in the entire sample. After comparison, the relevant data of imported robot enterprises selected in this article are highly comparable to other studies using the same dataset, such as Chen and Yao (2022). In this paper, enterprises that have imported robots are used as processing groups, and a series of control variables mentioned above are used as matching variables. To minimize any potential bias from how our sample was selected and to get a more accurate picture, we first employed a one-to-one nearest neighbor matching technique based on propensity scores. The results of this matching suggest that, on average, adopting robots leads to a 0.002 bump in the skill premium, a result that’s statistically significant at the 1% level. Essentially, this implies that companies that have brought in industrial robots see a 0.002 higher labor skill premium compared to those that haven’t. Building on this, we then used a multi-period difference-in-differences model to dig deeper into how importing robots affects these skill premiums. The model we used is as follows: $${{SP}}_{ft}=\alpha +\phi {{treat}}_{f}\ast {{period}}_{ft}+{\beta }_{1}X+{\mu }_{t}+{v}_{f}+{\varepsilon }_{ift}$$ (7) Where, ${\rm{treat}}_{f}$ is a processing group virtual variable, with the value of 1 for enterprises that have imported industrial robots and 0 for enterprises that have not imported industrial robots; ${\rm{period}}_{ft}$ is a dummy variable for the processing period. For enterprises that have imported industrial robots in the current year and subsequent years, the value is 1, and for previous years, the value is 0. For enterprises that have not imported robots, the value is 0 in each year; ${\rm{treat}}_{f}\ast {\rm{period}}_{ft}$ represents a virtual variable of processing effects; The meaning of other variables is the same as that of model (1). The estimated results are shown in column (1) of Table 4. The coefficient $\phi$ of ${\rm{treat}}_{f}\ast {\rm{period}}_{ft}$ is 0.0048, which is significantly positive at the level of 5%, indicating that the skill premium of enterprises that have imported industrial robots will significantly increase. That is, improving production automation will significantly increase the skill premium of enterprises. This conclusion is consistent with the benchmark regression results. Replacing production automation with the total amount spent by the enterprise on importing industrial robots By applying Chen and Yao’s (2022) approach, the central explanatory factor has been swapped for the total expenditure incurred by companies on the import of industrial robots. The findings can be observed in column (2) of Table 4, where the estimated coefficient exhibits a statistically significant positive relationship at a 10% confidence interval. When juxtaposed against the baseline regression outcome presented in column (3) of Table 2, the trends align. While the coefficient’s magnitude grows, its significance diminishes, yet the essence of the result remains unaltered, suggesting that the initial regression result is indeed robust. Replace the sample interval Due to the relatively complete indicators and higher data quality in the industrial enterprise database from 2001 to 2007, this article uses the samples from 2001 to 2007 to retest the conclusions based on excluding the samples from 2008 to 2010. The regression analysis presented in column (3) of Table 4 demonstrates findings that align with the initial benchmark results. Both the direction and statistical significance of the production automation coefficients remain consistent, reinforcing the robustness of our conclusions. The proxy variable for production automation is expressed as the installation density of industrial robots Column (4) displays the regression outcomes, with no alterations to the coefficients’ direction or significance, affirming the robustness of the baseline regression findings. Beyond the auto sector, which isn’t included in the tally, we’re talking about sectors like railroads, marine vessels, aviation, and the production of other transport gadgets According to the stats from IFR, industrial robots are being utilized at a much higher rate in the automotive field compared to other business realms. Is the impact of production automation on skill premiums caused by large-scale use in the automotive industry? To test whether the impact of production automation on skill premiums is universally significant, Therefore, the automobile industry and other transportation equipment manufacturing industries were excludedFootnote 1. The regression analysis presented in column (5) of Table 4 reveals compelling findings. The coefficient for production automation demonstrates a statistically significant positive relationship at the 1% confidence level. This suggests that even outside the automobile and transportation equipment manufacturing sectors, automation continues to drive up skill premiums. These results further validate the robustness of our baseline regression model. Mechanism test Benchmark regressions and a series of robustness tests confirm that production automation significantly amplifies the skill premium. Does production automation then affect the skill premium by influencing the division of labor in firms and hence the skill premium? In this regard, to mitigate the endogeneity of the division of labor in firms, we refer to Zhang (2020) to conduct mechanism tests. In the first step, we empirically test the impact of production automation on the division of labor in firms; in the second step, we theoretically demonstrate the impact of the division of labor in firms on the skill premium by combining relevant literature. First, to empirically test the impact of production automation on the division of labor in enterprises, this paper constructs the following model: $$M=\alpha +{\beta }_{01}{{PR}}_{it}+{\beta }_{1}X+{\mu }_{t}+{v}_{f}+{\varepsilon }_{ift}$$ (8) In Eq. (8), ${\beta }_{01}$ represents the coefficient of production automation when the enterprise division of labor deepening M is taken as the explained variable, and the meaning of other variables is consistent with Eq. (1). If the ${\beta }_{01}$ coefficient in Eq. (8) is positive, it indicates that production automation will promote the deepening of enterprise division of labor. The significance of Eq. (8) is that the impact of production automation on skill premiums from this perspective is effective only when there is a significant impact of production automation on enterprise division of labor. The findings presented in Table 5 reveal a strong positive correlation between production automation and the specialization of labor within firms. All three proxy variables measuring enterprise division of labor demonstrate statistically significant coefficients at the 1% confidence level, clearly suggesting that automation technology plays a crucial role in driving more sophisticated workplace specialization. This is because the application of enterprise production automation can effectively improve the production efficiency of enterprises through reducing the error rate in production, reducing labor costs, and improving the degree of coordination between various elements, thereby expanding the production scale. Expanding the production scale is a boon for businesses to nab top-tier value chain components, which, in return, spurs the intensification of labor specialization and fortifies their standing in the global division of labor (Dai et al. 2017). In addition, industrial robots replace low-skilled labor in firms, and in the process the average quality of the labor force increases, thus contributing to firms’ division of labor deepening and international division of labor status. According to the existing literature, firms’ division of labor deepening expands the skill premium in three main ways. First, as firms deepen the division of labor, they have access to a wider variety of better-quality and lower-priced intermediate products in both domestic and international markets, which improves the cost markup. The higher the firm’s cost markup, the larger the profit margin (Yu and Zhi 2016). OECD (2013) notes that firms’ participation in the international division of labor will change the structure of China’s skilled labor force, while Jiang and Milberg (2013) argue that this structural change will have a significant impact on workers’ wages and bargaining power. Skilled labor has strong bargaining power in the profit distribution chain by virtue of its scarcity, and thus the skill premium for firms expands (Anwar and Sun 2012). Second, by engaging in both domestic and global specialization, companies steadily enhance their expertise through hands-on experience and the diffusion of technological know-how. As innovation increasingly favors advanced skills, the demand for highly trained workers grows, driving up their wages relative to less skilled labor. Third, the increase in the level of enterprise specialization, the level of GVC and the GVC position is conducive to taking on more skill-intensive production tasks, increasing the demand for skilled labor and thus the skill wage premium. It is worth noting that the deepening of the division of labor in firms may cause knock-on and spillover effects, i.e., the deepening of the division of labor may, in turn, require the introduction of more robots and thus affect the skill premium. This is because a high degree of division of labor will enable enterprises to continuously improve their specialized production capacity, and the complex production process will be further divided into repeatable production segments, which creates the conditions for the large-scale introduction of machines and equipment, accelerating the speed of enterprises to replace manual labor with machines, thus affecting the demand for skilled labor and further expanding the skill premium. The analysis reveals that production automation affects the skill premium by altering enterprise labor division. Table 5 Mechanism tests. Full size table Linkage and spillover effects of production automation and enterprise division of labor on skill premiums Production automation and enterprise division of labor are mutually causal, and deepening the division of labor will in turn require the introduction of more automation equipment and thus affect the skill premium. In order to identify the linkage and spillover effects of production automation and enterprise division of labor on the skill premium, the following model is constructed: $${{SP}}_{ft}=\alpha +{\alpha }_{1}{{PR}}_{it}+{\alpha }_{2}M+{\beta }_{02}{{PR}}_{it}\ast M+{\beta }_{1}X+{\mu }_{t}+{v}_{f}+{\varepsilon }_{ift}$$ (9) Among them, ${\alpha }_{1}$ and ${\alpha }_{2}$ denote the coefficients of the main effect term, ${\rm{PR}}_{it}\ast M$ represents the interaction between production automation and enterprise division of labor. This setting can identify the impact of production automation on the skill premium of enterprises with different degrees of division of labor, excluding the possibility of PR changes causing changes in the degree of enterprise division of labor, and then affecting the skill premium. The regression results are shown in Table 6, the main effect coefficients are all significantly positive, and the coefficients of the interaction terms of production automation and enterprise specialization index and value chain division of labor are all significantly positive at the 5% level, indicating that increasing the degree of production automation in the context of deepening the division of labor in the enterprise will more significantly expand the skill premium, which confirms that there is a knock-on and ripple effect of production automation on the skill premium in the perspective of deepening the division of labor in the enterprise at multiple levels. Table 6 The linkage and spillover effects of production automation on skill premiums in the context of deepening enterprise division of labor. Full size table Heterogeneity test The study revealed that the adoption of production automation greatly enhances the skill premium for businesses. It also demonstrates a cascading and ripple effect on the skill premium, particularly in the context of intensifying hierarchical labor segmentation. However, it has not been taken into account whether there is a heterogeneous impact in enterprises with different trade methods, regions, and ownership systems. Therefore, this article classifies the entire sample from the following three aspects. Select a sample of enterprises whose trade mode is processing trade or general trade for heterogeneity analysis. None of the coefficient values for production automation in columns (1)–(4) of Table 7 for processing trade firms are significant, nor are the coefficients of the interaction terms in the proxies for the multidimensional division of labor in firms in columns (2), (3), and (4), indicating that overall, there is no cascading and ripple effect of production automation on the skill premium in processing trade firms. A likely explanation is that processing trade firms primarily import raw materials and parts, leveraging China’s abundant and inexpensive workforce to specialize in low-value-added production within the global supply chain. The demand for unskilled labor is greater than skilled labor. The application of production automation in processing trade enterprises needs to be deepened, and the chain and spillover effects of production automation on skill premiums are not obvious in the context of deepening the division of labor among enterprises. The coefficient values of production automation and firm division of labor are significantly positive in columns (5)–(8) for general trading firms, and the coefficients of the interaction term between the proxy variables of firms’ multilevel division of labor and production automation are also significantly positive in columns (6), (7), and (8), suggesting that there is a cascading and rippling effect of production automation on skill premiums under the deepening of firms’ multilevel division of labor perspective in general trading firms. This is due to the fact that general trading enterprises mainly import intermediate products with a certain technological content, and the good complementarity between technology and skilled workers increases the demand for skilled labor. The higher the degree of production automation, the stronger the company’s capital strength. The complementarity between capital and skilled labor further drives the company’s skilled labor bias, thereby significantly increasing the skill premium. Table 7 The impact of production automation on skill premiums in enterprises with different trading methods. Full size table In columns (1)–(4) of Table 8 for Eastern and Central firms, the coefficient values of production automation and firm division of labor are significantly positive, and the coefficients of the interaction terms between the proxy variables for multilevel division of labor and production automation are also significantly positive in columns (2), (3), and (4), suggesting that there are cascading and ripple effects of production automation on skill premiums under the deepening of the multilevel division of labor in firms’ perspectives in the East and Central firms. In columns (5)–(8) for western firms, the coefficient values of production automation and division of labor are not significant, and the proxy variables for the multilevel division of labor in firms in columns (6), (7), and (8) are not significant, suggesting that there are no knock-on and ripple effects in western firms. This may be due to the large geographical differences in enterprise production automation. The varying degrees of economic advancement and labor market maturity between eastern and central regions lead to distinct responses among businesses when it comes to how automation affects employment. These regional disparities inevitably shape how companies allocate roles and responsibilities within their workforce. Firms operating in the Middle Eastern markets, in particular, find themselves navigating a more dynamic and fiercely competitive commercial landscape. The complementarity between automation and skilled workers will enable enterprises to adjust the structure of labor factors in a timely manner when undertaking different tasks in the international market, increasing the demand for skilled workers, and thereby increasing the wages of skilled workers; Enterprises in the western region are limited to labor abundance and labor market perfection, and their degree of marketization is relatively weak. They remain largely unaffected by shifts in workforce dynamics resulting from automation, and struggle to promptly source qualified workers who can adapt to evolving labor divisions. Consequently, as businesses intensify their specialization, the ripple effects of automated production on skill-based wage gaps remain minimal. Table 8 Impact of production automation on skill premium in enterprises in different regions. Full size table In columns (1)–(4) of Table 9 of state-owned enterprises, the coefficient values of production automation and division of labor among firms are insignificant, and the proxy variables for multilevel division of labor among firms in columns (2), (3), and (4) are insignificant, indicating that there is no cascading and ripple effect in state-owned enterprises. In columns (5)–(8) of non-state-owned enterprises, the coefficient values of production automation and enterprise division of labor are significantly positive, and the coefficients of the interaction terms between the proxy variables of enterprise multilevel division of labor and production automation are also significantly positive in columns (6), (7), and (8), suggesting that there are cascading and ripple effects of production automation on skill premiums under the perspective of deepening enterprise multilevel division of labor in non-state-owned enterprises. This may be due to the low degree of monopoly and high market sensitivity of non-state owned enterprises, which can quickly grasp the dynamics of market changes. In order to improve productivity and increase enterprise profits, it will stimulate enterprises to expand the use of automated capital, promote the deepening of enterprise division of labor, thereby undertaking more skilled intensive production tasks with high added value, increase the demand for skilled workers, and reduce the demand for unskilled workers, The skill premium has expanded (Raveh and Reshef 2016). However, state-owned enterprises generally have labor protection systems, and the substitution rate for unskilled workers is low. Some workers who have been replaced by automation will continue to work in posts with low degree of automation through job transfer or vocational training (Hu et al. 2021). The wages of employees in state-owned enterprises are less flexible with market changes, factor marketization lags behind, and the degree of linking wages to enterprise performance is relatively low (Sheng and Hao 2021), These factors are not conducive to deepening the division of labor in enterprises, and the wage gap between skilled and unskilled labor is not significant. Therefore, there is no chain and spillover effect in state-owned enterprises.	2025-06-01T00:00:00		https://www.nature.com/articles/s41599-025-05249-1	[ { "date": "2025/06/01", "position": 44, "query": "job automation statistics" } ]
The Impact of Artificial Intelligence, Robotics, and Automation ...	The Impact of Artificial Intelligence, Robotics, and Automation on Employment Trends and Income Inequality	https://iisppr.org.in	[ "Shoo Phar Dhie" ]	Many routine and mid-level jobs are at risk, and this can lead to job losses, a more divided job market, and rising income inequality. Data shows that people ...	authors: Amrudha Harini D, Prab Jot Kaur, Aanya Narula Introduction The transformative power of Artificial Intelligence (AI), robotics, and automation is redefining the global workforce, reshaping employment landscapes and widening socio-economic gaps. While these technologies offer unprecedented efficiency and innovation, they simultaneously raise profound concerns regarding labor displacement, wage stagnation, and income polarization. This article argues that the rapid deployment of AI and automation has accelerated job polarization and contributed to rising income inequality, disproportionately affecting routine and middle-skilled workers while amplifying advantages for high-skilled labor and capital owners. Acemoglu and Restrepo (2020) demonstrated that the rise of industrial robots in the U.S. led to significant declines in both employment and wages, particularly in routine occupations. Their more recent 2021 study further elaborated that AI technologies tend to augment high-skilled jobs while replacing tasks traditionally performed by middle- and low-skilled workers, contributing to labor market polarization (Acemoglu et al., 2021). Similarly, Frank et al. (2019, updated 2021) highlighted that AI’s diffusion is likely to benefit workers with digital and analytical skills, while others may experience job displacement or downward mobility. Choudhury et al. (2020) examined the platform economy and found that while automation can enable remote, flexible work, it also intensifies precarity and income volatility for gig workers. Moreover, the World Economic Forum (2020) predicted that while automation will create 97 million new jobs by 2025, it will also displace 85 million, particularly in sectors relying on repetitive or manual labor. This churn implies that without proactive policy intervention, inequality is likely to deepen. This article critically evaluates how the current wave of technological advancement is influencing employment trends and income distribution, and discusses the imperative for inclusive innovation strategies and workforce reskilling policies. Impact of AI on Job Creation and Displacement The nature of work is being transformed by AI and automation. Machines are getting smarter and faster however most people are concerned/ worried about their jobs (or job security). The question is whether AI will replace everything or new opportunities will emerge? The answer is both! 1) Jobs are being replaced: Automation of repetitive and mundane jobs is one of the largest transformations that AI introduces. These jobs include data entry, factory labour… driving. These jobs can be done quicker and cheaper using machines than without taking breaks. As a result, many middle- skill occupations are declining and employers in sectors like manufacturing, logistics and retail are under threat. Workers in professional jobs too find their work altered. With AI, the need for human intervention is minimised. AI can now interpret data, identify patterns, and make choices. As stated by the Head of Research at Gartner: By 2025, one out of every three occupations will be replaced by robots, software, and smart equipment. — Peter Sondergaard 2) New jobs are being created: AI is creating new, just replacing old ones. Jobs which require reactivity, emotional intelligence and complex decision making are rising. AI for example can’t substitute for teachers, designers and psychologists . In addition, there is a huge demand for people who can develop, administer and enhance AI systems-like data scientist, digital marketers. Flexible income opportunities are being provided by gig economy and freelancing websites. Emerging industries are expanding rapidly. Report Prediction from McKinsey: “AI could produce an extra $13 trillion in economic output, which would raise the global GDP by 1.2%.” 3) Skills Matter More than ever: In today’s fast changing world, the most significant determinants of success are education and skills. Individuals with digital, technical, or strong problem-solving skills have a higher likelihood of gaining new opportunities. But people with lower levels of education or outdated skills might find themselves struggling. That is why lifelong learning is a necessity now. Whether it is learning to operate new tools, enhancing your technical skills, or brushing up on soft skills such as communication and teamwork, continuous learning is the solution to keep you employable. AI is fueling the employment landscape with its ever evolving algorithms and learning. It is true- some jobs will disappear under the vast possibilities that come with AI, while some may require some upskilling. A more certain truth is that it will also come up with a horizon of new job opportunities. If we prepare well and arm ourselves with the updated skills while assisting those at risk, future work dynamics will have a different and better trajectory. The verdict is that AI should be viewed as a tool of assistance and not a threat to our displacement Unequal Impact: Who Gains? Who Loses? While artificial intelligence (AI) drives both productivity and job displacement, its impact is unevenly distributed across sectors, regions, and social groups. On one end of the spectrum, certain individuals and institutions reap significant benefits, while others face substantial losses. A 2021 study by the McKinsey Global Institute estimates that up to 800 million workers globally could be displaced by automation by 2030, with the most affected being those lacking tertiary education or digital skills. Globally competitive firms and those with labor market power, such as monopsonistic employers, are more likely to benefit significantly from automation, as they are better positioned to invest in advanced technologies (Keller, 2024). At the same time, AI and machine learning (ML) can automate not only manual tasks but also complex functions traditionally performed by skilled professionals. As a result, job losses are particularly pronounced in sectors such as administrative support, transportation, manufacturing, retail, customer service, finance, and data analysis. Older workers also face a unique challenge, as they often struggle to adapt to new technologies and may be left behind in the absence of targeted reskilling programs. On the other hand, AI also creates new employment opportunities in roles such as robotics technicians, AI specialists, cybersecurity professionals, and data analysts. These roles predominantly benefit highly skilled, tech-savvy individuals who can adapt quickly to technological shifts (Setati, 2024). This divergence contributes to rising income inequality: wages tend to increase for those with advanced technological skills, while they stagnate or decline for individuals in roles susceptible to automation. Consequently, the gap between high-skilled and low-skilled workers widens. Tasks that rely on non-cognitive abilities—such as creativity, emotional intelligence, and complex problem-solving—remain relatively resistant to automation, offering continued opportunities for individuals with these skills (Adams et al., 2022). Geographical and economic disparities also shape the winners and losers of automation. Developed countries are expected to see greater displacement, whereas developing countries may find new opportunities in AI-related growth—though these benefits are often unevenly shared (Brown, 2018). Several factors determine whether an individual stands to gain or lose in the age of automation, including education level, skill set, income status, location, and gender (Agrawal et al., 2024). High-skilled, well-educated individuals are more likely to benefit from AI integration. Conversely, low-income workers often lack access to the resources needed to acquire new skills or transition into emerging sectors. Urban areas—particularly those with established tech ecosystems—are better positioned to adopt and capitalize on AI technologies. As a result, the uneven distribution of AI’s advantages exacerbates income inequality, deepens the divide between skilled and unskilled labor, and reinforces the urban-rural gap. Without substantial investments in reskilling programs and digital infrastructure, many individuals risk being left behind in the rapidly evolving technological landscape. Income Inequality in the Age of Automation(Impact of Artificial Intelligence on Employment and Wages) Artificial Intelligence has taken the world by its rapid evolution and automation process from small- medium companies to large organisations in all sectors of work. Artificial Intelligence (AI) is rapidly transforming industries worldwide. According to the International Monetary Fund (IMF), in advanced economies, about 60% of jobs may be impacted by AI (Cazzaniga et al., n.d.), with roughly half potentially benefiting from AI integration and the other half facing reduced labor demand, leading to lower wages and reduced hiring(The Impact of AI on Job Automation and Employment, n.d.). By examining how AI and automation are reshaping the global job market (Research: How Gen AI Is Already Impacting the Labor Market, n.d.)deepening income inequality, and offering new opportunities, exploring the landscapes of Africa, Asia, America, and Europe to understand the multifaceted impacts of AI on employment and wages. Understanding Automation and AI Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction. Automation involves using technology to perform tasks with reduced human intervention(AI Impacts in BLS Employment Projections : The Economics Daily: U.S. Bureau of Labor Statistics, n.d.-a). Historically, automation has evolved from the mechanization during the Industrial Revolution to the current Fourth Industrial Revolution (4ir), characterized by the fusion of technologies blurring the lines between the physical, digital, and biological spheres. Current trends include machine learning, robotics, generative AI, and robotic process automation (RPA), all contributing to significant shifts in labor markets (AI Could Widen the Equality Gap between Rich and Poor Nations \| World Economic Forum, n.d.). Global Overview of AI’s Impact on Employment and Wages II.1 Job Displacement and Job Creation AI and automation are displacing routine and manual jobs. The U.S. Bureau of Labor Statistics projects that AI will primarily affect occupations whose core tasks can be replicated by generative AI, including roles in computer, legal, business, financial, and engineering sectors (AI Impacts in BLS Employment Projections : The Economics Daily: U.S. Bureau of Labor Statistics, n.d.-b). Conversely, AI is creating new job categories, such as data scientists, AI ethics consultants, and AI system trainers. These roles require advanced skills and are often concentrated in developed economies. II.11 Wage Polarization Developed vs. Developing Countries Technological advancements have contributed to increases in wage inequality across OECD countries. Evidence suggests that AI exposure may be associated with lower inequality between high- and low-wage workers within occupations, but overall, AI has not significantly altered the wage gap between high- and low-wage occupations (What Impact Has AI Had on Wage Inequality? \| OECD, n.d.). In advanced economies, AI poses greater risks and opportunities, with about 60% of jobs potentially impacted (International Monetary Fund (IMF), in Advanced Economies, about 60% of Jobs May Be Impacted by AI – Google Search, n.d.). In developing countries, the impact is less pronounced, with some evidence of job polarization in countries like Mexico and Brazil (Molina & Maloney, 2019). AI adoption in Africa is growing, with experts predicting that AI could contribute up to $1.2 trillion to Africa’s economy by 2030 (Teknikalitech.Com/Blog/Impact-of-Ai-on-Africa/, n.d.). AI is transforming key industries in Africa, including healthcare, agriculture, and education. For example, AI-powered chatbots are being used in public health to improve access to medical services. Africa faces challenges such as a skills gap, low R&D funding, and a digital divide, which hinder the full potential of AI adoption. Currently, Asia is leading in AI adoption, with developing economies in the Asia-Pacific region having gen AI adoption rates 30% higher than developed economies (Generation AI in Asia Pacific \| Deloitte Insights, n.d.). AI is impacting over 11 billion working hours per week across Asia Pacific, with students and employees leading the gen AI revolution (“Research,” n.d.). Despite high adoption rates, there are concerns about privacy risks, fraud, and employment impacts, which hinder widespread trust in AI technology (AI Adoption Accelerates In APAC, But Consumers Struggle With Trust, Security, And Job Displacement Concerns \| Scoop News, n.d.). AI is significantly impacting the U.S. job market, with occupations in computer, legal, business, financial, and engineering sectors being susceptible to AI-related impacts(AI Impacts in BLS Employment Projections : The Economics Daily: U.S. Bureau of Labor Statistics, n.d.-a). In Latin America, there is evidence of incipient job polarization in countries like Mexico and Brazil due to automation. Both regions face challenges in reskilling the workforce and addressing digital inequities to adapt to the changing job landscape. Europe has a strong policy framework for AI ethics and data privacy. AI adoption in the EU from 2012 to 2022 has been studied to understand its employment implications(Guarascio & Reljic, 2025). The EU is focusing on transitioning to green and tech jobs, with social safety nets and reskilling programs in place to support workers. Europe faces challenges such as an ageing population and regional disparities in AI readings. II.111 The Global Inequality Lens, Gender and Youth Dimensions AI is contributing to income inequality by disproportionately affecting low-skilled workers and certain demographics, leading to wage polarization. Developing countries may become dependent on foreign AI technologies, leading to concerns about digital colonialism and loss of autonomy. While AI boosts productivity, its benefits are not evenly distributed. High-income countries with better infrastructure, education systems, and digital readiness reap more rewards, deepening the divide with lower-income regions. Within countries, individuals with advanced digital skills and capital access benefit more, while low-skilled workers face greater displacement risks. Women and youth are disproportionately impacted by automation. A 2023 IMF report indicated that women are overrepresented in administrative jobs that are more likely to be automated. Young people entering the labor market face uncertainty about which skills will remain relevant. Countries that invest in AI education, upskilling, and accessible digital infrastructure are better positioned to minimize inequality. Artificial Intelligence is neither inherently good nor bad. it is a powerful tool shaped by how we choose to use it. Its impacts on employment and wages reflect broader systemic issues, inequitable education, infrastructure gaps, and uneven access to technology. If global and regional actors work together, governments, industry, academia, and civil society, we can ensure that AI uplifts rather than divides. In low-resource settings like Africa, targeted investments in inclusive innovation, local talent, and ethical AI use will be key to ensuring no one is left behind in this technological transformation. Policy Responses and Recommendations As AI takes a toll on the job market and continues to exacerbate income inequalities and job displacement, by taking up jobs of humans and performing tasks conveniently, targeted policy responses are essential to mitigate their disruptive effects and promote inclusive growth. Without any intervention, it shall only exaggerate the problem and technological advancements shall take place, with proper strategies in place. Following are a few policy responses and recommendations- Investment in skill development and education- A rise in investment expenditure with respect to human capital formation needs to be done in all economies to ensure and emphasize on critical thinking, digital literacy, and adaptability. Partnering with Ed-tech companies can prove to be useful for the government and providing training to youth can help upscale the workforce and make efficient use of the human capital. Vocational training and community colleges can play a pivotal role in equipping workers with skills for emerging fields such as data analysis and AI management. Ensuring social safety- Enhanced social protection is crucial as automation and rise of AI replaces a plethora of jobs. Policymakers should expand unemployment insurance, healthcare access, and pension schemes to cover gig workers and informal laborers. Minimum wage system, ensuring basic healthcare and lifestyle amenities could also aid in minimizing the problems caused. Coordinated Global Governance- Since technological disruption is spread everywhere beyond national boundaries, international cooperation is crucial. Shared frameworks on data rights, AI ethics, and cross-border labor standards can prevent a race to the bottom in wages and protections. In sum, strategic and inclusive policymaking can ensure that the gains from automation are broadly shared, transforming technological disruption into an opportunity for widespread economic empowerment. Conclusion: The global job market is clearly going through a big shift because of the rapid growth of automation, robotics, and artificial intelligence. These technologies can boost productivity, spark innovation, and open up new economic opportunities but they also bring serious challenges. Many routine and mid-level jobs are at risk, and this can lead to job losses, a more divided job market, and rising income inequality. Data shows that people doing routine tasks or with moderate skills are more likely to be affected, while those who own businesses or have advanced tech skills tend to benefit the most. If governments don’t take thoughtful and inclusive action, the gap between rich and poor could grow even wider, especially across different regions, backgrounds, and education levels. Still, this tech revolution doesn’t have to come at the cost of fairness. With smart investments in things like digital infrastructure, social safety nets, education, and helping people learn new skills, societies can adapt. Encouraging lifelong learning will be a key part of making sure no one is left behind. References:	2025-05-19T00:00:00	2025/05/19	https://iisppr.org.in/the-impact-of-artificial-intelligence-robotics-and-automation-on-employment-trends-and-income-inequality-copy/	[ { "date": "2025/06/01", "position": 47, "query": "job automation statistics" } ]
These Jobs Won't Exist in 24 Months: What the Future of ...	These Jobs Won’t Exist in 24 Months: What the Future of Work Looks Like	https://medium.com	[ "Eva Lau" ]	Routine data entry is increasingly handled by robotic process automation (RPA) tools. These software robots can input, process, and verify large volumes of data ...	These Jobs Won’t Exist in 24 Months: What the Future of Work Looks Like Eva Lau 3 min read · Jun 17, 2025 -- Share Photo by Saulo Mohana on Unsplash In a recent The Diary of the CEO YouTube live stream with the Godfather of AI Geoffrey Hinton the host provides a sobering look at the rapid pace of change in the job market. According to the video, several roles that are common today could be obsolete within just two years. This article breaks down the specific jobs highlighted in the video, explores the forces driving these shifts, and considers what this means for workers and job seekers. The Jobs Facing Extinction The video points to automation, artificial intelligence, and changing business models as key reasons why certain jobs will disappear soon. Here are some of the main roles the host mentions: 1. Telemarketers and Cold Callers With AI-powered chatbots and advanced customer relationship management (CRM) systems, companies are automating outbound calls and customer engagement. According to a report by McKinsey, up to 50% of sales-related tasks could be automated…	2025-06-17T00:00:00	2025/06/17	https://medium.com/@writerdotcom/these-jobs-wont-exist-in-24-months-what-the-future-of-work-looks-like-7a95230cb548	[ { "date": "2025/06/01", "position": 58, "query": "job automation statistics" } ]
Stitch Fix Careers & Employment \| Come Work With Us	Stitch Fix Careers & Employment	https://www.stitchfix.com	[]	Machine Learning Engineer - Recommendation Algorithms and Foundational Models ... Go-to-Market (GTM) Lead. Remote, USA. Merchandising. Assistant Buyer. Remote ...	We’re a team of bright, kind individuals who are motivated by challenge and who care deeply about achieving great things. We know our individual strengths, but believe we only win as a team. We’re transforming the way people find what they love - and we need your big ideas. We just might be the perfect fit.	2025-06-01T00:00:00		https://www.stitchfix.com/careers/jobs?srsltid=AfmBOootz0wYDLt1E2l8WjxgbWDQvURrDAFCqp5ZYpgI05yVlI2KmEME	[ { "date": "2025/06/01", "position": 86, "query": "machine learning job market" } ]
Will the AI takeover spare politicians? Expert predicts 3 unexpected ...	Will the AI takeover spare politicians? Expert predicts 3 unexpected careers that could survive by 2045	https://m.economictimes.com	[]	He proposes bold experiments in social structure, such as universal basic income and new models of wealth distribution, to ensure that progress ...	Three Jobs May Survive, But That’s Not Comforting You Might Also Like: AI can't steal this one human skill, and it could be your ticket to career success before it catches up From a Work Economy to a Post-Work Society? You Might Also Like: Bill Gates predicts only three jobs will survive the AI takeover. Here is why What AI Still Can’t Do The Ethical Bet of the Century In a bold and unsettling forecast, Adam Dorr, Director of Research at RethinkX , has painted a future where most human jobs may no longer exist by 2045. In an interview with The Guardian, Dorr outlined how the relentless pace of AI and robotics development is fast rendering traditional human labor obsolete. According to his research—based on over 1,500 cases of historical technological disruptions—AI is following a well-trodden but accelerated trajectory: once a new tech takes hold, it replaces existing systems within 15 to 20 years.This time, the target isn’t a tool or a technique—it’s human labor itself.In a twist of irony, the three professions that Adam Dorr believes will withstand the AI onslaught are also among humanity’s oldest: politicians, sex workers, and ethicists. Their resilience lies not in resistance to change, but in their uniquely human foundations—power, intimacy, and morality.Politics has existed in organized form since ancient civilizations like Mesopotamia, Egypt, and Greece. At its core, governance is about navigating human complexity, building consensus, reading emotional undercurrents, and exercising judgment in unpredictable social environments. Despite advances in AI-generated policy simulation, leadership still hinges on trust, charisma, and interpersonal negotiation—qualities machines have yet to convincingly replicate.Sex work, often called “the world’s oldest profession,” similarly depends on human connection, physical presence, and emotional nuance. While AI-powered companionship and virtual intimacy are on the rise, some experts might contend that the core experience of human closeness and vulnerability remains beyond artificial reproduction.Ethicists, meanwhile, hold the moral compass of society. As we usher in powerful technologies capable of reshaping civilization, the need for ethical reasoning and philosophical guidance becomes more urgent—not less. Machines can process data, but they cannot weigh values, assess right and wrong in grey areas, or guide societies through moral dilemmas that lack precedent.While the prediction is stark, Dorr isn't entirely pessimistic. He envisions a future defined not by scarcity but by “super-abundance,” where machines meet most human needs. But such a vision demands urgent and radical changes to how society defines work, value, and ownership.“If we fail to act now,” Dorr cautions, “the consequences will be enormous—economic inequality on a scale we’ve never seen before.” He proposes bold experiments in social structure, such as universal basic income and new models of wealth distribution, to ensure that progress doesn’t leave humanity behind.Interestingly, even as machines learn to perform complex medical diagnoses or develop apps on command, they continue to falter in one essential area: soft skills. Emotional intelligence, empathy, collaboration, and ethics—these remain stubbornly human domains. In a recent USA Today report, HR expert Madeline Mann noted that soft skills are now more important than ever. “It’s how people experience you,” she said. “That’s the edge AI can’t replicate.”Even in the most technical fields, over 40% of in-demand skills are those AI still can’t master, such as critical thinking, adaptability, and authentic human communication.As AI systems inch closer to replacing cognitive labor at scale, the ethical dilemma becomes unavoidable: Just because we can replace humans—should we? And if we do, how do we preserve dignity, purpose, and human connection in a world run by machines?Perhaps the real question is not about what AI can do, but what we choose to let it do. Because the biggest risk isn’t just job loss—it’s losing sight of what it means to be human.	2025-06-01T00:00:00		https://m.economictimes.com/magazines/panache/will-the-ai-takeover-spare-politicians-expert-predicts-3-unexpected-careers-that-could-survive-by-2045/articleshow/122434833.cms	[ { "date": "2025/06/01", "position": 97, "query": "universal basic income AI" } ]
The Green Party's Universal Basic Illusion - Community Scoop	The Green Party’s Universal Basic Illusion	https://community.scoop.co.nz	[]	Another justification for UBI is the coming wave of automation. As jobs are replaced by AI and machines, we are told, we need a universal income ...	Press Release – Aotearoa Workers Solidarity Movement The Green Partys UBI is a reformist containment strategy, not a pathway to liberation. The Green Party of Aotearoa New Zealand, long considered the progressive conscience of Parliament, has proposed an Income Guarantee, a universal, unconditional payment that would replace or simplify several parts of the welfare system. Framed as a liberating policy to reduce poverty, support unpaid labour, and prepare for a future where work may be scarcer, it has garnered enthusiastic support among progressives. But this proposal is not the radical solution it pretends to be. Instead, it reflects a greenwashed attempt to stabilise capitalism by offering just enough relief to avoid revolt. Far from challenging the structural roots of inequality, private property, wage labour, and capitalist accumulation, the Green Party’s UBI functions as a sedative, dulling the sharp edges of exploitation while entrenching the system that causes it. The Green Party’s UBI is a reformist containment strategy, not a pathway to liberation. Its implementation would cushion the worst aspects of capitalist life, but in doing so, it would pacify resistance, entrench private ownership, and ultimately protect the interests of capital. What the Greens Propose In 2023, the Green Party unveiled a rebranded version of UBI called the Income Guarantee. This scheme offers: A weekly payment of at least NZD $385 to all adults not in paid work, including students and carers. Higher rates for single parents and families with children. A restructuring of existing welfare benefits, replacing Jobseeker, Sole Parent Support, and Working for Families with a unified baseline payment. A new agency (replacing ACC) to guarantee 80% of minimum wage for those unable to work due to illness or disability. No work obligations, sanctions, or means-testing for this baseline. The Greens frame this as a way to value unpaid work, decouple survival from employment, and support dignity in a time of rising precarity. They also claim that it simplifies bureaucracy and builds trust in people to use the payment in ways that work for their lives. But while these ideas may seem empowering on paper, they carry deep contradictions, particularly when implemented within a capitalist framework. Reforming the System That Creates Poverty The first and most glaring issue with the Greens’ Income Guarantee is that it leaves intact the very system that causes poverty and precarity in the first place. People are not poor because there is no universal income; they are poor because the means of production, land, housing, food, energy, are privately owned and controlled by a small class of capitalists. By funnelling a state stipend into a market dominated by landlords, bosses, and corporate monopolies, the Greens’ UBI model subsidises capital, not challenges it. The landlord still sets the rent. The supermarket still sets the price of bread. The corporation still determines wages and hours. A “universal income” becomes a universal transfer of public money to private pockets. This is not wealth redistribution, it’s redistribution of dependency. The Greens imagine that by putting cash in your pocket, they are empowering you. But as long as that cash has to pass through the hands of property owners and profiteers, it simply recirculates back into the capitalist machine. Flat Payments in an Unequal World The Green Party’s rhetoric of “universality” masks a dangerous flattening of difference. By giving the same baseline income to all regardless of need, the policy shifts away from needs-based welfare to a market-mediated minimalism. This sounds fair on the surface, but it has regressive implications. A wealthy investor and a single parent receive the same base rate. Meanwhile, tailored supports for disability, illness, or chronic hardship are pared back, replaced with a one-size-fits-all payment that ignores the complexity of human need. While the Greens claim that specialised supports would still exist, the logic of simplification, driven by administrative efficiency and cost, risks future erosion of more expensive targeted benefits. This is not an idle concern. Across the world, UBI experiments have been used to justify welfare cutbacks, particularly under conservative governments that follow. In the long run, a flat payment becomes an excuse to individualise poverty, treating everyone the same while leaving structural inequalities untouched. UBI as Austerity in Disguise UBI can become a tool of austerity, not generosity. By packaging welfare reform as “universal empowerment,” the state absolves itself of responsibility for meeting complex needs. It shifts risk back onto the individual giving them a cash payment, but removing the broader safety net that once protected people from market volatility. In practice, this leads to privatised hardship – disabled people navigating inaccessible housing markets on a flat income; sole parents forced to stretch meagre funds across rent, food, transport, and children’s needs; sick workers unable to afford care once the specialised benefits disappear. UBI may be universal, but its effects are not equal. It entrenches the neoliberal logic that you are responsible for surviving the system, even as the system remains rigged against you. The Work Fetish in Reverse A key selling point of the Green UBI is that it allows people to work less and to study, care for whanāu, volunteer, create art, or simply rest. This is undeniably attractive. For many, the dream of decoupling survival from employment is liberatory. However, UBI doesn’t abolish work, it just reorganises who gets to do less of it. The means of production still belong to someone else. People may reduce hours or leave exploitative jobs but they still must buy back access to life from those who own it. Without seizing control of land, housing, food systems, and workplaces, UBI only offers a slower treadmill, not a way off. True liberation from work requires not just the absence of compulsion, but the presence of collective power to shape what, how, and why we produce. Under capitalism, UBI is not freedom from work it is still just freedom to consume what others profit from. Automation and the Myth of Post-Work Capitalism Another justification for UBI is the coming wave of automation. As jobs are replaced by AI and machines, we are told, we need a universal income to ensure people aren’t left behind. This argument is both outdated and naïve. Automation is not new it has always accompanied capitalism. And rather than freeing us from labour, it has consistently resulted in: Job displacement for the many, Wealth concentration for the few, And a race to the bottom for those still working. Without changing the ownership of technology and the surplus it generates, automation becomes a weapon against workers, not a liberation. UBI does not challenge this, it merely proposes a bribe to stay quiet while the rich get richer from robotic productivity. If we want automation to free us, we must demand common ownership of its fruits, not a state-managed allowance. Depoliticising the Class Struggle UBI has a profoundly depoliticising function. By providing everyone a basic income, it suggests that class conflict can be solved through technocratic redistribution, rather than collective struggle. It individualises economic survival and replaces mutual aid with state-administered charity. The Greens often present this as “trusting people.” But in truth, it is a move away from politics altogether, away from strikes, occupations, assemblies, and direct action. It encourages people to become passive consumers of state policy rather than active agents of transformation. This is no accident. UBI fits comfortably within the liberal logic of non-confrontational progressivism – small gains, managed well, with no need to question who owns what or why. But anarcho-communists know that liberation is not granted it is seized. The abolition of wage labour, rent, and bosses does not come from a Treasury paper. It comes from resistance, solidarity, and revolt. The Green Fetish for Policy Without Revolution Ultimately, the Green Party’s UBI is a reflection of their broader political project – a capitalism with a conscience. Their aim is to regulate, reform, and humanise the existing system not to overturn it. This is the great tragedy of Green politics: it mobilises the language of justice to protect the architecture of oppression. They speak of liberation while fearing confrontation. They dream of balance sheets, not barricades. The Income Guarantee is not a step toward socialism. It is a safety valve for capitalism, designed to prevent breakdown by making survival just bearable enough to forestall uprising. As long as the Greens seek legitimacy in Parliament, they will remain managers of compromise, not agents of emancipation. Toward a Real Alternative Anarcho-communists do not oppose the idea of everyone having their needs met. But we reject the idea that this must come in the form of a wage or income. We do not want better access to markets we want a world without them. Imagine a society where housing is free because it is collectively owned. Where food is grown and shared in community gardens, not bought. Where care work is respected and supported through mutual aid, not commodified. Where education, transport, and health are decommodified. Where people work not for profit, but for one another. This is not utopia. It exists in fragments already in marae, solidarity kitchens, workers’ co-ops, and mutual aid networks. These are the embryos of a post-capitalist future. We don’t need a basic income. We need basic expropriation. We need the end of property, not its pacification. No Wages, No Compromise The Green Party’s UBI plan, however well-intentioned, is not a solution to poverty. It is a reformist illusion, an elegant attempt to stabilise a decaying system without addressing the violence at its core. It replaces welfare with technocracy, struggle with dependence, and solidarity with state charity. We say: No wages. No landlords. No bosses. No income guarantees only freedom from all need for income at all. We do not ask for a universal basic income. We demand a universal reclaiming of life itself. Content Sourced from scoop.co.nz Original url	2025-06-01T00:00:00		https://community.scoop.co.nz/2025/07/the-green-partys-universal-basic-illusion/	[ { "date": "2025/06/01", "position": 99, "query": "universal basic income AI" } ]
What It Means for Workforce Planning and Talent Acquisition	The Ripple Effect of AI Adoption: What It Means for Workforce Planning and Talent Acquisition	https://www.linkedin.com	[ "Diego Cresceri", "Steve Gard", "Siobhan Savage", "Athar Musharraf", "Policy Support Representative Looking To Grow Within The Underwriting Field." ]	As AI adoption transforms job roles, employees need to see clear paths for growth, or they'll look elsewhere. Investing in reskilling and internal mobility isn' ...	AI is no longer a future-facing buzzword. It’s a present-day force actively reshaping how businesses operate, compete, and hire. From streamlining internal operations to revolutionizing customer experiences, artificial intelligence is being integrated at every level, across many functions and industries. But amid the headlines, one ripple effect often gets overlooked: how AI adoption is changing the makeup of the modern workforce. As a staffing and consulting firm, we’re seeing it up close, and not just from a hiring lens. Increasingly, clients are turning to us, not only to identify the right talent, but also to consult on where and how AI can enhance their business. Here's how AI is altering the talent landscape, and what organizations need to consider to stay ahead. 1. The Skills Gap Is Shifting The acceleration of AI is widening an already-complex skills gap. But today, it's not just about hard skills, such as Python or data modeling. Soft skills like adaptability, critical thinking, and cross-functional collaboration are becoming just as critical. Companies are now rethinking how they define “qualified.” Roles that didn’t require technical literacy five years ago now demand some level of comfort with digital tools and automation. As a result, workforce planning requires a more nuanced view, one that balances current capabilities with the potential to grow into new roles. At Brilliant, we’re helping clients assess team strengths and identify where AI-driven tools can improve processes. Whether it’s integrating ChatGPT into workflows or evaluating platforms for financial automation, we bring a practical approach to tech adoption. 2. Job Titles Are Changing, Not Just Disappearing There's a lot of noise about jobs being replaced by AI. But in reality, many jobs are being redefined, not removed. Traditional roles are evolving into hybrid positions that combine traditional responsibilities with new tech fluency: Administrative Assistants are becoming Workplace Technology Coordinators Recruiters are learning to leverage AI-driven sourcing tools Financial Analysts are expected to interpret AI-generated insights alongside their own Forward-looking companies are investing in upskilling and job redesign, not just replacement. It’s less about headcount reduction, and more about workforce evolution. Brilliant supports this evolution by providing senior-level accounting and finance professionals, who are proficient in utilizing automation tools to optimize reporting, enhance analysis, and strengthen compliance functions. 3. Talent Acquisition Needs a New Playbook AI may help recruiters automate some of their workflows, but it’s also forcing a rethink of what “top talent” looks like. Today, it's not just about technical proficiency, it's about learning agility. Hiring managers must now evaluate candidates for their ability to adapt to rapidly evolving tools and processes. That requires a more strategic, long-term approach to talent acquisition. It also reinforces the value of contract, contract-to-hire, and project-based staffing models, which allow companies to stay agile while evaluating true fit in a changing environment. 4. Internal Mobility and Retention Just Got More Important As AI adoption transforms job roles, employees need to see clear paths for growth, or they’ll look elsewhere. Investing in reskilling and internal mobility isn’t just a retention strategy, it’s essential to staying competitive. We’re seeing companies take a fresh look at their workforce through the lens of potential, not just past experience. Internal talent marketplaces, upskilling programs, and cross-training are becoming key tools in retaining adaptable employees in a shifting market. Final Thoughts AI is changing the game, but not in a one-size-fits-all way. Each company’s journey with automation will look a little different, but the common thread is this: the future of work will reward agility, both in people and in planning. For organizations, this means rethinking workforce strategy to align with evolving roles, emerging skills, and a more dynamic view of talent. That’s where we come in. At Brilliant Management Resources, we do more than identify the right talent. We partner with you to assess where AI and emerging technologies can improve workflows, elevate roles, and unlock capacity.	2025-06-01T00:00:00		https://www.linkedin.com/pulse/ripple-effect-ai-adoption-what-means-workforce-planning-dkk7c	[ { "date": "2025/06/01", "position": 16, "query": "workplace AI adoption" } ]
Companies are hiring less in roles that AI can do	The heart of the internet	https://www.reddit.com	[]	AI powered robots are also now starting to be able to do blue collar work and manual labor. Previously automation led to poeple moving to the jobs that were not ...	A subreddit devoted to the field of Future(s) Studies and evidence-based speculation about the development of humanity, technology, and civilization. -------- You can also find us in the fediverse at - https://futurology.today Members Online	2025-06-01T00:00:00		https://www.reddit.com/r/Futurology/comments/1l5sdpm/new_data_confirms_it_companies_are_hiring_less_in/	[ { "date": "2025/06/01", "position": 38, "query": "workplace AI adoption" } ]
ZDNet: How AI is changing the workplace	AI Adoption Accelerates Across Businesses – But What Does It Mean for Workers?	https://www.linkedin.com	[ "View Profile" ]	AI Adoption Accelerates Across Businesses – But What Does It Mean for Workers?** A new report from ZDNet highlights a significant shift in how businesses ...	AI Adoption Accelerates Across Businesses – But What Does It Mean for Workers? A new report from ZDNet highlights a significant shift in how businesses are leveraging artificial intelligence, with adoption rates surging dramatically. Salesforce and Slack’s recent survey of 5,000 desk workers globally reveals that daily AI usage has more than doubled within the last six months. This represents a substantial increase compared to previous trends and indicates a growing integration of AI tools into everyday workflows. The survey data points to a concerted effort by business leaders to encourage and facilitate employee use of AI-powered applications. While the specific drivers behind this push remain varied – including increased efficiency, automation of routine tasks, and potential cost savings – the rapid uptake raises important questions about the impact on the workforce. Several factors contribute to this trend. The increasing availability of accessible and user-friendly AI tools is undoubtedly a key element. Furthermore, businesses are recognizing the potential for AI to augment human capabilities rather than replace them entirely, focusing on streamlining processes and freeing up employees for more strategic work. However, concerns regarding training needs, data privacy, and the evolving role of workers in an increasingly automated environment are also emerging. The survey’s findings underscore a critical juncture for businesses – one where thoughtful implementation and proactive workforce development strategies will be essential to maximizing the benefits of AI while mitigating potential challenges. It's clear that this isn’t just about adopting new technology; it’s about fundamentally rethinking how work gets done. Don't forget to follow me to stay up to date with the latest in AI and business!	2025-06-01T00:00:00		https://www.linkedin.com/posts/hamishfromatech_business-leaders-continue-to-push-workers-activity-7344519966555033601-976J	[ { "date": "2025/06/01", "position": 60, "query": "workplace AI adoption" } ]
New Data Confirms That AI Is Already Taking Human Jobs, Roles	New Data Confirms That AI Is Already Taking Human Jobs, Roles	https://www.businessinsider.com	[ "Aki Ito" ]	Most people assume it will take years for AI to reshape the job landscape. But tasks that chatbots can do are already vanishing from job ...	In March, Shopify's CEO told his managers he was implementing a new rule: Before asking for more head count, they had to prove that AI couldn't do the job as well as a human would. A few weeks later, Duolingo's CEO announced a similar decree and went even further — saying the company would gradually phase out contractors and replace them with AI. The announcements matched what I've been hearing in my own conversations with employers: Because of AI, they are hiring less than before. When I first started reporting on ChatGPT's impact on the labor market, I thought it would take many years for AI to meaningfully reshape the job landscape. But in recent months, I've found myself wondering if the AI revolution has already arrived. To answer that question, I asked Revelio Labs, an analytics provider that aggregates huge reams of workforce data from across the internet, to see if it could tell which jobs are already being replaced by AI. Not in some hypothetical future, but right now — today. Zanele Munyikwa, an economist at Revelio Labs, started by looking at the job descriptions in online postings and identifying the listed responsibilities that AI can already perform or augment. She found that over the past three years, the share of AI-doable tasks in online job postings has declined by 19%. After further analysis, she reached a startling conclusion: The vast majority of the drop took place because companies are hiring fewer people in roles that AI can do. Next, Munyikwa segmented all the occupations into three buckets: those with a lot of AI-doable tasks (high-exposure roles), those with relatively few AI-doable tasks (low-exposure roles), and those in between. Since OpenAI released ChatGPT in 2022, she found, there has been a decline in job openings across the board. But the hiring downturn has been steeper for high-exposure roles (31%) than for low-exposure roles (25%). In short, jobs that AI can perform are disappearing from job boards faster than those that AI can't handle. Which jobs have the most exposure to AI? Those that handle a lot of tech functions: database administrators, IT specialists, information security, and data engineers. The jobs with the lowest exposure to AI, by contrast, are in-person roles like restaurant managers, foremen, and mechanics. This isn't the first analysis to show the early impact of AI on the labor market. In 2023, a group of researchers at Washington University and New York University homed in on a set of professionals who are particularly vulnerable: freelancers in writing-related occupations. After the introduction of ChatGPT, the number of jobs in those fields dropped by 2% on the freelancing platform Upwork — and monthly earnings declined by 5.2%. "In the short term," the researchers wrote, "generative AI reduces overall demand for knowledge workers of all types." At Revelio Labs, Munyikwa is careful about expanding on the implications of her own findings. It's unclear, she says, if AI in its current iteration is actually capable of doing all the white-collar work that employers think it can. It could be that CEOs at companies like Shopify and Duolingo will wake up one day and discover that hiring less for AI-exposed roles was a bad move. Will it affect the quality of the work or the creativity of employees — and, ultimately, the bottom line? The answer will determine how enduring the AI hiring standstill will prove to be in the years ahead. Some companies already appear to be doing an about-face on their AI optimism. Last year, the fintech company Klarna boasted that its investment in artificial intelligence had enabled it to put a freeze on human hiring. An AI assistant, it reported, was doing "the equivalent work of 700 full-time agents." But in recent months, Klarna has changed its tune. It has started hiring human agents again, acknowledging that its AI-driven cost-cutting push led to "lower quality." "It's so critical that you are clear to your customer that there will always be a human," CEO Sebastian Siemiatkowski told Bloomberg. "Really investing in the quality of the human support is the way of the future for us." Will there be more chastened Siemiatkowskis in the months and years ahead? I'm not betting on it. All across tech, chief executives share an almost religious fervor to have fewer employees around — employees who complain and get demotivated and need breaks in all the ways AI doesn't. At the same time, the AI tools at our disposal are getting better and better every month, enabling companies to shed employees. As long as that's the case, I'm not sure white-collar occupations face an optimistic future. Even Siemiatkowski still says he expects to reduce his workforce by another 500 through attrition in the coming year. And when Klarna's technology improves enough, he predicts, he'll be able to downsize at an even faster pace. Asked when that point will come, he replied: "I think it's very likely within 12 months." Aki Ito is a chief correspondent at Business Insider.	2025-06-02T00:00:00		https://www.businessinsider.com/ai-hiring-white-collar-recession-jobs-tech-new-data-2025-6	[ { "date": "2025/06/02", "position": 72, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 72, "query": "AI employment" }, { "date": "2025/06/02", "position": 70, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 72, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 91, "query": "AI employment" }, { "date": "2025/06/02", "position": 87, "query": "AI employment" }, { "date": "2025/06/02", "position": 68, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 84, "query": "AI employment" }, { "date": "2025/06/02", "position": 54, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 51, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 47, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 73, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 69, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 68, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 51, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 59, "query": "AI employment" }, { "date": "2025/06/02", "position": 92, "query": "AI employment" }, { "date": "2025/06/02", "position": 68, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 71, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 48, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 49, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 92, "query": "AI employment" }, { "date": "2025/06/02", "position": 65, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 71, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 47, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 73, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 51, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 61, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 70, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 3, "query": "ChatGPT employment impact" }, { "date": "2025/06/02", "position": 46, "query": "artificial intelligence employment" } ]
Future Jobs: Robots, Artificial Intelligence, and Digital Platforms in ...	Future Jobs: Robots, Artificial Intelligence, and Digital Platforms in East Asia and Pacific	https://www.worldbank.org	[]	Robots are displacing industrial workers in routine manual task occupations (e.g., assembly line operators). AI threatens to displace services ...	Key insights & Data highlights New technologies are affecting labor markets and the nature of work by displacing, augmenting or creating new tasks performed by workers. Robots are displacing industrial workers in routine manual task occupations (e.g., assembly line operators). AI threatens to displace services workers not only in routine tasks (e.g., risk assessors) but increasingly in nonroutine cognitive tasks (e.g., interpreters). AI-empowered robots could also impact workers in nonroutine manual occupations. Both AI and digital platforms may lead to the creation of new tasks (e.g., AI-prompt engineers and cloud engineers). Interact with the chart below by clicking on it to reveal the elements of the framework.	2025-06-02T00:00:00		https://www.worldbank.org/en/region/eap/publication/future-jobs	[ { "date": "2025/06/02", "position": 12, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 22, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 84, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 10, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 36, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 13, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 36, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 94, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 89, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 90, "query": "artificial intelligence employers" }, { "date": "2025/06/02", "position": 19, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 90, "query": "artificial intelligence employers" }, { "date": "2025/06/02", "position": 11, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 90, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 89, "query": "artificial intelligence employers" }, { "date": "2025/06/02", "position": 12, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 19, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 94, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 36, "query": "artificial intelligence employers" }, { "date": "2025/06/02", "position": 19, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 91, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 85, "query": "artificial intelligence employers" }, { "date": "2025/06/02", "position": 12, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 18, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 11, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 94, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 14, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 91, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 11, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 87, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 19, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 90, "query": "AI labor market trends" }, { "date": "2025/06/02", "position": 11, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 81, "query": "artificial intelligence employers" }, { "date": "2025/06/02", "position": 11, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 12, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 87, "query": "artificial intelligence employers" }, { "date": "2025/06/02", "position": 86, "query": "artificial intelligence employers" }, { "date": "2025/06/02", "position": 19, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 38, "query": "AI impact jobs" }, { "date": "2025/06/02", "position": 11, "query": "robotics job displacement" }, { "date": "2025/06/02", "position": 20, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 38, "query": "artificial intelligence labor union" }, { "date": "2025/06/02", "position": 9, "query": "automation job displacement" }, { "date": "2025/06/02", "position": 25, "query": "future of work AI" }, { "date": "2025/06/02", "position": 6, "query": "job automation statistics" }, { "date": "2025/06/02", "position": 4, "query": "robotics job displacement" } ]
Navigating the AI skills gap: aligning leadership vision with frontline ...	Navigating the AI skills gap: aligning leadership vision with frontline capabilities	https://tech.yahoo.com	[]	As AI transforms ways of working and skills gaps widen, organizations must act now to equip employees with the knowledge necessary to understand ...	When you buy through links on our articles, Future and its syndication partners may earn a commission. Credit: Shutterstock As the promise of AI to rapidly reshape industries intensifies, the gap between having an understanding of AI capabilities and the skills to implement AI solutions continues to widen. This divide is particularly pronounced between senior leadership, who drive digital transformation, and frontline workers, who are expected to implement and adapt to these changes and use this technology in their everyday work. Recent research highlights the consequences of the AI skills gap, with one-third of UK employees feeling unprepared to adopt AI in the next one to three years. This disconnect between strategy and day-to-day execution on the ground is further underscored by the fact that 77% of UK tech workers admit to pretending they know more about AI than they actually do – illustrating the urgent need for organizations to bridge this gap and promote organization-wide AI literacy. Advertisement Advertisement Advertisement To address this divide, businesses must move beyond top-down mandates and build AI literacy across their entire workforce. Let’s explore why its important to act now, and how to achieve this in a scalable and effective way. Why businesses need AI literacy now According to the World Economic Forum’s 2025 Future of Jobs Report, 39% of current skills in the workforce will become outdated within the next five years, with skills gaps remaining the biggest obstacle to organizational preparedness for future markets. As AI transforms ways of working and skills gaps widen, organizations must act now to equip employees with the knowledge necessary to understand AI applications and leverage them effectively. First and foremost, businesses must recognize that AI literacy is no longer a nice-to-have, but a necessity. Employees need a foundational understanding of how AI works, where it adds business value and how it can be integrated into daily operations. AI has the power to enhance efficiency, streamline workflows and improve business operations, transforming organizations across industries. A key element to upskilling efforts beyond understanding general AI capabilities is equipping team members with the ability to identify the opportunities for AI. They should also focus on building the mindset and awareness required to use AI effectively. Advertisement Advertisement Advertisement For IT professionals, understanding AI fundamentals, such as ethical use, large language modelling and data privacy, is crucial. But technical proficiency alone isn’t enough. Power skills, like critical thinking, communication, experimentation, curiosity and resilience, will be equally important for navigating complex environments and driving innovation. A combination of technical and power skills ensures employees can thrive in their current roles, adapt to evolving technologies and build skills for the future. To embed AI literacy across the entire organization, leadership must take an active role in championing AI literacy initiatives. Without visible executive support, companies risk fragmented adoption and widening disparities in AI understanding between senior leaders and frontline workers. AI must be embraced holistically across all levels, from the boardroom to the frontline. Assessing existing skillsets With concern over the AI skills gap growing, 66% of C-Suite executives plan to recruit external AI-skilled talent, while 34% intend to ‘build’ talent internally by training existing employees. This split reflects the broader challenge of staying competitive in a landscape where AI capabilities are impacting the business landscape at a rapid pace. However, as skill lifespans shorten, especially in areas like machine learning, generative AI and data science, businesses can’t solely rely on external hires to stay ahead. The pace of change means that today’s skills can quickly become outdated and hiring new talent each time a skill becomes obsolete is not sustainable or cost-effective. Instead, organizations should strike a balance between hiring new talent and investing in continuous learning and reskilling for existing teams. Advertisement Advertisement Advertisement This starts by assessing the existing skillsets in their team. By conducting baseline evaluations, businesses can compare current skills against benchmarks to identify areas for improvement. This targeted approach ensures learning initiatives are relevant, measurable and aligned with strategic business goals, maximising resource efficiency and impact. Bringing existing employees along on this journey by assessing their existing AI skills and upskilling them appropriately will lead to deeper benefits beyond technical proficiency. This approach also boosts employee retention by demonstrating a clear investment in their growth while also improving the quality of and engagement in their work. Creating an AI literacy framework Rather than relying on ad hoc training sessions, organizations should establish structured, strategic AI literacy programs that equip frontline workers with the knowledge and skills required to identify AI use cases and drive AI adoption. Building this requires a multifaceted approach to learning, including programs that provide access to foundational AI and data skills, but they are only one piece of the puzzle. Programs such as instructor-led sessions that contextualize AI within specific roles and industries and simulation-based learning allow employees to engage with realistic, AI-powered scenarios. By embedding these learning experiences into workforce development, organizations can future-proof their workforce with the skills needed for the AI revolution. Advertisement Advertisement Advertisement Additionally, continuous learning and adaptability must be central to organizational culture, equipping employees with current and future required skilling opportunities, as technical skill lifespans shorten. Creating AI literacy frameworks ultimately helps teams stay ahead of technological shifts while building overall resilience. Achieving organization-wide AI literacy AI literacy is no longer just for tech teams. It’s a business imperative across the entire workforce. For businesses to reduce the AI skills gap, it becomes even more crucial to bridge the divide between senior leadership and frontline workers. By assessing existing skill sets, implementing comprehensive AI upskilling throughout the organization and fostering a culture of continuous learning, businesses can build an AI-ready workforce that is both prepared for and on board with their business strategy. We've featured the best productivity tool. This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro	2025-06-02T00:00:00	2025/06/02	https://tech.yahoo.com/ai/articles/navigating-ai-skills-gap-aligning-140839335.html	[ { "date": "2025/06/02", "position": 52, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 48, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 51, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 50, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 50, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 59, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 48, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 56, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 49, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 52, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 50, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 56, "query": "AI skills gap" } ]
AI Economic Disruption Is Changing Everything – Are You Ready?	AI Economic Disruption Is Changing Everything – Are You Ready?	https://www.birchgold.com	[ "Peter Reagan" ]	AI's massive economic impact is already here – from misinformation to unemployment. Here's how to secure your financial future…	News AI Disruption is Already Here: What It Means for Your Financial Security Artificial Intelligence is already disrupting entire industries and replacing human workers. And most of us aren’t prepared for the brave new world ahead. Learn how the AI economic revolution will change everything – especially your savings… While artificial Intelligence (AI) is the new kid on the block for technological innovation, it is already causing massive changes in our society and world. Most people don’t yet realize just how big and widespread these changes are, though. In some ways, the “brave, new world” is sneaking up on us. Now, I'm not going to try your patience with the details of programming or the debate about true artificial intelligence vs. today’s large language models (LLMs) like ChatGPT and Gemini. (Large language models are what most people are calling AI right now. If you have an inclination towards the philosophical and the technical, the debate on whether they show true sentience or if that is an illusion is an interesting topic to read up on.) No, what I'm going to get into is the more immediate impact of AI in our world already. Because these tools can access so much information so quickly, they can massively increase productivity. For example, if you have a smartphone (most of us in the U.S. do), you may have noticed, with more recent phone updates, prompts asking if you want to use AI to do things on your phone. It’s a rather enlightening experiment to do. AI provides some interesting and, at times, unnerving answers to your questions and requests. It provides them very quickly, often quite accurately. In fact, there are a number of tasks AI does significantly better than humans… Where AI is already better (and cheaper) than people Researchers and corporations have adopted current AI tools for a number of tasks – here are a few examples: Law: AI models that help review massive document sets in legal discovery, outperforming humans in identifying key documents. AI models that help review massive document sets in legal discovery, outperforming humans in identifying key documents. Medicine: In medical imaging interpretation, AI systems have been proven to outperform human radiologists in certain tasks, like detecting pneumonia or cancerous nodules in chest X-rays and CT scans. A notable example is Google's DeepMind AI, which matched or exceeded radiologists in identifying breast cancer from mammograms. In medical imaging interpretation, AI systems have been proven to outperform human radiologists in certain tasks, like detecting pneumonia or cancerous nodules in chest X-rays and CT scans. A notable example is Google's DeepMind AI, which matched or exceeded radiologists in identifying breast cancer from mammograms. Translation: AI-powered translation tools now provide more accurate translations for many language pairs than a typical human translator. AI-powered translation tools now provide more accurate translations for many language pairs than a typical human translator. Research: A specially-trained AI model outperforms biologists in protein folding prediction. A specially-trained AI model outperforms biologists in protein folding prediction. Logistics: IBM’s Watson AI manages supply chains better than human managers. These are just a few examples to give you an idea of just how rapidly AI is not only outperforming, but replacing, human workers. CEOs love this! Replacing a full-time employee with an always-on, never-sleeps computer saves money and increases profits. Great for companies, not so great for workers… I want to make this perfectly clear: Jobs are already being lost to AI. For example: …the list goes on. It’s no wonder a recent survey showed that most Americans want businesses to be cautious with AI. More than three-quarters of Americans (77%) want companies to create AI slowly and get it right the first time, even if that delays breakthroughs, the 2025 Axios Harris 100 poll found. When you break down the details of that survey, the age group most comfortable with racing headlong into an AI powered future is Millennials, and even 63% of them want businesses to be cautious. Nearly all (91%) of Baby Boomers polled wanted businesses to be cautious. What’s the concern? Everything is changing. In some parts of the economy, jobs are simply vanishing. While AI hasn’t revolutionized every industry, that doesn’t mean that it won’t in the future. Other concerns come up, though: “Others doubt AI's world-changing promises and instead see an innovation that will kill jobs and flood the world with bad information.” AI does a lot of things better than humans – but not everything. AI’s impact on education AI may be nearly brand new to the world, but it’s already throwing a giant wrench into the education world, just probably not in ways that you expected. You may have thought that AI would disrupt how teachers work and maybe displace teachers themselves, and that is a distinct possibility in the future. There are, after all, already efforts to create AI “life coaches" and AI "therapists.” Teaching could be not far behind. The way that education is being disrupted doesn’t have to do with taking away teachers’ jobs at this point. It is very different. Erica Pandey with Axios writes, Use [of AI] is ubiquitous in college. A survey of college students taken in January 2023, just two months after ChatGPT's launch, found that some 90% had already used it on assignments, New York Magazine reports. That’s right, AI is being used to cheat on college campuses right now. Maybe you’re saying that it’s just college campuses, though, and they’re old enough to know the fallout of their actions (to which I would point you to the reputation of college frat parties and the lack of understanding of consequences, but I digress…). On an amusing side note: [Stephen Cicirelli, an English professor at St. Peter’s University in Jersey City, New Jersey] captured the zeitgeist with a viral post on X about how one of his students got caught submitting an AI-written paper – and apologized with an email that also appeared to be written by ChatGPT. Funny, yes – but sad, too. It’s not just in colleges, though. 1 in 4 13- to 17-year-olds say they use ChatGPT for help with schoolwork, per a recent Pew survey. That’s double what it was in 2023. That's right 25% of middle school and high school students last year were already using AI to help with schoolwork. And that only mentioned ChatGPT. That statistic doesn’t include X's AI, Google’s AI, Facebook’s AI, or any other AI, so the true number of middle and high school AI users is probably already higher than 25%. AI is radically changing how students are approaching education. If our educational system doesn’t adapt, the next generation could graduate without learning anything beyond how to ask ChatGPT for answers to their homework. While that’s a real concern, it’s not as immediate as AI’s impact on the workforce… AI adoption means fewer jobs With every technological development, the economy changes. New firms rise, old firms fall – new skillsets become vital, while others become obsolete. This has always been true. Technology changes the economy and the job market. It always has! Here’s an example: Before petroleum products like kerosene and gasoline came along, whaling was a massive industry. Every year, thousands of whalers sailing the oceans, risking life and limb to harpoon whales. Why? Simply because whale oil was the best known source for both lantern fuel and industrial lubricants. But once engineers figured out how to process crude oil, kerosene emerged as a cheaper, safer, and more efficient alternative. Petroleum-based grease worked just as well as whale oil, too. The whaling industry virtually collapsed overnight. These economic shifts can be wrenching, but they’re nothing new. They’re simply the price of progress. Just like in the whaling example, the overall economy benefits. Even when individuals (whether they’re expert harpooners or whaling ship captains) suffer. They lose their jobs and, since the entire industry vanishes, their skills are no longer nearly as valuable. Back to the AI revolution. Will there be a permanent loss of employment? An article about Wharton Business School trained economist Paul Zane Pilzer notes: “...unemployment is not a macroeconomic problem, he says. It is a micro-economic issue, typically related to a skill deficiency on an individual level.” Historically, every technological advance has eliminated some jobs while creating new ones. Think of the Industrial Revolution: factory machines wiped out many artisan trades, but created a wave of new manufacturing and service jobs. In the long run, total employment typically expanded, though certain groups of workers faced painful transitions. With AI, we’re seeing a similar dynamic, but with a crucial twist: AI’s speed and scope of impact are unprecedented. Tasks in fields like data analysis, customer service, basic legal work, and even some creative work are being automated faster than new roles are emerging. This could create short-term net job losses in those sectors, especially for routine, repetitive work. Longer term, AI might boost productivity enough to spur new economic growth, creating new jobs we can’t even imagine today. Some economists argue that AI will become a powerful tool that augments human skills rather than replacing them entirely. But the transitional pain – especially for middle-skill workers in administrative or clerical roles – could be significant. Technology may or may not change the total number of employees a healthy, vibrant economy needs – we just don’t know. The specific skills workers need will definitely change. There will be tens of thousands of well-paid, professional and expert employees see their entire careers disappear before their eyes. (Who knows, I might be one of them!) That’s a huge amount of uncertainty to deal with! So how can we prepare ourselves today for a future we can’t see (and maybe can’t imagine)? There are some things technology can’t change Technology doesn’t change everything. Technology doesn’t change human nature. People will still want to eat, sleep comfortably, be entertained, spend time with their loved ones and feel like they have some control over their lives. Technology doesn’t change the nature of business – CEOs will seek to maximize efficiency, boost profits and (all too often) eliminate jobs. A lot of businesses will find their niche completely occupied by AI tools and simply cease to exist. During times of transition, like the one we’re in right now, there’s no guarantee that those lost jobs will come back. The 8,000 human resource workers IBM terminated may never get another job in HR. Microsoft’s 2,000 laid-off software engineers find their skills as highly valued by the new economy. Worse still, there’s no way of knowing whether or not that will happen… So let’s focus on what we can control. Technological advancements have yet to come up with a better inflation-resistant, safe-haven store of value than physical gold. Even those whose jobs disappear can benefit from diversifying with inflation-resistant stores of wealth. If they’re smart and start planning now, they can ensure there’s always food on the table and a roof over their heads. Because physical gold is in your control. Not an employer’s, not a government’s – and certainly not under AI control. If you’re a radiologist, a software developer, a supply chain expert or currently employed in any of the dozens of fields where AI is encroaching, I strongly recommend you look into diversifying your savings with physical precious metals. And if you’re not, you might want to consider the many other benefits of diversifying with gold and silver. You can start your due diligence to take control of your financial world by getting our free 2025 Precious Metals Information Kit.	2025-05-28T00:00:00	2025/05/28	https://www.birchgold.com/blog/news/ai-economic-disruption/	[ { "date": "2025/06/02", "position": 17, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 18, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 18, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 15, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 17, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 16, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 15, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 16, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 20, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 41, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 28, "query": "AI economic disruption" }, { "date": "2025/06/02", "position": 26, "query": "AI economic disruption" } ]
The Ever-Changing Legal Landscape of State and Federal ...	The Ever-Changing Legal Landscape of State and Federal Regulations for Using AI in Candidate Recruiting and Screening	https://www.steptoe-johnson.com	[]	Additionally, many states have introduced legislation establishing various requirements for employers using AI for employment-related decisions.	Details According to a University of Southern California study, 55% of businesses are investing in automated recruiting measures that use artificial intelligence (AI). Using AI tools in employee recruiting and screening offers a range of potential benefits to employers, including increasing the efficiency and speed when it comes to finding the most qualified candidates, reducing the workload for HR teams, and lowering operational costs. However, while using AI in hiring decisions can reduce candidate screening times and increase efficiency, many observers have raised concerns that it can introduce bias and reduce transparency in the hiring process. Numerous individuals have already filed lawsuits alleging they were summarily denied consideration for positions as a result of AI screening tools that discriminate on the basis of race, gender, age, or disability. Additionally, many states have introduced legislation establishing various requirements for employers using AI for employment-related decisions. Recent Litigation In Mobley v. Workday, Inc., the plaintiff filed a putative class action in a California federal court against Workday, a software company that provides algorithm-based applicant-screening tools to thousands of companies, including several Fortune 500 firms. The plaintiff alleged he was denied 80 to 100 jobs by several companies using Workday’s algorithm because of inherent bias embedded in the algorithm. The court denied Workday’s motion to dismiss, finding that the amended complaint adequately alleged that Workday was an agent of its client-employers and thus fell within the definition of an “employer” under Title VII, the Age Discrimination in Employment Act, and the Americans with Disabilities Act. The case is still pending and is in the discovery stage. Employers using AI screening tools should be familiar with the processes and methods the tools use to screen candidates and be ready to articulate clearly the role these tools play in hiring decisions and demonstrate that use of the tools does not result in disparate impacts on protected groups. Recent Legislation Although neither the court in Mobley nor other courts have yet decided whether a company’s use of AI screening tools violated federal or state antidiscrimination laws, numerous states have introduced legislation establishing various requirements for employers using AI for employment-related decisions. For example, the Colorado Artificial Intelligence Act (CAIA), which will take effect on February 1, 2026, requires developers and implementers of certain “high-risk AI” systems to disclose to consumers that they are interacting with an AI system and protect Colorado residents from any risks of algorithmic discrimination, including in employment-related decisions. The CAIA is broad in scope and applies to any employer utilizing a high-risk AI system to make essentially any employment-related decision affecting a Colorado resident, regardless of whether the employer has a physical presence in the state. Similarly, in Illinois, effective January 1, 2026, amendments to the Illinois Human Rights Act will prohibit employers from using AI that subjects employees to discrimination on the basis of a protected class. Last week, House Republicans proposed an amendment to their proposed signature budget bill, titled One Big Beautiful Bill Act, which would impose a temporary ban on state and local regulation of AI, with the stated goal of establishing uniform federal oversight. However, this proposal has faced bipartisan criticism and is unlikely to survive under the Senate’s procedural rules. Recommendations Employers using or planning to use AI tools in their hiring process should stay informed about state-specific AI regulations affecting employment practices and take measures to reduce the possibility of litigation. Engaging legal counsel to navigate the complex and rapidly changing landscape of AI employment laws is one of the best methods for ensuring compliance and reducing litigation risk. Steptoe & Johnson’s Labor & Employment attorneys represent employers in all aspects of labor, employment, employee benefits, and regulatory compliance, including compliance with complex and ever-changing AI-related legislation. For more information, reach out to the authors of this alert or other members of the Labor & Employment department.	2025-06-02T00:00:00		https://www.steptoe-johnson.com/news/the-ever-changing-legal-landscape-of-state-and-federal-regulations-for-using-ai-in-candidate-recruiting-and-screening/	[ { "date": "2025/06/02", "position": 12, "query": "AI regulation employment" }, { "date": "2025/06/02", "position": 12, "query": "AI regulation employment" }, { "date": "2025/06/02", "position": 11, "query": "AI regulation employment" }, { "date": "2025/06/02", "position": 17, "query": "AI employers" }, { "date": "2025/06/02", "position": 33, "query": "AI regulation employment" }, { "date": "2025/06/02", "position": 32, "query": "artificial intelligence employment" } ]
Navigating the AI skills gap: aligning leadership vision with frontline ...	Navigating the AI skills gap: aligning leadership vision with frontline capabilities	https://www.techradar.com	[ "Orla Daly", "Social Links Navigation" ]	Recent research highlights the consequences of the AI skills gap, with one-third of UK employees feeling unprepared to adopt AI in the next one ...	As the promise of AI to rapidly reshape industries intensifies, the gap between having an understanding of AI capabilities and the skills to implement AI solutions continues to widen. This divide is particularly pronounced between senior leadership, who drive digital transformation, and frontline workers, who are expected to implement and adapt to these changes and use this technology in their everyday work. Recent research highlights the consequences of the AI skills gap, with one-third of UK employees feeling unprepared to adopt AI in the next one to three years. This disconnect between strategy and day-to-day execution on the ground is further underscored by the fact that 77% of UK tech workers admit to pretending they know more about AI than they actually do – illustrating the urgent need for organizations to bridge this gap and promote organization-wide AI literacy. To address this divide, businesses must move beyond top-down mandates and build AI literacy across their entire workforce. Let’s explore why its important to act now, and how to achieve this in a scalable and effective way. Orla Daly Social Links Navigation Chief Information Officer at Skillsoft. Why businesses need AI literacy now According to the World Economic Forum’s 2025 Future of Jobs Report, 39% of current skills in the workforce will become outdated within the next five years, with skills gaps remaining the biggest obstacle to organizational preparedness for future markets. As AI transforms ways of working and skills gaps widen, organizations must act now to equip employees with the knowledge necessary to understand AI applications and leverage them effectively. First and foremost, businesses must recognize that AI literacy is no longer a nice-to-have, but a necessity. Employees need a foundational understanding of how AI works, where it adds business value and how it can be integrated into daily operations. AI has the power to enhance efficiency, streamline workflows and improve business operations, transforming organizations across industries. A key element to upskilling efforts beyond understanding general AI capabilities is equipping team members with the ability to identify the opportunities for AI. They should also focus on building the mindset and awareness required to use AI effectively. For IT professionals, understanding AI fundamentals, such as ethical use, large language modelling and data privacy, is crucial. But technical proficiency alone isn’t enough. Power skills, like critical thinking, communication, experimentation, curiosity and resilience, will be equally important for navigating complex environments and driving innovation. A combination of technical and power skills ensures employees can thrive in their current roles, adapt to evolving technologies and build skills for the future. To embed AI literacy across the entire organization, leadership must take an active role in championing AI literacy initiatives. Without visible executive support, companies risk fragmented adoption and widening disparities in AI understanding between senior leaders and frontline workers. AI must be embraced holistically across all levels, from the boardroom to the frontline. Assessing existing skillsets With concern over the AI skills gap growing, 66% of C-Suite executives plan to recruit external AI-skilled talent, while 34% intend to ‘build’ talent internally by training existing employees. This split reflects the broader challenge of staying competitive in a landscape where AI capabilities are impacting the business landscape at a rapid pace. However, as skill lifespans shorten, especially in areas like machine learning, generative AI and data science, businesses can’t solely rely on external hires to stay ahead. The pace of change means that today’s skills can quickly become outdated and hiring new talent each time a skill becomes obsolete is not sustainable or cost-effective. Instead, organizations should strike a balance between hiring new talent and investing in continuous learning and reskilling for existing teams. This starts by assessing the existing skillsets in their team. By conducting baseline evaluations, businesses can compare current skills against benchmarks to identify areas for improvement. This targeted approach ensures learning initiatives are relevant, measurable and aligned with strategic business goals, maximising resource efficiency and impact. Bringing existing employees along on this journey by assessing their existing AI skills and upskilling them appropriately will lead to deeper benefits beyond technical proficiency. This approach also boosts employee retention by demonstrating a clear investment in their growth while also improving the quality of and engagement in their work. Creating an AI literacy framework Rather than relying on ad hoc training sessions, organizations should establish structured, strategic AI literacy programs that equip frontline workers with the knowledge and skills required to identify AI use cases and drive AI adoption. Building this requires a multifaceted approach to learning, including programs that provide access to foundational AI and data skills, but they are only one piece of the puzzle. Programs such as instructor-led sessions that contextualize AI within specific roles and industries and simulation-based learning allow employees to engage with realistic, AI-powered scenarios. By embedding these learning experiences into workforce development, organizations can future-proof their workforce with the skills needed for the AI revolution. Additionally, continuous learning and adaptability must be central to organizational culture, equipping employees with current and future required skilling opportunities, as technical skill lifespans shorten. Creating AI literacy frameworks ultimately helps teams stay ahead of technological shifts while building overall resilience. Achieving organization-wide AI literacy AI literacy is no longer just for tech teams. It’s a business imperative across the entire workforce. For businesses to reduce the AI skills gap, it becomes even more crucial to bridge the divide between senior leadership and frontline workers. By assessing existing skill sets, implementing comprehensive AI upskilling throughout the organization and fostering a culture of continuous learning, businesses can build an AI-ready workforce that is both prepared for and on board with their business strategy. We've featured the best productivity tool. This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro	2025-06-02T00:00:00	2025/06/02	https://www.techradar.com/pro/navigating-the-ai-skills-gap-aligning-leadership-vision-with-frontline-capabilities	[ { "date": "2025/06/02", "position": 88, "query": "AI skills gap" }, { "date": "2025/06/02", "position": 99, "query": "AI skills gap" } ]
Navigating AI Automation: Understanding Workforce Transformation ...	Navigating AI Automation: Understanding Workforce Transformation & Career Worth	https://vskumar.blog	[ "Coach For Cloud", "Devops Job Skills" ]	Introduction As industries undergo rapid digital transformation, AI automation is increasingly replacing traditional roles, reshaping job ...	Introduction As industries undergo rapid digital transformation, AI automation is increasingly replacing traditional roles, reshaping job markets, and redefining career paths. Many IT professionals, especially those with legacy skill sets, struggle to assess their worth in an AI-driven landscape. While many have attended numerous job interviews, their lack of AI expertise has become a major hurdle in securing opportunities. This article explores where AI automation can be implemented, the human efforts saved, who automates AI, how professionals can evaluate their worth in the evolving tech industry, and what roles humans play after automation. The Three Worlds of Work: Manual, Digital, Automated The transition from manual work to complete automation can be categorized into three stages: 🔹 3rd World (Manual) – Humans work primarily with hands, handling labor-intensive tasks. 🔹 2nd World (Software) – Humans interact with software, optimizing workflows with digital tools. 🔹 1st World (Automated) – Software interacts with software, creating a fully automated ecosystem with minimal human intervention. Understanding these phases highlights how professionals must adapt their roles to stay relevant in an increasingly automated world. Where Can AI Automation Be Implemented? AI automation is transforming industries by enhancing efficiency and reducing manual effort. Some key areas include: Business & Enterprise Automation ✅ Customer Support – AI-powered chatbots handle routine inquiries. ✅ HR & Recruitment – AI-driven resume screening and automated candidate matching. ✅ Finance & Accounting – Automated fraud detection and expense tracking. IT & Cloud Automation ✅ DevOps & CI/CD Pipelines – AI-driven automation for software deployment. ✅ Cloud Resource Optimization – AI-powered cost management and scaling. ✅ Cybersecurity – AI-based threat detection and response automation. Manufacturing & Supply Chain ✅ Predictive Maintenance – AI monitors equipment health to prevent failures. ✅ Inventory Management – AI-driven demand forecasting and automated restocking. ✅ Logistics Optimization – AI-powered route planning for transportation efficiency. Healthcare & Life Sciences ✅ Medical Diagnostics – AI assists in disease detection and predictive analytics. ✅ Drug Discovery – AI streamlines research for faster pharmaceutical development. ✅ Patient Care Automation – AI-powered virtual health assistants. Retail & E-Commerce ✅ Personalized Recommendations – AI suggests products based on consumer behavior. ✅ Automated Pricing Strategies – AI-powered dynamic pricing models. ✅ Fraud Prevention – AI transaction monitoring for security risks. Human Efforts Saved Through AI Automation AI automation reduces human workload across various industries: ✅ Data Processing & Analysis – AI automates data collection and transformation, reducing manual effort by 50-70%. ✅ Customer Support – AI chatbots handle 80% of routine queries. ✅ DevOps & Cloud Automation – AI-driven CI/CD pipelines reduce manual intervention by 40-60%. ✅ Manufacturing & Supply Chain – AI-driven logistics cut human workload by 30-50%. ✅ Healthcare & Diagnostics – AI-assisted medical imaging improves efficiency, reducing human effort by 40-60%. Who Automates AI? Key Roles in AI & DevOps Automation AI automation is implemented by specialized professionals across industries: AI & Machine Learning Roles ✅ Machine Learning Engineers – Develop AI models for automation. ✅ Data Scientists – Analyze data and create predictive AI solutions. ✅ AI Researchers – Innovate new AI techniques. Cloud & DevOps Automation Roles ✅ Cloud Engineers – Automate cloud infrastructure using AI-driven scaling. ✅ DevOps Engineers – Implement CI/CD pipelines for AI model deployment. ✅ Site Reliability Engineers (SREs) – Ensure efficient AI-powered cloud operations. AI-Powered Business Automation Roles ✅ AI Product Managers – Define AI automation strategies for enterprises. ✅ Process Automation Engineers – Implement AI workflow automation solutions. ✅ AI Consultants – Advise businesses on AI adoption strategies. Human Roles After Automation After automation, humans play a critical role in overseeing, refining, and innovating AI-driven systems. Instead of performing repetitive tasks, professionals shift towards strategic, creative, and decision-making roles. Here’s how human roles evolve: 🔹 Key Human Roles in an Automated World ✅ AI & Automation Oversight – Humans ensure AI models function correctly, troubleshoot errors, and refine automation workflows. ✅ Strategic Decision-Making – AI provides insights, but humans interpret data, make ethical decisions, and drive business strategies. ✅ Creative & Innovation Roles – AI automates routine tasks, allowing humans to focus on design, problem-solving, and innovation. ✅ Human-AI Collaboration – Professionals work alongside AI, training models, optimizing prompts, and ensuring AI aligns with business goals. ✅ Ethical AI Governance – Humans monitor AI biases, ensure fairness, and implement responsible AI practices. ✅ Advanced Technical Roles – AI Engineers, DevOps Specialists, and Cloud Architects develop, deploy, and maintain AI-powered systems. This shift from manual execution to strategic oversight ensures that humans remain indispensable in an AI-first world. Assessing Your Worth in an AI-Driven Job Market Many IT professionals envision their worth based on their legacy experiences, but many legacy profiles are now obsolete due to AI advancements. This creates a skills gap that hinders career progression. To bridge this gap, professionals must self-evaluate their industry relevance and upgrade their expertise to match AI-driven opportunities. How to Determine Your Worth for Your Next IT Role Here are key steps to assess your IT career value: 1️⃣ Assess Your IT Career Value for Salary Negotiation 2️⃣ Know Your True Market Worth for IT Salary Discussions 3️⃣ Evaluate Your Position in the Competitive IT Job Market 4️⃣ Optimize Your Career Potential with Strong Negotiation Strategies 5️⃣ Understand the Key Factors Defining an IT Professional’s Worth	2025-06-02T00:00:00	2025/06/02	https://vskumar.blog/2025/06/02/navigating-ai-automation-understanding-workforce-transformation-career-worth/	[ { "date": "2025/06/02", "position": 84, "query": "AI workforce transformation" } ]
Artificial intelligence isn't ruining education; it's exposing ...	Artificial intelligence isn’t ruining education; it’s exposing what’s already broken	https://edsource.org	[ "William Liang" ]	A recent study from the National Education Association found that 72% of high school students use AI to complete assignments without really understanding the ...	July 10, 2025 - Hundreds of thousands of children could lose federally-funded food stamps and health care under the new law. Credit: Allison Shelley/The Verbatim Agency for EDUimages A few weeks ago, my high school chemistry class sat through an “AI training.” We were told it would teach us how to use ChatGPT responsibly. We worked on worksheets with questions like, “When is it permissible to use ChatGPT on written homework?” and “How can AI support and not replace your thinking?” Another asked, “What are the risks of relying too heavily on ChatGPT?” Most of us just used ChatGPT to finish the worksheet. Then we moved on to other things. Schools have rushed to regulate AI based on a hopeful fiction: that students are curious, self-directed learners who’ll use technology responsibly if given the right guardrails. But most students don’t use AI to brainstorm or refine ideas — they use it to get assignments done faster. And school policies, built on optimism rather than observation, have done little to stop it. Like many districts across the country, our school policy calls students to use ChatGPT to brainstorm, organize, and even generate ideas — but not to write. If we use generative AI to write the actual content of an assignment, we’re supposed to get a zero. In practice, that line is meaningless. Later, I spoke to my chemistry teacher, who confided that she’d started checking Google Docs histories of papers she’d assigned and found that huge chunks of student writing were being pasted in. That is, AI-generated slop, dropped all at once with no edits, no revisions and no sign of actual real work. “It’s just disappointing,” she said. “There’s nothing I can do.” In Bible class, students quoted ChatGPT outputs verbatim during presentations. One student projected a slide listing the Minor Prophets alongside the sentence: “Would you like me to format this into a table for you?” Another spoke confidently about the “post-exilic” period— having earlier that week mispronounced “patriarchy.” At one point, Mr. Knoxville paused during a slide and asked, “Why does it say BCE?” Then, chuckling, answered his own question: “Because it’s ChatGPT using secular language.” Everyone laughed and moved on. It’s safe to say that in reality, most students aren’t using AI to deepen their learning. They’re using it to get around the learning process altogether. And the real frustration isn’t just that students are cutting corners, but that schools still pretend they aren’t. That doesn’t mean AI should be banned. I’m not an AI alarmist. There’s enormous potential for smart, controlled integration of these tools into the classroom. But handing students unrestricted access with little oversight is undermining the core purpose of school. This isn’t just a high school problem. At CSU, administrators have doubled down on AI integration with the same blind optimism: assuming students will use these tools responsibly. But widespread adoption doesn’t equal responsible use. A recent study from the National Education Association found that 72% of high school students use AI to complete assignments without really understanding the material. “AI didn’t corrupt deep learning,” said Tiffany Noel, education researcher and professor at SUNY Buffalo. “It revealed that many assignments were never asking for critical thinking in the first place. Just performance. AI is just the faster actor; the problem is the script.” Exactly. AI didn’t ruin education; it exposed what was already broken. Students are responding to the incentives the education system has given them. We’re taught that grades matter more than understanding. So if there’s an easy shortcut, why wouldn’t we take it? This also penalizes students who don’t cheat. They spend an hour struggling through an assignment another student finishes in three minutes with a chatbot and a text humanizer. Both get the same grade. It’s discouraging and painfully absurd. Of course, this is nothing new. Students have always found ways to lessen their workload, like copying homework, sharing answers and peeking during tests. But this is different because it’s a technology that should help schools — and under the current paradigm, it isn’t. This leaves schools vulnerable to misuse and students unrewarded for doing things the right way. What to do, then? Start by admitting the obvious: if an assignment is done at home, it will likely involve AI. If students have internet access in class, they’ll use it there, too. Teachers can’t stop this: they see phones under desks and tabs flipped the second their backs are turned. Teachers simply can’t police 30 screens at once, and most won’t try. Nor should they have to. We need hard rules and clearer boundaries. AI should never be used to do a student’s actual academic work — just as calculators aren’t allowed on multiplication drills or Grammarly isn’t accepted on spelling tests. School is where you learn the skill, not where you offload it. AI is built to answer prompts. So is homework. Of course students are cheating. The only solution is to make cheating structurally impossible. That means returning to basics: pen-and-paper essays, in-class writing, oral defenses, live problem-solving, source-based analysis where each citation is annotated, explained and verified. If an AI can do an assignment in five seconds, it was probably never a good assignment in the first place. But that doesn’t mean AI has no place. It just means we put it where it belongs: behind the desk, not in it. Let it help teachers grade quizzes. Let it assist students with practice problems, or serve as a Socratic tutor that asks questions instead of answering them. Generative AI should be treated as a useful aid after mastery, not a replacement for learning. Students are not idealized learners. They are strategic, social, overstretched, and deeply attuned to what the system rewards. Such is the reality of our education system, and the only way forward is to build policies around how students actually behave, not how educators wish they would. Until that happens, AI will keep writing our essays. And our teachers will keep grading them. ••• William Liang is a high school student and education journalist living in San Jose, California. The opinions expressed in this commentary represent those of the author. EdSource welcomes commentaries representing diverse points of view. If you would like to submit a commentary, please review our guidelines and contact us.	2025-06-02T00:00:00		https://edsource.org/2025/artificial-intelligence-isnt-ruining-education-its-exposing-whats-already-broken/733854	[ { "date": "2025/06/02", "position": 5, "query": "AI education" } ]
OPINION: Educators have the tools but not the training or ...	OPINION: Educators have the tools but not the training or ethical framework to use AI in education wisely. And that’s a problem	https://hechingerreport.org	[ "Michael Goergen", ".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow", "Class", "Wp-Block-Co-Authors-Plus", "Display Inline", ".Wp-Block-Co-Authors-Plus-Avatar", "Where Img", "Height Auto Max-Width", "Vertical-Align Bottom .Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow .Wp-Block-Co-Authors-Plus-Avatar", "Vertical-Align Middle .Wp-Block-Co-Authors-Plus-Avatar Is .Alignleft .Alignright" ]	The use of AI in education has risks, but it could help personalize learning and free teachers to spend more time doing what only humans can do: connect, ...	The Trump administration wants to bring artificial intelligence into K-12 classrooms. At first glance, this isn’t a terrible idea. Used well, AI can be a patient tutor. It doesn’t get frustrated. It doesn’t lose focus. It doesn’t roll its eyes, check the clock or give up. AI could help personalize learning, diagnose learning disabilities, ease administrative burdens and free teachers to spend more time doing what only humans can do: connect, mentor, care. But such outcomes aren’t automatic. They depend on thoughtful design, clear oversight and shared values. And yet, the federal government still isn’t allowing most of its own workforce to use generative AI tools, citing both security concerns and a lack of clear policy. These are trained adults — scientists, analysts, engineers. Yet many are still waiting for guidance on how (or whether) they can use the same tools we now propose putting in front of third graders. Let’s take the time to ensure we get this right — by aligning educators and tech experts around what matters most: student outcomes. We can’t just bolt AI onto the current system. We have to rethink our educational values: Not just efficiency and test scores, but also ethical tech use and human connection. That kind of shift must be designed — openly, and with the people who know students best: teachers. Related: A lot goes on in classrooms from kindergarten to high school. Keep up with our free weekly newsletter on K-12 education . The truth is, AI is already in the classroom. More than half of U.S. K-12 teachers reported using AI tools in 2024 — double the previous year’s figure. Yet a recent Pew survey found that one in four teachers believe AI in education does more harm than good. Another third say the impact is mixed. The risks are real: biased algorithms, privacy breaches, overreliance on automation. But so are the possibilities. Done thoughtfully, AI could restore something our schools desperately need: time. Time for students to go deeper. Time for teachers to be present to coach student thinking. Time for sparking curiosity. There could be time for building trust — time for learning to be more human, not less. But to reap these benefits, we should not make AI the next Google — adopted first, questioned later, if at all. We must build an ethical framework alongside the tools. We must pilot, assess and revise before we deploy at scale. And we must create space for teachers, parents and students to shape these decisions — not just companies and politicians. This is a moment for humility, not hype. The question isn’t whether AI belongs in the classroom. It’s whether we are ready to make it serve the people in the classroom. If we let AI reshape education without purpose or care, the companies will keep building the algorithms. And when they fail, our students will feel the cost. That is disruption paired with negligence, leaving our teachers, not the tech companies, to deal with the fallout. We’ve been here before. Just ask Google. Over the past decade, schools across the country quietly embraced Google’s suite of tools. Google Docs, Gmail, YouTube — these products now form the digital backbone of American classrooms. During the pandemic, their adoption accelerated. In the U.S., more than 30 million students were using Google’s education apps as early as 2017. Globally, that number has since ballooned to over 150 million students, teachers and administrators, according to the company itself. In many districts, Chromebooks, based on Google’s operating system, are standard issue. Related: Kids who use ChatGPT as a study assistant do worse on tests But this embrace came with few questions asked: Who owns the data? What’s being tracked? Who profits? We didn’t stop to ask the hard questions about letting a big tech company mediate so much of the learning experience — and now, we’re scrambling to catch up. We’d be wise to learn from that experience. If we fail to build AI guardrails now, we risk letting AI flatten education — turning schools into testing labs for corporate algorithms, not communities for human growth. Done right, AI would be designed with and for teachers — not as a shortcut around them. It would focus on tasks that free teachers to do what only humans can do. Imagine a chatbot that gives a student real-time feedback while they draft an essay, flagging confusing phrases so their ideas flow faster and they build confidence — without waiting days for corrections. Or an exam platform that doesn’t just mark wrong answers, but explains why the answers are wrong, helping students learn from their mistakes while the memory is fresh. In both cases, AI isn’t replacing a teacher’s work — it’s reinforcing it, turning feedback loops into learning loops. Take the calculator. When it entered classrooms, many worried that it would destroy basic math skills. Today, we allow students to use calculators — even on the SAT — but with clear standards. We treat them as assistants, not replacements. AI poses a bigger challenge than calculators ever did — but the lesson endures: the right design, the right rules and the right purpose can make any new technology a tool for deeper learning. We remember the teachers who challenged us, who believed in us — not the calculator they taught us to use. If we get this right, AI will stay in the background, and the human moments will shine. Michael Goergen is a writer and policy professional focused on sustainability, technology and ethics. He has worked in science and government and believes the future of learning depends on remembering what makes us human. Contact the opinion editor at [email protected]. This story about AI in education was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for Hechinger’s weekly newsletter.	2025-06-02T00:00:00	2025/06/02	https://hechingerreport.org/opinion-educators-have-the-tools-but-not-the-training-or-ethical-framework-to-use-ai-wisely-and-thats-a-problem/	[ { "date": "2025/06/02", "position": 90, "query": "AI education" } ]
California Employers: Artificial Intelligence in Hiring Brings ...	California Employers: Artificial Intelligence in Hiring Brings New Compliance Risks	https://www.goldbergsegalla.com	[ "Scott R. Green", "Caroline J. Berdzik" ]	Use of Artificial Intelligence in hiring and promotion is undergoing regulatory scrutiny in California. Tools that screen resumes, rank candidates, or assess ...	Key Takeaways Use of Artificial Intelligence in hiring and promotion is undergoing regulatory scrutiny in California Tools that screen resumes, rank candidates, or assess interview responses can lead to legitimate claims of bias or discrimination Employers must ensure AI tools are transparent, explainable, and tested for adverse impact on protected groups The EEOC and the Civil Rights Department may view reliance on opaque (non-transparent) or unvalidated (not tested or verified for accuracy or bias) AI as a violation of anti-discrimination laws Federal and California agencies have increased their focus on how AI and automated decision-making tools are being used in hiring, promotions, and employment screening processes. Employers using such technologies may be exposed to discrimination claims if these tools lead to disparate treatment or impact on certain races, national origins, genders, ages, or other protected classes, whether intentional or not. With AI increasingly integrated into recruiting software, such as automated resume filters, personality assessments, and video interview analysis, employers must understand that their use can trigger compliance obligations under federal, state, and local laws, including but not limited to Title VII, the Fair Employment and Housing Act (FEHA), and other emerging AI-specific laws. A misstep could result in reputational damage as well as time-consuming and costly investigations, lawsuits, or enforcement actions. California is leading the charge in efforts to regulate AI. Earlier this year, a bill known as the “No Robo Bosses Act” was introduced in California. If this bill is passed as written, it will impose strict oversight on automated decision systems (ADS) to prevent discrimination in the workplace. For employers who use ADS and other AI tools for hiring and management, the proposed bill imposes compliance burdens on the employers in addition to compliance requirements for developers of ADS and AI tools. The penalties imposed could be up to $25,000 per violation. California employers should be mindful that AI tools must be transparent, explainable, and tested for adverse impact on protected groups. The Equal Employment Opportunity Commission and the California Civil Rights Department may view an employer’s reliance on opaque (non-transparent) or unvalidated (not tested or verified for accuracy or bias) AI as a violation of anti-discrimination laws. Action Items for Employers Conduct an internal audit of all recruitment and HR tools using automation or AI. Request documentation from vendors showing that tools are validated and tested for bias. Train HR and hiring managers on permissible use of AI in decision-making and ensure transparency with applicants. Update your privacy and hiring policies to reflect how these tools are used and how candidates can opt out, if applicable. Employers should proactively evaluate whether their use of AI and automation complies with evolving regulatory standards. Our team is available to help assess risk, recommend compliant practices, and review vendor contracts and systems. Please contact us to ensure your hiring technology does not create unintended liability.	2025-06-02T00:00:00		https://www.goldbergsegalla.com/news-and-knowledge/knowledge/california-employers-artificial-intelligence-in-hiring-brings-new-compliance-risks/	[ { "date": "2025/06/02", "position": 10, "query": "AI employers" } ]
AI Boosts Small Business Productivity, But Employee ...	AI Boosts Small Business Productivity, But Employee Training Lags Behind	https://www.business.com	[ "Chad Brooks Is The Author Of", "How To Start A Home-Based App Development Business", "Drawing Over A Decade Of Experience To Mentor Aspiring Entrepreneurs In Launching", "Scaling", "Sustaining Profitable Ventures. With A Focused Dedication To Entrepreneurship", "He Shares His Passion For Equipping Small Business Owners With Effective Communication Tools", "Such As Unified Communications Systems", "Video Conferencing Solutions", "Conference Call Services.", "As Business.Com'S Managing Editor" ]	42% of small-to-midsize businesses adopt AI with promising results, but over half of workers feel unprepared to fully harness its capabilities.	Open AI’s launch of ChatGPT in November 2022 sent shockwaves through the working world as employees panicked about being displaced by machines. Since then, many companies have experimented with artificial intelligence (AI) platforms, finding ways to boost productivity and innovation in their organizations. However, new data reveal that workers lack confidence in implementing AI in their day-to-day duties, signaling a need for more employee training and support. To understand more about AI adoption in small to medium-sized businesses (SMBs), business.com conducted a study of 1,175 Americans working in organizations with less than 250 employees. Our research reveals how AI tools have impacted employee productivity and satisfaction and the types of training required to maximize AI’s value in the modern workplace. Key findings 42 percent of small to medium-sized businesses are already using AI, and more than half of these companies report financial savings as a result. Almost three-quarters of employees agree AI has enhanced their productivity, and 60 percent feel more satisfaction at work. Only 52 percent of companies using AI are training their employees in the technology. More than half of employees feel they need more AI training, and only 37% of SMB employees expressed confidence in their AI skills. Though 90% of employees who received AI training reported better performance, only half of SMBs have trained their employees or established AI usage policies. AI adoption among small businesses is rising fast The rise of companies adopting AI technologies like machine learning, natural language processing, and computer vision has been sharp. However, a National Bureau of Economic Research paper showed that only six percent of U.S. companies were using AI in 2017, mainly in large companies in healthcare, manufacturing, or IT. In stark contrast, our research shows that 42 percent of small to medium-sized businesses (SMBs) have invested in artificial intelligence, an increase over 2023. ChatGPT is mainly responsible for this uptick, marking a significant internet milestone when it amassed one million users within its first five days. While ChatGPT is far from the only AI tool available, this conversational platform has brought generative AI to the forefront of public attention and shown the world what machines can produce from a simple prompt. Which workers are adopting AI the fastest? In companies that have already adopted AI, 68 percent of workers are using the technology. Though young people are often early adopters of new technology, we found that Gen Z workers use AI at work less often than their millennial and Gen X colleagues. This could be due in part to the types of roles young professionals hold at the beginning of their careers. We also discovered male workers are more likely than female workers to use AI. This aligns with an earlier business.com study, which revealed a similar gender gap with ChatGPT usage in the workplace. The University of Chicago also reports a difference between men and women regarding ChatGPT usage. Their researchers found that women are 20 percent less likely to use ChatGPT at work than men in the same occupation, even though men are 11 percent likelier to be restricted by their employer. The most revealing statistic is that 48 percent of women report needing training to use the technology, compared to just 37 percent of male employees. [Learn how to build an online business according to ChatGPT.] Nevertheless, the future looks bright for AI adoption. business.com’s research revealed that 30 percent of the SMBs that still need to adopt artificial intelligence plan to invest in it soon. This signals that any initial fears over AI have been overwritten by excitement about its potential. How SMBs are implementing AI Small to medium-sized businesses are still in the exploratory stages of using AI. This is apparent in our data, which reveals that 77 percent of SMBs choose tools pre-equipped with AI elements. Why? It’s an easy switch to experiment with AI functionality in a platform they already use. For example, project management tools like monday.com or ClickUp have built-in AI, allowing users to speed up tasks like drafting emails or product documentation. Once employees are comfortable using these add-on features, adopting a dedicated AI tool into their workflows is less challenging. [Check out our monday.com review to learn how it can help your business.] How does AI support different business functions? AI usage is as vast and varied as the teams it supports. Here are just a few business functions leaning on AI in their routine tasks and projects: Top business functions where small-to-medium companies use AI Among companies that have begun to implement AI Function Percent of companies using AI for functions Customer service 65% Marketing and sales 64% Product development and Innovation 58% Cybersecurity 55% Operations and supply chain management 53% Financial management 48% Human resources 47% While AI is often linked to tasks involving analytical thinking and problem-solving, such as data science or cybersecurity, our data shows it’s also used in more creative and people-oriented activities like marketing and customer service. Some SMBs are deploying AI-powered chatbots to answer customer queries round the clock, while marketing trainer and consultant Elizabeth Taylor swears by feeding a customer persona template into Google Gemini to create profiles that shape targeted marketing campaigns. “For years, I relied heavily on intuition and limited data to create these profiles. That all changed when I leveraged AI in my persona creation process. The initial output is usually good, but I always follow up with additional questions to add depth to the persona.” SMBs are concerned about AI compliance As SMBs quickly harness AI to improve efficiency across their organizations, they’re also hurrying to develop training and AI usage policies this year. Here’s what we discovered: 52 percent of companies are training their employees to use AI. 51 percent are communicating with stakeholders such as customers and business partners about their use of AI at work. 50 percent are creating an AI usage policy. These critical activities build guardrails around how employees rely on technology to complete their workloads, providing information on what is and isn’t allowed. Inputting sensitive employee or client data into a prompt is a top concern. AI systems learn from human-fed data sets, including the questions and prompts that users type in. This collection, processing, and storage of sensitive data could have significant legal implications for companies of all sizes. For example, SMBs using generative AI to craft performance reviews must understand whether entering employees’ personal details into a tool is legal and ethical. Business leaders should always consult legal professionals while developing AI use policies, specifically concerning the General Data Protection Regulations, the California Consumer Privacy Act, and any local or industry-specific compliance regulations. How are workers and employers benefiting from AI? AI has proved highly beneficial for companies of all sizes when implemented with proper training and usage guidelines. Initial fears that AI will phase out human employees currently appear unwarranted. Employers view artificial intelligence as providing numerous advantages beyond lowering their wage bills, as evidenced by our data in the table below. Percent of small to medium businesses experiencing various AI benefits, by level of implementation AI benefits Among workers at companies implementing AI at least partially in ONE area Among workers at companies implementing AI at least partially in FOUR areas AI has saved our company time. 73% 93% Our company encourages the use of AI. 60% 90% AI has saved our company money. 55% 83% Our customer experience has improved because of AI. 68% 83% Our company has made improvements based on AI-generated insights. 60% 90% For companies that use AI in one business area, time savings are the top benefit, experienced by 73 percent of respondents. When companies save time, this goes hand in hand with the financial savings reported by 55 percent. AI-generated insights also resulted in stronger decision-making for 60 percent, as the technology can generate key insights and predictions at speed then present them in an array of visual charts. A further 68 percent felt that artificial intelligence produced a better customer experience; for example, cosmetics company FC Beauty leverages the technology to create personalized product recommendations for its customers. The benefits of AI were even more pronounced for companies that had implemented it more extensively. For example, 93 percent of organizations that had implemented AI in at least four different business functions reported saving time, saving money for 83 percent of them. Research by the Small Business & Entrepreneurship Council takes a closer look at the impact of these resource savings. When AI is deployed in small businesses, owners report it does the job of 2.1 full-time employees and 1.5 part-time employees. By making these time savings on human capital, 25 percent of companies are able to increase employee wages and benefits, 20 percent pay down company debt more quickly, and 25 percent set aside extra capital for emergencies. Employees also report numerous benefits when they incorporate AI in their work. 73 percent of workers reported increased productivity due to AI, while 60 percent said they felt more satisfaction in their work thanks to AI tools. These improvements are likely because AI is excellent at eliminating repetitive tasks like sending email notifications or updating project management boards, which eat time and require constant tab-switching. When AI handles these mundane tasks, users can explore more creative sides of their roles. One Microsoft paper sums it up, with 23 percent of employees reporting that AI means they’ll never have to absorb unnecessary or irrelevant information again. The data clearly shows that employees still need to understand more about using AI to maximize its value in their specific roles. Only 41 percent of employees prioritize AI training, while the same percentage have yet to make the same commitment to learning. [Find out how to improve the transfer of learning in the workplace.] Employees need more AI training to reap its benefits fully Artificial intelligence implementation is too fast for many of the employees we surveyed. Only 37 percent of all SMB employees express confidence in their AI skills, and 54 percent believe they need more training in the technology. When employees don’t receive training, they’re less likely to use AI frequently, as demonstrated in the table below: AI use frequency at work, by training received Use AI once a week or less Use AI 2-4 times a week Use AI every day Company offered no training 72% 36% 25% Company offered training 28% 64% 75% AI isn’t a checkbox exercise – only companies willing to commit to a culture of lifelong learning will fully master and keep up with its evolution. Those who don’t will experience a knowledge gap that prevents them from reaping the full benefits of this powerful technology. The good news is that 90 percent of SMB employees who have received training from their employer report it makes them better at their role. The top ways people received AI training in the past 12 months are spread across several categories, as follows: On-the-job training enables employees to practice prompts and receive outputs relevant to their roles, explaining why this is the most popular learning method. In second place, online courses and certifications, such as those offered by LinkedIn or Coursera, are easy to complete alongside employee workloads. [See which courses we recommend to prepare for the future of business.] Networking with AI experts and coaching from experienced professionals were less popular training methods, perhaps highlighting a lack of AI specialists available to approach for this type of training. Overall, sentiment towards AI is very positive, with 90 percent of SMB employees keen to continue upskilling by taking company-provided AI training. This far outweighs the 24 percent who feel the technology is more trouble than it’s worth and the 27 percent who believe that learning AI has increased their workload.	2025-06-02T00:00:00		https://www.business.com/articles/ai-usage-smb-workplace-study/	[ { "date": "2025/06/02", "position": 86, "query": "AI employers" }, { "date": "2025/06/02", "position": 38, "query": "ChatGPT employment impact" }, { "date": "2025/06/02", "position": 29, "query": "workplace AI adoption" } ]
SPUI25: The Future of Journalism (Studies) in the Age of AI	SPUI25: The Future of Journalism (Studies) in the Age of AI	https://www.uva.nl	[ "Universiteit Van Amsterdam" ]	This panel examines the range of approaches currently used in journalism studies to analyze AI and news.	In recent years, rapid developments in generative artificial intelligence (GenAI) have presented both disruptive challenges and unique opportunities for a wide array of industries, including the media and information domains, such as journalism. In this media landscape, the narrative of “AI is transforming journalism” has gained considerable traction, often portrayed with a sense of inevitability. However, preliminary findings from academic research reveal a striking disconnect between this popular discourse and the on-the-ground realities in newsrooms. This panel aims to delve deeper into this schism, seeking to understand the nuances and subtleties that characterize the integration of artificial intelligence in journalism and its impact on professional roles. As these AI technologies evolve and become more integrated into journalistic workflows, they are simultaneously transforming the way journalism is taught, studied, and conceptualized. For journalism research in particular, discussions about AI have thus far focused on what these technologies mean for journalists and their work, ranging from the ethics of using chatbots to craft news texts to questions about human displacement and the biases associated with applying LLMs in the way news organizations gather, filter, and disseminate information. About the speakers Tomás Dodds (moderator) is an Assistant Professor in Journalism and Mass Communication at the University of Wisconsin-Madison and a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University. He is also a fellow in the AI, Media & Democracy Lab in the Netherlands, the Institute for Advance Study at the University of Amsterdam, and the Artificial Intelligence and Society Hub [IA+SIC] in Chile. Seth Lewis is Professor, Director of Journalism, and the founding holder of the Shirley Papé Chair in Emerging Media in the School of Journalism and Communication at the University of Oregon—a position he has held since joining UO in 2016. He has held visiting or affiliated fellow positions with the Tow Center for Digital Journalism at Columbia University, the Information Society Project at Yale Law School, and Oxford’s Reuters Institute for the Study of Journalism, among others. From 2020 to 2022, he served as the elected Chair of the International Communication Association’s Journalism Studies Division, the world’s largest scholarly group dedicated to the study of journalism. Natali Helberger is Professor of Information Law at the UvA and a member of the board of directors of the UvA’s Institute for Information Law. Since 2019, she is one of the leaders of the ‘Human (e) AI’ Research Priority Area at the UvA. She is also founder and Principal Investigator of ‘Information and Communication in the DataSociety’- an interdisciplinary research initiative into the way AI and algorithms affect the role, impact and regulation of data-driven communication and information platforms Nicolas Mattis is a Postdoctoral Researcher at the University of Amsterdam. He is a (computational) social scientific researcher with a particular interest in the interplay of new technologies, the (news) media, and democratic society. The majority of his research engages with responsible design practices for digital platforms.	2025-05-22T00:00:00	2025/05/22	https://www.uva.nl/en/shared-content/faculteiten/en/faculteit-der-rechtsgeleerdheid/events/2025/06/spui25-the-future-of-journalism-studies-in-the-age-of-ai.html	[ { "date": "2025/06/02", "position": 25, "query": "AI journalism" }, { "date": "2025/06/02", "position": 38, "query": "artificial intelligence journalism" } ]
Who tells our stories? Automated journalism and ...	Who tells our stories? Automated journalism and fundamental rights	https://www.apc.org	[ "Catalina Balla For Derechos Digitales" ]	... artificial intelligence (AI) tools, not everything is cause for alarm. There are concrete efforts being made to promote an ethical use of AI in journalism.	As we witness the widespread use of artificial intelligence (AI) tools, not everything is cause for alarm. There are concrete efforts being made to promote an ethical use of AI in journalism. Derechos Digitales describes such efforts in this article originally published in Spanish on its website. In the last few years, artificial intelligence tools have gained an increasingly more visible role in Latin America media newsrooms. From assistants that help transcribe interviews to systems that generate articles automatically, the promise is clear: greater efficiency, lighter workloads, lower costs and greater productivity. But in a context of growing job insecurity, media concentration and widespread misinformation, the questions we pose cannot be merely technical. What does automating the production of news entail? What do we sacrifice when we replace human discretion with algorithm-based decisions? And what rights are at stake when we delegate key functions to opaque technologies that may compromise people’s privacy or expose sensitive data without any clear safeguards? Technologies are neither neutral nor inevitable. How these technologies are designed, who they benefit and under what conditions they are incorporated are all profoundly political decisions. When we talk about AI in journalism, what is at stake is not just the future of the profession, but the quality of public debate, the diversity of voices and the right to verified, plural and free information. AI does not make mistakes, it simply makes things up One of the greatest dangers of AI use in journalism lies not only in its technical limitations, such as its ability to generate fake content that sounds convincing, but also in how these tools are incorporated into journalistic practices without proper controls. These falsehoods are known as “hallucinations”: errors that are not simply inaccuracies, but fictitious constructions, stylistically presented as facts. The risk increases when these tools are integrated into workflows without human revision, or when they are used to cover sensitive issues with little verification, replacing editorial discretion with automated results that are context-free and not bound by any professional responsibility. During the international panel discussion on “Artificial Intelligence and Journalism: How Media Outlets Are Covering and Using AI in Latin America”, organised by UNESCO to mark this year’s World Press Freedom Day, journalists, government officials and experts from around the world drew attention to this phenomenon. Participants agreed that, far from replacing the work of journalists, AI calls for a new layer of responsibility, as all content generated or assisted by automated systems must be verified, contrasted and contextualised. Using these tools without human oversight is not innovation; it is an accelerated way of eroding public trust. Between job instability and automation The adoption of AI is taking place against a backdrop of structural crisis for journalism. In Latin America, many newsrooms are facing budget cuts, outsourcing of editorial tasks and pressure to produce more content in less time. In this context, generative technology emerges as an easy fix, offering the possibility of automating the production of economic, climate or entertainment news. This logic, however, poses a false dichotomy: saving costs in exchange for undermining the profession. There are documented cases of serious blunders made by international media outlets that used AI without editorial control, including the publication of fabricated articles or interviews with nonexistent people. This not only harms the reputation of the outlet, it increases the job instability of journalists, editors and copy editors, consolidating a model in which automation replaces human abilities without generating more decent or sustainable conditions for those who are the backbone of the profession. At a time when journalism is suffering a credibility crisis throughout much of the region, indiscriminate automation is not a solution; it merely exacerbates the problem. Journalism is not just content production; it involves critical thinking, situating the narrative, considering the context and exercising public responsibility. When it is replaced by systems that favour quantity over quality, the result is not efficiency, but misinformation cloaked in apparent legitimacy, magnified by the institutional credibility of the media outlets that reproduce it. Uncritical automation undermines both the quality and the trust in journalism as an instrument of democracy. Sources are not prompts One of the most delicate – and often invisible – aspects of the use of generative AI in journalism has to do with the protection of sources. Feeding confidential information into systems such as ChatGPT or Gemini to, for example, prepare a draft article or summarise an interview entails handing over that information to companies whose terms of use allow them to store, analyse or use it to continue training their models. In practice, this means that names, descriptions, sensitive details about events or even fragments of accounts from sources can remain stored in external servers, with no assurance as to whether they will be deleted or used in the future. This not only goes against the basic principle of journalistic confidentiality. It also exposes people who gave their accounts under the promise of confidentiality, especially in contexts of risk, such as gender violence, corruption or socio-environmental conflicts, where an information leak can have serious consequences. Technology can simplify tasks, but it should never endanger sources or erode the conditions under which their information is safeguarded. In Latin America, with attempts to regulate AI and new rules governing personal information, such as Chile’s Personal Data Protection Act, which sets out clear obligations regarding consent, proportionality and purpose of data processing, this takes on even greater significance. Journalists have the responsibility of applying those principles, not only with respect to third parties, but also with respect to the platforms they use to conduct their work, bearing in mind that all digital tools are part of the risk environment that must be critically assessed. Principles and proposals for responsible AI use In this scenario, not everything is cause for alarm. There are concrete efforts being made to promote an ethical use of artificial intelligence in journalism. One of the most relevant efforts in this sense is the Paris Charter on AI and Journalism issued by Reporters Without Borders (RSF) and published in 2023, which sets out 10 ethical principles to address current challenges. Among its recommendations, the charter highlights the need for transparency in the use of automated tools, the traceability of AI-generated content, mandatory human editorial oversight, ensuring that AI does not replace essential journalist functions and commitment to a human rights-centred technological governance. This charter is aimed not only at media outlets and journalists, but also at governments, developers and technology platforms, in the understanding that journalistic work cannot be dependent on systems whose logic excludes public accountability. In practical terms, there are initiatives already underway that are working to implement these principles. Some media outlets have begun to explicitly label all AI-generated content, establish revision protocols and train their staff in the critical use of these tools. Other measures under discussion are the incorporation of ethical clauses in contracts with technology providers and the establishment of collective assessment and accountability mechanisms, especially in independent and community newsrooms. Technology can be an ally, but only under frameworks of transparency, professional ethics and respect for individuals. This entails understanding its limitations, critically assessing its design and effects and refraining from delegating to automated systems any decisions that require context, editorial discretion and human responsibility. Using it prudently not only protects journalism, it also guarantees the right of the public to receive information that is reliable, diverse and produced with standards of integrity. Journalism, democracy and technology Automation in journalism is not a neutral scenario. It can help address operational tasks in situations of work overload, staff reduction or the need to improve accessibility to certain content. But when it is adopted without justice, fairness and responsibility criteria, it can also aggravate existing problems, including job insecurity, loss of diversity in coverage and erosion of editorial judgment. Informing is a public function and it entails a responsibility to society; it is not a task to be replicated without context by opaque systems. In Latin America, where civic space is being restricted, where access to information often depends on independent news outlets and where threats to journalists are increasingly common, protecting journalism also means protecting democracy. Consequently, decisions regarding how AI is used in news production cannot be left in the hands of a few technological players. It must involve journalists, audiences, legislators, editors, academics, civil society and human rights defenders. Because if journalism changes it must do so without losing its essence: its ability to listen, question and draw attention to the stories that need to be told.	2025-06-02T00:00:00		https://www.apc.org/en/blog/who-tells-our-stories-automated-journalism-and-fundamental-rights	[ { "date": "2025/06/02", "position": 32, "query": "AI journalism" } ]
Journalism AI	Omedi News Network	https://omedinewsnetwork.org	[]	As artificial intelligence (AI) becomes increasingly integrated into journalism, one of its most critical applications is war and crisis reporting.	We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept”, you consent to the use of ALL the cookies.	2022-01-23T00:00:00	2022/01/23	https://omedinewsnetwork.org/journalism-ai/	[ { "date": "2025/06/02", "position": 50, "query": "AI journalism" } ]
News & Insights	News & Insights	https://www.theajp.org	[]	Product & AI Studio · Local News Incubator · Our Portfolio · News, Insights & Research Show submenu for “News, Insights & Research”. News & Insights · Industry ...	The American Journalism Project and JPMorganChase announced an expansion of their collaboration to support three additional members of the American Journalism Project’s portfolio, bringing the total number of news organizations as part of this collaboration to 10.	2025-06-02T00:00:00		https://www.theajp.org/news-insights/	[ { "date": "2025/06/02", "position": 89, "query": "AI journalism" } ]
Microsoft slashes 6000 jobs worldwide in strategic shift ...	Microsoft slashes 6,000 jobs worldwide in strategic shift toward AI	https://interestingengineering.com	[ "Neetika Walter" ]	AI push spurs Microsoft to slash 6,000 Jobs in largest layoffs in two years. The tech giant said the layoffs will span all levels, teams, and geographies ...	The cuts, concentrated heavily in Washington, come despite strong recent earnings, signaling a strategic reshuffle rather than financial distress. As many as 1,985 employees have been laid off from Microsoft’s Redmond headquarters alone. Of those, about 1,500 worked on-site, while 475 were remote, according to a notice filed with Washington state authorities. Their official last day of employment is scheduled for July. Layoffs across levels, teams, & geographies The tech giant said the layoffs will span all levels, teams, and geographies, but will primarily target managerial roles, affecting all areas of the business, including LinkedIn and Xbox. “I think many people have this conception of layoffs as something that struggling companies have to do to save themselves, which is one reason for layoffs but it’s not the only reason,” Associated Press quoted Daniel Zhao, lead economist at workplace reviews site Glassdoor, as saying.	2025-06-02T00:00:00		https://interestingengineering.com/culture/microsoft-lays-off-6000-employees	[ { "date": "2025/06/02", "position": 36, "query": "AI layoffs" } ]
'Can't ChatGPT do it?' The growing cull of office jobs	‘Can’t ChatGPT do it?’ The growing cull of office jobs	https://observer.co.uk	[]	Hiring is slowing but AI may not be up to the task of fully replacing workers. The predictions for white-collar work are apocalyptic – up to half of entry-level ...	The predictions for white-collar work are apocalyptic – up to half of entry-level professional jobs eliminated and unemployment surging by the end of the decade. And across a range of industries over the past few weeks, the AI-related job losses have already begun. From marketing to consulting to PR, swathes of jobs have been cut, and senior executives say hiring is cooling, with pushback from HR departments whose first question is: couldn’t ChatGPT do that? In advertising, a chill is sweeping through the labour market, with automation already eliminating many junior roles. Daren Rubins, co-founder of Conker, an executive search firm, described recent cuts in the advertising industry as “brutal” and extending to executive roles. Rubins said: “We have been absolutely inundated with people, including senior people.” A drumbeat of announcements has signalled the shift. At Microsoft nearly a third of code is written by artificial intelligence (AI). CrowdStrike, the cybersecurity provider, announced it was cutting 500 jobs, around 5% of its workforce, and that AI would “flatten” growth in future hiring. Labour market economists suggest this is a more likely immediate outcome than direct layoffs. Meanwhile, the government’s public spending review could lead to as many as 50,000 civil service jobs being cut by the end of the decade, partly aided by the shift to AI; internal analysis has found that at the most junior levels, nearly two-thirds of bureaucrats’ time is spent on tasks that can be automated. The drivers are complex: many service-sector firms are grappling with economic uncertainty, and in some cases there are specific challenges: for McKinsey, which has shed 10% of its workforce, there is also the costly legal fallout from its work for US opioid manufacturers. But even more optimistic voices paint a picture of a thorough transformation in patterns of cognitive work driven by the increasing abilities of large language models. One senior executive at an advisory firm said: “We are not at the point of needing fewer people. We are deploying them differently.” But the same executive said that a colleague at a large European company who wanted to hire new staff was asked to justify why each of the tasks they were recruiting for could not be performed by AI. “On most items, they did not win the debate,” the executive said. The picture is not uniformly bleak. Klarna, the payment company, was bullish about AI adoption, but Sebastian Siemiatkowski, its chief executive, has beaten a partial retreat, admitting that the AI’s work was of lower quality. Economists debate whether, as in previous waves of automation, AI will lead to job creation as well as destruction; the Institute for Public Policy Research has modelled a range of scenarios, from a worst-case situation of “full displacement”, with 8 million jobs lost from the UK labour market, to the rosiest picture of “full augmentation”, in which all at-risk jobs are supplemented by machine intelligence and there are no job losses. But even optimists accept that junior roles in law and consulting firms are at great risk, meaning a steep reduction in the hiring of graduate trainees. AI startup Anthropic’s chief executive, Dario Amodei, predicts that up to half of entry-level white-collar jobs will vanish in the next five years. Tech executives such as Amodei and Sam Altman of Open AI tend to be gloomy about jobs, predicting a world of radical and rapid but uneven change, along with increased prosperity – at least for them. Whichever scenario comes to pass, it has become clear that the cull of white-collar work is happening now, with existing levels of AI technology. Partners in consulting businesses say there is a premium on in-person interaction and being able to think on the spot. However, elements of consultancy work which involved pattern recognition have been replaced by AI, and in other areas – such as interrogating large datasets – the use of AI has been an advantage. One of the drivers of work now is adapting to technological change. Three senior consultants said business is booming as companies seek to implement AI technologies: the familiar story of consultants helping other companies trim their workforces, but this time with tech. A partner at a major consulting firm said: “ A client will hire a consultant to say, ‘Can you help me replace my typical marketing process … and replace it with AI?’” Photograph by Jason Alden/Bloomberg via Getty Images	2025-06-02T00:00:00		https://observer.co.uk/news/business/article/cant-chatgpt-do-it-the-growing-cull-of-office-jobs	[ { "date": "2025/06/02", "position": 31, "query": "ChatGPT employment impact" } ]
10 Ways EHS Pros Are Using ChatGPT (and other AI ...	10 Ways EHS Pros Are Using ChatGPT (and other AI Assistants)	https://benchmarkgensuite.com	[ "Gerardo Gámez" ]	Impact Report · Contact Us. Impact Benchmark Gensuite ... That's where AI-powered tools like ChatGPT are stepping in to transform how safety leaders work every ...	Environmental, Health, and Safety (EHS) professionals are under increasing pressure to do more with less—manage compliance, reduce incidents, train teams, and communicate clearly across departments. That’s where AI-powered tools like ChatGPT are stepping in to transform how safety leaders work every day. Whether it’s drafting policies, analyzing incidents, or preparing for audits, AI prompt engines are quickly becoming must-have assistants in the EHS toolkit. Here are 10 real-world ways EHS pros are using ChatGPT and similar AI tools to streamline their workflows, improve safety outcomes, and stay ahead of compliance risks. Writing and Updating Safety Policies and SOPs 📝 Creating or revising safety procedures can be tedious. AI makes it faster and easier by drafting clear, consistent, and regulation-aligned policies from scratch—or refining your existing ones. Examples of useful prompts: “Write a forklift safety policy for a warehouse, including training, inspection, and operation guidelines.” “Create a lockout/tagout (LOTO) policy for a facility with CNC machines and robotic arms.” Conducting Regulatory Research 📚 Need to decipher OSHA or ISO requirements? AI can break down complex language, highlight key takeaways, and even help you avoid common compliance pitfalls. Prompts to try: “Summarize OSHA’s confined space entry requirements for general industry.” “What are the machine guarding standards for punch presses in a metal shop?” Pro Tip: Try customizing prompts with your industry, site conditions, or workforce characteristics to get the most relevant and actionable results. Supporting Incident Investigations 🔍 After an incident, time is of the essence. AI can guide you through tools like the 5 Whys or Fishbone diagrams, helping structure reports and uncover root causes faster. Prompts that help: “Use the 5 Whys to analyze a slip incident on an oily loading dock floor.” “Guide a root cause analysis for a hand injury during conveyor maintenance.” Creating Safety Training Content 📢 From toolbox talks to quiz questions, AI tools help EHS managers develop training that’s timely, relevant, and engaging—without starting from scratch. Effective prompt ideas: “Write a 10-minute toolbox talk on proper PPE use for corrosive chemicals.” “Create a toolbox talk about the safe use of compressed air in machinery cleaning.” Simplifying Safety Data Sheets (SDS) 📄 SDS documents can be overwhelming. AI can summarize key hazards, PPE needs, and emergency procedures in plain language for frontline workers. Try prompts like: “Summarize this SDS for hydrochloric acid for non-technical staff.” “What are the hazards and storage instructions for acetone used in parts cleaning?” Assisting with Risk Assessments and JHAs ⚠️ AI makes it easier to develop Job Hazard Analyses (JHAs), identify hazards, and recommend controls based on industry best practices. Examples include: “Draft a JHA for changing a valve on a high-pressure steam line.” “List hazards and controls for operating a hydraulic press in manufacturing.” Streamlining Safety Communication and Reporting 🗞️ Need to share updates across your plant or leadership team? AI can help write newsletters, incident summaries, or leadership reports that are clear and concise. Prompt inspiration: “Write a monthly safety newsletter for a mid-sized manufacturing facility, including a recent near miss and upcoming training reminders.” Preparing for Audits and Compliance Reviews ✅ AI can generate checklists based on ISO, OSHA, or EPA standards and help identify areas where your site may be falling short. Prompts worth trying: “Create a self-audit checklist based on ISO 14001:2015 environmental requirements.” “Generate an OSHA audit prep checklist for a metal stamping facility.” Supporting Behavior-Based Safety (BBS) Programs 👥 From coaching conversations to campaign slogans, AI can help drive engagement in BBS programs with positive reinforcement and practical tools. Prompt examples: “Write a coaching script to praise an employee who reported a near miss.” “Help me create a conversation guide after observing poor lifting technique.” Developing KPIs and Dashboards 📊 Want to visualize performance trends? AI tools can recommend key safety metrics and guide you in presenting them in compelling, easy-to-read dashboards. Use prompts like: “Suggest 5 leading and 5 lagging safety KPIs for manufacturing operations.” “How should I display near-miss trends and incident rates for plant leadership?” Final Thoughts As AI technology continues to evolve, EHS professionals who embrace tools like ChatGPT are gaining a serious edge—working smarter, communicating better, and driving a stronger culture of safety. Whether you’re building a safety training from scratch, preparing for an audit, or crafting your next policy update, there’s likely a prompt that can help. In addition to tools like ChatGPT, there are also AI solutions that are purpose-built for EHS & Sustainability tasks. Benchmark Gensuite’s Genny AI was developed by EHS leaders, for EHS leaders to help practitioners with tasks just like the scenarios we walked through above. Contact us to see Genny AI in action and learn what AI can do for your program.	2025-06-02T00:00:00	2025/06/02	https://benchmarkgensuite.com/ehs-blog/10-ways-ehs-pros-are-using-chat-gpt-and-other-ai-assistants/	[ { "date": "2025/06/02", "position": 85, "query": "ChatGPT employment impact" } ]
AI in public employment services: Unlocking potential ...	AI in public employment services: Unlocking potential, avoiding pitfalls	https://blogs.worldbank.org	[]	Public employment services are rapidly turning to artificial intelligence (AI) to develop a range of sophisticated digital tools.	Public employment services are rapidly turning to artificial intelligence (AI) to develop a range of sophisticated digital tools. According to a recent survey, half of the public employment agencies in OECD countries have implemented AI solutions such as chatbots that assist with service inquiries, profiling models to assess jobseeker needs, tools that guide job-search strategies, and job matching systems. AI can help these agencies optimize resources and improve service delivery. However, implementation remains challenging. We recently completed a project in cooperation with the European Commission, funded by the European Union via the Technical Support Instrument, developing a jobseeker profiling model based on machine learning for the Greek public employment service (DYPA). While this technical assistance focused on one application and one AI methodology, it illustrated the potential – and pitfalls – of applying AI to the operations of public employment agencies. Using AI to profile jobseekers Profiling models are used by virtually all public employment agencies to estimate the intensity of services that each jobseeker is likely to need. This is done by predicting a jobseeker’s “distance from the labor market”, based on personal characteristics, employment history, and the labor market situation. These models can help with a chronic problem that almost all agencies face - how to allocate scarce resources across a large pool of jobseekers. DYPA is no exception - its limited resources, along with a very high caseload, make it difficult to provide the intensive support that unemployed workers often need. Over half of jobseekers never meet a counselor and when they do, meetings may last for less than 15 minutes. In addition to strengthening service delivery by public employment agencies, profiling models can also serve as a valuable resource for policy-making entities such as the Greece’s Unit of Experts in Employment, Social Insurance, Welfare and Social Affairs. These models can not only identify which groups are likely to remain in long-term unemployment but, when combined with administrative data, they can provide evidence on which interventions lead to successful transitions for different types of jobseekers. Several European countries are now using AI to profile jobseekers, moving beyond traditional econometric models. The machine learning model we developed in Greece was effective in predicting unemployment duration, capturing complex patterns that traditional models miss. However, its ultimate success hinges on overcoming key challenges when introducing AI tools, such as ensuring transparency, addressing staff and client resistance, and continuous monitoring. For DYPA, three hurdles stood out - navigating AI’s legal and ethical concerns, assembling quality data, and tailoring the model to fit the agency’s operational needs. Ethical and legal challenges confronting AI in public employment services AI is still uncharted territory for many governments, with legal and ethical challenges that are tough to navigate – including around bias and discrimination, data privacy, accountability, and the protection of individual rights. Even in countries with sophisticated public employment services, missteps have occurred. For example, Austria’s service faced backlash when a chatbot was accused of discriminating against women in providing information on training and career orientation to jobseekers. In addressing these challenges, France stands out for its strong legal and ethical framework, with clear guidelines that govern the use of AI, dedicated oversight teams, and capacity-building initiatives to ensure expertise. AI tools are only as effective as the quality of the data they rely on Governments generate vast amounts of data every day through their administrative processes that can support the development of effective AI tools. However, making data usable for machine learning is no simple task, as ensuring quality and linking databases properly can be highly complex. This was certainly a challenge in our engagement. Extracting, interpreting, and connecting multiple rich yet poorly documented databases from DYPA and other government agencies required intensive collaboration between operational and technical teams. The need for coordination In the end, the job profiling project underlined the need for close coordination between the technical team that is developing the tools and the agency personnel who will be using them. Yet this is not always an easy partnership. At different stages, DYPA’s operational staff asked for a profiling model with features that were not feasible with a machine learning approach and available data. At other times, the AI experts proposed models that were technically sound but did not fully meet DYPA’s business needs. Numerous discussions across these two divides were needed – and in fact will continue to be needed during the implementation stage and beyond - to ensure the models are effectively tailored to meet current operational needs. Lessons for public employment agencies While our machine-learning profiling project focused on one specific application, it offered more general lessons on how public employment agencies can realize the potential of AI while avoiding the pitfalls. AI can be a powerful tool to enhance employment services, but its impact will ultimately be shaped by how well agencies navigate the complexities of data, ethics, and human expertise. The challenge now is not just to build smarter models, but to ensure they work in practice through human oversight - helping jobseekers, supporting counselors, and strengthening the labor market as a whole. Lessons learned in an EU member country like Greece can help less developed countries where AI implementation faces steeper hurdles due to weak digital infrastructure, limited data availability, and low institutional capacity.	2025-06-02T00:00:00		https://blogs.worldbank.org/en/digital-development/ai-in-public-employment-services--unlocking-potential--avoiding-	[ { "date": "2025/06/02", "position": 28, "query": "artificial intelligence employment" }, { "date": "2025/06/02", "position": 30, "query": "artificial intelligence labor union" } ]
Generative AI used to copy and clone French news media ...	Generative AI used to copy and clone French news media in French-speaking Africa	https://rsf.org	[]	Fake radio broadcasts created with artificial intelligence are gradually making their way into the arsenal of those who disinform.	On 20 May, the French international news broadcaster Radio France Internationale (RFI) reported that it had again been the victim of identity theft using generative AI. In a video bearing RFI’s logo, fake voices discussed Cameroonian opposition leader Maurice Kamto’s possible presidential candidacy for more than five minutes. The admissibility of his candidacy in next October’s presidential election is currently a matter of debate in political circles in Cameroon. The dialogue in the video is laborious, the tone is crudely robotic and the format does not conform to RFI’s editorial practices. But RSF has learned that this is not preventing social media userss from commenting on it and circulating as if it were authentic, or WhatsApp groups from sharing it. This is not the first time generative AI has been used in an attempt to fabricate content produced by RFI or other French public broadcasters. In the Democratic Republic of Congo in April, a fake radio news programme simulated the voices of Arthur Ponchelet (of RFI) and Aurélie Bazzara (of France 24) to lend credence to a public apology supposedly made by Corneille Nangaa, the leader of the AFC/M23 rebel coalition that is active in the provinces of South and North Kivu.	2025-06-02T00:00:00		https://rsf.org/en/generative-ai-used-copy-and-clone-french-news-media-french-speaking-africa	[ { "date": "2025/06/02", "position": 16, "query": "artificial intelligence journalism" } ]
Artificial Intelligence, EU Regulation and Competition Law ...	Artificial Intelligence, EU Regulation and Competition Law Enforcement: Addressing Emerging Challenges	https://www.quinnemanuel.com	[]	... Union to ensure compliance with the EU AI Act's requirements.[7]. The EU AI ... Moreover, employers must inform workers and their representatives of the ...	Artificial Intelligence (“AI”)[1] has emerged as a uniquely powerful tool that promises to revolutionise businesses and society alike in unprecedented ways. It is trite to observe that companies increasingly make use of AI to conduct business, and that consumers do so to in their day-to-day lives. At the forefront of this technological revolution is generative AI,[2] which has garnered worldwide attention as a result of its use in AI chatbots that rely on natural language processing to create human-like conversational dialogue. AI is rightly viewed as a colossal and unmissable investment opportunity by both private and public actors. Notably, the European Commission (the “Commission”) announced on 11 February 2025 its InvestAI initiative, which aims to secure EUR 200 billion for investments in AI, including a EUR 20 billion fund to invest in AI gigafactories.[3] Similarly, President Trump announced on 21 January 2025 a private sector investment of up to USD 500 billion in AI infrastructure. The project, called Stargate, will take the form of a joint venture between OpenAI, Oracle and Softbank.[4] But because the immense power of AI can be used not only for good but also for ill, regulators around the world are in the process of adopting rules that seek to mitigate the risks arising from some of the most nebulous aspects of its deployment and use. And AI is also increasingly in the crosshairs of antitrust enforcers on both sides of the Atlantic. The present note discusses the key European Union (“EU”) regulations and competition law enforcement priorities in this rapidly evolving field. I. EU Regulatory Framework Governing AI (i) The EU AI Act The EU has been a pioneer in AI regulation with the adoption on 13 June 2024 of the Artificial Intelligence Act (the “EU AI Act”), the first-ever comprehensive legal framework on AI worldwide.[5] The EU AI Act imposes obligations on six categories of economic actors active in the AI sector, namely providers, importers, distributors, product manufacturers, and deployers of AI systems, as well as appointed authorised representatives. These are collectively referred to as “operators”.[6] The regulation has extraterritorial effect. As such, it applies not only to businesses established in the EU, but also to those based outside the EU if their AI systems are placed on the EU market, used within the EU, or if their outputs are intended for use within the EU. Non-EU providers of both AI systems and general-purpose AI (“GPAI”) models are therefore required to appoint an authorised representative established in the Union to ensure compliance with the EU AI Act’s requirements.[7] The EU AI Act adopts a risk-based approach that differentiates between AI systems placed on the market, or deployed, in the EU according to the different levels of risk that they pose, namely (i) unacceptable risk; (ii) high-risk; (iii) limited-risk; (iv) minimal risk; and (v) in the case of GPAI models, systemic risk. The EU AI Act prohibits outright those AI practices that are considered as posing an “unacceptable risk”.[8] These include AI systems that manipulate people’s decisions or exploit their vulnerabilities,[9] that evaluate or classify people based on their social behaviour or personal characteristics (social scoring),[10] that predict a person’s risk of committing a criminal offence,[11] that create or expand facial recognition databases by scraping images from the internet or CCTV footage,[12] that infer emotions in the workplace or educational institutions (unless they serve a medical or safety purpose),[13] and that categorise people based on their biometric data,[14] or enable real-time remote biometric identification.[15] This prohibition of AI practices that entail an “unacceptable risk” came into force on 2 February 2025. Failure to comply with the prohibition will attract a fine capped at EUR 35 million or 7% of the operator’s total worldwide annual turnover in the preceding financial year.[16] The EU AI Act does provide, however, for a limited exception to the outright prohibition of ‘real-time’ remote biometric identification systems. These are permissible if they are necessary for law enforcement purposes, and provided that proper safeguards, including prior judicial or administrative authorisation, are in place to ensure the protection of fundamental rights.[17] “High-risk” AI systems are those believed to create significant potential harm to health, safety and fundamental rights. They include AI systems that are used as a product, or as a safety component of a product. Such systems are required to undergo a third-party conformity assessment under the legislation referenced in Annex I of the EU AI Act,[18] which includes, inter alia, regulations governing the safety of toys, medical devices and lifts.[19] Other AI systems such as those intended to be used for biometric identification systems, biometric categorisation, emotion recognition, as safety components in the management and operation of critical infrastructure, or by law enforcement authorities,[20] also fall within the “high-risk” category.[21] All providers and deployers of such “high-risk” AI systems must comply with strict obligations, including the implementation of a risk management system, quality controls, record-keeping and transparency obligations, and some level of human oversight.[22] These obligations will start applying as of 2 August 2026 for those AI systems referred to in Annex III, and as of 2 August 2027 for those referred to in Annex I EU AI Act.[23] Penalties of up to EUR 15 million or 3% of the company’s total worldwide annual turnover in the preceding financial year can be imposed in case of non-compliance.[24] AI systems that do not fall within the prohibited or “high-risk” categories and are considered as presenting limited risks will be only subject to information and transparency obligations as of 2 August 2026.[25] These “limited-risk” AI systems include those intended to interact with natural persons or to generate content, which may pose specific risks of impersonation or deception. For instance, the regulation provides that, when interacting with chatbots, users must be made aware of this. Similarly, deployers of AI systems that generate or manipulate image, audio or video content (i.e., deep fakes), must disclose that such content has been artificially generated or manipulated (with very limited exceptions, including instances in which such content is being used to prevent a criminal offence).[26] Providers of AI systems that generate large quantities of synthetic content must implement sufficiently reliable, interoperable, effective and robust techniques and methods (e.g., watermarks) to ensure that it can be easily determined that the output has been generated or manipulated by an AI system and not by a human being. Moreover, employers must inform workers and their representatives of the deployment of AI systems in the workplace. Non-compliance with these transparency obligations can lead to fines similar to those imposed on “high-risk” AI systems.[27] AI systems that present minimal risks for individuals (e.g., spam filters) are not subject to any additional obligations and are required simply to comply with other legislation already in force such as the General Data Protection Regulation (the “GDPR”).[28] Finally, the EU AI Act contains special rules regarding GPAI[29] models and GPAI models that pose systemic risks.[30] GPAI models are subject to transparency obligations (e.g., the obligation to make detailed summaries of training data sets publicly available) and EU copyright protection obligations applicable.[31] In addition to these, the providers of GPAI models posing systemic risk are required to constantly assess and mitigate the risks they pose[32] and to ensure cybersecurity protection by, inter alia, documenting and reporting serious incidents (e.g., violations of fundamental rights) to the AI office and implementing corrective measures.[33] Non-compliance with these obligations risks the same level of penalties as those applicable to “high-risk” and “limited risk” AI systems.[34] Since the enactment of the EU AI Act, the Commission has taken and/or is preparing to take several actions to clarify the text of the regulation and/or facilitate compliance: On 4 February 2025, it published a set of Guidelines on prohibited practices [35] providing practical examples and further specifying the measures that may be taken to avoid offering or using AI systems in ways that are likely to be prohibited by Article 5 of the EU AI Act. Inter alia, the Guidelines clarify that emotion recognition systems that are not considered as posing an “unacceptable risk” [36] will nevertheless be considered “high-risk” AI systems. [37] The same applies to certain AI-based scoring systems, such as those used for credit-scoring or risk assessment by health and life insurance companies, which do not fulfil the conditions for outright prohibition provided by Article 5(1)(c) of the regulation. [38] The Guidelines also explain how, in the Commission's view, the EU AI Act will intersect with other related or overlapping EU statutes, [39] notably, the GDPR, the Law Enforcement Directive, [40] and Regulation (EU) 2018/1725. [41] providing practical examples and further specifying the measures that may be taken to avoid offering or using AI systems in ways that are likely to be prohibited by Article 5 of the EU AI Act. Inter alia, the Guidelines clarify that emotion recognition systems that are not considered as posing an “unacceptable risk” will nevertheless be considered “high-risk” AI systems. The same applies to certain AI-based scoring systems, such as those used for credit-scoring or risk assessment by health and life insurance companies, which do not fulfil the conditions for outright prohibition provided by Article 5(1)(c) of the regulation. The Guidelines also explain how, in the Commission's view, the EU AI Act will intersect with other related or overlapping EU statutes, notably, the GDPR, the Law Enforcement Directive, and Regulation (EU) 2018/1725. On 11 March 2025, the AI Office published the third draft of the “General-Purpose AI Code of Practice”, with the final version expected in May 2025. [42] By outlining commitments and detailed implementation measures, the purpose of this code is to support providers of GPAI models in meeting their obligations under the EU AI Act. A dedicated AI Act Service Desk is expected to be launched in summer 2025 to accompany the progressive entry into force of the EU AI Act’s obligations. This platform will provide stakeholders with interactive tools to help them assess their legal obligations and compliance steps. [43] The creation of the AI Act Service Desk is one of the Commission’s strategic priorities set out in its AI Continent Action Plan, unveiled on 9 April 2025. [44] It is clear that the EU AI Act has enormous practical implications, not least because it increases significantly the regulatory burden on businesses. Companies must now evaluate their current as well as future use of AI and undertake a thorough self-assessment to ensure compliance with the new regulation. They will also need regularly to monitor any updates, as the list of practices currently prohibited by the EU AI Act is likely to change over time. Providers should stay informed about the Commission’s upcoming post-market monitoring plan, due by 2 February 2026, and prepare their strategies accordingly. If already subject to post-market requirements, especially in regulated sectors like finance, companies should consider integrating compliance with the EU AI Act into their wider compliance program. In cases where a company’s product interacts directly with individuals or presents AI-generated content, transparency must be embedded from the outset by implementing clear user disclosures and developing mechanisms to label AI-generated material. As regards GPAI models, companies must prepare technical documentation in accordance with the EU AI Act’s requirements, considering how design choices, training data, and risk assessments may affect long-term compliance. They should also determine the appropriate timing for the system’s launch in the EU market and conduct a thorough legal assessment, particularly regarding copyright and data usage. Monitoring updates to systemic risk thresholds is equally important, as these may change through delegated acts. Moreover, given the technical and legal complexity of the issues at stake, obtaining independent legal advice from experts in this area, strong governance and proper documentation, compliance training, as well as assigning compliance oversight to a dedicated AI officer or a team within the business will be crucial. Notably, a robust risk management framework is critical, with regular reviews, mitigation strategies, and ongoing monitoring. Comprehensive documentation should be maintained for each AI system, outlining its function, design, and performance. Data must be regularly reviewed to eliminate biases that could result in discriminatory outcomes, as explicitly prohibited by the EU AI Act. In sum, the EU AI Act is a highly ambitious regulation that has inevitably attracted serious criticism, including that it sets forth very stringent requirements that create barriers for non-EU companies, potentially discouraging market entry due to the cost and complexity of compliance, which could reduce competition and hinder innovation within the EU. Conversely, European SMEs and startups may also find themselves at a disadvantage compared to rivals operating in less regulated regions. Compounding these challenges, the new regulation also raises enforcement issues, arising in particular from the lack of clarity of some of its provisions and/or the presence of seemingly contradictory ones. This gives rise to the risk that it will be enforced in legally defensible but unanticipated ways, increasing the likelihood of inconsistent implementation by the different EU Member States. Gaps in the EU AI Act’s coverage have also drawn criticism. Notably, in February 2025, fifteen cultural organisations sent a letter to the Commission arguing that it provides insufficient protection to content creators such as writers and musicians. Others have argued that the regulation fails to fully address copyright issues linked to generative AI, especially regarding the broad interpretation of text and data mining exemptions, which could enable large tech firms to exploit creative content at scale. This concern has already led to legal action from artists and authors.[45] It remains to be seen whether, despite these shortcomings, the EU AI Act will succeed in its desire to shape a transparent and ethical AI landscape in the EU without undermining Europe’s competitiveness. (ii) Application of the Digital Markets Act and Digital Services Act to AI In addition to the EU AI Act, the Commission may also seek to rely on the Digital Markets Act (the “DMA”)[46] and the Digital Services Act (the “DSA”)[47] to regulate the field of AI. More specifically, the Commission may use the DMA to regulate AI in circumstances where AI-related services are integrated or embedded into designated “core platform services”, or where it designates key inputs for AI applications as “core platform services”. To date, the Commission has not designated AI and cloud computing services[48] as such "core platform services".[49] However, this could change in the future, with the new Commissioner for Competition, Ms Teresa Ribera, having vowed to intensify its vigorous enforcement and targeted implementation of the DMA in 2025, including by expanding its scope. Moreover, certain Member States,[50] and the European Parliament’s Working Group on the DMA implementation have been pressuring the Commission to monitor closely AI and cloud services, and to designate them as “core platform services”.[51] We would thus expect the Commission to launch a market investigation under Article 19 of the DMA, with a view to including AI and cloud computing services in the list of core platform services laid down in Article 2(2) of the DMA. The DSA is equally set to play an important role in AI regulation, as shown by the steps already undertaken by the Commission (DG Connect) to that effect. Specifically, in March 2024, DG Connect sent requests for information to Microsoft (Bing), Google (Search and YouTube), Meta (Facebook and Instagram), Snapchat, ByteDance (TikTok) and X asking them about the measures they were adopting to mitigate systemic risks[52] linked to generative AI.[53] Similarly, in May 2024, the Commission sent an additional request for information to Microsoft concerning an alleged failure by the company to disclose certain documents related to risks stemming from the Bing search engine's generative AI features, namely, “Copilot in Bing” and “Image Creator by Designer”.[54] It has also been reported that the Commission is monitoring ChatGPT with a view to potentially designating it as a systemic platform in light of its online search functionality.[55] (iii) AI and Product Liability To address AI-specific issues, the EU has also modernised its (no-fault) product liability framework, pursuant to which consumers are entitled to claim damages caused by defective products without needing to prove negligence or fault on the part of the seller or supplier. The new Product Liability Directive explicitly applies to consumer-facing generative AI systems (including chatbots) and other AI tools, expanding the definition of “product” to include digital files, online platforms, and all types of software, including applications, operating systems, and AI systems.[56] Moreover, claimants will now be entitled to seek compensation if they suffer damages due to missing or insufficient software updates, weak cybersecurity protection, or the destruction or corruption of data.[57] The new Directive will apply to products placed on the market as of December 2026.[58] By contrast, the EU recently abandoned a proposed directive on adapting non-contractual civil liability rules to AI.[59] The Commission argued that the proposed directive would have reduced the evidentiary burden required for claimants, thereby mitigating some of the inherent challenges posed by the black-box nature of most AI models and algorithms by enabling claimants with a plausible damages claim to request disclosure of evidence about specific “high-risk” AI systems suspected of having caused damage, and introducing a targeted rebuttable presumption of causality. II. Antitrust and Competition Enforcement in the AI Sector Competition authorities have traditionally focused on algorithmic collusion in cases in which AI tools are used to monitor and adjust prices, or to facilitate the sharing of competitively sensitive information. Most recently, however, competition concerns have been raised about the AI sector as a whole, accompanied by numerous statements emphasising the need for cooperation between regulators to ensure fair competition in the AI fields. Notably, the Commission, the U.K. Competition and Markets Authority (“CMA”), the U.S. Department of Justice and the U.S. Federal Trade Commission (“DoJ” and “FTC”) issued a joint statement outlining their views on the issue.[60] Similarly, the competition authorities of Canada, France, Germany, Italy, Japan, United Kingdom and United States laid down guiding principles following the G7 summit held in October 2024.[61] These declarations identified three primary competition concerns: (i) the concentrated control of key components for developing AI foundation models, such as specialised chips, computing power, cloud capacity, large-scale data and specialist technical expertise, (ii) the entrenchment or extension of large incumbent digital firms’ market power in AI-related markets, and (iii) anti-competitive partnerships and arrangements involving key AI players. Ensuring fair dealing, interoperability, and choice among diverse products and business models were also identified as common principles for safeguarding competition in the AI sector. AI and cloud computing also rank high in Ms Ribera’s agenda, and the EU is likely to become a key jurisdiction for antitrust enforcement in the AI space. Testament to this is the Competition’s Policy Brief on Generative AI published in September 2024,[62] which reaffirms the Commission’s avowed interest in investigating emerging market trends involving large digital players holding critical inputs for AI. Albeit not novel, the main antitrust issues and theories of harm identified by the Commission’s Policy Brief in relation to AI are: (i) exclusivity arrangements leading to the exclusion or marginalisation of rivals; (ii) discrimination/preferential access arrangements; (iii) self-preferencing; (iv) refusal to supply, (v) tying or bundling, (vi) non-compete and lock-in strategies; (vii) margin squeeze by vertically integrated players; (viii) anticompetitive agreements between rivals in the AI space; and (ix) killer or reverse acquisitions. The Commission indicates that it will examine any such issues taking into account the specific characteristics of the digital and AI space, including any barriers to entry resulting from the highly complex and technical nature of the sector, ecosystem dynamics,[63] network effects, and the need for highly specialised employees, in particular engineers. In particular, the Commission will focus on vertical integration, exclusivity and/or preferential access arrangements between incumbent large digital players that may currently enjoy preferential access to key components of generative AI (e.g., Graphic Processing Units (“GPUs”), supercomputing power, cloud capacity, as well as data or specialised engineers), and certain third parties such as AI foundation model developers, such that the dominant players could deprive rivals of access to such key components, or reduce the quality/number of available components. The risk identified is that such partnerships may increase the degree of market concentration and dependency on a few dominant players, thus making access to critical inputs more difficult and increasing the likelihood of market foreclosure.[64] For instance, the Commission sought to investigate the USD 13 billion partnership between Microsoft and OpenAI to build new Azure AI supercomputing technologies. Although the deal in question did not qualify as a notifiable concentration under the EU Merger Regulation[65] because it did not result in an acquisition of control on a lasting basis, the Commission expressed a willingness to investigate it under the abuse of dominance prohibition set out in Article 102 TFEU.[66] Similarly, in the UK, the CMA investigated Amazon’s and Google’s strategic collaboration with Anthropic, although – like the Commission – it eventually concluded that no relevant merger situation had been created.[67] The Commission is also expected to carefully monitor the development of smaller AI foundation models capable of running on mobile devices and offline, such as the integration of Google’s Gemini Nano AI model in Samsung’s Galaxy S24 and S25 series.[68] The Commission will examine, in particular, whether such integration could raise anticompetitive concerns, taking the form of exclusivity agreements and default pre-installation on popular device brands that could lead to the anticompetitive foreclosure of rivals.[69] Moreover, the hiring of highly skilled employees in the AI sector, which is critical for the development of AI but may be relatively difficult for small companies, has also attracted the attention of competition authorities. For example, Microsoft’s hiring of Inflection employees was the subject of a referral request to the Commission by several competition agencies of EU Member States under Article 22 of the EU Merger Regulation, although the request was subsequently withdrawn[70] following the Court of Justice’s ruling in Illumina.[71] Finally, the Commission can be excepted to scrutinise deals and conduct related to key components necessary for the development of AI foundation models, as it considers that these are most likely to result in increased barriers to entry or expansion, or lead to anticompetitive foreclosure. In particular, the Commission considers that access to large and qualitative datasets may be hindered by the cost of data licensing agreements entered into between the holders of high-quality/large amount of data and players active in the AI-space. The necessity to obtain specialised chips supporting AI neural networks, such as GPUs, Tensor Processing Units and other AI accelerators, may similarly be impeded by their cost and long lead times.[72] For instance, the Commission recently used Article 22 of the EU Merger Regulation to review NVIDIA’s acquisition of Run:ai, as both are active in the GPU industry.[73] III. Conclusion It is undeniable that AI is a groundbreaking technology with unprecedented power to change our world. Various concerns about the potential misuse of AI will be legitimate, and some overarching measures ought to be taken to mitigate such risks. Similarly, seeking to ensure that key AI technologies and products do not end up concentrated in the hands of just one or two very powerful players and that competition in the AI sector will thrive is also laudable. However, over-regulation of nascent technologies could also hamper innovation and handicap European companies, including the SMEs and startups of which Europe is in dire need to spur economic growth. Enforcement of the sector-specific and antitrust rules already in place should not come at the cost of progress, and it would be wise for the Commission and other national authorities to limit their regulatory and antitrust enforcement action to what is strictly necessary and proportionate to safeguard fundamental rights and promote competition. *** If you have any questions about the issues addressed in this memorandum, or if you would like a copy of any of the materials mentioned in it, please do not hesitate to reach out to: Marixenia Davilla Email: [email protected] Phone: +32 2 416 50 13 Miguel Rato Email: [email protected] Phone : +32 2 416 50 04 Nicolas Papageorges Email: [email protected] Phone: +32 2 416 50 15 To view more memoranda, please visit www.quinnemanuel.com/the-firm/publications/ To update information or unsubscribe, please email [email protected] [1] According to a report issued by the Commission, AI refers to “systems that display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals”. See “A definition of Artificial Intelligence: main capabilities and scientific disciplines”, 18 December 2018, available at https://digital-strategy.ec.europa.eu/fr/node/2226. [2] According to the Commission, generative AI refers to “neural networks that can generate high-quality text, images, and other forms of content based on the data they were trained on”. Competition Policy Brief, “Competition in Generative AI and Virtual Worlds”, September 2024, p. 1, available at https://competition-policy.ec.europa.eu/document/download/c86d461f-062e-4dde-a662-15228d6ca385_en (“Competition Policy Brief on Generative AI”). [3] See Press Release, “EU launches InvestAI initiative to mobilise €200 billion of investment in artificial intelligence”, 11 February 2025, available at https://ec.europa.eu/commission/presscorner/detail/en/ip_25_467. [4] See Reuters, “Trump announces private-sector $500 billion investment in AI infrastructure”, 22 January 2025, available at https://www.reuters.com/technology/artificial-intelligence/trump-announce-private-sector-ai-infrastructure-investment-cbs-reports-2025-01-21/. [5] Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act). [6] Id., recitals 1-3, 6-8 and art. 1. [7] Id., recitals 9-11, 82 and arts 2, 22 and 54. [8] Id., art. 5. [9] Id., arts 5(1)(a) and (b). [10] Id., art. 5(1)(c). [11] Id., art. 5(1)(d). [12] Id., art. 5(1)(e). [13] Id., art. 5(1)(f). Recital 44 of the EU AI Act elaborates on the risks justifying the prohibition: “There are serious concerns about the scientific basis of AI systems aiming to identify or infer emotions, particularly as expression of emotions vary considerably across cultures and situations, and even within a single individual. Among the key shortcomings of such systems are the limited reliability, the lack of specificity and the limited generalisability. Therefore, AI systems identifying or inferring emotions or intentions of natural persons on the basis of their biometric data may lead to discriminatory outcomes and can be intrusive to the rights and freedoms of the concerned persons. Considering the imbalance of power in the context of work or education, combined with the intrusive nature of these systems, such systems could lead to detrimental or unfavourable treatment of certain natural persons or whole groups thereof”. [14] Id., art. 5(1)(g). [15] Id., art. 5(1)(h). [16] Id., art. 99(3). [17] For instance, art. 5(1)(h) of the EU AI Act provides that the use of ‘real-time’ remote biometric identification systems in publicly accessible spaces for the purposes of law enforcement is prohibited, unless and in so far as such use is strictly necessary for one of the following objectives: (i) the targeted search for specific victims of abduction, trafficking in human beings or sexual exploitation of human beings, as well as the search for missing persons; (ii) the prevention of a specific, substantial and imminent threat to the life or physical safety of natural persons or a genuine and present or genuine and foreseeable threat of a terrorist attack; and (iii) the localisation or identification of a person suspected of having committed a criminal offence, for the purpose of conducting a criminal investigation or prosecution or executing a criminal penalty for certain types of offences. See also, id., art. 5(2)-5(7). [18] Id., art. 6(1). [19] Directive 2009/48/EC of the European Parliament and of the Council of 18 June 2009 on the safety of toys (OJ L 170, 30.6.2009, p. 1); Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC; and Directive 2014/33/EU of the European Parliament and of the Council of 26 February 2014 on the harmonisation of the laws of the Member States relating to lifts and safety components for lifts. [20] These include AI systems intended to be used: (i) to assess the risk of a natural person becoming the victim of criminal offences; (ii) as polygraphs or similar tools; (iii) to evaluate the reliability of evidence in the course of the investigation or prosecution of criminal offences; (iii) for assessing the risk of a natural person offending or re-offending not solely on the basis of the profiling of natural persons as referred to in Article 3(4) of Directive (EU) 2016/680, or to assess personality traits and characteristics or past criminal behaviour of natural persons or groups; and (iv) for the profiling of natural persons as referred to in Article 3(4) of Directive (EU) 2016/680 in the course of the detection, investigation or prosecution of criminal offences. See EU AI Act, Annex III. [21] Id., art. 6(2) and Annex III. [22] Id., chapter III. [23] Id., art. 113. [24] Id., art. 99(4). Art. 99(6) provides that such an amount should be lower in the case of small and medium sized enterprises (“SMEs”). [25] Id., art. 50. [26] Id., art. 50(4). [27] Id., art. 99. [28] Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). [29] According to art. 3(63) of the EU AI Act, “’general-purpose AI model’ means an AI model, including where such an AI model is trained with a large amount of data using self-supervision at scale, that displays significant generality and is capable of competently performing a wide range of distinct tasks regardless the way the model is placed on the market and that can be integrated into a variety of downstream systems or applications, except AI models that are used for research, development of prototyping activities before they are placed on the market”. [30] According to arts 3(64) and 51, a GPAI model will be classified as posing systemic risk if it has or considered as having high impact capabilities (i.e. capabilities that match or exceed the capabilities recorded in the most advanced GPAI models). [31] Id., art. 53. [32] Art. 3(65) of the EU AI Act explains that “’systemic risk’ means a risk that is specific to the high-impact capabilities of general-purpose AI models, having a significant impact on the Union market due to their reach, or due to actual or reasonably foreseeable negative effects on public health, safety, public security, fundamental rights, or the society as a whole, that can be propagated at scale across the value chain”. See also id., recital 110 which further clarifies that systemic risks include “any actual or reasonably foreseeable negative effects in relation to major accidents, disruptions of critical sectors and serious consequences to public health and safety; any actual or reasonably foreseeable negative effects on democratic processes, public and economic security; the dissemination of illegal, false, or discriminatory content,” […] “chemical, biological, radiological, and nuclear risks, such as the ways in which barriers to entry can be lowered, including for weapons development, design acquisition, or use; offensive cyber capabilities, such as the ways in vulnerability discovery, exploitation, or operational use can be enabled; the effects of interaction and tool use, including for example the capacity to control physical systems and interfere with critical infrastructure; risks from models of making copies of themselves or ‘self-replicating’ or training other models; the ways in which models can give rise to harmful bias and discrimination with risks to individuals, communities or societies; the facilitation of disinformation or harming privacy with threats to democratic values and human rights; risk that a particular event could lead to a chain reaction with considerable negative effects that could affect up to an entire city, an entire domain activity or an entire community”. [33] Id., art. 55. [34] Id., art. 101. [35] Annex to the Communication from the Commission - Approval of the content of the draft Communication from the Commission - Commission Guidelines on prohibited artificial intelligence practices established by Regulation (EU) 2024/1689 (AI Act), C(2025) 884 final (“Guidelines on prohibited AI practices”). [36] EU AI Act, art. 5(1)(f). [37] Pursuant to art. 6(2) and Annex III, point (1)(c) of the EU AI Act. [38] EU AI Act, art. 5(1)(c). See also id., recitals 37-38, 58 and Annex III. [39] Guidelines on prohibited AI practices, paras 42-52. See also, id., paras 135-145, 178-183, 219-221, 238 and 287-288. The Commission has approved the draft guidelines, but not yet formally adopted them as of the date of publishing of the present note. [40] Directive (EU) 2016/680 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data, and repealing Council Framework Decision 2008/977/JHA. [41] Regulation (EU) 2018/1725 of the European Parliament and of the Council of 23 October 2018 on the protection of natural persons with regard to the processing of personal data by the Union institutions, bodies, offices and agencies and on the free movement of such data, and repealing Regulation (EC) No 45/2001 and Decision No 1247/2002/EC. [42] See Press Release, “Third Draft of the General-Purpose AI Code of Practice published, written by independent experts”, 11 March 2025, available at https://digital-strategy.ec.europa.eu/en/library/third-draft-general-purpose-ai-code-practice-published-written-independent-experts. [43] See Call for Tenders, “Commission launches a call for tender as part of the efforts to establish the AI Act Service Desk”, 16 April 2025, available at https://digital-strategy.ec.europa.eu/en/funding/commission-launches-call-tender-part-efforts-establish-ai-act-service-desk. [44] See Press Release, “The AI Continent Action Plan”, 9 April 2025, available at https://digital-strategy.ec.europa.eu/en/library/ai-continent-action-plan. The Commission’s strategic roadmap to advance AI development and adoption across the EU focuses on five areas: (i) building a large-scale AI computing infrastructure; (ii) expanding access to high-quality data; (iii) promoting AI deployment in strategic sectors; (iv) strengthening AI-related skills and talent; and (v) facilitating the implementation of the EU AI Act through the creation of the AI Act Service Desk. [45] See The Guardian, “EU accused of leaving ‘devastating’ copyright loophole in AI Act”, 19 February 2025, available at https://www.theguardian.com/technology/2025/feb/19/eu-accused-of-leaving-devastating-copyright-loophole-in-ai-act. [46] Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and far markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act). [47] Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market For Digital Services and amending Directive 2000/31/EC (Digital Services Act). [48] DMA, art. 2(i). [49] See, e.g., MLex, “AI, cloud competition risks must face DMA scrutiny, EU lawmakers say”, 23 January 2025, available at https://content.mlex.com/#/content/1626113/ai-cloud-competition-risks-must-face-dma-scrutiny-eu-lawmakers-say?referrer=search_linkclick. [50] See MLex, “Tech companies should see tighter AI competition rules, EU countries say”, 10 February 2025, available at https://content.mlex.com/#/content/1630052/tech-companies-should-see-tighter-ai-competition-rules-eu-countries-say?referrer=search_linkclick. [51] See MLex, “AI, cloud competition risks must face DMA scrutiny, EU lawmakers say”, 23 January 2025, available at https://content.mlex.com/#/content/1626113/ai-cloud-competition-risks-must-face-dma-scrutiny-eu-lawmakers-say?referrer=email_instantcontentset&paddleid=202&paddleaois=2000. [52] Under art. 35 of the DSA, “[p]roviders of very large online platforms and of very large online search engines shall put in place reasonable, proportionate and effective mitigation measures, tailored to the specific systemic risks identified pursuant to Article 34, with particular consideration to the impacts of such measures on fundamental rights”. These include, for instance, the adaptation of the design of the services, the terms and conditions, or the content moderation processes. [53] See Press Release, “Commissions sends requests for information on generative AI risks to 6 Very Large Online Platforms and 2 Very Large Online Search Engines under the Digital Services Act”, 14 March 2024, available at https://digital-strategy.ec.europa.eu/en/news/commission-sends-requests-information-generative-ai-risks-6-very-large-online-platforms-and-2-very. [54] See Press Release, “Commission compels Microsoft to provide information under the Digital Services Act on generative AI risks on Bing”, 17 May 2024, available at https://digital-strategy.ec.europa.eu/en/news/commission-compels-microsoft-provide-information-under-digital-services-act-generative-ai-risks. [55] MLex, “ChatGPT faces possible designation as a systemic platform under EU digital law”, 30 April 2025, available at https://content.mlex.com/#/content/1650470/chatgpt-faces-possible-designation-as-a-systemic-platform-under-eu-digital-law?referrer=search_linkclick. [56] Directive (EU) 2024/2853 of the European Parliament and of the Council of 23 October 2024 on liability for defective products and repealing Council Directive 85/374/EEC (“Product Liability Directive”), art. 4. [57] Id., arts 7-8 and 11. [58] Id., art. 21. [59] Proposal for a Directive of the European Parliament and of the Council on adapting non-contractual civil liability rules to artificial intelligence (AI Liability Directive), 2022/0303 (COD). [60] M. Vestager, S. Cardell, J. Kanter and L. Khan, “Joint Statement on Competition in Generative AI Foundation Models and AI Products”, 23 July 2024, available at https://competition-policy.ec.europa.eu/document/download/79948846-4605-4c3a-94a6-044e344acc33_en?filename=20240723_competition_in_generative_AI_joint_statement_COMP-CMA-DOJ-FTC.pdf. [61] G7 Competition Authorities and Policymakers’ Summit, “Digital Competition Communiqué”, 4 October 2024, available at https://en.agcm.it/dotcmsdoc/pressrelease/G7%202024%20-%20Digital%20Competition%20Communiqu%C3%A9.pdf%20%20. [62] Competition Policy Brief on Generative AI, fn. 2. [63] See, e.g., Commission Decision of 5 September 2023 in Case M.0615 – Booking Holdings/eTraveli Group, C(2023) 6376 final, paras 904 et seq. See also Competition Policy Brief on Generative AI, p. 8; French Competition Authority Opinion 24-A-05 of 28 June 2024 on the competitive functioning of the generative artificial intelligence sector, pp 58 et seq.; Portuguese Competition Authority Issues Paper of 6 November 2023 on competition and generative artificial intelligence, November 2023, pp 33 et seq.; and CMA AI strategic update of 29 April 2024, available at https://www.gov.uk/government/publications/cma-ai-strategic-update/cma-ai-strategic-update. [64] Competition Policy Brief on Generative AI, p. 3. [65] Council Regulation (EC) No 139/2004 of 20 January 2004 on the control of concentrations between undertakings (the EC Merger Regulation), art. 3. [66] See Speech by EVP Margrethe Vestager at the European Commission workshop on “Competition in Virtual Worlds and Generative AI”, 28 June 2024, available at https://ec.europa.eu/commission/presscorner/detail/en/speech_24_3550. [67] CMA’s Decision on relevant merger situation, “Amazon.com Inc.’s partnership with Anthropic PBC”, ME/7100/24; and CMA’s Decision on relevant merger situation, “Alphabet Inc.’s partnership with Anthropic PBC”, ME/7108/24. [68] See https://www.pcmag.com/news/google-gemini-ai-assistant-samsung-galaxy-s25. [69] Competition Policy Brief on Generative AI, p. 4. [70] Press Release, “Commission takes note of the withdrawal of referral requests by Member States concerning the acquisition of certain assets of Inflection by Microsoft”, 18 September 2024, available at https://ec.europa.eu/commission/presscorner/detail/en/ip_24_4727. [71] Joined Cases C-611/2 P and C-625/22 P, Illumina v Commission, EU:C:2024:677. [72] Competition Policy Brief on Generative AI, p. 4. [73] Commission Decision of 20 December 2024 in Case M.11766 - NVIDIA/Run:ai, C(2024) 9365.	2025-06-02T00:00:00		https://www.quinnemanuel.com/the-firm/publications/artificial-intelligence-eu-regulation-and-competition-law-enforcement-addressing-emerging-challenges/	[ { "date": "2025/06/02", "position": 87, "query": "artificial intelligence labor union" } ]
Deep Dive: Will AI take your job? How tech disruption ...	Deep Dive: Will AI take your job? How tech disruption affects work	https://www.workingnation.com	[ "Michael S. James" ]	“There's also evidence from the past that when the state does get involved on behalf of workers to protect them, and when there are robust unions, that workers ...	The future of work is changing fast. But then again, it often has. Past generations of workers lived through changes driven by the Industrial Revolution, or the introduction of the loom, steam power, electricity, the assembly line, factory automation, personal computers, and the internet. For 200 years or more, new technologies have transformed the workplace. And here’s some good news: Once the changes took hold, the labor market usually ended up with more jobs and newly freed-up workers to fill them. And many of those jobs would have been hard to imagine before the disruption. “People still go to work, they still make incomes, they support their families – and that’s despite a century of tremendous technological change and an economy where work looks incredibly different than it used to,” says Daniel Gross, an associate professor of business administration at Duke University’s Fuqua School of Business, and a faculty research fellow at the National Bureau of Economic Research (NBER). “The question people come to is, ‘Well, is this time different?’” Gross says. “People have asked that question essentially every time one of these waves has arrived of: ‘We don’t think this is like the others.’ Right? ‘This is the calamity we’ve all been waiting for’ – and that hasn’t really borne out. And given the historical pattern, I’m skeptical that it will this time, either.” However, though some workers merely saw their jobs evolve or improve during prior disruptions, others were unable to adapt. Former factory towns fell on hard times. Careers were diverted or derailed. Workers saw fulfilling jobs carved up into unsatisfying tasks built around automation. “Based on historical precedent, there’s no reason to think that all work will come to an end,” says Jason Resnikoff, author of “Labor’s End: How the Promise of Automation Degraded Work.” “There is reason to think that much of the (new) work won’t be good.” So which will it be this time? Not everyone agrees, but there are common threads suggesting it will be a little bit of both – plus some new wrinkles thrown in. White-Collar Workers in Danger, but More Jobs Coming? For one thing, whereas many past disruptions replaced physical power with automated machines, some of today’s disruptions, such as AI, could replace brainpower. That could put more white-collar jobs in jeopardy and affect a different geography, according to a Brookings Institution report. Till Leopold, head of work, wages and job creation, World Economic Forum But overall, “we do … not see any indication in our research that there would be a long-term structural issue with the quantity of jobs,” says Till Leopold, head of work, wages and job creation at the World Economic Forum, whose Future of Jobs Report 2025 projects the creation of 170 million new jobs and the displacement of 92 million existing ones worldwide in the next five years. “The challenge,” Leopold adds, “is the very fast and accelerating change in the types of jobs being created and destroyed, and the fact that the skills requirements in those newly emerging jobs often look very different from the ones in the jobs that are being disrupted.” Which Jobs Face Change? As with past disruptions, that will require retraining or upskilling workers directly in the path of change. In this case, that may be driven by forces such as artificial intelligence and emerging technologies, the green transition, and demographic shifts. Even in existing jobs, the World Economic Forum projects 40% of core skills, on average, will change by 2030. And the McKinsey Global Institute’s latest U.S. projection anticipates that 30% of hours currently worked could be automated in that time. Anu Madgavkar, partner, McKinsey Global Institute That McKinsey projection has increased recently because “advancements in gen AI have just made it so much more possible to do more of the higher cognitive work … through automation,” says Anu Madgavkar, a partner at the McKinsey Global Institute focused on the future of work and related topics. McKinsey reports its “updated modeling of the future of work finds that demand for workers in STEM-related, health care, and other high-skill professions would rise, while demand for occupations such as office workers, production workers, and customer service representatives would decline.” Broadly, McKinsey expects technological, social and emotional skills to be in higher demand. It forecasts that cognitive skills will be less sought after. Madgavkar suggests skilled laborers such as plumbers and electricians, or patient care workers in health care could be relatively safe. “There are a host of physical tasks which are still really hard to automate,” she notes. But how soon will most of us feel the effects? A recent working paper examining Danish labor data found that generative AI chatbots have not yet impacted the earnings of workers in 11 exposed occupations. Those 11, measured into 2024, are accountants, customer support specialists, financial advisors, HR professionals, IT support specialists, journalists, legal professionals, marketing professionals, office clerks, software developers, and teachers. Anders Humlum, assistant professor of economics, University of Chicago Booth School of Business “Despite substantial investment from both employers and workers, these tools have so far had zero impact on workers’ earnings, recorded hours, or wages,” one of the study’s co-authors, Anders Humlum, an assistant professor of economics at the University of Chicago Booth School of Business, told WorkingNation. “In the short run, our evidence shows that the tools have not moved the needle on the hard economic outcomes.” Believe it or not, the period from 1990 to 2017 actually was less disruptive to American work than any period going back to 1880, according to an NBER working paper by a trio of Harvard University scholars, David J. Deming, Christopher Ong and Lawrence H. Summers, while their analysis found the period from the 1940s to the 1960s to be the most volatile. But in the same paper, the authors suggest AI could be a labor market disruptor on par with steam power and electricity, and the pace of change seems to have accelerated since the pandemic. Judging by the past, the labor economy might organically adapt. Disruption could affect some jobs gradually over generations, while others might feel the tide turn more quickly, if they’re not feeling it already. Some displaced workers will need new skills and careers right away, while others might gradually learn new skills at work as their job descriptions shift. Ways Change Can Benefit Some Workers In what could be an encouraging sign for workers, Humlum says the data he’s looked at suggests high performers appear to be most avidly adopting AI chatbots. Could that be an indication that AI initially may augment high-skill work rather than displace its workers? “If they were just the perfect substitute for their high-skilled workers, you would … expect to see the opposite path: that a highly skilled worker would have no reason to use it, but only the lowest performers would have an incentive to do so,” he says. However, another recent paper found that generative AI has the power to level the playing field and reverse polarization by allowing less-skilled workers to rise toward the level of their more highly skilled colleagues. “AI, if used well, can assist with restoring the middle-skill, middle-class heart of the U.S. labor market that has been hollowed out by automation and globalization,” writes David Autor, an economist at MIT who authored the paper. Daniel Gross, associate professor, strategy, Duke University Fuqua School of Business Even if workforce disruption does accelerate, as some predict, history suggests a few of those in fading fields could actually see a surge in demand for their talents as incoming workers forsake disrupted occupations and labor shortages arise. As an example, Gross cites outside research on how “anticipatory dread” created a shortage of horse-drawn freight wagon drivers as motorized trucks loomed on the horizon but didn’t immediately arrive – or how aging experts in COBOL, a coding language now rarely taught, can be highly paid to repair old devices still running COBOL because younger workers didn’t enter the field. “Automation often takes much longer than a casual observer would expect,” Gross says. “And there are a number of reasons for that. But that’s a pattern, and I think it’s one that, to me, resonates as much today as ever.” AI Disruption: Decades? Or Mere Years? However, some suggest today’s changes could come faster than earlier ones, perhaps requiring faster intervention and retraining. “The transition from a predominantly agrarian economy to an industrial one played out over decades,” says Leopold of the World Economic Forum, “whereas timeframes now are significantly more time-compressed, creating a much more immediate and less-gradual adaptation challenge, which will require a concerted effort from all parts of economy and society to manage.” But while one study found technological disruptions have indeed affected work faster more recently, such cycles still tend to be measured in decades before full deployment of the transformative technology, rather than months or years. A longer-than-expected transition could happen this time, says Humlum of the University of Chicago. “In terms of thinking about productivity and the labor market and the broader economy, there’s several additional adjustments that need to take place,” he says. “Firms need to reorganize how they structure work in response to these technologies, and those adjustments take time.” How Disruption Can Create New Jobs Regardless of whether the coming change will be slow or rapid, history suggests there might be more plentiful jobs at the end. In a 2018 podcast discussion available online, Richard Cooper, then an economics professor at Harvard University, and Susan Lund, at the time a McKinsey Global Institute partner who had just co-authored a McKinsey report on disruptive workplace technologies, use the example of the automobile assembly line to show how that might work. In that case, fewer workers could produce more cars – but instead of decreasing employment, the changes drove jobs. That’s because the increased productivity allowed the price of cars to fall dramatically and drove up demand. That, in turn, required more workers to work the assembly lines and fueled whole new industries – filling stations, mechanics, tourism, and more – to support a new automotive culture. Eventually, cars allowed longer commutes to work, bringing suburban sprawl. And they allowed people to take up leisure activities, like skiing, previously inaccessible mountain areas. Both trends drove even more jobs to serve the new realities, Cooper notes. Of course, the assembly line also caused horse- and carriage-based industries to decline over several decades. That cost some jobs, but far fewer than were gained. “That’s a central problem with public discussion of this issue,” Cooper says in the podcast. “The jobs that are lost are tangible. There are people in them. We can sympathize with the people who lose their jobs and so forth. There’s a human dimension. The jobs that are created or made possible have no one in them, and, in fact, we don’t even know exactly where they’re going to be until, you know, time goes on.” New Jobs Could Be Worse Jason Resnikoff, assistant professor of contemporary history, University of Groningen But Resnikoff – an assistant professor of contemporary history at the University of Groningen in the Netherlands and a former organizer for the United Autoworkers Union – says automation also can degrade work and lead to less-fulfilling replacement jobs. In the case of the assembly line, Resnikoff says, “that’s a highly skilled job up until, oh, 1912, 1914, when Henry Ford brings in the assembly line. Then … you can bring in semi-skilled labor to do what before you would’ve had to be an extremely skilled mechanic to achieve.” Some already see AI driving such degradation in computer coding jobs. The New York Times recently reported that some workers feel they must work faster to support AI on tasks they used to do themselves. From ‘Mechanization’ to ‘Automation’ Resnikoff argues job degradation may result from disruptive technology being directed at workers by bosses. He cites the evolution after World War II from “mechanization,” a “more neutral term” for applying technology to work, to “automation.” Before that, he argues, captains of industry, “didn’t say that every time they brought in a new machine it meant less work. And it wouldn’t have been credible if they did because, especially beginning in the late 19th century, the Industrial Revolution is producing more work, not less. “All of these immigrants who are coming off the boat at Ellis Island are being brought into factories. It’s a booming industrial economy. It requires a great deal of labor. And they certainly weren’t saying it was imminent that (new production machinery) was about to get rid of all the work.” On the other hand, the term automation “was invented by Ford and the creation of their automation department in 1947 (as) … a way basically to fight their newly unionized workers on the shop floor,” he says. “This allowed Ford Motor Company to attack the job classifications of workers on the shop floor and to claim that they, themselves, weren’t doing it … (that) this is merely the result of technological progress.” De-Skilling Labor? He says Ford’s promotion of post-war “technological optimism,” a sense that technology would elevate workers and society beyond manual labor, soon became, and remains, the consensus mindset. And he sees it continuing. “Many of the same functions that automation had as a way of telling a story about technology at work, that storytelling work is now being done by the term ‘AI,’” he says. “I think that’s what a lot of the fear is about, that now … white-collar people seem more at risk,” he says. “On the other hand, what isn’t new is that this is exactly how industrial capitalism has been operating for 200 years. And people are, every generation, they’re surprised anew when employers find ways to use machines to de-skill or, let’s just say, change skilled labor. … That’s why it’s both old and new. It’s the same process, but it’s happening in a new place or a new sector. “Whether it’s a programmer or some screenwriter in LA, these were jobs that were thought to be, you couldn’t get a machine to do them,” he says. “And now you can … (get AI to) do part of the job, if not the whole job.” How is this a cycle that we’ve seen before? “There would’ve been a time when you would’ve said making steel can’t be mechanized,” Resnikoff says. “It was also a very highly skilled job. But then, in fact, it was achieved – and it made some (new) unskilled jobs and it made some skilled jobs.” Change Jolts Telephone Operators, Especially Older Workers At least there were jobs at the end of it. Not all workers are so lucky. Gross has studied the displacement of telephone operators over the first half of the 20th century. Back then, callers would pick up their phone and tell an operator who they were calling. The operator, often female, would then plug a cable into a jack to connect them. But phones started coming with dials so callers could direct their own calls. Over decades, the phone company installed local automated switchboards that could do the rest. “There was considerable concern over what this would do, particularly to the young women who were affected,” Gross says. “This even rises to the level of congressional hearings that are more generally on automation and economic conditions, but where the mechanization of the telephone system was a point of focus.” What happened next wasn’t good for displaced operators, particularly the older ones. “When we observed them in the census a decade later … we find them being less likely to be employed and, conditional on being employed, more likely to be in a lower-paying occupation,” Gross says. “These effects are particularly strong for older operators as opposed to younger existing operators. “What we think that this points to is a bit of an age gradient,” he says. “Older workers have a more difficult time adjusting to new technology and transitioning to other jobs, other sectors. And younger workers are more fluid, up to the point where those who are not yet of working age at the time that automation takes place are essentially perfectly fluid. They can prepare for careers in different sectors from the start.” ‘Dislocation Is Very Painful’ So disruption was not so good for the telephone operators. And perhaps it could derail those directly in AI’s path who don’t revise their skillsets. “Dislocation is very painful,” says Madgavkar, the McKinsey partner. “There is, I think, no question about that.” But for the rest of us? “In general, over long periods of time, the quality of work people have been doing has improved,” Madgavkar says. “I do think there is less drudgery in work of all kinds today.” Adds Gross: “It’s easy to take a view that automation is destructive to work and to workers and to therefore be reluctant to embrace it. But if we inhabit that view over the past century, there are so many things that we wouldn’t even be able to have today.” Managing Change He says change can be managed so it is less painful, and some commentators suggest more should be done now to mitigate the effects of AI on workers and jobs. Gross cites the example of containerized shipping and port automation in the 1960s, when some workers gained compensation for being displaced and others got new jobs operating the automation. The changes also brought benefits to consumers and importers by making goods cheaper. According to Resnikoff, “It’s fair to say (that) if employers are allowed to use technology however they please, and we are unable to have any rules either through the government or through collective bargaining to stop them, the history is very clear on that: Employers usually want control and they want more for less. And so there will still be jobs, but they won’t be very good. “There’s also evidence from the past that when the state does get involved on behalf of workers to protect them, and when there are robust unions, that workers can, in fact, safeguard for themselves at least certain kinds of protections,” he says. “And this will make the job not-so-terrible, perhaps, and make the job better.” Throughout recent history, an ever-evolving labor market has brought jobs that could not have been imagined by prior generations. Some may have arisen from technological disruptions. Others, as Resnikoff argues may be more common, could have been a natural evolution driven by independent social factors, market demand, and changing trends. Whatever the reason, Gross says, “We’re still here, we’re still working, we are still feeding our families. “If anything,” he adds, “on average and for most of the population, our quality of life has improved. We’re richer than we used to be. We live longer than we used to. We’re safer than we used to be. And ultimately, my perspective is to see technology as the root of progress and something that we ought to continue supporting.”	2025-06-02T00:00:00	2025/06/02	https://www.workingnation.com/ai-jobs-work-disruption-automation/	[ { "date": "2025/06/02", "position": 96, "query": "artificial intelligence labor union" }, { "date": "2025/06/02", "position": 6, "query": "automation job displacement" } ]
How will Artificial Intelligence (AI) impact the future of work	How will Artificial Intelligence (AI) impact the future of work	https://rinewstoday.com	[ "Mary T. O'Sullivan" ]	They tout the benefits as reducing or eliminating repetitive, boring, and time consuming tasks that many humans spend their entire careers performing. Imagine ...	By Mary T. O’Sullivan, MSOL, contributing writer on business “AI won’t replace humans—but humans with AI will replace humans without AI.” – Professor Karim Lakhani, Harvard Business School Although people tend to resist using or relying on AI, leaders are determined to incorporate automation more and more into the workplace. They tout the benefits as reducing or eliminating repetitive, boring, and time consuming tasks that many humans spend their entire careers performing. Imagine no longer having to toil entering data, creating schedules and reports, filling out government forms, or researching arcane information like labor rates in Texas. Simply make a query, and the information pops up in the correct format, and with the same accuracy as the organization’s database. The assumption is that the database is accurate and the AI tool has learned enough about the user and the organization to fashion the required documents as well or better than a human. The lure of AI is not only its potential for accuracy, but speed as well. Reports that would normally take a week, now can be accomplished in a few hours. Large masses of data, like the thousands of pages required for submission to the state or federal government, or to major companies (like pharmaceutical or defense) can be quickly spit out in correct form, including dates, organizational formats, proprietary statements, and other details that otherwise would need to be checked and double checked by experts. The AI tool has those details embedded in its memory. AI doesn’t need lessons learned, it automatically learns from its mistakes, and never repeats them again, unlike humans who often rotate jobs within the organization and often face new tasks, otherwise known as the “learning curve”. Once AI learns tasks, it has the ability to incorporate that learning into new information when needed; there is no “learning curve”. But with such speed and accuracy, why do employees resist embracing this groundbreaking technology at work? According to Harvard Business Review, “in a 2023 Gartner survey, 79% of corporate strategists said that the use of AI, automation, and analytics would be critical to their success over the next two years. But only 20% of them reported using AI in their daily activities.” The relevance of AI in an organization heavily depends on people’s willingness to use it. What is the root cause of harboring such widespread reservations? In several recent studies, a number of reasons for human reluctance to embrace AI have been isolated. Two stood out as stellar examples of people’s fear of “robots” taking over. Not surprisingly, the number one reason given by both Harvard Business Review and NILG.AI (an AI training platform), is fear of job loss. This apprehension arises when humans realize that various mental tasks can be performed by AI, like analyzing medical records, sales prediction and forecasting, and personalized recommendations, like dating. What turns the “Doubting Thomas’s” around is adjusting people’s mindset. In one study, people were asked to predict whether AI could detect cancerous tissue better than a doctor. Scans of a skin cancer sample were examined by both AI and a physician. The participants’ attitudes changed once they were told the results – the AI tool was in fact more accurate than the human diagnosis. This demonstration made them less biased against using AI, at least for medical purposes. Another fear emerges when people believe that AI is emotionless. People are naturally skeptical that AI can accomplish subjective tasks. The fact that AI can identify emotions in human faces, and perform face recognition out of thousands of faces, and produce still images and videos from written human suggestions shows that AI already uses many subjective skills. However, people are more likely to trust an AI financial advisor than an AI dating App, especially if the dating App has no human qualities. In fact, multiple studies have shown that when making the AI tool more human-like, as in Amazon’s Alexa, people are more accepting of AI. Alexa has a female voice and a name. When more human characteristics are added to the tool, humans are more accepting. In a study using self-driving cars, people were more comfortable when the car possessed a human-like avatar and voice. Resistance to AI is common in the workplace, but it can be overcome by addressing the root causes of resistance, fear of job loss, and lack of humanization. When strategies are put in place to encourage the adoption of AI, reluctance to the tool lessens. If businesses want to fully utilize the power of AI to improve their operations and outcomes, they must accommodate the human element. Regardless of how much money organizations invest in AI, the leadership team must consider the psychological barriers to general acceptance by employees. Every AI deployment will face barriers to full rollout, so don’t leap into a new system and expect people to love it. It’s the job of the leader to recognize these potential obstacles to success and support the team, customers and stakeholders while they adapt to the new reality. “…in the age of AI, skills that would actually matter are those that make us humans. From creativity to critical thinking, soft skills are expected to take the lead in becoming the most valuable and in-demand skills in the market.” – EuroNews ___ Mary T. O’Sullivan, Master of Science, Organizational Leadership, International Coaching Federation Professional Certified Coach, Society of Human Resource Management, “Senior Certified Professional. Graduate Certificate in Executive and Professional Career Coaching, University of Texas at Dallas. Member, Beta Gamma Sigma, the International Honor Society. Advanced Studies in Education from Montclair University, SUNY Oswego and Syracuse University. Mary is also a certified Six Sigma Specialist, Contract Specialist, IPT Leader and holds a Certificate in Essentials of Human Resource Management from SHRM.	2025-06-02T00:00:00	2025/06/02	https://rinewstoday.com/how-will-artificial-intelligence-ai-impact-the-future-of-work-mary-t-osullivan/	[ { "date": "2025/06/02", "position": 35, "query": "future of work AI" } ]
The biggest barrier to AI adoption in the business world isn ...	The biggest barrier to AI adoption in the business world isn't tech—it's user confidence	https://techxplore.com	[ "Greg Edwards", "The Conversation" ]	It's here and already beginning to transform industries. But despite the hundreds of billions of dollars spent on developing AI models and platforms, adoption ...	This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility: Believe in your own decision-making. Credit: Pixabay/CC0 Public Domain The Little Engine That Could wasn't the most powerful train, but she believed in herself. The story goes that, as she set off to climb a steep mountain, she repeated: "I think I can, I think I can." That simple phrase from a children's story still holds a lesson for today's business world—especially when it comes to artificial intelligence. AI is no longer a distant promise out of science fiction. It's here and already beginning to transform industries. But despite the hundreds of billions of dollars spent on developing AI models and platforms, adoption remains slow for many employees, with a recent Pew Research Center survey finding that 63% of U.S. workers use AI minimally or not at all in their jobs. The reason? It can often come down to what researchers call technological self-efficacy, or, put simply, a person's belief in their ability to use technology effectively. In my research on this topic, I found that many people who avoid using new technology aren't truly against it—instead, they just don't feel equipped to use it in their specific jobs. So rather than risk getting it wrong, they choose to keep their distance. And that's where many organizations derail. They focus on building the engine, but don't fully fuel the confidence that workers need to get it moving. What self-efficacy has to do with AI Albert Bandura, the psychologist who developed the theory of self-efficacy, noted that skill alone doesn't determine people's behavior. What matters more is a person's belief in their ability to use that skill effectively. In my study of teachers in 1:1 technology environments—classrooms where each student is equipped with a digital device like a laptop or tablet—this was clear. I found that even teachers with access to powerful digital tools don't always feel confident using them. And when they lack confidence, they may avoid the technology or use it in limited, superficial ways. The same holds true in today's AI-equipped workplace. Leaders may be quick to roll out new tools and want fast results. But employees may hesitate, wondering how it applies to their roles, whether they'll use it correctly, or if they'll appear less competent—or even unethical—for relying on it. Beneath that hesitation may also be the all-too-familiar fear of one day being replaced by technology. Going back to train analogies, think of John Henry, the 19th-century folk hero. As the story goes, Henry was a railroad worker who was famous for his strength. When a steam-powered machine threatened to replace him, he raced it—and won. But the victory came at a cost: He collapsed and died shortly afterward. Henry's story is a lesson in how resisting new technology through sheer willpower can be self-defeating. Rather than leaving some employees feeling like they have to outmuscle or outperform AI, organizations should invest in helping them understand how to work with it—so they don't feel like they need to work against it. Relevant and role-specific training Many organizations do offer training related to using AI. But these programs are often too broad, covering topics like how to log into different programs, what the interfaces look like, or what AI "generally" can do. In 2025, with the number of AI tools at our disposal, ranging from conversational chatbots and content creation platforms to advanced data analytics and workflow automation programs, that's not enough. In my study, participants consistently said they benefited most from training that was "district-specific," meaning tailored to the devices, software and situations they faced daily with their specific subject areas and grade levels. Translation for the corporate world? Training needs to be job-specific and user-centered—not one-size-fits-all. The generational divide It's not exactly shocking: Younger workers tend to feel more confident using technology than older ones. Gen Z and millennials are digital natives—they've grown up with digital technologies as part of their daily lives. Gen X and boomers, on the other hand, often had to adapt to using digital technologies mid-career. As a result, they may feel less capable and be more likely to dismiss AI and its possibilities. And if their few forays into AI are frustrating or lead to mistakes, that first impression is likely to stick. When generative AI tools were first launched commercially, they were more likely to hallucinate and confidently spit out incorrect information. Remember when Google demoed its Bard AI tool in 2023 and its factual error led to its parent company losing US$100 billion in market value? Or when an attorney made headlines for citing fabricated cases courtesy of ChatGPT? Moments like those likely reinforced skepticism—especially among workers already unsure about AI's reliability. But the technology has already come a long way in a relatively short period of time. The solution to getting those who may be slower to embrace AI isn't to push them harder, but to coach them and consider their backgrounds. What effective AI training looks like Bandura identified four key sources that shape a person's belief in their ability to succeed: Mastery experiences, or personal success Vicarious experiences, or seeing others in similar positions succeed Verbal persuasion, or positive feedback Physiological and emotional states, or someone's mood, energy, anxiety and so forth. In my research on educators, I saw how these concepts made a difference, and the same approach can apply to AI in the corporate world—or in virtually any environment in which a person needs to build self-efficacy. In the workplace, this could be accomplished with cohort-based trainings that include feedback loops—regular communication between leaders and employees about growth, improvement and more—along with content that can be customized to employees' needs and roles. Organizations can also experiment with engaging formats like PricewaterhouseCoopers' prompting parties, which provide low-stakes opportunities for employees to build confidence and try new AI programs. In "Pokemon Go!," it's possible to level up by stacking lots of small, low-stakes wins and gaining experience points along the way. Workplaces could approach AI training the same way, giving employees frequent, simple opportunities tied to their actual work to steadily build confidence and skill. The curriculum doesn't have to be revolutionary. It just needs to follow these principles and not fall victim to death by PowerPoint, or end up being generic training that isn't applicable to specific roles in the workplace. As organizations continue to invest heavily in developing and accessing AI technologies, it's also essential that they invest in the people who will use them. AI might change what the workforce looks like, but there's still going to be a workforce. And when people are well-trained, AI can make both them and the outfits they work for significantly more effective. This article is republished from The Conversation under a Creative Commons license. Read the original article.	2025-06-02T00:00:00		https://techxplore.com/news/2025-06-biggest-barrier-ai-business-world.html	[ { "date": "2025/06/02", "position": 52, "query": "workplace AI adoption" } ]
The AI workplace revolution: What HR needs to know now	The AI workplace revolution: What HR needs to know now	https://hrzone.com	[ "Natasha Wiebusch", "Read More Natasha Wiebusch", "Quentin Millington", "Becky Norman", "Kerry Nicholson" ]	Meanwhile, flawed project execution is costing time and money. Many organisations rush into AI adoption using outdated project management methods. Experts ...	AI’s workplace revolution is well underway, but most organisations are still catching up. From confusion about where to start, to project failure rates nearing 80%, HR leaders are under pressure to implement AI with purpose, precision, and responsibility. Early adoption trends point to three key focus areas where employers must act fast: skills, project management, and ethical use. But one area that demands urgent attention is compliance in AI-assisted hiring. As regulators tighten rules and lawsuits mount, including a recent legal challenge against Workday alleging AI-driven age discrimination. HR must be vigilant. Our latest guide, Avoiding compliance pitfalls in the evolving AI legal landscape, breaks down emerging laws in jurisdictions like New York City and Illinois, and offers practical steps to prevent algorithmic bias and protect your brand. Download the guide now Tackling the AI skills gap Right now, AI is being adopted unevenly across functions, mostly inwardly to support employee performance rather than external outputs. Generative AI is the exception: tools like chatbots and copy generators are already in use, albeit without consistent oversight. This lack of governance is risky. Publicly available tools raise privacy concerns, can’t cite sources, and are already facing copyright litigation. For HR, the skills gap is one of the biggest barriers. According to Amazon Web Services, 73% of employers now prioritise hiring AI-skilled workers, yet few have the strategies to identify roles at risk, align reskilling programmes, or secure executive buy-in. Managing complex AI projects Meanwhile, flawed project execution is costing time and money. Many organisations rush into AI adoption using outdated project management methods. Experts recommend a hybrid approach – combining agile with data-centric methodologies to properly handle the complexity AI projects demand. Prioritising ethical AI guidelines And finally, ethics must move up the priority list. Just 21% of employers using generative AI have formal policies in place. That leaves organisations vulnerable to legal, reputational, and DEI risks. A responsible approach includes implementing policies, training staff, and forming cross-functional AI ethics groups with C-suite representation. AI is not just another digital tool – it’s a transformational force. For HR leaders, success means embracing it with the right guardrails in place. Request a quote from Brightmine.	2025-05-29T00:00:00	2025/05/29	https://hrzone.com/the-ai-workplace-revolution-three-focus-areas/	[ { "date": "2025/06/02", "position": 54, "query": "workplace AI adoption" } ]
AI at work: How artificial intelligence can boost productivity ...	AI at work: How artificial intelligence can boost productivity and benefit UK workers	https://www.theworkersunion.com	[ "Workers Union" ]	For British workers, the benefits of AI adoption are substantial. Properly implemented, AI can reduce workloads, cut through bureaucracy, and free up time ...	Artificial intelligence is no longer a concept confined to the world of science fiction. It’s here, it’s evolving, and it’s already reshaping the future of work across the United Kingdom. Whether you’re a seasoned professional or new to the workforce, understanding how AI can support productivity—and how to ask your employer for the tools to harness it—has never been more crucial. AI tools and technologies are revolutionising the way organisations operate. From automating routine administrative tasks to providing insights that inform better decision-making, AI offers opportunities to streamline processes, improve services and, most importantly, support workers to focus on higher-value, human-centred work, yet fewer than one in four workers use AI in daily operations. A national shift is already underway According to a 2024 report by the National Audit Office, while AI use in government is still in its infancy, a significant 70% of public sector bodies are currently piloting or planning to implement AI-based solutions. The message is clear: this isn’t a temporary trend—it’s a long-term shift. And that means workers up and down the UK must be part of the conversation, and part of the solution. For British workers, the benefits of AI adoption are substantial. Properly implemented, AI can reduce workloads, cut through bureaucracy, and free up time for more strategic, creative, or customer-focused tasks. It can also make data more accessible and enhance accuracy—an essential advantage in sectors such as healthcare, manufacturing, retail, and finance. But for these benefits to be realised, AI must be implemented with workers, not to them. How workers can champion AI adoption If you’re in a role where AI could help streamline your day-to-day tasks—or open the door to more rewarding responsibilities—asking your employer for support with AI training or tools is a proactive first step. Many organisations are already looking to integrate AI, but widespread adoption is only successful when employees are on board and equipped to use these AI technologies confidently. Here’s how you can start the conversation: Assess your current workflow : Where are the bottlenecks? What tasks feel repetitive or manual? Identifying these areas helps you understand where AI might help. : Where are the bottlenecks? What tasks feel repetitive or manual? Identifying these areas helps you understand where AI might help. Make a case for training : A recent Microsoft report found that just 39% of workers using AI had received any formal instruction. Ask your manager or HR department for training workshops, webinars or hands-on sessions tailored to your job. : A recent Microsoft report found that just 39% of workers using AI had received any formal instruction. Ask your manager or HR department for training workshops, webinars or hands-on sessions tailored to your job. Request support networks : Suggest the creation of internal AI champions—colleagues trained to provide support and advice—which can help ease wider adoption. : Suggest the creation of internal AI champions—colleagues trained to provide support and advice—which can help ease wider adoption. Start small : Advocate for pilot schemes or trial runs of AI tools in specific teams. This allows for testing and learning before full-scale implementation. : Advocate for pilot schemes or trial runs of AI tools in specific teams. This allows for testing and learning before full-scale implementation. Ask for measurable goals: Request that the organisation define clear goals for AI adoption—whether that’s time saved, reduced errors, or increased job satisfaction. Employers must take responsibility too Leadership buy-in is key. When managers demonstrate their commitment to new technologies—whether by leading training initiatives or communicating openly about the benefits—teams are more likely to engage positively. Transparency, support, and access to resources make all the difference. Employers who ignore the needs of their workers during AI implementation risk more than just failed projects. They risk alienating the very people AI is meant to support. A perfect example is Googles call to arms as a golden opportunity for Britain to embrace a £200bn bonanza. Workers must also feel confident that AI will enhance—not replace—their roles. That confidence can only come through ongoing training, honest communication, and a culture of collaboration. A strategic approach to future success It’s vital that businesses don’t rush blindly into AI adoption. The most successful organisations will be those that assess their current capabilities, identify skills gaps, and plan for incremental change. By breaking down the AI journey into manageable steps—starting with pilot projects, setting clear goals, and monitoring results—organisations can adapt smoothly. Along the way, offering digital coaching, building internal support networks, and making space for continuous feedback will ensure that AI becomes an asset, not an obstacle. Above all, leaders must invest in people. A workforce that understands and feels confident using AI is one that will drive transformation—rather than be overwhelmed by it. The Workers Union Says… AI holds enormous potential to support British workers—boosting productivity, reducing stress, and creating space for more meaningful, skilled, and rewarding work. But its success depends not only on software, but on support. If you believe AI could help you in your role, it’s time to start the conversation with your boss. Ask for training, ask for tools, and ask to be involved in shaping the way your organisation adapts to this technological shift. This is not about machines replacing humans—it’s about making sure humans have the best tools to succeed in an ever-changing workplace. AI is here to stay. It’s time we made it work for the workforce. Reach out to our press team about this article	2025-06-02T00:00:00	2025/06/02	https://www.theworkersunion.com/2025/06/02/ai-at-work-how-artificial-intelligence-can-boost-productivity-and-benefit-uk-workers/	[ { "date": "2025/06/02", "position": 69, "query": "workplace AI adoption" } ]
Will AI replace your job? Perhaps not in the next decade	Will AI replace your job? Perhaps not in the next decade	https://www.dallasfed.org	[ "Mark A. Wynne", "Lillian" ]	Future employment trends lack certainty ... There is very little evidence of artificial intelligence taking away jobs on a large scale to date.	Will AI replace your job? Perhaps not in the next decade Mark A. Wynne and Lillian Derr Recent rapid improvements in the capabilities of artificial intelligence (AI) have raised concerns about these technologies’ impact on employment, specifically, the rollout of generative AI models such as ChatGPT that can create new work product from existing inputs. Unease about new technologies displacing workers is not new. It can be traced back at least to the earliest days of automation during the Industrial Revolution. Technologies such as the steam engine and the dynamo inspired similar fears in their day, as did information technology when computers were first introduced. But the jobs AI is expected to touch in the years to come are different from those impacted by previous waves of technological change. The ultimate effects of AI on the workforce will depend on the extent to which AI augments (or complements) rather than automates (or substitutes for) workers’ tasks. Will this new technology aid workers or replace them? To understand AI’s possible occupational implications, we explore the workforce effects of the last technological advance thought to put jobs at risk: computerization. We look at the impact on occupations believed vulnerable to computerization 10 years ago and what that analysis may say about job vulnerability to generative AI today. First, computers loomed over occupations Before the advent of AI, the main workforce concern was whether computerization would displace jobs. A widely cited University of Oxford study by Carl Benedikt Frey and Michael Osborne, first circulated in 2013, examined the susceptibility of different occupations to computerization. It ultimately determined that 47 percent of total U.S. employment was at risk in 2013. The authors assigned a probability of computerization to each of 702 occupations. Jobs deemed most at risk included telemarketers, title examiners, sewer workers, mathematical technicians, insurance underwriters, watch repairers, cargo and freight agents, tax preparers, photographic process workers, new account clerks, library technicians and data entry keyers, all with a 99 percent likelihood of computerization. Among those deemed least at risk were recreational therapists, occupational therapists, health care and social workers and choreographers. All had a less than a half-percent likelihood of computers taking over. Move over, here comes AI A decade later, with the November 2022 release of ChatGPT and subsequent rapid advance of generative AI since then (DeepSeek, Stargate, Manus, for example), AI has overtaken computers as a leading risk to long-standing occupations. A recent study by Edward W. Felten, Manav Raj and Robert Seamans attempts to identify occupations most vulnerable to automation from generative AI from both the perspective of language modeling (learning language and creating sentences after analyzing huge amounts of existing text) and image generation (creating images based on assimilation of large data sets) in a similar fashion to the earlier work by Frey and Osborne. Both studies identify occupations by the same occupation codes, making these two measures easily comparable. The AI study authors created two scales for automation risk: from language modeling and image generating AI, labeling hundreds of occupations accordingly. The jobs identified as most susceptible to automation by language modeling AI include telemarketers, many types of teachers, sociologists, political scientists and arbitrators. The jobs identified as most susceptible to automation by image generating AI include interior designers, architects, chemical engineers, art directors, astronomers and mechanical drafters. Computerization, AI threaten different jobs Comparing the occupations thought to be most likely to be computerized a decade ago with those most likely to be automated by AI today, the lists vastly differ (apart from telemarketers) (Chart 1). The risks to specific occupations from earlier computerization and AI language modeling appear little correlated. The results for image-generating AI are similar. Today’s at-risk occupations aren’t the same ones threatened earlier. Bureau of Labor Statistics offers 10-year employment projections Every year, the Bureau of Labor Statistics (BLS) issues 10-year projections of employment in detailed occupations. The most recent set of projections was published in August 2024 covering 2023 to 2033. The BLS assumes that “labor productivity and technological progress will be in line with the historical experience” but recognizes that recent AI advances could cause the future to differ greatly from the past. These projections have historically been fairly accurate. We looked at previous projections to better understand when they proved more or less accurate amid major technological change. We asked whether there is any evidence that the BLS’ misses by occupation were somehow related to the susceptibility of that occupation to computerization, as estimated by Frey and Osborne (Chart 2). To the extent that the BLS projections missed the mark, anticipation of computerization doesn’t appear to have been the cause. The colored dots in Chart 2 represent occupations classified as being most susceptible to generative AI automation by Felten, Raj and Seamans. Again, there is little correlation with the BLS projection errors. Considering this question from a different angle, we looked at whether changes in the growth rate of occupations from 2003–13 to 2013–23 were correlated with Frey and Osborne’s measures of computerizability (Chart 3). Again, there is no correlation between an occupation’s change in growth rate and its susceptibility to computerization. Charts 2 and 3 together, thus, point to the idea that Frey and Osborne’s computerizability concerns did not have as significant an effect on the workforce as perhaps anticipated. Alternatively, the charts suggest that the BLS methodology for long-term employment projections at the occupational level is relatively robust at incorporating major technological innovations. Looking ahead, to see if AI might yield different results, we explored the correlation between Felten, Raj and Seamans’ measure of exposure to language modeling AI versus the BLS 2022–32 employment projections (Chart 4). The measure of exposure to image generating AI vs. the BLS 2022–32 employment projections shows similar results (Chart 5). The lack of correlation in both charts demonstrates the BLS’ conservative approach to including AI in its workforce projections. For example, employment in some occupations, such as statisticians in Chart 4, is expected to grow in the next decade despite a high degree of exposure to AI. However, employment in other occupations, such as telemarketers, is projected to decline, which is more like what one might expect from an occupation with a high likelihood of automation (to both computerization and AI). Future employment trends lack certainty There is very little evidence of artificial intelligence taking away jobs on a large scale to date. Correlation between AI exposure and the projections of job growth or decline over the next decade remains low. Furthermore, just 10 years ago, our concerns about which jobs were at risk were quite different from the ones we are concerned about today, demonstrating that such considerations evolve. Many jobs once feared to be at risk did not end up showing major decline in employment data. AI is such a rapidly changing field, we do not know much about its ability to one day have a large overall workforce impact, especially as current studies are mainly speculative. However, like the many technological changes that came before it, AI is a tool. Though rapid improvements in AI capabilities could lead to large workforce effects, over the next decade that worry can be tempered by the current data and the fact that concerns about technological unemployment are not new and rarely come to pass as first anticipated. Share this	2025-06-03T00:00:00		https://www.dallasfed.org/research/economics/2025/0603	[ { "date": "2025/06/03", "position": 97, "query": "AI labor market trends" }, { "date": "2025/06/03", "position": 92, "query": "AI labor market trends" }, { "date": "2025/06/03", "position": 24, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 23, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 99, "query": "AI labor market trends" }, { "date": "2025/06/03", "position": 98, "query": "AI labor market trends" }, { "date": "2025/06/03", "position": 26, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 25, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 27, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 26, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 25, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 24, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 15, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 28, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 97, "query": "AI labor market trends" }, { "date": "2025/06/03", "position": 25, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 25, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 26, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 97, "query": "AI labor market trends" }, { "date": "2025/06/03", "position": 24, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 26, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 96, "query": "AI labor market trends" }, { "date": "2025/06/03", "position": 98, "query": "AI labor market trends" }, { "date": "2025/06/03", "position": 23, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 26, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 29, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 24, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 7, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 8, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 11, "query": "AI impact jobs" }, { "date": "2025/06/03", "position": 8, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 10, "query": "AI impact jobs" }, { "date": "2025/06/03", "position": 8, "query": "AI replacing workers" }, { "date": "2025/06/03", "position": 10, "query": "AI unemployment rate" }, { "date": "2025/06/03", "position": 9, "query": "ChatGPT employment impact" }, { "date": "2025/06/03", "position": 4, "query": "job automation statistics" } ]
Why Business Leaders Need an AI-First Mindset - Oracle	Why business leaders need an AI-first mindset	https://www.oracle.com	[]	Miranda Nash of Oracle and XD Huang of Zoom explore how businesses can create an AI-first mindset and drive AI adoption.	In the latest episode of AI Now with Oracle, I had the pleasure of sitting down with Xuedong (XD) Huang, the CTO at Zoom, to explore how businesses can create an AI-first mindset and drive AI adoption. AI Now: Building an AI-First Culture (21:56) This is such an important topic as generative AI is changing the pace of innovation. According to XD, the most successful organizations in the next decade will be those that integrate AI not as an add-on, but as a foundational layer in their strategy, operations, and culture. Or put another way, organizations that can implement an AI-first strategy have the best chance of succeeding in the future. It was a fascinating conversation with key takeaways for every business leader. AI as a creative companion We talked about how AI is evolving from a tool into a companion. As XD noted, AI is redefining productivity. It’s helping teams move faster, explore more directions, and get from “meetings to milestone” in a fraction of the time. For product development, brand campaigns, and customer experience design, this means faster cycles, better insights, and more adaptive execution. Business leaders looking to unlock the value of AI should ask themselves: Are we using AI to optimize what we already do, or are we reimagining what’s possible? Building an AI-first culture XD emphasized that adopting an AI-first mindset requires more than implementing tools—it demands a shift in how organizations think about creativity, capability, and talent. Teams need to be empowered not just to use AI, but to think with it. This, of course, means upskilling, but it also means giving teams the space and leadership support to experiment, refine, and build new muscles around continuous learning. We also discussed the importance of keeping a human in the loop. AI can handle repetition and scale, but real differentiation comes from how humans direct it. The organizations that will win with AI will be those that combine human insight with machine capability in intentional ways. It’s not just about having the technology—it’s about having the vision to use it well. Thinking AI-first to get ahead For business leaders, the call to action is clear: Don’t wait. Start now. Identify where AI can save time, open new revenue streams, or create a competitive edge. And most importantly, lead the mindset shift yourself. AI is already becoming as integral to day-to-day work as digital tools were 20 years ago. The difference now is speed. Organizations that hesitate may find themselves outpaced by players who are already leveraging AI not just to execute—but to reimagine and lead. This isn’t about keeping up; it’s about staying ahead. And it starts with thinking AI-first.	2025-06-03T00:00:00		https://www.oracle.com/applications/ai-now-mindset/	[ { "date": "2025/06/03", "position": 27, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 27, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 24, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 25, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 26, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 25, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 25, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 25, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 25, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 43, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 25, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 25, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 25, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 34, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 18, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 17, "query": "AI business leaders" } ]
Generative AI for Business Leaders: Moving Beyond the Hype	Generative AI for Business Leaders: Moving Beyond the Hype	https://emeritus.org	[ "Sunil Sharma", "About The Author", "Read More About The Author", "Supriya Sarkar", "Srijanee Chakraborty" ]	Course leader Sunil Sharma explores how generative AI for business leaders can unlock real value beyond the hype.	Generative AI for Business Leaders: Driving Growth and Moving Beyond the Initial Hype Synopsis: Course leader Sunil Sharma explains how business leaders can move beyond the hype and harness generative AI for business leaders to unlock real enterprise value through strategic implementation, cross-functional use cases, and AI-powered transformation. The latest report from Forrester paints an exciting picture of the future of generative AI. With a projected annual growth rate of 36% through 2030, this artificial intelligence technology is poised to capture a substantial 55% share of the AI market.1 Beyond its automation capabilities, the report highlights the new revenue prospects that generative AI offers to organizations not directly involved in its development. Technology revolutions like this one are poised to reshape industries and open doors to innovation and growth. For business leaders, the opportunity isn’t just about automation—it’s about reimagining business models, advancing AI-driven productivity, and unlocking entirely new revenue streams. For organizations seeking a competitive edge, generative AI for business leaders represents a critical lever—one that can reshape industries, unlock innovation, and drive long-term growth. How Business Leaders Can Drive Real Transformation With Generative AI Planning use cases: Enterprises must prioritize practical, valuable applications for genAI for business leaders and avoid getting caught up in overhyped use cases. Opportunities often lie within existing enterprise scenarios, including IT operations, business automation, customer insights, and data analytics. For consumers, generative AI serves four main utility categories: Efficiency: covering areas such as health plans, product discovery, and research support Instruction: offering learning guidance, personalized content, and language instruction Creation: encompassing content generation, video editing, interior design mockups, and fashion curation Entertainment: involving game design, virtual avatars, 3D environments, and music remixing At the enterprise level, use cases become more complex, demanding features such as proven ROI, customization, security, and support. Generative AI use cases for enterprises include personalized retail experiences, automated customer service in banking, and code debugging in manufacturing—all widely applicable and rich with potential. Building the tech stack, designing processes, and gathering data: Organizations must navigate the complex AI technology landscape to effectively harness generative AI for business leaders . In-house expertise or trusted tech partners are essential to design and implement the right solutions. The generative AI tech stack includes infrastructure—computing, networking, storage, and microprocessors—as well as applications powered by foundation models trained on substantial data. An effective AI transformation strategy for leadership involves designing intelligent processes and gathering enterprise-grade data assets to fuel powerful, responsible AI outcomes. Mobilizing teams to operationalize generative AI: Successful artificial intelligence and machine learning–driven transformations depend on proficient process management teams. Implementing generative AI transformation strategies requires structured deployment, customization, and change management efforts. Collaboration and iterative development are crucial. Proactive engagement with an advisory ecosystem can offer first-mover advantages, favorable pricing, and greater confidence in experimenting with new solutions. Demonstrating leadership success: GenAI for business leaders has moved beyond promises and is delivering tangible business results. Data and technology teams should confidently present generative AI’s use cases and implementation plans to organizational leadership. A well-documented generative AI transformation portfolio should outline how adopting this technology can differentiate an organization from its competitors and enhance resilience to disruptions. For today’s thought leaders , this is a call to lead from the front, shaping the trajectory of AI-enabled enterprise transformation. Best Practices to Avoid Generative AI Pitfalls To ensure effective business transformation with generative AI, organizations can follow these best practices to avoid common pitfalls: Avoid overhyped use cases: focus on real-world AI projects that enhance existing processes, improve efficiency, reduce costs, and deliver measurable ROI. Evaluate technical feasibility: involve both domain and technical experts to assess feasibility, align success metrics with business value, and account for available resources and timelines. Think long-term AI life cycle: plan for the full AI solution life cycle—from deployment to end-user support and maintenance—and ensure users receive the necessary training. Enhance data and AI capabilities iteratively: recognize that data development is an ongoing process in AI projects. Begin with existing data, extract maximum value, and use project success to justify further investments in data assets and pipelines. No organization can afford to carry out zombie AI projects during challenging economic times. AI projects driven by unrealistic expectations or limited team understanding drain resources and hinder innovation. Teams should be empowered to terminate them, ensuring that valuable lessons are applied to more viable initiatives. Risk Management for Generative AI To mitigate risks in generative AI projects and ensure responsible AI adoption, you must address data biases and involve diverse technical and subject matter experts. Ensure that AI models trained on proprietary data adhere to privacy standards through encryption, anonymization, and source traceability. Stay vigilant about evolving regulations while maintaining data confidentiality and ensuring secure collaboration with multi-tenant artificial intelligence models. Consider that generative AI query and prompt costs can be up to ten times higher than index-based queries, though they typically decrease over time. Assess the economic factors in internal business cases and customer pricing models to support adoption. Additionally, prioritize workforce planning and upskilling, as high-ROI use cases can enhance productivity but also pose job displacement risks as models advance. In conclusion, generative AI for business leaders is more than a technological milestone—it’s a strategic catalyst for redefining how organizations operate and compete. For business leaders, the mandate is clear: move beyond experimentation, embrace responsible AI adoption, and shape a new work paradigm in which human creativity and machine intelligence coevolve. Thought leaders who spearhead this transformation will not merely adapt—they will define the future of their industries. (Sunil Sharma is the course leader for the Stanford Business Digital Transformation Playbook Program and the Berkeley Data Strategy Program. He also leads the Agile Strategy Execution elective in the INSEAD Chief Operating Officer (COO) Programme, Chief Strategy Officer (CSO) Programme, and Sustainability Leadership Programme for Senior Executives. All views expressed here are his own.) Reference:	2025-06-03T00:00:00	2025/06/03	https://emeritus.org/blog/sme-generative-ai-business-leaders/	[ { "date": "2025/06/03", "position": 91, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 82, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 64, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 97, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 83, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 65, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 75, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 52, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 83, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 83, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 77, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 63, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 83, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 52, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 62, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 66, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 83, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 66, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 42, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 66, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 96, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 66, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 82, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 66, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 83, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 42, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 55, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 96, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 77, "query": "artificial intelligence business leaders" }, { "date": "2025/06/03", "position": 99, "query": "AI business leaders" }, { "date": "2025/06/03", "position": 78, "query": "artificial intelligence business leaders" } ]
PwC 2025 Global AI Jobs Barometer	PwC 2025 Global AI Jobs Barometer	https://www.pwc.com	[ "Contact Us", "Imran Javaid", "Global Corporate Affairs", "Communications", "Senior Manager", "Pwc United Kingdom", "Dan Barabas", "Manager" ]	AI linked to a fourfold increase in productivity growth and 56% wage premium, while jobs grow even in the most easily automated roles · Job ...	Workers see rising wages: AI-skilled workers see average 56% wage premium in 2024, double the 25% in the previous year Confounding expectations, data shows job availability grew 38% in the roles more exposed to AI, albeit below the growth rate in less exposed occupations Industries ‘most exposed’ to AI saw 3x higher growth in revenue per employee (27%) compared to those ‘least exposed’ (9%) The skills sought by employers are changing 66% faster in jobs ‘most exposed’ to AI LONDON, 3 June 2025 – AI is making workers more valuable, productive, and able to command higher wage premiums, with job numbers rising even in roles considered most automatable, according to PwC’s 2025 Global AI Jobs Barometer, released today. The report is based on analysis of close to a billion job ads from six continents. The report finds that since GenAI’s proliferation in 2022, productivity growth has nearly quadrupled in industries most exposed to AI (e.g. financial services, software publishing), rising from 7% from 2018-2022 to 27% between 2018-2024. In contrast, the rate of productivity growth in industries least exposed to AI (e.g. mining, hospitality) declined from 10% to 9% over the same period. 2024 data shows that the most AI exposed industries are now seeing 3x higher growth in revenue per employee than the least exposed. Carol Stubbings, Global Chief Commercial Officer, PwC, said:	2025-06-03T00:00:00		https://www.pwc.com/gx/en/news-room/press-releases/2025/ai-linked-to-a-fourfold-increase-in-productivity-growth.html	[ { "date": "2025/06/03", "position": 60, "query": "AI wages" }, { "date": "2025/06/03", "position": 47, "query": "AI wages" }, { "date": "2025/06/03", "position": 87, "query": "AI wages" }, { "date": "2025/06/03", "position": 85, "query": "AI employment" }, { "date": "2025/06/03", "position": 71, "query": "AI wages" }, { "date": "2025/06/03", "position": 70, "query": "AI wages" }, { "date": "2025/06/03", "position": 65, "query": "AI wages" }, { "date": "2025/06/03", "position": 84, "query": "AI wages" }, { "date": "2025/06/03", "position": 54, "query": "AI employers" }, { "date": "2025/06/03", "position": 7, "query": "AI labor market trends" }, { "date": "2025/06/03", "position": 20, "query": "job automation statistics" } ]
Artificial Intelligence and Generative AI for Media & Journalism	Artificial Intelligence and Generative AI for Media & Journalism	https://guides.lib.unc.edu	[ "Madison Dyer" ]	AI has the potential to enhance the efficiency, accuracy, and reach of journalism, while also presenting new challenges and ethical considerations for the ...	Artificial intelligence (AI) is poised to significantly impact journalism in several key ways: Automated Reporting: AI can generate news articles on routine topics such as sports scores, financial reports, and weather updates. This allows journalists to focus on more complex and investigative stories. Data Analysis: AI tools can analyze large datasets quickly, uncovering trends and insights that might be missed by human analysts. This is particularly useful for investigative journalism and data-driven stories. Personalized Content: AI can help news organizations deliver personalized content to readers based on their interests and reading habits. This can increase reader engagement and satisfaction. Fact-Checking and Verification: AI can assist in fact-checking by quickly cross-referencing information against reliable sources. This helps in combating misinformation and ensuring the accuracy of news reports. Language Translation: AI-powered translation tools can make news content accessible to a global audience by translating articles into multiple languages in real-time. Audience Engagement: AI can analyze reader behavior and preferences, helping news organizations tailor their content strategies to better engage their audience.	2025-06-03T00:00:00		https://guides.lib.unc.edu/generativeAI/ai-journalism	[ { "date": "2025/06/03", "position": 1, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 4, "query": "AI journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 1, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 1, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 3, "query": "AI journalism" }, { "date": "2025/06/03", "position": 1, "query": "artificial intelligence journalism" }, { "date": "2025/06/03", "position": 2, "query": "AI journalism" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence journalism" } ]
I tested 8 AI tools for graphic design, here are my favorites	I tested 8 AI tools for graphic design, here are my favorites	https://blog.hubspot.com	[ "Rachael Nicholson" ]	Discover how AI tools revolutionize graphic design, enable experimentation, and save time. Here's my top tools for their design ...	Graphic design has come a long way since I started in the arts nearly a decade and a half ago. Improvements in tools like Photoshop or my personal favorite, Procreate, from then to now have helped remove creative roadblocks and speed up the design process. Then, user-friendly design tools like Canva have made design more accessible to non-specialists. Now, we find ourselves in the throes of the “AI revolution.” We’re met with a ton of AI tools for graphic design that promise to streamline our creative workflows and more. But is the juice really worth the squeeze with these tools? Today, we find out. Table of Contents How to Use Generative AI in Content Marketing Find out how to integrate AI tools into your content workflows. Understanding generative AI. Understanding generative AI. Limitations of AI. Limitations of AI. Rolling out AI to your team. Rolling out AI to your team. And more! Download for Free Learn more Download for Free Download Free All fields are required. You're all set! Click this link to access this resource at any time. Download Now Why use AI for graphic design? From removing creative blocks to saving time, here are three reasons to consider adding AI to your graphic design process. Remove Creative Blocks One of the main benefits I’ve found by using AI is that it helps me remove creative blocks. So, when I’m writing and hit a wall, rather than staring at a blank screen, I might ask ChatGPT to generate an example use case for something. Even if the text it generates is clunky and unusable, it at least gives me a starting point. When it comes to design, specifically, the same goes for something like color pairing. Without AI assistance or pre-created palettes, I struggle to choose more than one color for a design. (As you’ll see later, a specific AI tool for graphic design can help remove that creative block.) Creative Experimentation As an Illustration graduate who specialized in printmaking, I can tell you first-hand that creative experimentation in real life gets messy. Not only is the process often messy, but it can take up a lot of space. Before you know it, multiple physical versions of your experimentations are mounting up, and the next issue is where to store them. Aside from that, non-digital creative experimentation is often riskier. Without the faithful “Ctrl Z” or equivalent at your disposal, you’re always one move away from ruining a design. The crux? It’s enough to put you off even trying, and that’s not good for business. But I’m not just some random ex-art grad on a soap box lecturing you about creative expression without just cause. I’m also a business owner shouting from the rooftops about one key fact: Experimentation is the driving force behind innovation. So, the more you can encourage it in yourself and, if relevant, your organization, the better. Save Yourself Time There’s a reason 95% of professionals using AI say it helps them spend less time on manual tasks. And a further 83% say it helps them focus on the creative aspects of their role. Through automation, AI can help you simplify your creative workflow, be it through bulk edits or generating designs in a few prompts. Also, by removing creative blocks and providing a way to experiment at speed, you can get from a concept to a final design much quicker. That said, there’s something magical about creating a physical piece that you can hold in your hands. And without all those years spent in the print room or experimenting with different mediums — from textiles to gloopy oil paints — I don’t think I’d understand how to use AI to achieve my desired outcome. How I Tested the Best AI Graphic Design Tools I tested each tool against the following criteria: Price. I wanted to know if you could get started with the tool for free. (As a side note, it’s worth exploring how much it will cost you to actually download and use the end product for commercial purposes.) I wanted to know if you could get started with the tool for free. (As a side note, it’s worth exploring how much it will cost you to actually download and use the end product for commercial purposes.) Ease of use. I wanted to test how intuitive and user-friendly the design platform was. I wanted to test how intuitive and user-friendly the design platform was. Design capabilities. I wanted to know how broad each tool’s design capabilities were. More specifically, I tried to understand whether the tools’ AI elements were overhyped or genuinely helpful. I wanted to know how broad each tool’s design capabilities were. More specifically, I tried to understand whether the tools’ AI elements were overhyped or genuinely helpful. Licenses and copyright. I wanted to know if you could use the end products commercially. Bonus points if the companies behind the tools actively protect people from potential legal action after using designs created through the platform. I wanted to know if you could use the end products commercially. Bonus points if the companies behind the tools actively protect people from potential legal action after using designs created through the platform. Ethics. As a creative, I was curious about how each company trained the AI models. I personally would prefer not to use a tool that didn’t work with creatives fairly or didn’t openly state how it trained the models. HubSpot's AI Image Generator Transform text into AI-customized images that perfectly match your content. Go to your preferred content tool Go to your preferred content tool Find the image generator feature Find the image generator feature Enter a text prompt or content input Enter a text prompt or content input Generate and insert your image Learn More AI Tools for Graphic Design I road-tested six tools for graphic design. Here’s what I found. 1. Adobe Express Best for: Professional designers and businesses needing high-quality marketing materials Adobe Express is an all-in-one AI content creation tool that allows users to make stunning social posts, marketing images, flyers, and more. Its AI tools are powered by Adobe Firefly, a generative machine-learning model specifically for design. Though Adobe Express is available for everyone, HubSpot's newest integration allows HubSpot users to use the AI content creation app to make aesthetically pleasing marketing materials without leaving HubSpot. A popular use case for Adobe Express is to “create stunning, life-like images.” However, you can also use the tool for AI photo editing (i.e., changing backgrounds and removing unwanted elements from your images). I decided to try Adobe Express for generating images from scratch. For context, remember what I said earlier about having an Illustration degree and spending a lot of time in a printmaking studio. So, I’d definitely say my bias is toward the more painterly/illustrative side of graphic design. My prompt: “A simple black outline of a mountain drawn in the style of Tolkien's Lord of the Rings drawings colored with random splotches of drawing ink in magenta, blue, purple, and gold.” I was actually quite impressed with the output, especially considering I only prompted the tool and didn’t configure any of the settings initially. But how does Adobe Express stack up when you get more specific with the settings? I chose Art as the content type to find out. In my opinion, the setting change didn’t make that much difference to the output. But that could be because the original prompt was biased towards an “art” type of output anyway. As a further experiment, I also adjusted the prompt to bring more whitespace into the mountain portion of the design. Adobe Express interpreted that as more whitespace, in general, rather than in the mountain section itself. Long story short, that didn’t work out as I envisioned, so I ditched that portion of the prompt. I will say that with something like this (personal work), it’s often about the journey as much as the destination. And even though it took mere seconds to produce, it wasn’t as fun to create this digitally as it would’ve been by using traditional printing or even with biro and drawing ink. What I like: Adobe Express first piqued my interest in 2023 when Sarah Rogers, a Contributing Artist at Cricut, posted about the tool on LinkedIn. Best for: Adobe Express is best for individual graphic designers, design teams, students, students and teachers. And anyone looking to improve their design skills using a responsible AI tool. Pricing: Get started for free. Source I’d followed Sarah’s thoughtful LinkedIn posts about AI for a while. And we both seemed to have a similar mindset regarding its use within creative endeavors. I don’t want to speak for Sarah, but as for me, here’s my mindset: Yes, you might be able to speed up your creative process with AI — if you know how to use it. No, you shouldn’t fire all of your creative team and replace them with AI. And yes, you should keep a healthy level of skepticism regarding the application of AI within your business. (A healthy level of skepticism, specifically about the output of the tools, legal issues like licensing, and how the models are trained.) So, what caught my eye the most about Sarah’s endorsement of Adobe Firefly was that Adobe is, seemingly at least, acting responsibly in this space. Source They’re arguably the most responsible folks in the design tool world regarding AI. That makes me feel more confident about any potential licensing issues and that “no creatives were harmed” in training the models. But you should try it out for yourself, here's a couple ways you can: Graphic Design Prompts to Try for Adobe Express: "Create a minimalist logo for a sustainable coffee shop with earthy tones." "Design a social media post for a summer sale with bright colors and beach elements." Pros: Strong integration with Adobe ecosystem High-quality professional templates Cons: Steeper learning curve compared to others Premium features require subscription 150+ Free Content Creation Templates Access ebook, blog post, CTA, case study, and more content templates. Ebook Templates Ebook Templates CTA Templates CTA Templates Blog Post Templates Blog Post Templates And more! Get Your Free Templates Learn more Get Your Free Templates Download Free All fields are required. You're all set! Click this link to access this resource at any time. Download Now 2. Canva Best for: Social media content, presentations, and quick designs for non-designers Canva is a free online graphic design tool. You can use it to create a range of designs, such as social media posts, logos, and presentations. Canva has integrated AI into its platform in several different ways, including Magic Design, a text-to-image generator, and Magic Studio, which includes AI-driven photo editing features and text-to-video generation. These days, I mostly use Canva to make (some might say, hilarious) memes for my LinkedIn. However, I used the tool a lot when I offered social media marketing services, so I wanted to use Magic Studio to create a social media image. I started with a time-saving social media template — an Instagram post specifically. I chose a template titled "Cream Minimalist New Collection Instagram Post" by Kinley Creative. I wanted to customize the image, so I uploaded a picture of some of my own artwork. I also wanted to upload a font I’d recently downloaded from Type Colony. (This is TC Kuareen if you’re interested.) Source To upload the font, I clicked on the “new collection” templated text, selected the font drop-down menu, and clicked “Upload a font.” Once I’d come this far, I realized I’d not used any AI features. So my next task was to try to find some. But, try as I might, I could only find two noticeable AI features within the image editor. One of them was “Magic Write.” I could see that being helpful for designers or business owners who need help writing copy. That said, if you don’t know how to use AI well, it’s no replacement for working with a trained copywriter. Of course, the same goes for design. The other AI feature was “Translate.” Once again, I could see this being helpful. However, like copy and design, AI translation is no replacement for having an actual translator to safeguard against translation mishaps. This could be my misunderstanding of the tool, but I found it hard to see a specific AI use case for social media graphic creation. But I think the tool could be really handy for AI image editing. For example, the “Magic Eraser” edit feature gets rid of unwanted design elements, and “Magic Edit” adds to, replaces, or edits an image in a few clicks. What I like: Canva is really user-friendly. I feel like people with varying levels of design knowledge, and even those with little experience using design tools could use it. I also like that the platform has introduced an “industry-leading collection of robust trust, safety, and privacy tools” through Canva Shield. It seems like Canva is also safeguarding against intellectual property claims for Enterprise customers. Plus, they’re compensating Canva creatives for their work through an AI royalty program. Best for: Individual graphic designers, design teams, and small businesses to enterprise businesses. Pricing: Get started for free. But you should try it out for yourself, here's a couple ways you can: Graphic Design Prompts to Try for Canva: "Generate a modern podcast cover with vibrant gradients and geometric shapes." "Create an Instagram story template with a clean layout for a fitness brand." Pros: User-friendly interface for beginners Extensive template library Cons: Advanced customization can be limited Free version has restrictions 3. Designs.ai Best for: Quick brand identity creation and logo design for startups Designs.ai is an integrated Agency-as-a-Service platform powered by AI technology. It’s a one-stop shop for everything from logo design to social media and image generation. You can even convert text to speech for voice-over content. I tried creating a social media image to see how Designs.ai compares to Canva, mainly because I wanted to know if the AI aspects of this tool were more prominent. My first thought was that if you’ve used Canva before, it won’t take you long to get to grips with the layout for this section of the tool. But even if you haven’t, the Designs.ai platform is straightforward and intuitive. I can see most people being able to pick up this tool and run with it to some degree. At first glance, the social media section is very similar to Canva in terms of picking templates to customize based on the channel (Facebook, Instagram, etc.). So, it’s pretty standard stuff, really. The “Wizard” option, however, caught my eye. The default format is “Business Card,” but you can choose from different options, such as “Quotes,” “Product Listings,” and “YouTube Thumbnail.” I picked “Instagram Post” to compare the results to Canva. In addition to the different design format options, you can also choose from predetermined categories like “Events & Celebrations,” “Business, Legal & Finance,” and “Animal & Pet.” I selected “Art, Design & Inspiration.” As a side note, I had to sign up/sign in to upload my own title image. But I did everything until that point via the website without signing up for the platform. After inputting the design variables, I hit Generate. The options the tool spat out weren’t standout designs, but they were better than I expected — a pleasant surprise! Next, I selected one of the suggested designs to see what the image editor was like. As with much of Designs.ai, the layout is similar to Canva. For the sake of continuity, I could’ve missed something, but unlike Canva, as far as I’m aware, you can’t upload your own fonts to Designs.ai. Overall, I found the platform easy to navigate and use. That said, I don’t think this would serve your needs if you wanted to create complex designs. I also couldn’t find any information about how Designs.ai trained its models, so I’m wary of that aspect. There is information about licensing, though. Generally, “finished projects made with our creative AI tools can be distributed to promote and advertise your business.” Still, there are specific Do’s and Don’ts segmented by each aspect of the tool (Logo, Social Media, Video, etc.) that you might want to pay attention to. What I like: In terms of AI, Designs.ai goes a step beyond Canva. I can also see the “Bulk Edit” function coming in handy if you want to automate mass edits. Best for: Small businesses at the start of their journey who don’t have the budget for a designer. Individual graphic designers or design teams specializing in holistic marketing. Pricing: Get started for free. Graphic Design Prompts to Try for Designs.ai: "Design a tech company logo with blue and purple futuristic elements." "Create a modern business card with a clean layout and professional color scheme." Pros: AI-powered design automation Good for brand identity creation Cons: Fewer customization options than traditional tools Output quality can be inconsistent 150+ Free Content Creation Templates Access ebook, blog post, CTA, case study, and more content templates. Ebook Templates Ebook Templates CTA Templates CTA Templates Blog Post Templates Blog Post Templates And more! Get Your Free Templates Learn more Get Your Free Templates Download Free All fields are required. You're all set! Click this link to access this resource at any time. Download Now 4. Visme Best for: Interactive presentations, infographics, and data visualization Visme is a comprehensive graphic design platform enhanced by artificial intelligence, designed to facilitate the creation of impressive interactive visual content. Its no-code feature empowers individuals, even those without a design background, to achieve professional-level design proficiency. With Visme’s user-friendly interface and drag-and-drop functionality, it effectively meets a wide range of design requirements. I encountered a LinkedIn post about Visme from a direct connection, which immediately piqued my interest. As I delved into Visme's features, I was particularly impressed by its intuitive interface and user-friendly design, making it accessible to users of all skill levels. Visme can help you level up your design efforts without needing any intensive manual design work. It has an interactive interface, millions of design assets, and 100% customizable templates. I quickly explored everything in under 5 min. At that moment, I was not ready to buy any of its premium versions. Therefore, I chose to create something that won’t cost me anything. I’m a design fanatic. Great design visuals give me a thrill. I love testing various design tools from time to time for creative tasks, creating calendars, and managing my kids' tasks. I decided to give Visme a try to create a lesson plan for my kid for the holidays, and the results amazed me. My prompt was: “Create a weekly lesson plan for my kid where I can list activities for each day.” Moreover, you can enhance the design and aesthetics of graphics using Visme's advanced features, such as its AI image search and AI image generator. By utilizing Visme's AI image search, you can locate pertinent images for projects like lesson plans. Visme sources relevant images from platforms like Unsplash or employs AI to generate suitable images, which can be incorporated into your design to improve its visual appeal. It is remarkable to have a tool like Visme that facilitates the creation of impressive designs with ease and confidence. To the left side, you will find various design assets like images, fonts, blocks, figures, colors, illustrators, lines, etc. You can use Visme’s drag & drop functionality to add them to your design project. What I like: Visme stands out as an exceptional design tool, primarily due to its user-friendly interface. It serves as a comprehensive design platform enhanced by artificial intelligence, enabling the creation of visually appealing content suitable for various marketing materials. Whether you need a straightforward graphic for social media or a detailed infographic to simplify complex information, Visme is an ideal choice. The platform has effectively integrated AI into its diverse features, such as its image generator, image search, and designer (beta), among others. Best for: Visme is best suited for a diverse range of users, from individual creators to large teams, who seek to produce high-quality visual content without the need for extensive design experience. Pricing: Get started for free. 5. AutoDraw Best for: Quick illustrations and converting rough sketches to clean drawings AutoDraw is an AI tool that combines machine learning and drawings from artists. You can use the tool to “draw stuff fast.” In terms of graphic design use cases, you could use AutoDraw to make learning materials and custom graphics. And for any design that requires a quick outline, I can see Autodraw speeding up the process. A warning: I don’t have my graphics tablet set up. So everything you’re about to witness — hilarious though it may be — was done with just a mouse. I’m guessing the tool’s capabilities are far greater with a tablet or a stylus at hand. However, without giving too much away, it proves that you can input a terrible drawing into AutoDraw and get something better back. I decided to keep things simple with a good old smiley face. First, I used the “Shape” tool to create a circle outline, and then I used “AutoDraw” for the eyes and nose. As you can see, the AutoDraw elements inputted by me are … lacking finesse, shall we say. But that’s not a problem. The “Do you mean” section on the top toolbar gives various options to finesse your drawing. So even if your attempt to draw a smiley face with just a mouse didn’t turn out so well, one click on a smiley face up top, and you’re golden. As you can see the smiley face is now a little less unbearable to behold. Next I used Select to select and then delete the initial circle shape I added. (It turned out to be unnecessary.) Et voila! A shiny happy clipart style person laughing … or something. Regarding training the models, Google used“the same technology to guess what you’re trying to draw,” as Quick, Draw!, which relied upon “artists, designers, illustrators, and friends of Google” to add drawings to the doodling data set. I doubt the artists were compensated for their work. Still, at least they shared designs willingly rather than having them scraped by AI without their consent. What I like: I really like that the tool is simple to use, free, and, let’s be honest, fun! However, it wouldn’t be ideal for complex design work. That said, if you don’t have a lot of time and need to visualize an idea quickly, AutoDraw can help. Best for: Anyone who needs to convey ideas and concepts at speed. Pricing: Get started for free. Graphic Design Prompts to Try for AutoDraw: "Convert my rough sketch of a bicycle into a clean illustration." "Create a simple cartoon character from my drawing." Pros: Free to use Instantly converts rough sketches into clean illustrations Cons: Limited functionality beyond basic drawings Few customization options 6. Khroma Best for: Color palette exploration and color scheme development Khroma is an AI graphic design tool that helps you match your favorite colors into a series of palettes. The tool also blocks the colors you don’t like, so they’ll never find their way into your palettes. I love that this tool is so specialized for a specific purpose. And I can see this being a big time saver if you struggle with color pairing like me. For context, I can pick a few colors that I like, no problem. But I’m not always confident they go together and can get lost in analysis paralysis. As a result, I tend to buy pre-made color palettes for my go-to illustrative tool, Procreate. After I clicked Generate, I was prompted to choose 50 colors “to train a color generator algorithm” personalized to me. I dove right in and picked the colors that stood out to me at a glance. As I picked the colors, the “likes to go” section counted how many colors I still had to choose. The color bar also started filling up with the ones I’d selected so far. After picking my 50 colors, I hit Start Training. Then, the results came in. The layout for the color pairings is beautiful, and I see a lot of potential in this tool. Another interesting element of Khroma is that you can visualize your color pairings in different ways, including “Type” (the view above) and “Gradient” (the view below). You can also see how your color choices look as posters, images, and within broader color palettes. Since Khroma helps you pair colors already in existence, I can’t see it being exploitative to creators or needing specific licenses for commercial use. But I can’t say that for certain, so do your due diligence. What I like: When you click the information icon against each color pairing, Khroma provides you with the color codes. That will be such a time saver if you want to color match in another design tool. Best for: Individual designers and design teams looking to save time on color selection and pairing. Pricing: Get started for free. Graphic Design Prompts to Try for Khroma: "Generate a color palette inspired by Mediterranean coastal themes." "Create accessible color combinations for a healthcare website." Pros: AI-powered color palette generation Personalized to your preferences over time Cons: Focused only on color selection Limited integration with other design tools HubSpot's AI Image Generator Transform text into AI-customized images that perfectly match your content. Go to your preferred content tool Go to your preferred content tool Find the image generator feature Find the image generator feature Enter a text prompt or content input Enter a text prompt or content input Generate and insert your image Learn More 7. Looka Best for: Logo design and brand identity packages for small businesses Looka is a platform specifically for logo and brand design. It uses artificial intelligence and machine learning to create designs based on your input. I started my test by entering an example company name and clicking Get started. From then on, Looka took me through a series of steps to help me create a logo. The first step was to pick my industry. As you can see, there is a range of sectors to choose from. I was then prompted to select some logos I liked, followed by some colors. The following steps were to add a company name (again, for some reason?) accompanied by a slogan and then to choose some symbol types. An observation: I liked that Looka gave me notes about my company name and slogan choices as I inputted them. This could be handy advice for beginners. Plus, you can also pick your own symbols if you want to be more hands-on with the design. After that, Looka generated a few different logos for me. While they were competent logos, they were too “out of the box” for me and lacked the creative flair needed for brand differentiation. That said, I didn’t go too deep into customizing the logo. This tool shines more in the presentation of the designs than in the designs themselves. For example, I like that Looka provides design mock-ups so you can see how your logo will look on a business card, website, social media, and more. I couldn’t find any specifics about how Looka trained its AI models, but they at least address the potential negative impact on human designers here: In terms of licensing and copyright, Looka says: “You may not use any of Looka’s End Products outside of the Site, whether for commercial or personal use, without paying all applicable and respective Fees in advance. This includes both digital and physical use of the End Products.” What I like: Overall, the platform is intuitive and easy to use. I like that Looka doesn’t use templates; rather, it generates each design based on your specific input. There is also a wide range of font, layout, and color options. Best for: New businesses without the budget to work with a designer. Individual designers and design teams working specifically in branding. Pricing: Get started for free. Graphic Design Prompts to Try for Looka: "Design a modern tech startup logo with blue and gray colors." "Create a complete brand identity for a yoga studio with calming colors." Pros: Complete brand identity packages Simple interface for non-designers Cons: Limited to logo and brand identity Less flexibility for custom designs Looking to pair your designs with AI-powered text? Get started with HubSpot AI today. 8. Kittl Best for: Vintage aesthetics, badges, labels, and designs with texture effects Kittl is a free, easy-to-use online design platform that lets you create high-quality designs without the steep learning curve of traditional graphic design software. Because it's web-based, Kittl is perfect for designers on the go or those seeking a simple design solution without the hassle of installations, updates, and storage space. One of Kittl's most notable qualities is its emphasis on "quick and easy creation of professional designs," which makes it especially well-liked by users who need to quickly and easily create visually appealing social media images, typography, and logos. But don't be fooled by its simplicity — Kittl provides sophisticated customization options that allow you to adjust every layout element with precision. Kittl offers several AI-based tools such as an AI logo generator, AI vectorizer, AI product background generator, and more. To have a better sense of the platform, I decided to specifically investigate their AI Image Generator. While choosing what to create, I discovered tons of free templates for specific projects such as T-shirt designs, business cards, POD products, and more. I settled on designing a logo and aimed to create a modern yet natural look using a pre-made template, an AI image, and Kittl's extensive collection of fonts. After logging in to Kittl, I started a New Project, navigated to templates, and selected a pre-made logo design from a pretty hefty template library. This added a new artboard to my project with a customizable design. With my template selected, I needed to create my AI image. The Image Generator was very straightforward and even if you don’t have much experience using AI design tools, you should be able to navigate the features with ease. The concept for my logo design was, "a sleek, nature-inspired logo in muted earth tones, with elegant typography." The prompt I entered into the image generator was “a dreamy desert oasis.” I was pleasantly surprised that I could choose from a variety of styles such as “cartoon,” and even “synthwave.” I settled on the “watercolor” style. Using the AI Background Remover, I got rid of the resulting image's white background with a click - and viola! I was left with the image below…which is pretty awesome. I didn’t think the bright image matched the natural look I wanted so I tried another AI tool: the AI Vectorizer. This was surprisingly easy to use and I was able to choose how many colors would remain in the vector image. From here I tweaked some things, like the artboard color, text color, and font. The amount of unique fonts Kittl has made it difficult to choose! I even duplicated the artboard to experiment with other brand colors. After I finished the graphic I went to the mockup section and chose the sticker mockup how real does that look? The text wraps around the contours of the image! All in all, creating a custom logo on Kittl was super straightforward. What stood out with Kittl was just the ease of navigation and use. It was incredibly easy to adjust each aspect with precision in just a few clicks. What I like: If we’re thinking of AI tools, then I like how Kittl strikes a mix between ease of use and advanced capabilities. Because the design tools are simple to use, I could explore and make adjustments without any hassle. Best for: Beginner or expert-level graphic designers, design teams, and POD creators. Kitt’s particularly good for individuals wanting professional quality designs without the steep learning curve of other design software. Pricing: Get started for free. Graphic Design Prompts to Try for Kittl: "Create a vintage-style label for a craft beer with ornate elements." "Design a retro badge logo with custom typography and texture effects." Pros: Excellent for vintage and retro-style designs Strong typography and effects options Cons: Less suitable for minimalist modern designs Learning curve for advanced features The Bottom Line on AI Tools for Graphic Design Let’s be honest: A tool is only as good as the person wielding it. So, if you don’t know much about graphic design concepts to begin with, it’s unlikely you’ll create a brand-differentiating end product. However, if you know your way around your colors, typography, alignment, visual hierarchy, balance, and the rest, AI can speed up your creative process. Personally, I loved testing out Adobe Firefly. The end output exceeded that sterile “out of the box” template, which feels common with other tools. I also liked that you could create something painterly in style. Plus, I love Adobe’s ethical approach to using AI. They are working with creatives to train their models responsibly and protecting product users against potential licensing and legal issues. Editor's note: This article was originally published May 2024 and has since been updated for comprehensiveness.	2025-06-03T00:00:00		https://blog.hubspot.com/marketing/ai-for-graphic-design	[ { "date": "2025/06/03", "position": 4, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 4, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 6, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 5, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 4, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 6, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 4, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 4, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 6, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 5, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 5, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 5, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 6, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 5, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 4, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 4, "query": "artificial intelligence graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 5, "query": "AI graphic design" }, { "date": "2025/06/03", "position": 2, "query": "artificial intelligence graphic design" } ]
AI Employee Overview - HighLevel Support Portal	AI Employee in HighLevel: Automate Calls, Chats & Workflows	https://help.gohighlevel.com	[]	The AI Employee is a suite of features that includes Voice AI (AI Agents), Conversation AI, Reviews AI, Funnel & Website AI, Content AI, and Workflow AI ...	What is the AI Employee? The AI Employee is a suite of features that includes Voice AI (AI Agents), Conversation AI, Reviews AI, Funnel & Website AI, Content AI, and Workflow AI Assistant. It’s designed to reduce manual workloads, improve client communication, and help businesses scale by automating key interactions. There are 2 pricing models to access AI Employee features. Usage-based or a monthly unlimited plan. AI Employee Pay Per Usage $97/monthly Unlimited Plan Voice AI $0.13/min Unlimited Usage Conversation AI $0.02/message Unlimited Usage Reviews AI $0.08/review Unlimited Usage Content AI $0.09/1000 Words & $0.06/ Image Unlimited Usage Funnel AI $0.99/funnel Unlimited Usage Workflow AI Assistant $0.02/request Unlimited Usage Enabling AI Employees for your Agency and Sub-accounts To enable "AI Employee" for your agency, check the AI employee toggle in the "Company" tab of your Agency Settings. Click the agency switcher tab at the top left corner of the sidebar. Click "Switch to Agency View" tab. Look for "Settings" tab at the bottom left corner of the sidebar. Click "Company" tab to get into the company menu. Once you are inside the company menu, scroll down until you see the "AI Employee" feature. Enable it using the toggle. There you have enabled the AI Employee. Now you can customize the offering for individual sub accounts based on your clients usage and requirements. Granting/Limiting access to "AI Employee" feature to different sub accounts. Now lets see how to manage the access to your Sub-accounts from the "AI Employee" tab. In order to locate the "AI Employee" option, you will need to access agency settings. If you are in one of the sub-accounts then you will first need to "Switch to Agency View". Next, Click "Settings" tab located at the bottom left corner. Scroll down for "AI Employee" option. Here you can view the list of your sub accounts in the first column. There is a toggle button next to each sub account for you to enable or disable "AI Employee" option for each individual sub-account. You can also choose to enable or disable rebilling for each sub-account and set the rebelling amount. Enabling the toggle will open up a page where you will be able to set the rebilling amount. Lets turn one of the toggle on. Here you can set the Rebill amount by moving the pointer from left to right. In this example, we have set the amount to 2.5X. This means, your customer will be charged $2.5 for the cost of $1. Now that you have set the pricing, save your rebilling amount by clicking "Enable Rebilling" button. Along with usage-based pricing for each AI product (with the exception of AI Agents (Voice), AI employee also offers an Unlimited monthly plan for each sub-account to use all the above AI products for $97 per month per sub-account. In the next few steps you will learn how you can set your pricing model to offer unlimited monthly plan for your customers. If you are in the Agency View then you can just look for "Reselling" tab located at the navigation sidebar on the left side. If you are in one of the sub-accounts, you will first need to "Switch to Agency View" to be able to find the "Reselling" tab. Scroll down to find "AI Employee Reselling" option. This is the area where you can customize your offer. Enter the price in the "Your Price" field. "Save" your changes. Below are the the different areas where your customers will be able to see your Unlimited Monthly AI Employee plan inside their sub-account Conversation AI Voice AI Content AI Reviews AI In this example, we will see how your customer can see your Unlimited monthly offer when they are inside "Reviews AI" section. Below are the steps you can follow. Step 1: If you are in a "Agency View" you will need to go to the sub account. Click the Sub account switcher tab located at the top left corner. Step 2 : Select your desired sub-account from the list. You have to make sure that the sub account you are going to select has "AI Employee" feature already enabled. Step 3 : Click "Reputation" tab from the left side navigation menu. Next, Click the "Settings" button. Step 4 : Click "Upgrade to AI Employee" button. Remember we set $297 as monthly price in our agency settings? This is how your your customers will see the option to go from usage based pricing to a fix monthly price. Frequently Asked Questions Q: What is the AI Employee in HighLevel? The AI Employee is a collection of powerful AI tools designed to help businesses automate and streamline operations. It includes features like Voice AI, Conversation AI, Reviews AI, Funnel & Website AI, Content AI, and Workflow AI Assistant. Q: How much does the AI Employee cost? You can either pay per usage or opt into an unlimited plan for $97/month per sub-account, which grants access to all AI Employee tools without individual usage charges. Q: Can I control which sub-accounts have access to the AI Employee? Yes, you can enable or disable the AI Employee for each sub-account individually through your Agency Settings. Q: Is it possible to rebill clients for AI Employee usage? Absolutely! You can set up custom rebilling rates for each sub-account, allowing you to monetize the AI tools as part of your service offering. Related Articles	2025-06-03T00:00:00		https://help.gohighlevel.com/support/solutions/articles/155000003906-ai-employee-overview	[ { "date": "2025/06/03", "position": 55, "query": "AI workers" }, { "date": "2025/06/03", "position": 53, "query": "AI workers" }, { "date": "2025/06/03", "position": 51, "query": "AI workers" }, { "date": "2025/06/03", "position": 73, "query": "AI workers" }, { "date": "2025/06/03", "position": 52, "query": "AI workers" }, { "date": "2025/06/03", "position": 50, "query": "AI workers" }, { "date": "2025/06/03", "position": 52, "query": "AI workers" }, { "date": "2025/06/03", "position": 56, "query": "AI workers" }, { "date": "2025/06/03", "position": 52, "query": "AI workers" } ]
Landing your First Machine Learning Job: Startup vs Big Tech vs ...	Landing your First Machine Learning Job: Startup vs Big Tech vs Academia	https://towardsdatascience.com	[ "Piero Paialunga", "Shreya Rao", "Dr. Robert Kübler", "Christabelle Pabalan", "Tds Editors", "Joyce Annie George", "Pranay Dave", ".Wp-Block-Post-Author-Name Box-Sizing Border-Box" ]	A practical guide to landing your first Machine Learning job across startups, big tech, and academia.	This guide is for early-stage Machine Learning practitioners who have just graduated from university and are now looking for full-time roles in the Machine Learning field. Most of the experiences shared here come from companies and universities based in the United States. Keep in mind that this is blog post is inspired by my personal journey, so not everything may apply to your specific case. Use your best judgment and enjoy the read. 🙂 , I had just completed my Master’s Degree in Physics of Complex Systems and Big Data at the University of Rome, graduating with full marks. My master’s degree progressed fairly smoothly, and during my studies, I completed two internships and numerous practical Machine Learning projects. I also completed my Master’s Degree in 1.5 years instead of 2. I felt confident. I genuinely believed that people would be knocking at my door. I thought my master’s degree was a clear indication that I was capable of working and succeeding. Turns out I was not just “wrong”; I was terribly wrong. Unfortunately, the ability to “sell” your skills and get through the recruiting process is a skill in itself. Throughout the years after my Master’s Degree, I had to learn a set of soft skills and techniques that were not taught by my university classes, but they were crucial to finding a job. In particular, I learned that finding a job for a Research Lab/University* is completely different than finding a job in a Startup, and finding a job in a Startup is completely different than finding a job in a Big Tech Company. After finishing my PhD, I went through several hiring processes and ended up with offers from three very different places: a startup, a research lab, and a large tech company. Getting noticed, passing the interviews, and getting these offers wasn’t easy; it was the result of several mistakes and good choices I made along the way. This article wants to share my experience so that an early-stage Machine Learning practitioner can shine in the job-hunting process, whatever path they decide. Before going on, I’d like to be clear on two points: This article is just my experience. While I do believe that sharing it could be helpful to a lot of people, please consider what applies to you and use your best judgment. This article is not a “do your best, be yourself” kind of article. It is meant to be a no-fluff, no-hype, specific guide on what to do in order to succeed in the job-hunting process for Machine Learning roles. In order to get your Machine Learning job, there are 4 specific steps to follow: Image generated by author In the next chapters, I’ll break down each of these four steps so you’ll have a clear idea of how to approach every stage of the process. Let’s get started! 🚀 * Throughout this article, when I refer to a “Research Lab,” I don’t mean R&D roles at companies like Google or Meta. Instead, I’m talking about research positions in academic institutions, national laboratories, or public-sector research centers: places like MIT, Lawrence Livermore, or university-affiliated research groups. These roles are typically more focused on publishing, grants, and long-term scientific contributions than on product-driven innovation. 0. Do your homework. Before discussing the 4 main points of landing a job, I believe that there is an important point to make. The Machine Learning job market is very competitive, and facing it without a solid understanding of linear algebra, statistics, algorithms, data science models, and strong coding skills is basically impossible. Recruiters can quickly tell when someone is bluffing, and it’s surprisingly easy for them to spot when you don’t know what you’re talking about. I strongly suggest not trying to cheat the process. The rest of the guide assumes that you already have a strong Machine Learning foundation, both theoretical and practical, and that your coding skills are in good shape. Now, let’s go back to the process. 1. Know the job market. 1.1 Introduction The job hunting process starts with asking yourself some questions. Which path is best for you? Are you looking for startups? University/Research Lab positions? Or are you trying bigger companies? This part of the article explains the difference between these three sectors so you can have a clearer understanding of the job market and you can make your decision. 1.2. Working In a Startup When you work in a startup, you usually wear multiple hats. You will take care of a lot of things, like MLOps, Model Deployment, Data Acquisition, and all the software engineering that is in the middle. You will also learn how to communicate with investors, approach problems from different angles, and sharpen your soft skills in ways that more structured environments rarely allow. For these reasons, startups are usually considered a great place to start your career. The downside is that your employment in a startup is much more unstable than the one you would have in big tech companies or research labs. The reason is simple: startups are more prone to failure. In 2021, Harvard Business Review estimated that more than two-thirds of them never deliver a positive return to investors. In January 2024, Stripe confirmed that more than 90% of startups fail. Even Growthlist tells us that less than 50% of startups manage to survive. Startups also usually offer lower salaries than Big Tech. Wellfound tells us that the average salary in the USA is slightly below $130k/yr. Given the lower salary and the abovementioned risks associated with a startup, they usually provide you with a pretty decent equity package (0.5%-3% of the company). 1.3. Working In a Big Tech Company In contrast to startups, employment in a big tech company, such as Google, Meta, Amazon, Apple, or Microsoft, offers significantly more stability and structure. These companies have established business models, mature engineering practices, and the resources to support large-scale, long-term research and development. From a compensation perspective, big tech companies are among the highest-paying employers in the industry. According to Levels.fyi, entry-level Machine Learning Engineers (e.g., L3 at Google or E3 at Meta) typically earn $180k to $220k/year in total compensation, including base salary, bonuses, and stock options. These companies also offer generous benefits, including wellness stipends, retirement matching, parental leave, and internal mobility opportunities. A thing to consider about working in a big tech company is that the “structured” setup of a Big Tech Company allows you to grow in your specific area, but it might not be the best if you like to wear multiple hats and learn from multiple areas. For example, if you work on the LLaMA team at Meta, it’s highly unlikely that you’ll ever interact with the teams building the company’s virtual reality products. Your focus will be deep, but narrow. 1.4. Working in a Research Lab/University Ok, on this one, I want to be brutally honest. For the same seniority, academia will pay you way less than the industry. Even very successful professors with great publications would earn much more if they joined the board of a big tech company, for obvious reasons. Even if you become a professor in Machine Learning, you will still earn way less than you would as a Senior Machine Learning Engineer (check out the report from HigherEdJobs). On top of that, the academic world can be extremely competitive, as the academic track for prestigious universities puts you in direct competition with some of the most driven and talented researchers in the world. If you are still reading, that means that you really like academia. And if that’s the case, then it is worth exploring the other side of the moon. Because here’s the truth: despite the lower pay and intense competition, academia offers something incredibly rare: intellectual freedom. In the U.S., you can build your own lab, apply for grants, propose bold research directions, and explore questions that may have no immediate commercial value. That freedom is something industry often can’t offer. There are usually two kinds of Machine Learning research: you can either apply Machine Learning to existing research problems or perform research specifically on Machine Learning, creating new algorithms, neural networks, and optimization techniques. 1.5 Summary A quick comparison between the three settings, summarizing what we have said before, can be found in the picture below. Image made by author. The sources of the salary are here and here. Numbers relate to NYC as of May 2025. I want to reiterate a concept. Let’s say you don’t really know if you want to work in a startup, a big company, or a research environment. Maybe you had a couple of startup experiences, but you don’t know how life would be in a big company or a research laboratory. Is it bad? Not at all. At the beginning of your career, when you’re still figuring things out, the most important thing is to get started. Gain experience. Try things. You don’t need to have it all mapped out from day one. It’s fine not to know exactly where you want to end up. 2. Stand Out 2.1 Introduction A very important thing to worry about is how to stand out. Machine Learning is an extremely hot topic. You will find yourself competing with a pool of very well-prepared people, and somehow you will be the one who stands out. The goal of this part of the chapter is to provide some techniques for you to be appealing in the Machine Learning job market. 2.2 Your authenticity is your best weapon I’m going to say something that may sound a little weird, as we are all Machine Learning enthusiasts: please don’t blindly trust AI to generate resumes/cover letters/messages to recruiters. Let me be more precise. It is completely ok to ask ChatGPT to improve your “summary” section of the resume, for example. What I’m suggesting is to try to modify ChatGPT’s text to make it personal and let your personality shine. This is because recruiters are getting tired of seeing the same resume in 10,000 candidates. Your authenticity will distinguish you from the pool of candidates. Photo by Brett Jordan on Unsplash 2.3 Build a good resume The resume is your business card. If your resume is messy, full of columns, full of meaningless information (e.g., pictures or “fun facts”), the impression the recruiter will have of you it’s that of an unprofessional character. My most successful resume (the one that got me the most job offers) is this one: Image made by author Simple, no picture, no fluff. Every time you write something, try to be quantitative (e.g. “improving AUC by 14%” is better than “improving classification performance”), and make the formatting simple such that you don’t get filtered out by bots. Avoid putting information that is not related to the job you are applying for, and try not to exceed one page. 2.4 Build a portfolio One of the hardest parts after graduating is convincing recruiters that you’re not just someone who studied the theory, but you’re someone who can build real things. The best way to do so is to pick a topic you are passionate about, create your synthetic data or extract it from Kaggle (if you need a dataset), and build your Machine Learning project on top of the dataset. A smart thing to do is to build projects that you can link to a specific recruiter. For example, if you’d like to work at Meta, you could start a project about using LLama to solve a real-world problem. They don’t have to be paper-quality pieces. They just need to be captivating enough to impress a recruiter. Once you have the code, you can: Showcase the project on a blog post. This is my favorite way to do it because it allows you to explain, in plain English, the problem you had to face and how you managed to solve it. Add it to your own GitHub Page/website. This is also excellent. One could argue that a GitHub page gives more of the “software engineer” vibe, while a blog post is more “recruiter-friendly”. The reality is that both work very well to stand out. Also, every time you publish a project, it’s a great idea to share it with your LinkedIn network. This is how my portfolio looks. Screenshot made by the author on Towards Data Science. 3. Get the interview 3.1 Introduction Ok, so we have our resume, and we have our portfolio. This means that if a recruiter looks at my profile, they find a very well-organized portfolio, and they can reach out. Now, how do we actively look for a job? Let’s give a look. 3.2 Looking in person (Career Fair and Conferences) Throughout my career, the only way I found full-time opportunities was through my network, either my virtual network (LinkedIn) or my in-person network (through people I knew and career fairs). If you are still in university and you are looking for startups/big tech companies, don’t sleep on career fairs. Prepare 1-page resumes, study the companies beforehand, and rehearse your one-liner introduction so you own the conversation from the beginning. For example: “Hello, my name is [Your Name], it’s very nice to meet you. I noticed the job opening for [X]. I think I am a good fit for the role [Y], as I have developed projects [I,J,K]. This is my resume hand your resume“ Again, don’t feel discouraged if you leave the career fair without any immediate job interview. I left the career fairs with no interviews and, after a few months, I started receiving messages like these. Screenshot made by author If you are looking for Research Lab opportunities, your academic advisor is the best person to ask, and the best places where you can actively look are the conferences where you present your work. After the conference, invest some of your time in talking with presenters and see if they are hiring postdocs or visiting scholars. It’s usually not necessary to hand your resume, as they are not technically HR and they can evaluate your research by talking with you, reading your paper, and listening to your presentation. Remember to provide your email, and collect researchers’ emails and business cards so you can reach out. 3.3 Looking online This is a secret-not-so-secret routine I used to find jobs online. 0. (On LinkedIn only) On the LinkedIn search bar, search for “Hiring Machine Learning Engineer in [Location]” and filter for “more recent” and “posts” (see screenshot below). You will see the contact of the recruiter posting the job application, and you will see the job application before LinkedIn promotes it in the job section. Screenshot made by author. Apply for the position with a tailored cover letter (not more than 1 page). By “tailored”, I mean that you should look at the company’s website and find overlaps with your work. You should explicitly mention this overlap in your cover letter. You can prepare a template cover letter and tweak it based on the specific application to make things quicker. Find the recruiter who has posted that position (if you can) Send them a message/an email, saying something like (if you can): “Hello, my name is [Your Name], a Machine Learning Engineer graduating from [School]. I hope this message finds you well. I’m writing you this message regarding the [X] job post, as I think I am a great fit. Througout my career I did [J, K (make sure J and K are somehow related to X)]. I would love to borrow 15 minutes of your time to discuss about this. Please find my resume and porfolio attached [Attach Resume, Attach Portoflio/GitHub]” + Send Connection Request If you are applying at startups, most of the time you can directly talk to the CEO of the company. This is a huge plus, and it helps speed up the hiring process by a lot. A similar thing happens in research labs, where most of the time you can talk directly with the professor of the department that will eventually (hopefully) hire you. Please, keep this in mind. 9 people out of 10 will leave you on read. Maybe even 19 out of 20. The only thing you need is one person who is willing to give you a shot. Don’t get discouraged and trust the process. I strongly discourage using software to generate thousands of cover letters in seconds and apply to thousands of jobs. The quality of your application will be terribly low: your application will be exactly like the other 1000 full of em dashes job applications. Think about it. Why would the recruiter choose you? Would you choose yourself if you were the recruiter? 20 good applications a day, with a tailored cover letter and a personalized message to the recruiter, are way better than 1000 AI-generated ones. Please trust me on this one. 4. Pass the Interview 4.1 Introduction Ok, so there is a recruiter who feels like you could be a good fit. How do we get to the stage where they send us the job offer? Let’s give a look. 4.2 The Startup Interview Defining the startup interview is incredibly tough because it dramatically depends on the specific company. It is fair to assume coding exercises, questions about your previous work experience, and an informal talk about your work ethic, where they try to see if you are “fit” for the startup world. From my experience, the startup interviews are usually pretty short (one/two rounds). The best way to prepare for them is to study the startup mission and try to find an overlap between your past projects and the startup mission. Also, startups tend to close this process very quickly, so if you are interviewed, you are probably on a very short list of candidates. In other words, it’s an extremely good sign. 4.3 The Big Tech Interview Ok, this one is long and hard, and it is best to be prepared for a tough process. You typically have a main recruiter who helps you prepare and gives you advice. Throughout my experience, I have always found amazing people there. Remember: no one is there to see you fail. You can expect at least 2 coding rounds, at least 1 Machine Learning System Design round, and at least 1 behavioral round. This process usually takes between 1 and 2 months to complete. Sadly, getting interviewed is a good sign, but it is not a great sign. Rejections happen even at the last round. 4.4 The Academia/Research Interview In my opinion, this is the easiest of the three. If you have studied the research project enough, you are probably good to go. Try to approach the interview with an open-minded approach. Most of the time, the professor/interviewer will ask you questions without a precise answer in mind. So don’t panic if you are not able to answer. If you are able to provide a somewhat impressive and plausible suggestion, you have already aced it. I would not expect more than 2 rounds, maybe the first one online and the second one on-site. It is very important that you study the research project beforehand. 4.5 How to prepare Each round requires a different kind of preparation. Let’s talk about it. About the coding round. I’m not being paid by LeetCode, but if you can, I strongly suggest getting the premium version, at least for a short amount of time. Look for the typical questions the company asks (e.g. Glassdoor), prepare on breadth more than depth. time yourself, and practice thinking out loud. My impression is that nobody asks “easy” questions anymore. I would practice Medium and Hard level questions. With the premium LeetCode profile, you can also select the specific company (e.g., Meta) and prepare for the specific coding questions. Some standard coding questions I have been asked are binary trees, graphs, lists, string manipulation, recursion, dynamic programming, sliding windows, greedy, and heaps. When you prepare, make sure you are making it as realistic as possible. Don’t practice on your couch with your jazz playlist on. Make it challenging and real. These rounds are usually 30-45 minutes. In the system design round, a big company (which I won’t say the name of) recommended preparing on ByteByteGo. That is a good starting point. There are also a bunch of YouTube videos (this guy is incredibly good and funny) that are great to see how the interview should look. During these rounds, I have used embeddings, recommendation systems, two tower networks, latency vs accuracy vs size, recommendation metrics like MAP, precision@k, recall@k, and NDCG. The usual question is about an end-to-end recommendation system, but the specific considerations depend on the problem. Start by asking questions, keep your interviewer in the loop at all times, think out loud, and make sure you follow the hints. This is also 35-40 minutes. About the behavioral round. Be prepared to apply the STAR method (Situation, Task, Action, Result). Start describing a situation, say what your task was, what action you applied to achieve the task, and what the result of it was. Look at your resume and think of 4-5 stories like those. My advice is not to oversell your skills, it is ok to say that you have made some mistakes and you have learned from them. Actually, it is a good sign that you acknowledged and grew. It’s not a great sign if you don’t ask questions after the interview. Study your interviewer, follow them on LinkedIn, and prepare some questions for them. 5. The Elephant in The Room Following the steps of the process, I ended up signing for a Big Tech Company I really like, on a project that excites me, in New York City, which is a place I am in love with. Now, it would be very dishonest of my end to pretend that it was easy. I had impostor syndrome, felt like I wasn’t enough and worth it, countless sleepless nights, even more days when I didn’t even feel like getting out of bed, and when everything felt pointless and useless. I hope you won’t go through what I did, but if you are (or you will) go through this phase, just know that you are not alone. The Machine Learning market can be brutal at times. Remember, you are not doing anything wrong. The rejections are not a reflection of you not being good enough. You might not be a good fit for THAT specific company, you might be filtered out by a biased algorithm, they might have canceled the role, or they might have fired the recruiter. You have no control over those things. Reflect on your mistakes, grow, and do better next time. Now, a very important thing: you need to trust the process diligently. Looking for a job is a job per se. Set a fixed schedule and follow it. I know it’s hard, but try not to be emotional, stay rational, and keep yourself aligned with the daily goal. Finding a job is the result of a prolonged search, not the outcome of a one-shot trial. 6. Summary Thank you very much for being with me ❤️. I hope this article is helpful to you. Let’s wrap things up with the key takeaways from this guide. Start by understanding the three job paths : Research labs, startups, and big tech companies each offer something different. Research gives you intellectual freedom, but pays less. Startups give you fast growth but come with instability. Big tech pays the most and offers structure, but it is highly competitive and specialized. : each offer something different. Research gives you intellectual freedom, but pays less. Startups give you fast growth but come with instability. Big tech pays the most and offers structure, but it is highly competitive and specialized. Don’t underestimate your foundation : You need strong coding, solid ML knowledge, and a good understanding of math and stats. Don’t skip the fundamentals. Recruiters are trained to catch cheaters. : You need strong coding, solid ML knowledge, and a good understanding of math and stats. Don’t skip the fundamentals. Recruiters are trained to catch cheaters. Stand out with clarity and authenticity : You will need a clean, well-organized resume, a portfolio with your work, and an impactful LinkedIn profile. Please don’t use AI-em-dashes-obsessed text. Show your personality, especially in how you communicate your work. : You will need a clean, well-organized resume, a portfolio with your work, and an impactful LinkedIn profile. Please don’t use AI-em-dashes-obsessed text. Show your personality, especially in how you communicate your work. Build strong applications : You don’t need to apply to 1,000 jobs. Use Cover Letters, send messages to recruiters, network a bunch, and create tailored job applications. The work will pay off. : You don’t need to apply to 1,000 jobs. Use Cover Letters, send messages to recruiters, network a bunch, and create tailored job applications. The work will pay off. Preparation is non-negotiable : Know what kind of interviews you’re facing. The three fundamentals for ML interviews are coding , system design , and behavioral . Prepare accordingly, use the tools available (LeetCode, ByteByteGo, STAR method), and practice under real conditions. : Know what kind of interviews you’re facing. The three fundamentals for ML interviews are , , and . Prepare accordingly, use the tools available (LeetCode, ByteByteGo, STAR method), and practice under real conditions. Rejection is not failure: You will face no’s. You will feel impostor syndrome. Remember, one yes is all it takes. Stick to your schedule, trust the process, and take care of your mental health along the way. 7. Conclusions Thank you again for your time. It means a lot ❤️ My name is Piero Paialunga, and I’m this guy here: Image made by author I am a Ph.D. candidate at the University of Cincinnati Aerospace Engineering Department. I talk about AI and Machine Learning in my blog posts and on LinkedIn, and here on TDS. If you liked the article and want to know more about machine learning and follow my studies, you can: A. Follow me on Linkedin, where I publish all my stories B. Follow me on GitHub, where you can see all my code C. For questions, you can send me an email at [email protected] Ciao!	2025-06-03T00:00:00	2025/06/03	https://towardsdatascience.com/landing-your-first-machine-learning-job-startup-vs-big-tech-vs-academia/	[ { "date": "2025/06/03", "position": 30, "query": "machine learning job market" }, { "date": "2025/06/03", "position": 30, "query": "machine learning job market" }, { "date": "2025/06/03", "position": 30, "query": "machine learning job market" }, { "date": "2025/06/03", "position": 30, "query": "machine learning job market" }, { "date": "2025/06/03", "position": 30, "query": "machine learning job market" }, { "date": "2025/06/03", "position": 30, "query": "machine learning job market" }, { "date": "2025/06/03", "position": 31, "query": "machine learning job market" }, { "date": "2025/06/03", "position": 30, "query": "machine learning job market" } ]
PwC finds number of jobs and wages rising despite AI risk	PwC finds number of jobs and wages rising despite AI risk	https://www.siliconrepublic.com	[ "Suhasini Srinivasaragavan", "Suhasini Srinivasaragavan Is A Sci-Tech Reporter With Silicon Republic. Previously", "She Has Worked With The Journal S Noteworthy", "Rté S Prime Time", "Dublin Inquirer. However", "Her Most Favourite Activity Is Sleeping", "She Will Choose This Above All Else If At All Possible. Currently She Has Taken A Liking To Knitting", "Hopes To Finish A Scarf The Time Winter Arrives." ]	According to the report, Irish wages are growing twice as fast in industries more exposed to AI, as opposed to less exposed. AI-skilled workers ...	In Ireland, AI-exposed roles have almost doubled since 2019, PwC report says. A new PwC report finds that jobs exposed to artificial intelligence (AI) grew by nearly 40pc despite risks from the tech. The firm’s latest Global AI Jobs Barometer analysed nearly 1bn job ads from across the world and stated that AI is making employees more valuable, productive and capable of commanding higher wages. According to the report, Irish wages are growing twice as fast in industries more exposed to AI, as opposed to less exposed. AI-skilled workers saw a wage premium of 56pc on average last year – doubling from 25pc the year before. Business productivity has nearly quadrupled since generative AI (GenAI) began speedily proliferating throughout industries such as finance, software and publishing in 2022, PwC says. While in contrast, productivity growth declined in sectors less exposed to AI, such as mining and hospitality. As a result, employees skilled in AI are bringing higher revenue to their workplace. The PwC report finds that industries most exposed to AI saw three-times higher growth in revenue per employee last year. “AI is amplifying and democratising expertise, enabling employees to multiply their impact and focus on higher-level responsibilities,” said Laoise Mullane, director of workforce consulting at PwC Ireland. “With the right foundations, both companies and workers can re-define their roles and industries and emerge leaders in their field, particularly as the full gambit of applications becomes clearer.” However, occupations less exposed to AI saw stronger job growth at 65pc, when compared to more exposed sectors. Still, the growth of AI-exposed roles exceeds expectations at 38pc, finds PwC. Moreover, jobs that require AI skills continue to grow faster than all jobs – rising 7.5pc from last year, even as total job postings fell by 11.3pc. In Ireland, the study finds that job numbers in AI-exposed occupations have grown by nearly 94pc since 2019. There is also a higher demand for AI-related skills. PwC says that this transformation means that workers need to reskill and upskill more frequently. Stemming from this report, PwC recommends that businesses see AI as a tool for enterprise-wide transformation and treat the technology as a growth strategy and not just an efficiency strategy. “In Ireland we are also seeing the productivity prize from AI. PwC’s 2025 Irish CEO survey showed that 44pc of Irish CEOs reported AI had increased efficiencies in their employees’ time at work in the last 12 months,” said Gerard McDonough, a partner at PwC Ireland. “However, to reach full potential, close attention needs to be given to skills enhancement. PwC’s Irish 2025 GenAI Business Leaders survey revealed that 73pc of Irish business leaders are of the view that AI will require most of their workforce to develop new skills.” Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.	2025-06-03T00:00:00	2025/06/03	https://www.siliconrepublic.com/careers/pwc-global-ai-jobs-barometer	[ { "date": "2025/06/03", "position": 90, "query": "AI wages" }, { "date": "2025/06/03", "position": 92, "query": "AI wages" }, { "date": "2025/06/03", "position": 79, "query": "artificial intelligence employment" } ]
Artificial Intelligence at DHS \| Homeland Security	Artificial Intelligence at DHS	https://www.dhs.gov	[]	DHS uses AI to advance its critical missions, defend against new threats from AI, and support the DHS workforce.	Generative AI for the DHS Workforce The DHSChat (DHS-2433) is an AI chatbot for DHS personnel built by DHS and operated in a secure environment. DHSChat provides DHS personnel with the opportunity to leverage generative AI capabilities safely, and securely, which con assist in performing routine tasks more efficiently, such as summarizing complex documents and reports, generating computer code, and streamlining repetitive tasks like data entry. CBP Uses AI to Keep Fentanyl and Other Drugs Out of the Country In 2023, a car driving north from Mexico approached the Point of Entry at San Ysidro. Almost immediately, one of U.S. Customs and Border Protection's (CBP) Machine Learning (ML) models identified a suspicious pattern in the car’s border crossing history and flagged it for further review, which revealed 75 kilograms of narcotics hidden in the vehicle and led to the arrest of the driver. Image HSI Uses AI to Investigate Heinous Crimes In August 2023, Homeland Security Investigations (HSI) announced the completion of one of the most successful operations ever against online child sexual abuse. Operation Renewed Hope resulted in the identification of 311 previously unknown victims of sexual exploitation in less than a month by using AI and ML models, developed in partnership with the Science and Technology Directorate, to enhance old images and give investigators new leads using facial recognition. The operation also led to the rescue of several abuse victims and the arrests of multiple suspected perpetrators, including one international criminal. DHS Generative AI Public Sector Playbook The DHS GenAI Public Sector Playbook encapsulates the lessons learned from DHS’s pilot programs and offers a series of actionable steps for the responsible adoption of GenAI technologies in the public sector. DHS Delivered on AI in 2024 The DHS AI Roadmap outlined priorities, goals, and opportunities for DHS’s AI efforts and activities throughout 2024. DHS has delivered on this vision through milestones such as:	2025-06-03T00:00:00		https://www.dhs.gov/ai	[ { "date": "2025/06/03", "position": 84, "query": "government AI workforce policy" } ]
AI and Future of Work: What You Need to Know in 2025 - Freelearing	AI and Future of Work: What You Need to Know in 2025	https://freelearing.com	[ "Dr.Aqsa Naseem" ]	AI and Future Work: The Best Opportunities & Stay Ahead · What is Artificial Intelligence (AI)? · How AI is Changing the Workplace.	AI and Future Work: The Best Opportunities & Stay Ahead AI and Future: We’re living in a time where machines are not just assisting us—they’re becoming our coworkers. The rise of Artificial Intelligence (AI) is revolutionizing every aspect of work, from how we perform tasks to the jobs we pursue. But instead of fearing it, we should learn to ride the wave and explore the endless opportunities AI brings. What is Artificial Intelligence (AI)? A subfield of computer science called artificial intelligence (AI) builds devices that mimic human intelligence. Think of Siri, Alexa, ChatGPT (yes, like me), or recommendation engines on Netflix—they’re all using AI to process information and interact intelligently with humans. How AI is Changing the Workplace Remember those boring, repetitive tasks at work? AI is handling them now. Whether it’s scheduling meetings, processing invoices, or analyzing massive data sets, AI is doing it faster and smarter. But that’s just the beginning. The Growing Role of AI in Different Industries AI is not limited to tech firms—it’s everywhere. Here’s a peek into how it’s transforming industries: Healthcare and AI-Powered Diagnostics AI speeds up and improves the accuracy of medical diagnoses.Tools like IBM Watson can review thousands of medical cases in seconds. AI also plays a key role in predicting outbreaks and personalizing treatments. Finance and AI in Fraud Detection Ever had your bank freeze a suspicious transaction? Thank AI for that. AI algorithms are trained to detect unusual patterns and prevent fraud in real-time. It’s also making investment advice more personalized through robo-advisors. Retail and Personalized Shopping Experiences AI tracks your preferences and browsing behavior to suggest exactly what you want—even before you know it. From chatbots to smart inventory management, AI is enhancing both customer and employee experiences. Manufacturing and Automation AI-driven robots are streamlining production lines and reducing human error. Predictive maintenance powered by AI even prevents costly machinery breakdowns before they happen. AI and Future Opportunities for Career Growth You don’t need to be a coder to thrive in the AI-driven world. Here’s how to tap into the new wave of careers: Emerging AI-Driven Job Roles Machine Learning Engineers These are the brains behind the algorithms. If you love solving complex problems, this could be your dream role. These are the brains behind the algorithms. If you love solving complex problems, this could be your dream role. AI Ethicists As AI grows, so does the need for professionals who ensure it’s used responsibly. As AI grows, so does the need for professionals who ensure it’s used responsibly. Data Scientists and Analysts Data is the new gold, and people who can make sense of it are in high demand. Remote AI Jobs and Freelancing More AI jobs are going remote. Platforms like Upwork and Toptal are filled with gigs needing AI-savvy freelancers. AI solutions may enhance your abilities in any field, including customer service, automation, and content production. How to Maintain Your Lead in the AI Age Don’t just compete with AI—work alongside it. Here’s how: Upskill with AI and Tech Courses Websites like Coursera, Udacity, and edX offer affordable (even free) AI and machine learning courses. Certifications from Google AI or IBM can boost your credibility. Soft Skills That Complement AI Tools AI lacks human empathy, communication, and creativity—skills that are more valuable than ever. Strengthening these will make you irreplaceable. Lifelong Learning and Adaptability The job market is changing fast. Be open to learning new tools, experimenting with AI platforms, and adapting to new workflows. The Ethics and Responsibility of AI in the Workplace With great power comes great responsibility. AI raises some important ethical questions. Bias in AI Systems AI is only as unbiased as the data it learns from. This can lead to discrimination in hiring, loans, or law enforcement if not checked. Privacy Concerns AI systems collect a ton of data. Without proper regulations, personal info can be misused or exposed. Ensuring Human Oversight AI should be a co-pilot, not the pilot. Keeping humans in control ensures that ethical and emotional decisions remain human-led. AI in Entrepreneurship and Startups Thinking of starting a business? AI can be your co-founder. Leveraging AI for Business Ideas From fitness apps that analyze your posture to AI-powered virtual interior designers—innovation is endless. Entrepreneurs are using AI to solve real-world problems with creative solutions. AI and Future Tools for Marketing, Sales, and Support AI can write ad copy, respond to customers, generate leads, and track performance. Tools like Jasper, Copy.ai, and ChatGPT are helping businesses scale without breaking the bank. AI and Future of Work: What Will Change and What Will Stay? The nature of work is evolving—but humans are still at the center. Hybrid Work Models AI makes remote work easier by managing meetings, analyzing productivity, and supporting virtual collaboration. Expect hybrid work to become the norm. Human-AI Collaboration AI can’t replace human intuition or judgment. But together, we can be unstoppable. Imagine doctors with AI assistants or writers using AI to brainstorm content. Redefining Job Satisfaction and Purpose As AI takes over mundane tasks, people can focus on meaningful work—innovation, creativity, leadership, and connection. Conclusion AI isn’t just the future—it’s already here. While it may disrupt traditional jobs, it also brings a treasure chest of new opportunities for those willing to evolve. The key? Stay curious, stay learning, and embrace the tools that make you superhuman. Be sure you don’t fall behind as the workplace changes. FAQs Q1: What jobs are most threatened by AI? A: There is a significant chance that repetitive, rule-based jobs like cashiers, telemarketers, and data entry clerks will be automated. Q2: How can I start a career in AI? A: Begin by learning Python, statistics, and machine learning concepts. Free resources like Coursera or YouTube tutorials are great starting points. Q3: Is AI taking over all jobs? A: Not at all. AI will replace tasks, not entire professions. Human creativity, judgment, and empathy remain irreplaceable. Q4: Can small businesses benefit from AI? A: Absolutely. From automating marketing to managing customer queries with chatbots, small businesses can compete with larger firms using AI tools. Q5: What are some free AI tools for beginners? A: Try tools like ChatGPT, Canva’s AI features, Google Colab, and Hugging Face for experimentation and learning.	2025-06-03T00:00:00	2025/06/03	https://freelearing.com/ai-and-future-work/	[ { "date": "2025/06/03", "position": 79, "query": "future of work AI" } ]
Best AI Engineering Staffing and Recruitment Companies ...	Best AI Engineering Staffing and Recruitment Companies in 2025	https://www.hirewithnear.com	[ "Written By", "Hayden Cohen", "Published On", "Last Updated" ]	Compare top AI engineering staffing companies in 2025 and find the right partner to hire skilled AI engineers faster and more cost-effectively.	Finding the right AI engineers is hard enough. Doing it quickly, within budget, and without sacrificing quality? That’s even tougher. Staffing and recruitment partners can help streamline your hiring process, connect you with pre-vetted candidates, and reduce the risk of a bad hire, whether you’re hiring locally or looking abroad. This article highlights ten AI engineering staffing companies you should consider in 2025. You’ll learn what they offer, where they source talent from, and what kind of pricing models to expect. How Staffing and Recruitment Firms Help You Find Top Talent Before we get into the top companies, it’s worth unpacking what staffing and recruitment firms actually do—and how they can support your AI hiring goals. Recruitment firms focus on finding long-term hires. For each role, they handle sourcing, screening, and shortlisting candidates, then hand them off for you to interview and make the final decision. Some firms also offer Recruitment Process Outsourcing (RPO)—a more comprehensive model where they manage your entire recruitment function, not just one-off roles. In an RPO setup, the firm acts as an extension of your HR team, handling everything from workforce planning to employer branding. Staffing firms, on the other hand, usually specialize in contract or project-based roles. If you’re looking to fill a temporary gap or bring on a machine learning expert for a short-term project, a staffing firm will help you do that quickly—often within days. Many agencies offer both models to meet broader business needs. Why use a staffing or recruitment company to hire AI engineers? The demand for AI engineers continues to grow, with more companies competing for the same pool of specialized talent. As businesses rush to integrate AI capabilities, finding qualified engineers has become increasingly challenging—especially if you don’t have deep pockets or an established tech brand. With more businesses launching AI initiatives every day, this talent crunch isn’t going away anytime soon. This is exactly why more companies are turning to specialized staffing and recruitment partners. They’re not just another hiring expense—they’re a strategic advantage that helps you move faster, tap into hidden talent pools, and avoid the expensive mistake of a bad hire. Let’s break down when bringing in a recruitment partner actually makes sense, especially for technically complex roles like AI engineering. You need to hire fast Top AI engineers aren’t on the market for long. Agencies have access to candidate pipelines and talent pools that would take you months to build on your own. If speed is a priority, using a recruiting partner can cut your hiring timeline in half or more. You want access to pre-vetted talent Sorting through dozens (or hundreds) of resumes takes time—especially for technical roles like AI engineers, where you can’t just rely on a list of keywords. You need candidates who not only have the right skills on paper (like experience with LLMs, neural networks, or data pipelines) but who can also demonstrate real-world problem-solving and strong communication. Staffing and recruitment firms do that legwork for you. They filter out the noise, assess technical skills, soft skills, and cultural fit, and present you with a shortlist of candidates who meet your exact requirements—saving you weeks of sourcing and screening. Instead of guessing who might be a good fit, you only interview top-tier engineers who’ve already been vetted for the role. You don’t have in-house AI expertise If you don’t have a technical founder or senior AI leader on your team, evaluating candidates can feel like flying blind. How do you know if someone’s experience with LLMs, reinforcement learning, or model deployment stacks up? Staffing and recruitment firms bridge that gap. They know which questions to ask, how to interpret technical assessments, and can spot red flags that non-technical hiring managers might miss. This specialized screening ensures you’re not just hiring someone who talks a good game but can actually deliver the AI solutions your business needs. You’re trying to cut costs It may sound counterintuitive, but using a staffing or recruitment firm can be more cost-effective than running an in-house process, especially when you consider the cost of a bad hire. You’re paying for efficiency, accuracy, and access to talent that matches your business goals. And if you’re exploring offshore or nearshore hiring, specialist firms can help you tap into markets with highly competitive salaries compared to US norms—further offsetting their fees. You want hiring flexibility Some roles don’t need a full-time headcount. Maybe you’re launching a one-off AI project. Maybe you’re in the early stages of product development. Staffing firms give you the option to bring in contract talent on your terms without long-term commitments or overhead. You want support with international hiring Hiring outside the US opens up a much larger talent pool and gives you access to AI engineers with salary expectations 30–70% below their US peers, especially in regions with a combination of strong technical education and lower living costs like Latin America or Eastern Europe. But navigating international hiring comes with complexities that can quickly become overwhelming. Specialized staffing firms eliminate these headaches entirely. They handle everything from legal compliance and contract creation to payroll setup and tax requirements. They understand local labor laws, know market-appropriate salaries, and manage all the paperwork that makes international hiring daunting. The result? Hiring an AI engineer in Buenos Aires or Mexico City becomes as straightforward as hiring someone in Chicago or Seattle. You interview and select your ideal candidate, and your staffing partner manages all the behind-the-scenes logistics. You get the talent you need without the administrative burden—helping you scale faster while maintaining complete peace of mind about compliance. 10 Top Staffing and Recruitment Companies for Hiring AI Engineers in 2025 With hundreds of staffing and recruitment companies out there, finding the right partner to help you hire AI engineers can feel overwhelming. Who specializes in technical roles? Are they sourcing locally or globally? To save you hours of research, we’ve compiled a list of ten notable options for 2025. This list includes a diverse range of companies to help you evaluate different approaches based on your priorities: Some focus on US-based talent for teams wanting local hires Others specialize in international recruitment, helping you tap into global talent pools Many provide both staffing and recruitment services Whether you’re looking for a quick contract hire to tackle a specific AI project or building out an entire team, this range of options should help you identify partners that align with your specific needs, timeline, and budget. 1. Near We’re starting with a company we know best—ourselves. At Near, we help US companies hire top AI engineers in Latin America. Our approach combines speed, quality, and cost efficiency—most clients hire in under 21 days while making significant savings compared to US salary rates. We offer two flexible models to match your needs: either we handle everything (sourcing, compliance, payroll, and ongoing support), or we find your ideal candidates, and you manage the employment relationship yourself. What sets us apart is our deep connection to the Latin American tech community. Our recruitment team lives in the region, understands the local markets, and has built relationships that help us find exceptional talent that aligns with both your technical requirements and company culture. Key features Sources talent from: Latin America Latin America Service provided: Recruitment, Recruitment Process Outsourcing (RPO), and staffing options Recruitment, Recruitment Process Outsourcing (RPO), and staffing options Pricing: Interview candidates at no cost. Once you hire, choose a one-time fee (recruitment) or a monthly fee per hire (staffing). We offer a replacement guarantee and help most clients save 55–63% compared to US salaries while still offering competitive pay. 2. Insight Global Insight Global is a staffing agency specializing in connecting US businesses with skilled AI engineers. With over 70 offices across the US, Canada, and the UK, it offers extensive reach. Its dedicated tech recruiters can identify and screen candidates within 24–48 hours, facilitating onboarding in as little as 1–3 weeks. The firm offers contract, contract-to-permanent, and direct hire options, making it easier for businesses to scale teams based on project needs. Key features Sources talent from: 50+ countries 50+ countries Service provided: Staffing and recruitment for AI professionals Staffing and recruitment for AI professionals Pricing: For direct hires, a one-time payment fee is charged when a candidate starts. For contract roles, businesses pay an hourly or monthly rate during the assignment. 3. TECLA TECLA connects US companies with a vetted network of 50,000+ developers and engineers across Latin America, including specialists in AI and machine learning. It screens candidates for technical skills, English fluency, and time zone alignment to ensure smooth collaboration with US teams. TECLA offers flexible models, from staff augmentation to building dedicated nearshore teams, with an emphasis on fast integration and sustainable partnerships. Key features Sources talent from: Latin America Latin America Service provided: Staff augmentation and nearshore team building Staff augmentation and nearshore team building Pricing: Pricing available upon initial consultation; rates may vary based on role, region, and engagement type. 4. Scion Technology Scion Technology, a division of Scion Staffing, specializes in placing AI, machine learning, and data science professionals. Known for its personalized approach and tech-savvy recruiters, the firm works with startups and enterprise clients alike. It offers both temporary and direct-hire placements, with an emphasis on quickly sourcing hard-to-find, highly specialized AI talent. Key features Sources talent from: United States United States Service provided: Direct hire and contract staffing for AI and ML roles Direct hire and contract staffing for AI and ML roles Pricing: Pricing available upon initial consultation; varies based on position and engagement length 5. HopHR HopHR uses a proprietary AI-matching engine to connect US companies with top-tier AI, machine learning, and data science talent from Latin America. Its platform delivers pre-vetted candidates within 24-48 hours and supports both project-based and long-term hires. Key features Sources talent from: Latin America Latin America Service provided: AI-powered recruitment for AI, ML, and data science roles AI-powered recruitment for AI, ML, and data science roles Pricing: Pricing available upon initial consultation; flexible engagement models for contract and full-time hires. 6. LatamRecruit LatamRecruit helps US and Canadian tech startups hire senior software and AI engineers from Latin America. The company emphasizes high-level talent—typically engineers with 7+ years of experience—who are fluent in English and work US standard hours. Its recruiters deliver pre-vetted candidates within 48 hours and aim to complete hires in 4-6 weeks, streamlining the process for lean, fast-moving teams. Key features Sources talent from: Latin America Latin America Service provided: Technical recruitment and IT staff augmentation Technical recruitment and IT staff augmentation Pricing: Pricing available upon initial consultation; tailored for startup budgets and team scale. 7. SolGuruz SolGuruz helps businesses hire top AI engineers specializing in generative AI, machine learning, and natural language processing. Its team works with companies to define project needs and then matches them with engineers skilled in areas like model training, deployment, and integration. Clients can hire engineers for full-time roles, short-term projects, or as part of a dedicated development team, with flexible engagement options to fit different timelines and budgets. Key features Sources talent from: India India Service provided: AI engineer hiring for generative AI, machine learning, and NLP projects AI engineer hiring for generative AI, machine learning, and NLP projects Pricing: AI engineer hiring for generative AI, machine learning, and NLP projects 8. Data Teams Data Teams connects organizations with highly skilled AI engineers, data scientists, and analytics professionals through a specialized recruitment approach, using AI and manual efforts to screen candidates for technical depth and project-specific experience. Key features Sources talent from: Global talent pool Global talent pool Service provided: Contract and full-time recruitment services for AI, data science, and analytics roles Contract and full-time recruitment services for AI, data science, and analytics roles Pricing: Pricing available upon initial consultation; based on role type, seniority, and project scope 9. Turing Turing helps businesses hire pre-vetted AI engineers and machine learning specialists. Its platform matches companies with developers skilled in building machine learning models, natural language processing systems, recommendation engines, and generative AI applications. The company handles sourcing, vetting, and onboarding, helping businesses hire remote AI engineers for full-time roles, short-term projects, or contract work, with flexible options based on project needs. Key features Sources talent from: Global talent pool Global talent pool Service provided: Remote AI engineer and machine learning developer hiring Remote AI engineer and machine learning developer hiring Pricing: Available upon consultation; flexible models for contract, full-time, or project-based work 10. Teilur Talent Teilur Talent helps US startups and growing tech companies hire AI engineers from Latin America. Its vetting process screens for technical skills, English proficiency, and cultural alignment, making integration with US-based teams smooth and fast. With over 100,000 professionals in its network, Teilur offers flexible staffing models, including full-time and fractional hires, optimized for early-stage teams. Key features Sources talent from: Latin America Latin America Service provided: Technical recruiting and nearshore staffing Technical recruiting and nearshore staffing Pricing: Transparent pricing with a 20% commission Final Thoughts Hiring the right AI engineer is hard, but it doesn’t have to take months or stretch your budget—or patience. The staffing and recruitment companies we’ve highlighted can help you move faster, tap into pre-vetted talent, and avoid costly hiring mistakes. And if you’re ready to combine those time-saving benefits with the cost efficiency of hiring outside the US, Near can help make that happen. We’ve helped over 700 US companies successfully hire top talent from Latin America—including AI engineers—professionals who work your hours, speak your language, and perform like your best US hires, all while delivering significant cost savings. If you want to explore how Near can help you hire AI talent faster and smarter, let’s talk. Book a free no-commitment consultation call.	2025-06-03T00:00:00		https://www.hirewithnear.com/blog/best-ai-engineering-staffing-companies	[ { "date": "2025/06/03", "position": 60, "query": "AI employers" } ]
Artificial Intelligence and Machine Learning	Artificial Intelligence and Machine Learning	https://www.lockheedmartin.com	[]	AI/ML is transforming our customer's mission and our own business. Our customers have a need for AI-driven tools that increase decision speed & improve decision ...	The complexity of the battlespace is ever-evolving. AI provides us with the ability to shift roles between nodes in-mission, dynamically adjusting to the ever-changing battlespace, even shifting software-defined capabilities. Between cross-domain synchronization (land, air, space, sea, cyber), network-of-networks communication, and regular software/model updates, AI is necessary to keep pace. AI/ML is transforming our customer’s mission and our own business. Our customers have a need for AI-driven tools that increase decision speed & improve decision quality. AI isn’t magic. It doesn’t solve all our problems, and we have plenty of challenges ahead of us in transitioning AI capabilities to our end-users. But, when problems are beyond human scale to comprehend based on volume of data, complexity, or urgency, AI can help illuminate solution opportunities.	2025-06-03T00:00:00		https://www.lockheedmartin.com/en-us/capabilities/artificial-intelligence-machine-learning.html	[ { "date": "2025/06/03", "position": 80, "query": "AI employers" } ]
NiCE: AI Customer Service Automation Solutions	NiCE: AI Customer Service Automation Solutions	https://www.nice.com	[]	Get connected fast with pre-built integrations, open APIs, and simple tools that keep your business moving. Learn more. Dashboards & Reporting. Get one source ...	One platform. Endless possibilities. Every NiCE solution runs on the same smart, secure platform — built to scale, connect, and evolve with you. From conversations to operations, it all starts here.	2025-06-03T00:00:00		https://www.nice.com/	[ { "date": "2025/06/03", "position": 84, "query": "AI employers" } ]
2: Heads I Win, Tails You Lose: How Tech Companies ...	2: Heads I Win, Tails You Lose: How Tech Companies Have Rigged the AI Market	https://ainowinstitute.org	[ "Ai Now Institute" ]	This section maps the drivers that are securing Big Tech firms' advantage in the AI market, before turning to the question of who loses in the end.	Governments and investors are funneling billions of dollars into a speculative AI industry without a clear business model or pathway to profitability. In Chapter 1, we identified the myths undergirding the hype despite obvious red flags and warning signs. But the reality on the ground is far less distributive; here, we explain how a handful of firms are poised to capture the AI market. In some regards, the current market behavior of AI firms appears wholly irrational: Tech companies are pumping billions of dollars into an unproven technology with little market demand, firing their own workers, and acquiescing to the political demands of an administration defined by its tech factionalism and personal vendettas. On its face, the AI market appears to be driven more by AI “FOMO”—a fear of missing out—than sound business decisions, with AI firms throwing product use cases at the wall to see what sticks, and firms across the economy force-fitting AI solutions into their workflows, buckling under the generalized pressure that any competitive company must today have an “AI strategy.” Big Tech firms have guaranteed their own success by making the wall as sticky as possible, gaming the market to ensure they benefit if and when the returns come rushing in. Whether by locking customers into existing ecosystems, bending the law to work in their favor, co-opting political processes and media narratives, or pegging their own futures to an industrial strategy of national dominance and government investment, Big Tech firms are shaping the market to consolidate their own power and to hedge against the considerable risks they’re exposed to. The reality is that Big Tech firms and AI developers (propped up by Big Tech firms) can successfully gamble on AI’s future because the house always wins. Their deep pockets allow them to suffer short-term losses as they shuffle through product use cases and burn money, AI chips, and energy at an alarming rate, but ultimately they—and power players in adjacent industries that hinge on AI infrastructure build-out—are best positioned to net long-term gains in this market. This section maps the drivers that are securing Big Tech firms’ advantage in the AI market, before turning to the question of who loses in the end. Cloud Infrastructure Providers Benefit from Cycles of AI Dependence Because the quickest path to AI profit is through the increased demand in cloud services this market drives, Big Tech firms that offer cloud computing services and control cloud infrastructure (like Amazon, Microsoft, and Google) are best positioned to win the AI race. Because of the “bigger-is-better” paradigm, AI developers require more and more compute resources to effectively train their larger models and run “inference,” such as the queries returned each time you enter a prompt into ChatGPT. This dependency on compute has made large-scale AI development contingent on access to compute resources, which has led AI developers like OpenAI and Anthropic to secure partnerships with cloud companies like Microsoft and Amazon in order to successfully train and run their models. The early exclusive partnership between OpenAI and Microsoft has received the most attention among these: OpenAI received Microsoft’s cloud resources at a fraction of the cost; in return, Microsoft locked OpenAI into billions of dollars in cloud commitments and a share of OpenAI’s future revenue. OpenAI wasn’t alone: Anthropic developed arrangements with Google and Amazon, Deepmind solidified its cloud partnership as Google DeepMind, and Mistral struck a deal with Microsoft, for example. But the advantage these cloud firms hold is multifaceted: Unlike other cloud companies like Oracle and Coreweave, Amazon, Microsoft and Google also hold a dominant advantage along the AI supply chain, with advantages in access to data, paths to market, and talent. The partnership model between hyperscalers and AI developers is evolving from being predicated—as was the case in the 2018 deal between Microsoft and OpenAI—on exclusivity to being predicated on mutual dependence. For example, even though OpenAI is no longer locked into an exclusive partnership with Microsoft, Microsoft remains able to secure a market advantage where it matters most—AI model deployment—while ensuring their investment is recouped through circular spending agreements and revenue shares. Under the new partnership, Microsoft retains access to OpenAI’s IP (including insight into how OpenAI and Oracle will manage the new Stargate servers); OpenAI API is still exclusive on Azure (and pays more than $1 billion per year on Microsoft services); and revenue-sharing commitments are still in place (Microsoft retains a 20 percent share of OpenAI’s revenue and future profits up to $92 billion). Microsoft is also positioned to effectively block OpenAI’s effort to convert into a for-profit company, while OpenAI’s board can trigger a clause that prevents Microsoft from accessing its most cutting-edge tech, which OpenAI officials have reportedly proposed doing. In January 2025, markets were temporarily rattled by the announcement that Chinese startup DeepSeek was able to launch an AI model comparable to OpenAI’s latest release at a fraction of the compute cost. For some, DeepSeek cast doubt on the self-serving, bigger-is-better paradigm advanced by companies like OpenAI, projecting future efficiencies in compute resources. But DeepSeek’s release does not change the current paradigm of cloud company dominance: Despite the model’s smaller use of compute in the final training run, the technical advancements in advanced reasoning driven by the inference-time compute approach are still reliant on scale for their performance advantages. And any efficiency gains would likely be overridden by growth in demand, a phenomenon known as the Jevon’s paradox. As Satya Nadella declared in the wake of DeepSeek’s release, “As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can’t get enough of.” DeepSeek thus solved one pressing business problem for Microsoft—how to deal with its escalating expenditures on data centers—without disrupting the overall business proposition for the company: capturing the market through its control over the cloud ecosystem. Efficiency gains through models like DeepSeek’s also don’t necessarily undercut the advantage that Big Tech companies hold from their access to compute. For one, pushing ahead on performance gains at the cutting edge of the technology is still extremely compute intensive. (It is also widely believed that DeepSeek essentially “distilled” its model building off of OpenAI’s o1. ) Moreover, cloud companies reap consistent gains even as we—consumers and companies alike—figure out whether AI delivers what it promises: Every response generated on ChatGPT, every query run on Gemini, and every customer service chatbot integration incurs a cost that customers pay back to the hyperscalers. Now, if compute remains a scarce resource, Big Tech companies with cloud businesses win by controlling limited supply. Similarly, if models become more efficient, these firms still win because the efficiencies will lead to overall reduced infrastructure costs, allowing them to deliver more product at cheaper cost. This means that cloud firms are incentivized to boost AI demand either way, ensuring that AI demand balloons to fit a growing market for infrastructure that depends on its success. This relationship of dependence extends not just to AI developers but to cloud startups, too. For instance, CoreWeave is a new entrant into cloud computing that chipmaker Nvidia has invested in, and has marketed itself as a solution to compute bottlenecks in the AI market. But the company recently went up for public offering, and financial documents revealed that CoreWeave is saddled with debt and almost entirely dependent on Big Tech companies like Microsoft that need to offload their excess demand—the very companies it is attempting to compete with. Big Tech Firms Benefit from Leveraging Control over the Tech Ecosystem There is increasing consensus that AI models are becoming “commoditized,” meaning that gains in model efficiency decrease costs, and more large-scale models will emerge to compete. In response, firms like Microsoft are advising those in the market to “focus more on how they integrate these models with their own data and workflows.” This advice reflects Microsoft’s position in the market: It, like Google and Meta, has an advantageous position due to its dominant role in enterprise and consumer-facing software. This is precisely why, on the day that the chipmaking firm Nvidia’s stock fell nearly 17 percent following the DeepSeek news, Amazon, Meta, and Apple’s stock went up. Because if AI models become cheap to integrate—and compute becomes significantly cheaper—the firms who own AI products, distribution, and data centers are at an advantage. This makes ecosystem power—control over the paths to market—an important element in the AI market. Ads all the way down: Meta’s advertising ecosystem positions it well in the generative AI market. Because one of the main use cases for generative AI technology right now is generating a lot of content very quickly, Meta can deploy AI to consistently optimize for the highest-performing ads at unprecedented scale—driving revenue for themselves and advertisers at minimal marginal cost. Moreover, Meta can leverage its infinite stream of free, user-generated photo and video content across Facebook and Instagram to make ads “indistinguishable from content” by using AI to label and link every possible purchasable item in every possible piece of content. This helps explain the significant investment—$65 billion—Meta is making into AI infrastructure. Other Big Tech firms are moving in a similar direction: Microsoft dominates the enterprise software market, and is pushing Copilot integration and the upselling of security features, which require purchase of a premium subscription to their cloud platform, Azure. In the event that OpenAI is able to capitalize on its Chat-GPT user base—an unlikely prospect —Microsoft, as mentioned earlier, has a revenue-sharing agreement in place to give them 20 percent of OpenAI’s revenue. It has also opened up its own competing arm called Microsoft AI, led by Inflection CEO Mustafa Suleyman, which is devoted to product development both for Copilot and other consumer AI products. Google dominates the search and search-advertising ecosystems—so much so that a federal court found in 2024 that Google has an illegal monopoly over both internet search and search-advertising markets. As remedies work their way through the court, Google has leveraged its search dominance to integrate Gemini, its own AI model, into the search experience, providing Google with an unbeatable advantage to deploy AI to millions of captive users. The same logic that applies to Meta’s ad market applies to Google, where generative AI models can quickly and cheaply optimize advertising content to benefit advertisers and Google alike. Meanwhile, Google is launching its suite of AI tools across all of its consumer and enterprise products, from Workspace to Email, so computing efficiencies will make this process all the more profitable for Google. Finally, DeepMind, Google’s AI research laboratory, is slowly expanding into a product development org, revealing that Google’s biggest bet on AI is product integration rather than AI model development. Amazon, as the leading cloud infrastructure firm, is already positioned to take up a significant portion of the AI market, and has made several rounds of investments into the startup Anthropic. It has also made some initial effort to develop its own models, though its operation of a model marketplace seamlessly integrated with Amazon Web Services (AWS) is likely most reflective of its intent to offer AI-as-a-Service. Amazon is testing the rollout of AI models across its existing platforms and services, including Alexa, and a shopping tool called Interests onto its online marketplace. It is also developing its own chips: Inferentia is optimized for AI training runs, and Trainium is optimized for inference and training. Apple dominates the mobile-device ecosystem. If inference becomes dramatically cheaper and memory requirements substantially decrease, it becomes significantly cheaper to deploy the most powerful AI models on Apple’s devices. Its focus has been on releasing small models aimed at running on-device for iPhone, iPad and Mac, and a larger foundation model running on its private cloud servers, leveraging Apple’s position in the device market. AI Firms Benefit from the Data Center Boom As we described earlier, controlling access to cloud resources and services is a crucial way firms like Google, Microsoft, and Amazon are advantaged in the AI market. In 2024, Big Tech companies spent more than $180 billion on data center expansion and infrastructure. Just one year later, Google, Meta, Microsoft, and Amazon expect to spend an additional $300 billion on data center construction and infrastructure costs for AI. It is estimated that by 2030, the largest cloud service providers will host 60–65 percent of all AI workloads. The amount of energy that data centers are projected to use is staggering. Industry analysts anticipate a five-year load growth of 80 gigawatts to power AI data centers—the equivalent of adding the electric capacity of the entire state of California onto our existing grid. Energy utility companies tend to project even more aggressive numbers, like a Texas utility company claiming requests for 82 gigawatts of additional load in its service territory alone. If these numbers are even nominally accurate, the growth in power consumption by data centers is poised to wreak havoc on already fragile energy grids and markets that are incapable of meeting this extraordinary demand, especially in such a short time. These projections need closer scrutiny, however. Former National Economic Council Director Brian Deese has said that forecasters tend to overestimate electricity demand because they emphasize static load growth over efficiencies that are likely to develop over time. Utilities companies are also incentivized to overproject energy demands to grab investors’ attention. Furthermore, data centers tend to request services from multiple utilities, meaning that projected demand is likely captured in multiple utility companies’ projections. But these high-demand projections have acted as a strategic policy level for firms petitioning the government to quickly bring more power sources online as a matter of national importance. The strategy is working: The Department of Energy is set to announce plans allowing companies to build data centers and power plants on federal land by the end of 2025, in order to maintain America’s “global AI dominance.” In one sense, these inflated projections could seem risky for Big Tech companies. If the AI bubble bursts and energy projections don’t materialize, Big Tech companies risk sinking billions of dollars into stranded infrastructure. This is why the push for public investment is critically important as a de-risking measure, so they won’t be left footing the bill in the face of a market collapse. Big Tech Wins Even If the AI Boom Doesn’t Pan Out Big Tech firms are pursuing a multifaceted strategy to shore up their interests no matter how the AI market inevitably plays out. First, they pump up energy projections while banking on efficiency gains to drive more demand. Then they strike favorable deals with utility monopolies to ensure best-rate prices and pass off the remaining costs to ordinary ratepayers. Next, they integrate themselves across the entire energy supply chain, purchasing energy, selling energy technology, and making strategic long-term contracts with power plants to preference their own needs at the expense of other energy customers. Finally—and perhaps most importantly—they pit state and local governments against each other by dangling purported economic benefits of data center development, accruing tax breaks and subsidies, while lobbying to block any legislation that undermines their interests. Although data center facilities require massive capital expenditure to build—a hyperscale facility of a hundred megawatts requires up to $1.4 billion in up-front investments—data center capacity is relatively fungible if enough capital is available to maintain or repurpose the facility. But the specialized AI infrastructure being built today for GPU clusters that produce significantly greater heat isn’t as easily repurposed for general computational use. In the event of AI demand plummeting, Big Tech companies could repurpose data centers for other workloads, including traditional cloud and data storage servers. But they’ll be equally incentivized to leverage their market power to make demand happen, rather than accept huge write-offs. And since a portion of the data center infrastructures used by Big Tech firms are leased, companies have some flexibility to cancel or opt out of renewals based on demand—as Microsoft chose to do after it ended its exclusive agreement with OpenAI. Utility companies, on the other hand, may be left reeling. Despite having no long-term guarantees of future demand, utility companies across the country are planning to invest billions of dollars in new infrastructure to service new data centers. But minimum contracts for large load customers tend to be short—two years on average—and minimum charges are low, meaning that data centers can walk away from their large energy contracts with little risk, leaving utility companies—and ratepayers—left to carry the costs, even for unfinished projects. Data centers also pose risks to utility grid planning and management: They have the capacity to “disconnect” from the grid and switch to their own local, back-up power generators. This is a safety mechanism intended to protect data center equipment from damage that can arise from fluctuations in voltage, grid frequency, or natural disasters; yet when done at scale across multiple large data centers concentrated in one region, these disconnections can cause large surges in excess electricity that threaten grid reliability across a region. Several “near-miss” cases have already been documented across the country in the past year. The second way Big Tech companies guarantee favorable market terms for massive data center investment is by striking opaque and exclusive deals with utility monopolies to set preferential energy rates, which shift infrastructure costs onto ordinary ratepayers. As Eliza Martin and Ari Peskoe discuss in the report Extracting Profits from the Public: How Utility Ratepayers Are Paying for Big Tech’s Power, Big Tech companies do not need a rate discount: they are fully capable of funding their own infrastructure development costs. Nevertheless, utility companies offer special contracts to Big Tech companies to attract their business and then may raise electricity rates for other ratepayers to make up for the rate discounts to large customers. Already customers in Georgia have seen six rate increases in less than two years, increasing the electricity bills of ordinary ratepayers by 37 percent due to additional power demand from Georgia data centers. Dominion Energy, which services an area in Virginia known as “data center alley” because it houses the largest cluster of data centers in the world, is proposing fuel-rate increases that could raise average residential customer bills by as much as ten dollars per month. Technically, some special contracts must receive approval from regulators. But approvals tend to pass in uncontested hearings, with many contract details held confidential or redacted in public hearings. In some states, regulators face immense political pressure from utility companies (and in some states, elected officials seeking to gain favor) to approve special rate deals for large and influential companies like Big Tech firms. In addition to underpaying on rates, Big Tech firms also have the ability to game the system by reducing their energy load during the time frame when they know utility companies are measuring their uses for the purposes of calculating their “demand charges,” so that their data centers will be charged much less than their fair share of the system costs. While these companies are securing long-term contracts for energy and capacity at a stable and known price, they are driving the cost of energy higher for all other customers and taking valuable energy resources currently used to serve existing customers away to serve data centers’ rapacious needs instead. Third, Big Tech companies are embedding themselves across the entire energy supply chain in hopes of “cementing a technological lock-in effect” and ensuring dominance in whatever energy future takes hold. Big Tech companies are major purchasers of clean energy, but are also suppliers to renewable energy companies, selling technology to help companies organize their workspaces or manage their energy loads. For example, Alphabet (Google) has developed a product called Tapestry to help electricity grid operators map and manage their electricity grids; Alphabet also owns the thermostat company Nest, which utility companies can use to control customers’ thermostats under demand-response programs. Big Tech companies are also funding investments to bring new sources of power online or restart and expand dangerous sources that have closed, like Microsoft’s investment to reopen a unit at Three Mile Island, a nuclear power plant; or Google’s investment in offshore wind projects. These firms are directly funding investments in new energy companies, like Amazon’s investment in hydrogen electrolyzers, Google’s investment in geothermal startup Fervo, and Sam Altman’s investment in Helion Energy. And despite stated commitments to sustainability, tech firms have deep ties to the fossil fuel industry: They purchase fossil fuel energy; they sell AI to fossil fuel companies to speed extraction; and they are driving investments to delay the retirement of coal plants. Fourth, Big Tech companies are pitting state and local governments against each other by dangling economic development promises to secure generous subsidies and abatements that reduce their tax liability. In turn, localities, often desperate for additional sources of revenue, offer these companies packages of incentives to attract their business, including tax breaks for data center projects, sales and use tax exemptions, and property tax abatements. For example, in 2019 (years before the generative AI boom), Indiana passed a law exempting data centers from sales tax on materials and equipment needed to build and operate data centers for up to fifty years, as well as a sales tax exemption on purchasing energy. Meanwhile, ordinary Indianians pay a 7 percent sales tax for their electricity and any other goods they buy. This is particularly damning when considering how much data centers spend on electricity bills: Indiana Michigan Power estimated that a 1,000 megawatt data center would pay an annual electric bill of $492.6 million. Over a fifty-year period, the foregone sales tax revenue would total more than $1.7 billion. Amazon’s new data center campus coming to New Carlisle, Indiana, within this service territory could use double that energy once completed, and lead to even more lost revenue. Nevertheless, at least thirty-two states now offer similar subsidies to data centers, which will cost billions in foregone public revenue: Texas’s program, for example, could cost the state $1 billion in lost tax revenue in 2025 alone. Good Jobs First has tracked over $6 billion in data center subsidies given to Amazon in the United States, including a recent $1 billion property tax exemption in Oregon for a new data center in Morrow County and a $4.3 billion subsidy deal in St. Joseph County, Indiana. Other states are also passing legislation designed to unlock new energy sources and strip back consumer protection laws in a bid to court data center development. If states and localities refuse to offer desired incentives, Big Tech companies routinely say that they’ll build elsewhere. Martin and Peskoe provide over a dozen examples from Big Tech companies and data center developers testifying in rate cases that utility prices are an important factor for determining where they will build data centers. Similarly, an investigation into Meta’s decision to build a $10 billion data center campus in Louisiana reveals the project was a “non-starter” unless Louisiana provided a sales tax exemption on servers and equipment. But this might not always be true: As the Microsoft executive responsible for data center selection stated in the New York Times, “I can’t think of a site selection or placement decision that was decided on a set of tax incentives.” Relatively few states offer ideal sites for building data centers due to cost, climate, and the risk of natural disasters. This gives states and localities much more bargaining power than they are leveraging, causing them to lose out on significant tax revenue whenever they bow to corporate pressure to strike deals. Fifth, and finally, Big Tech firms lobby to block measures designed to protect consumers in state legislatures. For example, in January 2025, a bipartisan group of Virginia lawmakers proposed multiple bills to enshrine baseline protections for citizens, including oversight, transparency, sustainability, and cost-allocation measures. Big Tech companies fought these bills tooth and nail, with one political action committee connected to the Data Center Coalition, a Big Tech lobbying group, contributing over $100,000 to Virginia state lawmakers. In the end, all but one data center bill failed to pass. The bill that passed, which allows (not even requires) data centers to perform impact assessments of data centers’ effects on water and agricultural resources, was recently vetoed by Virginia’s Governor for creating “unnecessary red tape.” In Oregon, lawmakers introduced a bill to specify data center companies as a new customer class to ensure cost allocations are fair. But Big Tech companies are challenging the bill, claiming that this would unfairly single out data centers —a particularly ironic argument given that much of their strategy for securing lower rates with utility monopolies relies on using their power as a “special” customer to ask for a differentiated, discounted rate.	2025-06-03T00:00:00	2025/06/03	https://ainowinstitute.org/publications/2-heads-i-win-tails-you-lose-how-tech-companies-have-rigged-the-ai-market	[ { "date": "2025/06/03", "position": 91, "query": "AI employers" } ]
How AI reshapes editorial authority in journalism	How AI reshapes editorial authority in journalism	https://digitalcontentnext.org	[ "Rande Price", "Research Vp" ]	AI is actively reshaping editorial workflows, impacting how newsrooms select, edit, and present information.	Artificial Intelligence (AI) is changing how news organizations produce and distribute content. Newsrooms use AI to generate headlines, curate stories, automate writing, and optimize content for digital platforms. Rather than simply supporting journalists, AI is actively reshaping editorial workflows, impacting how newsrooms select, edit, and present information. In short, AI is reshaping the newsroom process and the news itself, which has broad impacts on the business of media and audience perception. Shifting controls As AI takes on more editorial roles, it shifts control from individual journalists to automated systems optimized for engagement and scalability. Felix M. Simon’s research on how the use of artificial intelligence shapes the way news gets produced and distributed explores this shift. Instead of relying on human judgment, experience, or what’s best for the public, news decisions are now more focused on numbers, data, and technology. This change is part of a bigger trend of running things in a more controlled, predictable, and measurable manner. Editors are using tools like algorithms and performance stats to decide what news to publish rather than just their instincts or values. This raises concerns that news is becoming too uniform, that editors have less freedom, and that journalism may not serve democracy as well as it used to. Simon explains this type of change using the “gatekeeping theory.” The theory views news production as a series of choices, like gates, where people decide what becomes news. In the past, journalists mostly made these choices using their judgment. Digital tools and social media platforms now play a significant role in shaping those decisions. Reshaping journalism Simon’s study analyzes AI’s role in reconfiguring journalism processes. Based on 143 interviews across 34 news organizations in the U.S., U.K., and Germany, he identifies how and where AI integrates into news workflows. He structures his research around these central questions: How does AI shape the gatekeeping process of the news in terms of production and distribution? Where do news organizations use AI in editorial processes? What effects result from its adoption in news production and distribution? Simon’s questions guide his investigation into how AI reshapes gatekeeping and journalism’s role in the public sphere, revealing three key patterns. First, AI boosts efficiency by automating routine, time-consuming tasks such as transcription, translation, and content formatting. Second, it enhances effectiveness by enabling previously impractical tasks, like large-scale data analysis and personalized content delivery. Third, AI supports greater optimization, particularly in refining distribution strategies and managing dynamic paywall systems, ultimately transforming how news reaches and engages audiences. AI reconfigures gatekeeping In many cases, AI systems influence news judgment not by replacing human editors but by steering their attention through data-driven cues. These cues include story performance metrics, SEO recommendations, or algorithmic predictions of audience interest. These subtle nudges shape editorial agendas and shift newsroom priorities over time. Additionally, AI’s influence extends into ethical domains, where concerns emerge about bias in training data, transparency in automated decisions, and the implications of delegating journalistic choices to opaque systems. These challenges push newsrooms to assess the values embedded in AI tools and develop policies for their responsible use. Further, researchers and practitioners continue to explore how AI alters productivity, quality, and editorial judgment. Simon’s findings show that AI is not revolutionary but evolutionary and deeply embedded in journalism’s digital transformation. AI retools news production and distribution without changing journalism’s core goals. Its impact is often subtle and only becomes clear over time or at scale. AI is a mix of technologies used across the entire news process, not just in editorial work. It continues a broader trend of automation and digital transformation in journalism. While its effects are real, they’re still hard to measure or quantify fully. Notably, Simon’s study shows how AI transforms journalistic gatekeeping and connects AI’s rise to a broader trend of automation and efficiency in journalism. While effects vary and remain difficult to measure, AI is shifting decision-making power and newsroom practices and media leaders need to be mindful of these shifts and their implications.	2025-06-03T00:00:00	2025/06/03	https://digitalcontentnext.org/blog/2025/06/03/how-ai-reshapes-editorial-authority-in-journalism/	[ { "date": "2025/06/03", "position": 1, "query": "AI journalism" }, { "date": "2025/06/03", "position": 1, "query": "artificial intelligence journalism" } ]
Lesson: Artificial intelligence in journalism	Lesson: Artificial intelligence in journalism	https://mediahelpingmedia.org	[ "Media Helping Media", "This Material Has Been Produced The Team At Media Helping Media", "Mhm", "Using A Variety Of Sources. They Include Original Research The Mhm Team As Well As Content Submitted Contributors Who Have Given Permission For Their Work To Be Referenced. Ai Is Also Used For Research", "Article Development But Only After Original Content Produced The Mhm Team Has Been Created. All Ai Produced Material Is Thoroughly Checked Before Publication." ]	This one-day lesson outline is designed for working journalists and editors who want to understand how artificial intelligence (AI) is reshaping journalism.	This one-day lesson outline is designed for working journalists and editors who want to understand how artificial intelligence (AI) is reshaping journalism. It’s based on five articles published on Media Helping Media which we recommend trainers study before adapting the following lesson outline for local needs. The lesson explores practical uses of AI, ethical issues, and verification strategies related to AI-assisted reporting and content creation. Lesson running order 09:30 – 10:00 \| Welcome and introduction Course objectives and schedule Participants’ expectations and prior experience with AI Brief overview of AI tools in journalism today 10:00 – 10:45 \| Session 1: Hey AI, what’s on the news agenda today? Focus: Using AI to support editorial planning Summary: This article demonstrates how tools such as Google’s Gemini, ChatGPT and Perplexity can generate a list of trending topics and offer treatment suggestions, helping journalists structure their news agenda with fresh perspectives. Discussion: Benefits and limitations of generative AI in setting the news agenda. Exercise: Use a generative AI tool to generate a news list for today. Compare with the homepage of a leading news site. Reflect on what was missed or added? Is there a clear news hierarchy? 10:45 – 11:30 \| Session 2: AI-assisted reporting Focus: Enhancing story development with AI Summary: This article explores how AI can serve as a partner in brainstorming, particularly for solo or under-resourced journalists. The aim is to show how AI can identify gaps in coverage and suggest fresh story angles. Exercise: Provide a story prompt to an AI tool. Ask for: follow-up angles, sources to approach, and suggested headlines. Share and discuss results in small groups. 11:30 – 11:45 \| Break 11:45 – 12:30 \| Session 3: Artificial intelligence assesses its role in journalism Focus: Opportunities and risks of AI tools Summary: This article, written by AI, evaluates its own potential in journalism. It highlights speed and scalability, but warns of risks including bias, lack of transparency, and misinformation. Exercise: Working in pairs, list three strengths and three weaknesses of AI in journalism. Group brainstorm: What editorial checks should exist when using AI outputs? 12:30 – 13:30 \| Lunch break 13:30 – 14:15 \| Session 4: How to detect AI-generated images Focus: Verifying visuals in the age of AI Summary: Based on real case studies, this article offers a step-by-step guide to spotting AI-generated visuals, such as those circulated after the Nepal earthquake. Tools introduced: Reverse image search, EXIF data checkers, watermark detectors Exercise: Review 5 images (some AI-generated, some real). Use tools and article guidance to verify authenticity. Discuss implications of falsely using AI images in journalism. 14:15 – 15:15 \| Session 5: AI and investigative journalism Focus: Pattern detection and data analysis with AI Summary: This piece explains how AI can mine large volumes of documents or datasets to identify potential story leads. It shows promise in automating parts of the investigative process. Exercise: Review a sample dataset (e.g. government spending). Use an AI tool to ask questions about irregularities or trends. Discuss how these findings might shape an investigation. 15:15 – 15:30 \| Afternoon break	2025-06-03T00:00:00	2025/06/03	https://mediahelpingmedia.org/lessons/lesson-artificial-intelligence-in-journalism/	[ { "date": "2025/06/03", "position": 18, "query": "AI journalism" }, { "date": "2025/06/03", "position": 15, "query": "artificial intelligence journalism" } ]
Faculty and AI - Artificial Intelligence and Generative AI for ...	Artificial Intelligence and Generative AI for Media & Journalism	https://guides.lib.unc.edu	[ "Madison Dyer" ]	Artificial Intelligence and Generative AI for Media & Journalism: Faculty and AI · How Faculty Can Utilize the Power of AI · Prompt Framework for Educators from ...	University faculty can utilize AI in the classroom in various innovative ways to enhance teaching and learning experiences: Personalized Learning: AI can help tailor educational content to meet the diverse needs of students. Adaptive learning platforms adjust the difficulty of tasks based on individual student performance, providing a customized learning experience. Automated Grading and Feedback: AI tools can assist in grading assignments and providing instant feedback. This not only saves time for faculty but also helps students understand their mistakes and learn from them more quickly. Enhanced Research and Data Analysis: AI can assist faculty and students in conducting research by analyzing large datasets, identifying patterns, and generating insights. This can significantly speed up the research process and improve the quality of findings. Interactive Learning Tools: AI-powered chatbots and virtual assistants can provide students with 24/7 support, answering questions about course material, reminding them of deadlines, and offering motivational support. Content Creation: AI can help faculty create engaging educational content, such as interactive simulations, personalized quizzes, and multimedia presentations. This can make learning more interactive and enjoyable for students. Accessibility: AI tools can make learning more accessible for students with disabilities by providing features like speech-to-text, text-to-speech, and real-time translation. Ethical and Critical Thinking: Faculty can use AI to teach students about the ethical implications and limitations of AI technologies. This includes discussions on data privacy, bias, and the responsible use of AI. By integrating these AI tools and strategies, university faculty can enhance their teaching methods, improve student engagement, and foster a more inclusive and effective learning environment. (Text generated by Microsoft Copilot).	2025-06-03T00:00:00		https://guides.lib.unc.edu/generativeAI/ai-faculty	[ { "date": "2025/06/03", "position": 29, "query": "AI journalism" }, { "date": "2025/06/03", "position": 63, "query": "artificial intelligence journalism" } ]
Untitled	UNESCO Highlights Opportunities, Challenges, and Threats of AI for	https://www.unesco.org	[]	This question is for testing whether you are a human visitor and to prevent automated spam submission. Red dot Audio is not supported in your browser. bottle	The 2025 World Press Freedom Day commemoration, themed "Informing in a Challenging World," focused on Artificial Intelligence’s transformative impact on press freedom and media ecosystems. To mark the occasion, UNESCO’s Regional Office for Central America, Mexico, and Colombia partnered with the UN Resident Coordinator’s Office in Costa Rica and the College of Journalists and Communication Professionals (COLPER) to host a critical discussion titled World Press Freedom Day 2025: The Impact of Artificial Intelligence on Journalism on May 14 at the UN House in San Pedro, Montes de Oca. Approximately 30 attendees gathered to reflect on AI’s benefits, challenges, and threats to freedom of expression, press freedom, and the right to information. The session also addressed AI’s implications for journalist safety, particularly for women in the field. Notable participants included Colombian AI Literacy Consultant Carlos Augusto Barrera Cuesta; Damián Arroyo, AI Editor for Costa Rica’s La Nación newspaper; Ana Saavedra, former Director of Colombia Check and specialist in disinformation and conflict reporting; and Larissa Tristán, Professor and Researcher at the University of Costa Rica’s School of Collective Communication. Panelists unanimously agreed that AI has irrevocably permeated all societal spheres, journalism included. They acknowledged its power as a journalistic tool—enhancing false-information detection, data analysis, and task automation—but also warned of its potential to amplify disinformation, hate speech, and censorship. A central conclusion emphasized the irreplaceable role of humans in deploying AI tools ethically. Participants stressed that training journalists and audiences in responsible AI use is now essential. The forum ultimately served as a vital space to confront AI’s unique challenges within journalistic practice.	2025-06-03T00:00:00		https://www.unesco.org/en/articles/unesco-highlights-opportunities-challenges-and-threats-ai-journalism-costa-rica	[ { "date": "2025/06/03", "position": 49, "query": "AI journalism" } ]
Should readers know if a news story was written by AI? The ...	Should readers know if a news story was written by AI? The ethical challenge of modern journalism	https://world.edu	[ "Sonia Parratt Fernández", ".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow", "Class", "Wp-Block-Co-Authors-Plus", "Display Inline", ".Wp-Block-Co-Authors-Plus-Avatar", "Where Img", "Height Auto Max-Width", "Vertical-Align Bottom .Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow .Wp-Block-Co-Authors-Plus-Avatar", "Vertical-Align Middle .Wp-Block-Co-Authors-Plus-Avatar Is .Alignleft .Alignright" ]	These and other ethical dilemmas will become increasingly common in media newsrooms, which need to innovate while maintaining journalistic principles. In fact, ...	Should a newspaper reader know that what they are reading was written with the help of artificial intelligence (AI)? Can journalists guarantee that a text generated with this technology does not contain sexist biases? These and other ethical dilemmas will become increasingly common in media newsrooms, which need to innovate while maintaining journalistic principles. In fact, the ethical dimension of the emergence of AI is one of the most important challenges for the media due to their social commitment and their service to democracy . AI is present in more and more areas of our lives, including the information we constantly receive from the media. Many journalists already use these tools for all kinds of tasks, including generating text, images, and sound. Thanks to what is known as generative AI, professionals can write headlines, summarize, translate texts, transcribe interviews, search for information to add context to the news they write, create graphics and images, and even generate news automatically. Ethical codes Aware of the potential implications of AI, some media outlets have begun creating their own ethical codes, a kind of guide with guidelines to help journalists use it responsibly. However, although half of newsrooms already use generative AI, only 20% have such codes, according to a global survey . In our study , we located 40 of these documents. These documents, in addition to containing guidelines on how to use AI in journalism ethically, are publicly accessible to maintain user trust. They belong to a total of 84 media outlets, news agencies, magazines, media groups, and media alliances from 15 countries across Europe—including Spain—the Americas, and Asia. What can academics contribute? Researchers from academia can indirectly contribute to the implementation and successful use of AI in newsrooms. Proof of this is that 90% of journalists participating in a global survey view universities as having a greater role in this process. In this way, media outlets would benefit from their knowledge , research, collaboration, and critical view of this technology. However, some studies show that connections between journalists and academics are often poor. In fact, as our study shows, there are academic proposals for the ethical use of AI in journalism that existing codes do not address, likely due to a lack of awareness. Here we present a summary that combines the academic proposals and guidelines from the professional documents examined in the study, as well as some guidelines on how to implement them. Follow the principles of accuracy and credibility . Verify the accuracy and credibility of the information provided by AI; simplify the process by which users can report errors resulting from the use of AI to the media. Improve accessibility . Make news stories adaptable to different platforms, improve their readability, and ensure that the style of automated texts is similar to the rest. Offer relevant content . Use AI not only to find popular and trending topics, but also to make the information published meaningful to people’s lives; AI-powered content personalization and recommendation services should not be detrimental to the public interest. Promote diversity . Show social diversity, perspectives, and points of view; avoid stereotypes and biases. Ensure transparency . Indicate when an algorithm was used to create a news piece and let the user know whether they are interacting with a human or an AI. Ensure responsible data and privacy management . Data providers must have the legal right to provide their data to journalists, and journalists must have the right to process and publish it; collect only the necessary personal data; anonymize irrelevant information and ensure secure storage of databases; assess whether it is worthwhile to share private and potentially competitive audience data with third parties; and users of conversational AI must be able to decide what data is collected, what it is used for, where it comes from, and how it is shared. Empower human presence . Regularly review the algorithm to prevent it from writing out-of-context information; consider the potential negative implications of delegating editorial decision-making to algorithms. Have interdisciplinary teams . Have teams that combine technical knowledge and ethical principles, and that investigate how AI can advance the principles that govern journalism. These eight recommendations can be useful for media outlets looking to create their own codes of ethics or improve existing ones. Social function of journalism As media outlets and their professionals continue to take steps toward integrating AI tools into their journalistic work, they will increasingly face ethical dilemmas that are not always easy to resolve. Sooner or later, everyone will need to take steps to guide journalists in the responsible use of AI without losing sight of the social function that good journalism serves. And in this task, researchers have much to contribute. Author Bio: Sonia Parratt Fernández is Professor of Journalism at Complutense University of Madrid	2025-06-03T00:00:00	2025/06/03	https://world.edu/should-readers-know-if-a-news-story-was-written-by-ai-the-ethical-challenge-of-modern-journalism/	[ { "date": "2025/06/03", "position": 60, "query": "AI journalism" } ]
How Data Journalism Can Future-Proof Your Content ...	How Data Journalism Can Future-Proof Your Content Visibility in Organic Search	https://ipullrank.com	[ "Carly Stoenner", "Francine Monahan", "Mike King" ]	Learn how non-media publishers can leverage data journalism principles to build authority and improve visibility in AI-powered organic search environments.	Nearly 70% of people believe that government officials, media, business leaders, and journalists deliberately mislead the public, according to a 2025 report by the global communications firm Edelman. As content markers and content strategists, it’s our job to change that. In some industries, businesses have even emerged as a leading player in addressing societal issues, with many people viewing companies as more competent and ethical than the government, according to Richard Edelman, CEO of Edelman. This means that as marketers, we have an immense opportunity (and the public’s blessing) to gain the trust of our audience. Data journalism is one of the best opportunities for brands to do so. Data-led brand storytelling helps demonstrate company-driven values, builds authority online, and has the power to differentiate brands in a world of AI slop (a term used for shoddy or unwanted AI content). And, with conversational and generative search becoming increasingly popular, original, trustworthy, and authoritative content is more valuable than ever. With data journalism, you can create content that fosters trust with your audience and performs well in both traditional and AI-driven search environments. You just need to stick to the basics— original data research, narrative storytelling, and newsworthy distribution.	2025-06-03T00:00:00	2025/06/03	https://ipullrank.com/journalism-organic-search	[ { "date": "2025/06/03", "position": 66, "query": "AI journalism" } ]
New Publication: "Study published on AI in Science Journalism"	New Publication: “Study published on AI in Science Journalism”	https://www.mscl.de	[ "Claudia Seelmann", ".Wp-Block-Post-Author-Name Box-Sizing Border-Box" ]	New Publication: “Study published on AI in Science Journalism”. June 3, 2025. •. Claudia Seelmann. We are pleased to draw your attention to a recent publication ...	We are pleased to draw your attention to a recent publication by Lars Guenther (LMU Munich), Jessica Kunert (JGU Mainz), and Bernhard Goodwin. They have published two papers about generative AI in German science journalism. They analyzed interviews of science journalists in a variety of different outlets and positions. Looking at all phases of the news-making process (selection, production, distribution), they paint a broad picture of potential consequences and how they are evaluated by journalists. The collaborators built on 30 interviews conducted by graduate students in Munich’s MA journalism program. The work was previously presented at AISCICOMM24 in Zurich. The publications are open access: * Guenther, L., Kunert, J., & Goodwin, B. (2025). My New Colleague, ChatGPT? How German Science Journalists Perceive and Use (Generative) Artificial Intelligence. Journalism Practice, 1–18. https://doi.org/10.1080/17512786.2025.2502794 * Guenther, L., Kunert, J. and Goodwin, B. (2025). “Away from this duty of chronicler and towards the unicorn”: How German science journalists assess their future with (generative) Artificial Intelligence JCOM 24(02), A06. https://doi.org/10.22323/2.24020206	2025-06-03T00:00:00	2025/06/03	https://www.mscl.de/new-publication-study-published-on-ai-in-science-journalism/	[ { "date": "2025/06/03", "position": 70, "query": "AI journalism" } ]
Big tech promised developers productivity gains with AI tools	Big tech promised developers productivity gains with AI tools – now they’re being rendered obsolete	https://www.itpro.com	[ "Ross Kelly", "News", "Analysis Editor", "Social Links Navigation" ]	While AI tools have delivered benefits for software developers, many now find themselves facing layoffs as big tech companies ramp up automation.	Coding tools quickly emerged as one of the key use cases in the generative AI boom, with big tech providers promising marked productivity boosts and reduced workloads. With an AI companion at their side picking up the drudge work, it’s not hard to see why developers would be enthusiastic about them. Research from GitHub last year seemed to back this up. Developers were flocking to AI coding tools and for good reason: they were unlocking significant benefits working with these tools, saving huge amounts of time in their weekly schedules, and speeding up development processes. Initial concerns over the quality of AI-generated code were also dissipating, the study noted, with 90% of US-based respondents reporting an improvement in this regard when using AI tools. All told, the technology heralded a new golden era in the software industry, at least if big tech was to be believed. One of huge productivity boosts and reduced manual toil – and it came at a critical time. Developers were overworked during the pandemic and reported surging levels of burnout and mental health issues. The reality at this point is the complete opposite, and some might now look back to the pandemic-era churn as a fond memory. The slow trickle of AI integration has now become a torrent and developers working on AI tools are now finding themselves rendered obsolete by the very platforms and solutions they’ve built. Get the ITPro daily newsletter Sign up today and you will receive a free copy of our Future Focus 2025 report - the leading guidance on AI, cybersecurity and other IT challenges as per 700+ senior executives Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors Microsoft, for example, announced fresh layoffs in May – its second batch in a matter of months. While tech industry workers are no stranger to layoffs at this stage, this latest round hit differently. Reports from Bloomberg showed a significant portion of the workers set to be cut were operating out of the tech giant’s Redmond headquarters. Moreover, around 40% of these were in software engineering, equivalent to more than 800 roles. For a company that builds software to begin cutting developers and engineers seems counterintuitive. But this is the new normal those in the profession face. Companies feel the need to justify their huge investment in AI by integrating it across the board, making humans surplus to requirements. Microsoft CEO Satya Nadella recently revealed that around 30% of the company’s code is now AI generated . Microsoft isn’t alone in this, either. Last year, Sundar Pichai said more than 25% of Google’s internal source code was also AI generated – that’s likely grown since then. That’s not to say that Google or Microsoft have AI running large parts of the show behind the scenes. Humans are still ‘in the loop’ in some capacity and this code is subject to rigorous scrutiny. But what this does suggest is that the days of expansive software engineering divisions might be coming to an end, with those left after cuts reduced to caretakers monitoring the output of AI tools designed to usurp them. The signs have been there all along It’s not like there haven’t been warning signs for developers in recent years, or for workers spanning a range of professions. Human resources was in the crosshairs from the get-go during the early days of the generative AI boom, with IBM specifically highlighting this as an area ripe for automation . The messaging from some big tech companies has been telling as well. Last year I discussed the fact that companies were quite obviously seeking ways to reduce headcount to compensate for AI investment – just without actually saying it. These efforts to cut workers were veiled under the guise of “focusing on high-growth areas”. That same messaging and approach seems to be applied to specific professions, particularly software development. The moment you hear terms like ‘drudge work’ when referring to core aspects of your role, you should probably start worrying. The same can be said when big tech providers evangelize about your ability to focus on the more “rewarding” aspects of your job, whatever they may be. While attending Salesforce’s Dreamforce conference in San Francisco last year, this was a common recurring term bandied around by figures at the company – and it conveniently came at the start of Salesforce’s big agentic AI push. In the months since, we’ve seen comments from CEO Marc Benioff specifically highlighting the potential for AI tools, particularly their own agentic AI solutions, to take over from human workers. During an appearance on 'The Logan Bartlett Show’ earlier this year, Benioff suggested the company might not need to hire software engineers as a result of AI agents. His comments on this topic echoed those made by Meta CEO Mark Zuckerberg, who told Joe Rogan that the company could also begin replacing engineers with AI within the next year or so. It’s not out of the ordinary for industry figures to make bold claims, after all it justifies their strategy and investment in the technology. But the sheer volume of comments like these – and their consistency – should have alarm bells ringing. Where do developers go now? There have been small glimmers of hope on the horizon for embattled developers and engineers - but these largely focus on the potential for upskilling. Research from Gartner last year showed that while AI will have a significant impact on the workforce, those best prepared to ride out the wave of automation will be the ones who adapt and upskill . Critically, however, the consultancy said 80% of the workforce will need to upskill by 2027 to contend with AI-related skills requirements. With such a large portion of the workforce expected to shake things up with regard to skills, this seems completely unrealistic. We already have casualties recorded at Microsoft, and there will undoubtedly be more.	2025-06-03T00:00:00	2025/06/03	https://www.itpro.com/software/development/ai-tools-software-development-workforce-layoffs	[ { "date": "2025/06/03", "position": 41, "query": "AI layoffs" } ]
AI will cost jobs, lots of them. Stop lying about it.	AI will cost jobs, lots of them. Stop lying about it.	https://telcodr.com	[ "Danielle Rios" ]	The alternative is lying until the last possible moment, then springing Friday afternoon layoffs. That approach creates fear, chaos, and resentment. And any ...	This week at TelecomTV’s DSP Leaders World Forum, the panels will buzz with talk of artificial intelligence (AI)—transformation, innovation, the future of networks. But underneath the excitement, a familiar lie will echo through the halls: “AI won’t take jobs.” Telco execs will use all the usual euphemisms: “We’re augmenting human capabilities.” “This is about efficiency, not elimination.” “Our people are our greatest asset.” They’ll avoid talking about headcount reductions, because no one wants to be That Guy. Bill Lumbergh, aka “That Guy,” from Office Space, 1999. But let’s stop pretending. Every executive in that room has seen the McKinsey decks and run the Goldman Sachs numbers. They know AI will eliminate thousands of jobs—from network operations to customer service to billing. They just haven’t figured out how to say it out loud. Lying to your people about AI’s impact isn’t just dishonest. It’s weak, short-sighted, and counterproductive. So stop doing it. The elephant in the room Let’s look at the facts: What about telco? I believe the impact will be even bigger—easily 40% or more. We’re built on systems, processes, and data: exactly what AI devours. Customer interactions, network monitoring, billing, provisioning, service activation—these are already software-driven and digital. AI can optimize or eliminate them entirely. Even physical infrastructure is increasingly managed through predictive, AI-powered tools. Regulation won’t save jobs. It just adds complexity to how we automate. Everyone inside your company can see what’s coming. They’ve watched Deutsche Telekom’s Frag Magenta chatbot replace agents. They’ve seen AT&T use AI to prevent outages that once required full-time monitoring. They know Telefónica’s automation isn’t “supporting” people—it’s replacing them. So when you get up and pretend AI won’t cost jobs, your people don’t believe you. They know better. And they lose trust in you as a leader. Why lying backfires Since 2009, I’ve worked as a turnaround CEO. And everyone knows that turnarounds start with one thing: firing people. I’ve let go of more than 10,000 employees across the US, UK, France, Germany, Croatia, India, Brazil, Australia, and Japan. I’ve managed transformations under some of the toughest labor laws out there. I’ve seen what happens when you lie to employees to “keep morale high” during change. Leaders convince themselves they need everyone “on board” for transformation, so they sugarcoat reality or delay tough conversations. They think it protects the company. It doesn’t. What really happens is that your best people—the ones you need most—see through your spin. They know what’s coming. And instead of sticking around, they update their LinkedIn profiles, start taking interviews, and move on. The people who stay are naive, checked out, or have nowhere else to go. Congratulations! You’ve just brain-drained your organization. Start with the truth You want to win in the AI era? Stop insulting your employees’ intelligence and tell them the truth. Tell them AI is going to fundamentally change how we operate. Some roles will be eliminated. We’re going to be transparent, give you time, and invest in retraining. Some of you will build careers here in new AI-enabled roles. Others will take these valuable skills elsewhere. Either way, we’ll help you prepare for the future. Sounds terrifying? It’s not. It’s leadership. I’ve told employees their exit dates a year in advance and watched them keep working productively to the very end. Why? Because I treated them like adults. I respected them enough to be honest. And they returned that respect. They trained their replacements. They stayed engaged. They left with dignity—and even gratitude. The alternative is lying until the last possible moment, then springing Friday afternoon layoffs. That approach creates fear, chaos, and resentment. And any strong performers you have left see how you’ve treated this group and assume that’s what’s coming for them. It feels terrible, wrecks your culture, and poisons your transformation. Lead with honesty Here’s how to do it right: 1. Tell it like it is Follow BT’s example—but go further. BT announced that 10,000 roles would be replaced by AI by 2030. That’s a start. Now put specifics behind your roadmap. Who’s affected? When? Help people plan their lives. You’d want that for yourself, wouldn’t you? 2. Start training everyone Don’t wait for AI systems to go live. Launch training programs immediately. Make your people AI-literate. Help them understand how roles and processes will change in the AI future. Start building followership and excitement NOW. 3. Create visible AI champions Highlight the people embracing change. Promote them. Give them new titles. Show everyone that adapting is rewarded. It shows you’re serious, and you’re walking the talk. 4. Use transparency as a retention tool Tell people early. Give them long runways. Respect their time and talent. If you do, they’ll stay and help you transition. I promise. 5. Don’t hide the transformation—involve your people Too many leaders design AI in a lab and roll it out once it’s “ready.” They treat it like a secret project, then spring it on the organization. That’s not leadership. That’s cowardice. You need your workforce to gain experience with how AI is reshaping work. Involve them in pilots. Let them experiment. Use your team as the lab. Because when you hide the change, you miss the opportunity to find your natural AI champions—and when the rollout comes, your people feel blindsided, not empowered. Time to lead Your employees aren’t clueless. They know what AI is doing, and what it means for their jobs. By not saying it out loud, you’re not protecting them. You’re pretending it’s not happening. But the truth doesn’t disappear because you ignore it. Help your team get ready for the inevitable future and start preparing them today. Be transparent. Be honest. Treat people like adults. When you do, something surprising happens: You build trust. You gain credibility. And your transformation moves faster—because everyone understands what it means for them. We are in the middle of one of the biggest technology transformations of our lives. There’s no turning back. Euphemisms won’t soften the blow. Telling the truth is hard. But it’s also what visionary leadership looks like. So stop pretending. And start leading.	2025-06-03T00:00:00	2025/06/03	https://telcodr.com/insights/telco-layoffs-and-ai/	[ { "date": "2025/06/03", "position": 51, "query": "AI layoffs" } ]
AI Could Wipe Out 50% of Entry-Level White Collar Jobs	Anthropic CEO: AI Could Wipe Out 50% of Entry-Level White Collar Jobs	https://www.marketingaiinstitute.com	[]	... layoffs we've been talking about are going to start to compound," he says. A ... Mike is the co-author of Marketing Artificial Intelligence: AI ...	AI might be on track to cure cancer and supercharge economic growth. But first, it may wipe out millions of jobs. That was the warning from Anthropic CEO Dario Amodei, who told Axios this week that AI could eliminate half of all entry-level white-collar roles within five years. And that unemployment could spike to 10-20% as a direct result. On Episode 151 of The Artificial Intelligence Show, I spoke to Marketing AI Institute founder and CEO Paul Roetzer about AI's impact on entry-level work. And what we found was as clear as it was alarming: Most people have no idea what’s coming. And the people who do aren’t saying enough about it. The Calm Before the Storm Amodei’s message is blunt. CEOs will quietly stop hiring, then replace humans with AI the moment it becomes viable to do so, a shift he says could unfold almost overnight. And despite his warnings, he says most government officials, CEOs, and workers are either unaware or unwilling to confront the implications. That contradiction, between the astonishing power of AI and the silence around its fallout, is what drove the conversation this week. Amodei is building the very technology that could displace millions, yet he’s also among the few executives willing to publicly admit what that could mean. "We, as the producers of this technology, have a duty and an obligation to be honest about what is coming," Amodei told Axios. And people are starting to listen. This One Hit a Nerve Roetzer’s own post on LinkedIn, reacting to the Axios piece, exploded: It garnered 151,000 impressions, hundreds of comments, and thoughtful, concerned debate from far outside the usual AI echo chamber. "This was a lot of people that I don't usually see in the comment threads posting thoughts, questions, and concerns about it," says Roetzer. "It just feels that, for some reason, [Amodei] saying this moved the dialogue forward, which I see as a very positive thing." That discussion is already moving beyond early adopters and into family conversations. Parents are increasingly asking Roetzer what career paths make sense for their kids. Students are wondering if degrees in business or computer science will still be valuable. And Roetzer believes that’s just the beginning. "My hypothesis is that we will start to see data emerge through the summer and into the fall that shows AI is having a clear impact on jobs that the quiet AI layoffs we've been talking about are going to start to compound," he says. A Ticking Clock for the Class of 2025 The timing matters. If Roetzer is right, the class of 2025 will hit a very different job market than the one their older siblings entered. And the class of 2026? They could graduate into a full-blown economic crisis. "This is going to be top of mind and core to economic discussions," says Roetzer. And, he notes, mid-2026 comes right in the middle of the start of midterm elections in the US. If unemployment and underemployment surge, the politics of AI could shift dramatically. Already, influential voices like Steve Bannon are predicting that AI's job-killing potential will be a major issue in the 2028 campaign, according to Axios. And it may not just be government that gets involved. Roetzer predicts other institutions—including the Catholic Church—may soon weigh in. "It's going to cross over into a true societal issue very soon," he says. From Conversation to Action Axios didn't just sound the alarm by interviewing Amodei. In a separate article, the company's CEO also offered a roadmap for responsible leadership. At Axios, for instance, managers are required to justify why AI won’t do a job before hiring for it. That's a good step for individuals and companies for individuals and companies to take, too. "Individually, we have to start looking at our own industries, looking at our own companies, being more proactive to prepare," says Roetzer. That also includes having frank conversations at home and in schools, preparing students to navigate a future where AI isn't just a tool—it's the competition. The Bottom Line The class of 2026 could graduate into an economy where AI agents outperform them at entry-level tasks—and where companies quietly stop offering those roles altogether. Axios' CEO goes so far as to tell employees: "You are committing career suicide if you're not aggressively experimenting with AI." It’s a hard truth. But it might be the only thing that gives us a chance to prepare before the job market changes forever.	2025-06-03T00:00:00		https://www.marketingaiinstitute.com/blog/dario-amodei-ai-entry-level-jobs	[ { "date": "2025/06/03", "position": 56, "query": "AI layoffs" } ]
Navigating the AI Skills Gap: Practical Challenges and ...	Navigating the AI Skills Gap: Practical Challenges and Solutions for Leaders	https://hrspotlight.com	[]	As AI and analytics reshape industries, organizations face the urgent task of equipping their workforce with the skills to thrive in this data-driven era.	The fear of replacement is real, and it’s the #1 challenge I see when helping teams adopt AI. The truth is, no tool works unless your people are on board. Right now, the most significant practical challenge across small and medium-sized enterprises isn’t the tool; it’s the trust. AI is moving faster than most employees can mentally process, and without the correct narrative from leadership, it quickly becomes a threat. Here’s the framework we recommend leaders follow to close the fear gap and make AI adoption stick: 1. Hold the first conversation early and make it about value: Don’t wait for the tools to arrive before addressing the elephant in the room. From day one, tell your team, “We’re not replacing you; we’re upskilling you.” Let them know the great staff will always be valued. AI is here to remove repetitive tasks, not humans. 2. Reframe AI as a teammate, not a threat: We call AI a digital assistant, not a system. The language matters. When staff feel like AI is working with them – answering FAQs, handling follow-ups, drafting notes – they stop resisting it. Show them where it saves time, not where it replaces them. 3. Identify and invest in your early adopters: In every company, there’s someone who’s quietly curious. Support them. Train them first and then let them teach others. This builds internal momentum far better than top-down mandates or external consultants alone. 4. Make upskilling part of the culture: Create a culture where learning AI is a badge of honour, like becoming ‘fluent in digital’. You don’t need full technical literacy; you need familiarity and confidence. Normalize this by hosting 30-minute demos, walk-throughs, or mini-workshops 5. Check in often because fear doesn’t vanish, it evolves: Staff need reassurance during rollout, not just before. Create weekly check-ins, anonymous Q&A sessions, or pulse surveys to understand where the resistance lies. Trust builds with communication, not silence. AI isn’t a threat to good people. It’s a multiplier for them. My most practical advice is to build a narrative around value, not fear. Help people build an identity as someone who works well with AI. That’s what’s going to matter most in the next five years.	2025-06-03T00:00:00	2025/06/03	https://hrspotlight.com/navigating-the-ai-skills-gap-practical-challenges-and-solutions-for-leaders/	[ { "date": "2025/06/03", "position": 10, "query": "AI skills gap" } ]
How Much Does AI Really Threaten Entry-Level Jobs?	How Much Does AI Really Threaten Entry-Level Jobs?	https://www.marketingaiinstitute.com	[]	Data appears to back this up. According to Raman's op-ed, since September 2022, the unemployment rate for college grads has jumped 30%—nearly double the rise ...	The AI revolution isn’t coming for the job market—it’s already here. And if you’re just starting your career, it may have already hit. On Episode 151 of The Artificial Intelligence Show, I talked to Marketing AI Institute founder and CEO Paul Roetzer about the quiet unraveling of entry-level work. The catalyst? Major warnings from LinkedIn executives, prominent journalists, and AI industry leaders. Together, their message is impossible to ignore: The bottom rung of the career ladder is breaking. And no one is moving fast enough to fix it. This Is Happening Now The conversation really kicked off with alarming comments from Anthropic CEO Dario Amodei this past week that he thinks AI will eliminate 50% of entry-level white collar jobs in the next 5 years. That kicked off a firestorm of conversation around how much AI really threatens entry-level jobs. And the verdict from several sources? This is already starting to happen. First, a New York Times op-ed from Aneesh Raman, LinkedIn’s Chief Economic Opportunity Officer. Raman doesn’t mince words: Entry-level jobs are disappearing now. AI is already taking on tasks once assigned to junior software developers, paralegals, and retail associates. Data appears to back this up. According to Raman's op-ed, since September 2022, the unemployment rate for college grads has jumped 30%—nearly double the rise among all workers. Gen Z is reporting the lowest workforce confidence of any age group. And in a survey of 3,000 senior executives, 63% admitted they expect AI to take over many of the routine tasks currently handled by entry-level employees. Second, Kevin Roose published a deep-dive in The New York Times. Roose reports that hiring freezes, rising unemployment among new grads, and a wave of AI-first decision-making are already remaking entry-level roles. Some companies are only hiring mid-level engineers, having phased out junior roles entirely. Others are quietly testing AI “virtual workers” to replace entire junior teams. The data, anecdotes, and sentiment all point in the same direction: The job market is shifting underfoot, and entry-level workers are feeling the first ripples. How Fast Is Too Fast? Roetzer argues that this isn’t just about hype or long-term speculation. It’s unfolding in real-time, with real data behind it. The gap between what AI can do and what entry-level employees are being asked to do is narrowing by the week. "I owned a marketing agency for 16 years," he says, and the types of knowledge work that AI can do now is what clients often paid his agency to do. That included both entry-level and sophisticated knowledge work. That fundamental shift in what work looks like doesn’t just disrupt junior roles—it redefines them. And the companies that move fastest aren’t necessarily cutting jobs maliciously. They’re just becoming smarter, leaner, and more efficient by design. Two Paths: AI Native vs. AI Emergent Companies Roetzer sees two kinds of organizations emerging. AI-native companies , like his own company SmarterX, are building from scratch with AI at the core. These teams are small, nimble, and can afford to pay more because they need fewer people. They’re using AI to create roles that wouldn’t exist otherwise. , like his own company SmarterX, are building from scratch with AI at the core. These teams are small, nimble, and can afford to pay more because they need fewer people. They’re using AI to create roles that wouldn’t exist otherwise. AI-emergent companies are the traditional businesses retrofitting AI into existing systems. These firms often aim to grow revenue without growing headcount. Or worse, they shrink staff if demand remains flat. As one executive at EY put it: “I like to think we can double in size with the workforce we have today." That’s not exactly a vision that includes more entry-level hiring. Time to Prepare—Not Panic So what can we do? Roetzer believes this is the moment to move from awareness to action. He's launching a SmarterX Impact Summit series aimed at catalyzing real conversations—and real solutions—around AI’s impact on jobs and education. The goal? Convene educators, economists, business leaders, and policymakers to begin designing a better future of work. Because right now, the entry-level collapse isn’t just a crisis of employment. It’s a crisis of opportunity. Without a clear first step, entire career paths vanish. And the consequences won’t just affect graduates. They’ll reverberate through entire industries, economies, and generations.	2025-06-03T00:00:00		https://www.marketingaiinstitute.com/blog/ai-entry-level-jobs	[ { "date": "2025/06/03", "position": 7, "query": "AI unemployment rate" }, { "date": "2025/06/03", "position": 34, "query": "artificial intelligence employment" } ]
How AI Search is Changing the Job Hunt and What ...	How AI Search is Changing the Job Hunt and What It Means for Recruiters and HR Software Vendors	https://blog.hiringthing.com	[ "Sean Johnson", "Sean Johnson Is A Seasoned Digital Marketer With Over Two Decades Of Experience Helping Brands Grow Through Data-Driven Seo", "Sem", "Content Strategies. As The Digital Marketer At Hiringthing", "Sean Applies His Deep Expertise In Ai", "Analytics", "Paid Search", "Strategic Planning To Scale Visibility", "Optimize Conversion Across Channels. He Was The Founder Of Strategy", "Where He Advised Startups" ]	Nowhere is this more evident than in the domain of job hunting and human resources technology. Platforms like ChatGPT search, Microsoft Copilot, and Google ...	How AI Search is Changing the Job Hunt and What It Means for Recruiters and HR Software Vendors AI search tools like ChatGPT and Gemini are changing how people look for jobs and research employers. Candidates now expect faster, more personalized, and transparent communication from recruiters and employers. Recruiters and HR tech vendors must adapt AI for both end users and job candidates. Companies are scrambling to optimize for candidates using AI search. AI has become deeply embedded in the way we live, work, and search for information. Nowhere is this more evident than in the domain of job hunting and human resources technology. Platforms like ChatGPT search, Microsoft Copilot, and Google Gemini have revolutionized how people access information about careers, employers, and the hiring process. These AI-powered tools are not just influencing job seekers; they're also transforming the expectations and behaviors of recruiters, software buyers, and HR SaaS resellers. Let’s dive into how AI search platforms are reshaping job-seeking behavior, the impact on recruiters and hiring workflows, and the implications for vendors and resellers of HR software. We also delve into trending queries, rising user expectations, and what proactive steps recruiting professionals and HR tech companies can take to stay competitive. The Rise of AI Search Platforms in Job Seeking AI search platforms offer far more than traditional keyword-based search engines. The use of AI tools that provide contextual, conversational search capabilities that allow users to engage in complex information queries is growing at an exponential rate. Rather than typing "best resume format," a user might now ask, "How can I tailor my resume to pass an ATS for a marketing job in healthcare?" This shift marks a major evolution in the way people use search technology. With AI tools capable of understanding nuance, preference, and personal context, job seekers are finding answers that are more aligned with their goals and aspirations. A recent study by 29% of U.S. adults have tried generative AI tools as of early 2024, and among those aged 18 to 34, the adoption rate jumps to 46%. Many of these users are leveraging these tools to research job opportunities, prepare application materials, or gather insight on potential employers. Search Behavior Is Evolving Rapidly The way users frame job-related questions is changing in real time. People are no longer simply browsing job boards. Instead, they are turning to AI platforms to simulate coaching conversations, compare job titles, or prepare for interviews. The types of queries being submitted reflect a deeper, more strategic level of engagement. Examples of rising AI search trends include: "How to beat an ATS" "Is [Company] a good place to work?" "What is a good answer to 'Tell me about yourself'?" "How do I write a resume for a career pivot?" These types of queries show that users want more than information. They want personalized guidance, strategic insights, and answers that take their specific situation into account. Implications for Recruiters and Hiring Teams The increasing reliance on AI tools is reshaping how recruiters must interact with job seekers. It has raised the bar on what candidates expect from employers, and it has introduced new challenges for identifying authenticity and assessing candidate fit. Recruiters Must Prepare for More Informed Candidates Candidates that have used AI search tools are arriving at interviews more prepared than ever. They're practicing answers with ChatGPT, asking Copilot to help them analyze job descriptions, and have used Gemini to identify the strengths and weaknesses of the company they’re applying to. This new level of preparedness can be beneficial, but it also means that recruiters must be more adept at digging below the surface. Applicants may come across as polished and articulate, but that polish might be the result of rehearsing with AI prompts. Recruiters will need to sharpen their interviewing techniques to uncover real-world experience and genuine competencies. Transparency Is Now a Competitive Advantage With AI tools summarizing employee reviews and Reddit discussions, and analyzing company culture from social media content, transparency is no longer optional. Companies with mismatched messaging and internal culture will quickly be exposed, and top talent will move on to better-aligned opportunities. Recruiters and employer branding teams must work together to ensure consistency across all touchpoints, from career pages and social content to employee review sites and candidate communications. Authenticity and alignment will be key to earning candidate trust. Personalization and Response Speed Are Non-Negotiable In an ecosystem where AI tools can help candidates apply to dozens of jobs in a single session, the competition for top talent has intensified. This has created a new expectation among job seekers for fast, personalized communication. Recruiters who rely on manual processes and delayed responses will lose candidates to organizations that provide streamlined, tech-enabled hiring experiences. Investing in automation tools that support personalized messaging and rapid response times is essential. The Growing Influence of AI on HR Software Buyers The influence of AI in the job market is not limited to candidates. Buyers of HR technology are also adopting AI tools to evaluate and select software solutions. Whether it’s an ATS, a workflow management tool for HR, or performance management software, decision-makers are leveraging AI to make more informed purchasing decisions. This behavior is redefining the sales funnel for HR SaaS vendors and resellers. Prospective customers are turning to AI to explore options, compare features, and get recommendations long before they ever engage with a sales representative. HR Buyers Are Asking Smarter Questions Software buyers are using AI to ask specific and strategic questions that go beyond surface-level comparisons. They want real answers, fast. Common queries include: "What are the top ATS platforms for small businesses?" "Which onboarding platforms integrate with ADP and offer white labeling?" "Compare JazzHR and HiringThing for workflow customization" "What are the best HR tech tools for hybrid teams?" These detailed queries mean that buyers are no longer waiting for vendors to present feature sets. Now they are looking at tailored comparisons from AI platforms that understand their needs. Content Must Be AI-Friendly and Structured Because AI tools rely on structured data and machine-readable formats to generate answers, vendors need to go beyond traditional marketing content. Websites, blogs, and comparison pages should use schema markup, bullet lists, FAQs, and clearly labeled features to make content easier for AI tools to parse. If your product information is buried in long paragraphs or poorly optimized tables, it may never be surfaced in AI responses. Clear formatting and accessible data presentation will become core components of AI-driven discoverability. AI Optimization Is the New SEO The landscape of digital visibility is shifting from traditional SEO to AI optimization (AIO). This new focus requires businesses to rethink how they present information. To thrive in this environment, HR tech vendors should: Create content designed to answer natural language questions. Provide structured product comparisons. Regularly audit how their brand appears in AI-generated summaries. Supply tools and integrations that work with AI agents and plugins. AI as the 24-Hour Job Coach AI is not just a tool for searching. It has become an always-on mentor, resume editor, and job coach. From drafting cover letters to preparing elevator pitches, AI is empowering job seekers in unprecedented ways. According to a 2024 report 46% of job seekers who used ChatGPT received interview callbacks, and 78% of those said it helped improve the quality of their resume. This shift is reducing the advantage of access to professional career services and democratizing high-quality job preparation. Candidates from all backgrounds now have tools to help them prepare for interviews, compare roles, and communicate effectively. AI tools are even helping users analyze job offers, estimate salaries, and simulate difficult conversations. This means candidates are entering negotiations better prepared, with data-backed expectations and higher confidence. What Recruiters and HR Tech Providers Must Do Now To remain competitive, recruiters and HR software vendors must align their practices and offerings with the realities of AI-driven behavior. That means staying informed, adopting new technologies, and optimizing for how people actually search, apply, and buy. Key Actions for Recruiters Recruiters should consider the following strategies to stay ahead: Audit job descriptions to ensure they are keyword optimized, inclusive, and clear. to ensure they are keyword optimized, inclusive, and clear. Ask deeper interview questions that move beyond AI-generated responses. that move beyond AI-generated responses. Align brand messaging across all platforms and ensure transparency. across all platforms and ensure transparency. Shorten feedback cycles and personalize communication throughout the process. Key Actions for HR Tech Vendors and Resellers Vendors and resellers must also adapt to meet the expectations of increasingly savvy buyers: Use schema markup and structured data to make job listings and related content for job seekers and end users more AI-friendly. and structured data to make job listings and related content for job seekers and end users more AI-friendly. Offer comparison pages that clearly differentiate your solution. that clearly differentiate your solution. Monitor AI summaries of your platform and actively shape your reputation. of your platform and actively shape your reputation. Educate your team on AIO strategies and incorporate them into your marketing workflows. AI Search Is Just the Beginning The widespread adoption of AI tools for job searchers and HR teams is not a passing trend. It represents a long-term evolution in how people interact with digital content, make decisions, and pursue careers. Job seekers will continue to demand smarter, faster, and more personalized experiences. Recruiters will need to evolve from gatekeepers to strategic partners. And HR tech vendors must rethink how they present their products in a world where AI search is the first point of discovery. Those who adapt early will build credibility, earn trust, and accelerate growth. Those who wait may struggle to catch up in an environment where the rules of engagement are being rewritten in real time. AI is not just changing how people search. It is changing how people think about work. The recruiting and HR software sectors will belong to those who embrace that change today. About HiringThing HiringThing is a modern recruiting, employee onboarding, and workflow management platform as a service that creates seamless talent experiences. Our white label solutions and open API enable HR technology and service providers to offer hiring and onboarding to their clients. Approachable and adaptable, the HiringThing HR platform empowers anyone, anywhere to build their dream team.	2025-06-03T00:00:00		https://blog.hiringthing.com/how-ai-search-is-changing-the-job-hunt-and-what-it-means-for-recruiters-and-hr-software-vendors	[ { "date": "2025/06/03", "position": 66, "query": "ChatGPT employment impact" } ]
Will AI wipe out the first rung of the career ladder?	Will AI wipe out the first rung of the career ladder?	https://www.theguardian.com	[ "Blake Montgomery" ]	The likeliest outcome of AI's impact on entry-level jobs is that companies will reformulate them into something new. The job market may settle somewhere between ...	Hello, and welcome to TechScape. This week, I’m wondering what my first jobs in journalism would have been like had generative AI been around. In other news: Elon Musk leaves a trail of chaos, and influencers are selling the text they fed to AI to make art. AI threatens the job you had after college Generative artificial intelligence may eliminate the job you got with your diploma still in hand, say executives who offered grim assessments of the entry-level job market last week in multiple forums. Dario Amodei, CEO of Anthropic, which makes the multifunctional AI model Claude, told Axios last week that he believes that AI could cut half of all entry-level white-collar jobs and send overall unemployment rocketing to 20% within the next five years. One explanation why an AI company CEO might make such a dire prediction is to hype the capabilities of his product. It’s so powerful that it could eliminate an entire rung of the corporate ladder, he might say, ergo you should buy it, the slogan might go. If your purchasing and hiring habits follow his line of thinking, then you buy Amodei’s product to stay ahead of the curve of the job-cutting scythe. It is telling that Amodei made these remarks the same week that his company unveiled a new version of Claude in which the CEO claimed that the bot could code unassisted for several hours. OpenAI’s CEO, Sam Altman, has followed a similar playbook. However, others less directly involved in the creation of AI are echoing Amodei’s warning. Steve Bannon, former Trump administration official and current influential Maga podcaster, agreed with Amodei and said that automated jobs would be a major issue in the 2028 US presidential election. The Washington Post reported in March that more than a quarter of all computer programming jobs in the US vanished in the past two years, citing the inflection point of the downturn as the release of ChatGPT in late 2022. Days before Amodei’s remarks were published, an executive at LinkedIn offered similarly grim prognostications based on the social network’s data in a New York Times essay headlined “I see the bottom rung of the career ladder breaking”. “There are growing signs that artificial intelligence poses a real threat to a substantial number of the jobs that normally serve as the first step for each new generation of young workers,” wrote Aneesh Raman, chief economic opportunity officer at LinkedIn. The US Federal Reserve published observations on the job market for recent college graduates in the first quarter of 2025 that do not inspire hope. The agency’s report reads: “The labor market for recent college graduates deteriorated noticeably in the first quarter of 2025. The unemployment rate jumped to 5.8% – the highest reading since 2021 – and the underemployment rate rose sharply to 41.2%.” The Fed did not attribute the deterioration to a specific cause. The likeliest outcome of AI’s impact on entry-level jobs is that companies will reformulate them into something new. The job market may settle somewhere between Amodei’s AI Ragnarok and the antediluvian days before ChatGPT. Familiarity with AI will be required in the way that Microsoft Office has, and bosses will demand a higher standard of productivity. If a robot can do most of the coding for you, a junior software engineer, then you should be producing five times the amount of code as before, they may say. Speaking of Microsoft and software engineers, CEO Satya Nadella claimed in late April that AI writes 30% of Microsoft’s code. That may be the future of software development. It is possible that is true; it is also possible that Nadella, head of the company that has reaped enormous gains from the AI boom, is trying to sell by example, overestimating how much of that code is usable. Meta’s Mark Zuckerberg has been more pointed in his assessments, asserting that his company will no longer need mid-level coders by the end of 2025. Shortly after, Meta announced a 5% staff reduction. View image in fullscreen Mark Zuckerberg, the head of Meta, last year. Photograph: Manuel Orbegozo/Reuters The short-term readjustment, however, is the pain point. Recent classes have graduated without AI being an integral part of their school life, and employers won’t believe those interim graduates have the necessary familiarity for a new professional landscape. That is not the fault of the graduates: employers themselves don’t know what they want yet from AI. Axios followed up Amodei’s doom and gloom with a piece about how AI job cuts are jumping the gun. Companies are not replacing departing workers, betting that AI will be able to perform the same functions, if not now, then hopefully by the time it would take to hire replacements. The example of journalism may be a canary in the coalmine. Entry-level jobs in journalism often involve aggregating news items from other outlets in the style of your own employer, a task AI is well suited to if the facts are straight. I spent several years doing just that when I started out. In the same way that we see Amodei’s predictions taking shape in LinkedIn’s data, I see the entry-level diminishment beginning in my own industry. Business Insider, a digital outlet focused on financial and business news, laid off 20% of its staff late last week. CEO Barbara Peng said the newsroom would go “all-in on AI” and become “AI-first” in her note eliminating the jobs. Axios itself footnoted its Amodei interview with a disclosure about its own practices with regard to AI. “At Axios, we ask our managers to explain why AI won’t be doing a specific job before green-lighting its approval. (Axios stories are always written and edited by humans),” the disclosure reads. The parenthetical indicates that Axios editors know that AI’s involvement in writing is bad for the brand. The part said outside the parentheses indicates Axios executives may not be backfilling vacated jobs, waiting for AI to catch up and close those openings. The week in AI Musk leaves behind a sticky trail like a slug View image in fullscreen Musk at the White House in April. Photograph: Bloomberg/Getty Images Elon Musk announced he would leave the White House last week, ending a contentious and generally unpopular run as a senior adviser to the president and de facto head of the so-called “department of government efficiency” (Doge). Donald Trump hosted a press conference for his departure, the same day that the New York Times reported that Musk had heavily used drugs on the campaign trail. My colleague Nick Robins-Early assessed the mess Musk leaves in his wake: As Musk moves on, he consigns a mess of half-realized plans and gutted agencies to his acolytes installed in key positions across the federal government. His departure throws Doge’s already chaotic impact on the government into an even grayer limbo, with questions over how much power the nebulous taskforce will have without him and who, if anyone, might rebuild the programs and services it destroyed. Musk’s initial pitch for Doge was to save $2tn from the budget by rooting out rampant waste and fraud, as well as to conduct an overhaul of government software that would modernize how federal agencies operate. Doge so far has claimed to cut about $140bn from the budget – although its “wall of receipts” is notorious for containing errors that overestimate its savings. Donald Trump’s new tax bill, though not part of Doge and opposed by Musk, is also expected to add $2.3tn to the deficit, nullifying any savings Doge may have achieved. Its promises of a new, modernized software have frequently been limited to artificial intelligence (AI) chatbots – some of which were already in the works under the Biden administration. The greater impact of Doge has instead been its dismantling of government services and humanitarian aid. Doge’s cuts have targeted a swath of agencies such as the National Oceanic and Atmospheric Administration, which handles weather and natural disaster forecasting, and plunged others such as the Department of Veterans Affairs into crises. Numerous smaller agencies, such as one that coordinates policy on homelessness, have been in effect shut down. Doge has brought several bureaus to their knees, with no clear plan of whether the staff Musk leaves behind will try to update or maintain their services or simply shut them off. As Musk returns to Tesla and SpaceX, the agencies he laid waste to are left to pick up the pieces. skip past newsletter promotion Sign up to TechScape Free weekly newsletter A weekly dive in to how technology is shaping our lives Enter your email address Sign up Privacy Notice: Newsletters may contain info about charities, online ads, and content funded by outside parties. For more information see our Newsletters may contain info about charities, online ads, and content funded by outside parties. For more information see our Privacy Policy . We use Google reCaptcha to protect our website and the Google Privacy Policy and Terms of Service apply. after newsletter promotion While Musk is returning to his tech empire, many of the former employees and inexperienced young engineers whom he hired to work for Doge are set to remain part of the government. One of the largest questions about what Doge’s future looks like is whether these staffers, some of whom gained near unfettered access to the government’s most sensitive data, will retain the same powers they enjoyed under Musk. Read a timeline of Musk’s stint in Washington. A mess of misinformation Influencers are selling the text that prompted AI to generate their art View image in fullscreen A ChatGPT logo on a keyboard. Photograph: Jaque Silva/NurPhoto/Rex/Shutterstock Would you buy instructions for ChatGPT? Two weeks ago, the Instagram account @voidstomper, which posts grotesque videos generated by AI to 2.2m followers, offered a novel kind of sale. Up for grabs were 10 prompts used to prod AI-powered engines into generating the videos that the account itself had posted. Voidstomper posted a video captioned: “I didn’t want to sell these. But I’m broke and these still go viral. So here: 10 raw horror prompts straight from my archive. Some got me hundreds of millions of views. Some barely make sense. All of them work. Use them in any AI video tool. Just don’t pretend you wrote them. VAULT_DUMP_1 is live. Link in bio. No refunds. You’re on your own.” The account’s administrator did not respond to a request for an interview. The account is not alone. Marketplaces exist for the sale of AI prompts. Ben Stokes, the founder of the PromptBase, says there are about 20,000 sellers on his site hawking prompts, with thousands sold monthly and, to date, seven figures paid out to their writers since 2022. He said social media influencers and other content creators selling their prompts is “still quite niche” and serves as a side hustle for graphic designers, artists and photographers. Voidstomper put prompts for sale that had been used to create specific videos. The product a user might receive when purchasing a prompt on PromptBase is likely to be closer to a generalized template than a finite sentence, Stokes said. “For example, if the prompt that creates posters of famous landmarks in a vintage style, there’d be sections within the prompt in square brackets like [LANDMARK NAME] that you could change to the landmark you’d like to create a poster for, like the local pier in your town,” he said. Why buy a string of text when you could type out your own, though? “There is a specific group of people who are looking for high-quality, robust prompts for their business applications. Specifically, they are looking to integrate AI into their product or workflow, which usually requires a prompt, and want to ensure it works well and produces great consistent outputs,” said Stokes. Though the general public thinks of ChatGPT as free, running enough generations to obtain specific and correct outputs can be quite expensive for businesses, he added. It may be more cost-effective to buy a prompt. Even within the niche of AI-generated art, some consider the sale of prompts ridiculous. The Instagram and TikToker HolyFool36, an AI art Instagram account that has been interviewed in this newsletter before, said he would never engage in the practice. “Frankly, I find it as an insult to my sensibilities,” he said via email. “Generative AI requires no skill – almost anyone can figure out how to reverse engineer his prompts for free. “I personally know Void Stomper and have had many interactions with him. I’ve explained that the best way to monetize this stuff is to build a brand and then sell real tangible products within that brand. He has clearly, directly, stated that he does not have the discipline to do that. I don’t judge him, everyone’s gotta pay rent somehow, I’d just never go about it that way personally,” he added. The wider TechScape	2025-06-02T00:00:00	2025/06/02	https://www.theguardian.com/global/2025/jun/02/artificial-intelligence-jobs-techscape	[ { "date": "2025/06/03", "position": 88, "query": "ChatGPT employment impact" }, { "date": "2025/06/03", "position": 82, "query": "artificial intelligence employment" } ]
ChatGPT for business. How to use it?	ChatGPT for business. How to use it?	https://addepto.com	[ "Edwin Lisowski" ]	In summary, while ChatGPT and AI technologies have the potential to automate tasks and change the nature of certain jobs, they are unlikely to replace jobs ...	81% of business executives consider AI a mainstream technology in their businesses. A Gartner report also estimates that customer satisfaction may grow by 25% in 2023 in organizations that use AI [1]. The relationship between businesses and AI technologies is poised to increase, especially with the release of new technologies and revolutionary chatbots like ChatGPT that can generate human-like text in a conversational context. This unique ability makes ChatGPT potentially useful in a wide variety of business applications, especially when automating interactions with customers. Read on as we explore the various ways you can use ChatGPT for business and the benefits it presents. Addepto introduces a game-changing AI Text Analysis Tool to supercharge your document analysis efforts. What is ChatGPT? ChatGPT is a large language model developed by OpenAI. The model is based on GPT-3 and uses Natural Language Processing (NLP)[2]. It is trained to understand and respond to user input conversationally. As a pre-trained language model, ChatGPT relies on a huge database of training data to understand the structure and pattern of natural language. The result is better and more coherent responses that enable it to generate a wide variety of texts, including articles, screenplays, stories, and more. It can also be used to generate code and other technical documentation [3]. Due to these natural language processing capabilities, companies are now considering using ChatGPT in businesses (especially for business applications such as content marketing, customer service, and personalization, among others). If you are interested to know more about this AI-driven technology, we encourage you to download a new eBook prepared by our AI experts! ChatGPT for business: benefits Businesses are continuously looking for effective and cost-effective ways to improve their product offering, service delivery, and scalability. To this effect, 91% of leading businesses invest in AI on an ongoing basis due to its ability to automate tasks and provide valuable insights [4]. Unlike other chatbots on the market, ChatGPT offers added benefits that cut across all areas of business – not just customer service. Here are some of the most notable benefits of using ChatGPT in the business: ChatGPT for businesses: improved productivity The average employee is only productive for 60% of their work day. Productivity levels are even worse for office workers, who are barely productive for two and a half hours a day[5]. This means that any business that wants to stay competitive must figure out better ways to keep its employees engaged and productive. One of the best ways to improve productivity in any business is through automation. Using ChatGPT in business can automate a wide variety of tasks, freeing up your employee’s time to focus on more intricate, strategic work. For instance, ChatGPT can generate reports, handle customer complaints, and even create content marketing materials such as email campaigns and social media posts. ChatGPT can also help keep employees engaged by automating routine tasks such as content creation, report generation, translation, and data analysis. The result is more engaged employees who stay focused for longer and get more work done. ChatGPT for businesses: marketing and lead generation In today’s fast-paced digital landscape, businesses are constantly seeking innovative ways to stay competitive and engage with their customers effectively. ChatGPT for marketing, powered by artificial intelligence and natural language processing, has emerged as a game-changer for marketing and lead generation strategies. Let’s delve into how ChatGPT for marketing can transform these crucial aspects of your business operations. Some of the biggest challenges content marketers face are getting ideas for new content, creating content that generates leads, and creating engaging content that resonates with their target audience [6]. ChatGPT for marketing can help content marketers alleviate some of these issues. For starters, content marketers can use the language model to generate marketing content such as product descriptions, and email newsletters. As a machine learning model, ChatGPT can learn the style and tone of a business’s marketing materials and generate new content that is consistent with the business’s brand and voice. Another effective way of using ChatGPT for marketing is using it to provide personalized content and recommendations. The language model can effectively analyze user data and behavior to generate personalized content that is not only relevant but also engaging. You can also use ChatGPT in business by incorporating it into your chatbots to engage visitors and help them find the information they need. One of the primary benefits of using ChatGPT for marketing strategy is its round-the-clock availability. It never sleeps, ensuring that potential customers can get information, assistance, and engage with your business at any time. While they’re at it, they can use ChatGPT’s capabilities to include a preset questionnaire to persuade site visitors to lead generation, which ultimately leads to higher conversion rates. Furthermore, ChatGPT for marketing can be seamlessly integrated into various communication channels, including websites, social media, and messaging apps. This ensures a consistent and cohesive brand presence across different platforms, making it easier for customers to engage with your business. ChatGPT for businesses: improved customer service The natural language processing capabilities of ChatGPT allow it to understand and respond to customer requests naturally and conversationally – almost as well as a human would. The effect is improved customer experience and loyalty. ChatGPT can also handle a large volume of customer requests simultaneously, thus allowing businesses to scale their customer service operations without necessarily having to add additional staff. This can greatly reduce response time and improve overall efficiency. You can also use ChatGPT in business to gather and process customer data so they can get insights into customer needs and preferences. The collected data can also be used to improve a company’s products, services, and overall customer experience. ChatGPT for businesses: scalability Scalability is a crucial aspect of any business, especially for those that are experiencing rapid growth. As a business expands, it needs to be able to accommodate an increasing volume of customer queries and transactions without compromising the quality of its customer service. This is where ChatGPT can play a significant role in helping businesses to scale. ChatGPT is highly scalable, meaning that it can handle a large volume of queries simultaneously. This makes it an ideal solution for businesses that are growing rapidly and need to scale their customer service capabilities. As more customers interact with the ChatGPT system, the system can adapt and handle the additional workload without the need for additional human staff. How to use ChatGPT for businesses and not only For personal use Using ChatGPT for business is pretty straightforward, thanks to its minimalistic interface. ChatGPT works much like a regular messaging app, where you type in text and press enter. The only difference is that instead of sending a message, ChatGPT answers the question. The language model is optimized for dialogue, which means that you don’t have to key in long blocks of text each time you have a follow-up question. You can simply ‘speak’ casually to it, and it will follow along with the conversation. All follow-up questions are typically added to the conversation thread, which the system can look back on. That said, you’ll need to refresh the chat with the ‘new chat’ button at the top left of the page to erase the details of your previous conversation when starting a new one. Below, is a comprehensive list on how to effectively use ChatGPT in various contexts, including professional applications and personal use: Assist with personal content creation , like writing essays, creating poetry, or even drafting creative fiction. , like writing essays, creating poetry, or even drafting creative fiction. For personal research or academic purposes , ChatGPT can assist in summarizing articles, reports, or even answering specific research questions. , ChatGPT can assist in summarizing articles, reports, or even answering specific research questions. ChatGPT can help you craft well-structured emails , improve communication, and even draft polite responses to personal messages. , improve communication, and even draft polite responses to personal messages. Quick translations while traveling, learning a new language, or communicating with friends and family who speak different languages. while traveling, learning a new language, or communicating with friends and family who speak different languages. Analyze personal data , such as finances, fitness, or personal project metrics, to gain better insights into your own life. , such as finances, fitness, or personal project metrics, to gain better insights into your own life. For leisure, ChatGPT can generate jokes, riddles, or even engage in casual conversation to keep you entertained. ChatGPT for web developers Although it’s not an absolute requirement, marketers need to learn to code. Coding skills make marketers better and more effective at their jobs. ChatGPT can be very helpful when creating small pieces of code or debugging a set of code to remove errors. Unlike with personal use, developers need to sign up for an OpenAI API key that gives them access to the model, so they can use it in their applications. As a developer, you can install and set up ChatGPT in your system through the following steps: Visit the OpenAI website and create a free account Browse through the selection of API keys on the API page to generate a new key Copy and store the API key, so you can access the model whenever you need to If, by any chance, you’re coding using Python programming language, you have to install the OpenAI Python Package to gain access to the model from the Python code. ChatGPT for content marketing Many content marketers are already using ChatGPT for content marketing. The model’s ability to generate compelling content marketing copy in the form of articles, email campaigns, and even newsletters is making it a game-changer in the content marketing industry. With that said, despite being one of the largest and most effective language models in the world, ChatGPT has a few limitations, particularly around being excessively wordy, overusing certain phrases, and providing the same response to multiple users for the same query, thus lacking personalization. Therefore, to effectively use ChatGPT for content marketing, content marketers need to employ their skills to provide domain knowledge, ask good questions, and review created text. Here’s how content marketers can set up ChatGPT for businesses: Visit the OpenAI website and sign up for an account Configure your preferred ChatGPT settings, including its tone, language, and response time Integrate the model into your social media channels or website chat functions Input specific prompts or queries and let the model generate a response Review and edit the response as needed to ensure relevance and accuracy Repeat the process as needed to assist with content creation, customer queries, and information-gathering tasks Final thoughts on ChatGPT for businesses ChatGPT is a game-changer in the world of business. The revolutionary technology can improve nearly all aspects of business, including streamlining customer service, lead generation, and even generating marketing content. Additionally, due to its ability to adapt and learn from its interactions, ChatGPT in businesses is a valuable asset for any organization looking to enhance user experience, boost productivity, and ultimately stay competitive – all the while without breaking the bank with overwhelming overhead costs. If you’re interested in incorporating ChatGPT for business or other generative AI models into your business or organization, make sure to check out our generative AI development services. Don’t hesitate to reach out and explore the possibilities of what generative AI can do for you. By utilizing the power of ChatGPT and other generative AI technologies, you can take your organization to the next level and stay ahead of the curve in this fast-paced and ever-evolving technological landscape. So why not start exploring the possibilities today? Will ChatGPT replace jobs? In recent years, there has been a significant increase in the development of artificial intelligence (AI) technologies, and many people are starting to wonder if these technologies will eventually replace human workers. ChatGPT is a powerful tool that can understand and generate human-like responses to text inputs. It has been trained on vast amounts of data and has the ability to learn and adapt to new information quickly. As a result, it has the potential to revolutionize the way we interact with technology and each other. However, the question remains: will ChatGPT replace jobs? The short answer is no. While ChatGPT for business is a powerful tool, it is still just that – a tool. It is designed to assist humans, not replace them. ChatGPT is not capable of performing all the tasks that humans can do. It lacks empathy, creativity, and critical thinking skills, which are essential for many jobs. ChatGPT is best suited for tasks that involve repetitive, data-driven processes, such as customer service inquiries or data analysis. In conclusion, while ChatGPT is a powerful tool that has the potential to transform many industries, it is unlikely to replace human jobs. Instead, it will create new opportunities and improve efficiency in many industries. Here are some key points to consider regarding the impact of ChatGPT on employment: Task Automation ChatGPT and AI systems, in general, can automate routine, repetitive, and data-driven tasks. This can lead to increased efficiency and reduced labor costs in industries where these tasks are prevalent. ChatGPT and AI systems, in general, can automate routine, repetitive, and data-driven tasks. This can lead to increased efficiency and reduced labor costs in industries where these tasks are prevalent. Job Transformation AI is more likely to transform jobs rather than replace them entirely. Workers may find themselves working alongside AI tools to perform tasks more efficiently. This shift can require upskilling and adapting to new tools and technologies. AI is more likely to transform jobs rather than replace them entirely. Workers may find themselves working alongside AI tools to perform tasks more efficiently. This shift can require upskilling and adapting to new tools and technologies. Improved Decision-Making AI can provide valuable insights and assist with decision-making by processing vast amounts of data quickly and accurately. This can lead to better-informed decisions across various industries, ultimately improving business performance and potentially creating opportunities for job growth. AI can provide valuable insights and assist with decision-making by processing vast amounts of data quickly and accurately. This can lead to better-informed decisions across various industries, ultimately improving business performance and potentially creating opportunities for job growth. Human-AI Collaboration Human-AI collaboration is increasingly common in fields like healthcare, finance, and manufacturing. AI can assist human professionals by handling data analysis, but the human touch remains crucial for complex decision-making, empathy, creativity, and ethics. In summary, while ChatGPT and AI technologies have the potential to automate tasks and change the nature of certain jobs, they are unlikely to replace jobs entirely. The impact of AI on employment will depend on various factors, including the industry, the adaptability of the workforce, the ethical use of AI, and the development of new roles related to AI technology. Rather than being a sole job disruptor, AI is more likely to act as a tool that complements and augments human capabilities, ultimately leading to greater productivity and efficiency in the workforce. ChatGPT for Business – How are Companies Using It? FAQ What is ChatGPT and how does it work? ChatGPT is a large language model developed by OpenAI based on GPT-3, utilizing Natural Language Processing (NLP). It is trained to understand and respond to user input conversationally, drawing from a vast database of training data to generate coherent responses. ChatGPT can be used to generate various types of text, including articles, screenplays, stories, and even technical documentation. How can businesses benefit from using ChatGPT? ChatGPT offers numerous benefits for businesses across various areas, including improved productivity, marketing and lead generation, enhanced customer service, and scalability. By automating tasks, generating marketing content, handling customer inquiries, and scaling customer service operations, ChatGPT can streamline business processes, boost efficiency, and improve customer satisfaction. Will ChatGPT replace human jobs? While ChatGPT and similar AI technologies have the potential to automate certain tasks, they are unlikely to replace human jobs entirely. Instead, they are more likely to transform job roles, create new opportunities, and improve efficiency. Human-AI collaboration is increasingly common, with AI assisting human professionals in tasks such as data analysis and decision-making. How can businesses effectively implement ChatGPT? Implementing ChatGPT for business use involves configuring the model’s settings, integrating it into communication channels such as websites and social media, inputting specific prompts or queries, and reviewing and editing generated responses as needed. Content marketers, web developers, and other professionals can leverage ChatGPT’s capabilities to streamline processes and enhance their business operations. Are there tools available to distinguish between AI-generated content and human creativity? Yes, there are various approaches and tools that can help differentiate between AI-generated content and human creativity, although none are foolproof. Methods include analyzing complexity and originality, assessing emotional depth and personal expression, examining contextual understanding, and utilizing forensic techniques. However, these tools may require constant refinement to keep pace with evolving AI capabilities. How can ChatGPT be integrated with existing business systems? Integrating ChatGPT with existing business systems involves several steps, including configuring the model’s settings, selecting appropriate communication channels, and implementing APIs or plugins to enable seamless interaction. Businesses may also need to train employees on using ChatGPT effectively within their workflow and ensure compatibility with existing software and platforms. Are there any legal limitations associated with using ChatGPT for business? While ChatGPT offers numerous benefits for businesses, there are legal considerations to be aware of, including data privacy and intellectual property rights. Businesses must ensure compliance with relevant regulations such as GDPR and CCPA when handling customer data. Additionally, using ChatGPT to generate content may raise copyright issues if not properly attributed or if it infringes on existing copyrights. What are some alternatives to ChatGPT for business? While ChatGPT is a powerful tool for businesses, there are several alternatives available, each with its own strengths and limitations. Some popular alternatives include IBM Watson, Amazon Lex, and Microsoft Azure Bot Service. These platforms offer similar natural language processing capabilities and can be tailored to suit specific business needs. Businesses should evaluate each option based on factors such as cost, scalability, and integration capabilities before making a decision. Editor’s note: This article was originally published on January 16th, 2023. It has since been revised and updated on October 12th, 2023 to reflect the latest information available References [1] Gartner.com. Top 10 trends in Digital Commerce. https://www.gartner.com/smarterwithgartner/top-10-trends-in-digital-commerce, Accessed January 11, 2023 [2] IBm.com. Natural Language Processing. URL: https://ibm.co/3IR1SrX. Accessed January 11, 2023 [3] Towardsdatascience.com. Pre-trained Language models Simplified. URL: https://towardsdatascience.com/pre-trained-language-models-simplified-b8ec80c62217. Accessed January 11, 2023 [4] Businesswire.com. 2020 Data and AI Executive Survey. URL: https://bwnews.pr/3QHpKA6. Accessed January 11, 2023 [5] Voucercloud.com. Office Worker Productivity. URL: https://www.vouchercloud.com/resources/office-worker-productivity. Accessed January 11, 2023 [6] Hubspot.com. Hubspot blog Marketing Industry Trends. URL: https://bit.ly/3ZI837B. Accessed January 11, 2023	2024-04-08T00:00:00	2024/04/08	https://addepto.com/blog/how-can-you-use-chatgpt-in-business/	[ { "date": "2025/06/03", "position": 91, "query": "ChatGPT employment impact" } ]
Educating Tomorrow's Tech Workforce: A New Map for AI- ...	Educating Tomorrow’s Tech Workforce: A New Map for AI-Era Skills	https://blogs.cisco.com	[ "Giuseppe Cinque" ]	Our research focuses on nine high-demand, entry-level ICT jobs, revisiting and expanding insights from the Consortium's broader study.	What happens when AI doesn’t replace jobs, but fundamentally transforms how they’re performed? This is the reality now facing the global technology workforce. While generative AI (GenAI) continues making headlines for its disruptive potential, our research reveals a more nuanced story: one of transformation rather than wholesale replacement. At Cisco, we recognized the urgent need to understand these changes at a granular level. Building upon the foundational work done within the AI-Enabled ICT Workforce Consortium—a coalition led by Cisco and nine other ICT industry leaders—Cisco Networking Academy has partnered with Lightcast to release a new white paper specifically designed for educators: “Educating Tomorrow’s ICT Workforce: The Role of Generative AI Skills in Entry-Level ICT Roles.” How generative AI is reshaping entry-level IT roles Our research focuses on nine high-demand, entry-level ICT jobs, revisiting and expanding insights from the Consortium’s broader study to address the specific needs of instructors and educators. Beyond analyzing AI’s impact, it provides a comprehensive methodology for forecasting how AI technologies will transform specific job roles—a crucial tool for educational planning in this rapidly evolving landscape. The paper examines the following job roles to identify how GenAI is reshaping skill requirements and task allocation: Cybersecurity Analyst Ethical Hacker SOC Analyst – Level 1 Network and IT Automation Engineer Network Support Technician Network Administrator IT Support Specialist Data Analyst Python Developer This white paper builds on broader research from the AI Workforce Enablement Consortium, which previously analyzed 47 jobs across seven job families ranging from business and cybersecurity to infrastructure and software. From roles to tasks—a more precise understanding of AI’s impact Rather than analyzing these job titles in isolation, our research breaks each role into discrete tasks and evaluates which are likely to be automated, which will be augmented by AI, and which remain largely unchanged. This task-level approach provides greater insights into how jobs may evolve. Low-risk, repetitive tasks—like documentation or data cleaning—are increasingly being delegated to machines. Meanwhile, high-risk or human-centered tasks—those requiring sound judgment or interpersonal skills—are more likely to be augmented rather than replaced. As a result, workers must shift focus from pure execution to defining problems, delegating appropriate tasks to AI, verifying outputs, and maintaining accountability for outcomes. This transition demands a workforce that is fluent not just in the specific technology and task, but also in how to collaborate effectively with intelligent systems on the task. Building upon this task-level mapping, once we have established which skills support specific tasks, we can extend the impact analysis to the skills themselves. This deeper analysis allows us to identify which skills will become more or less relevant and highlights new skills that will become indispensable in an AI-driven work environment, informing the evolution of educational programs. What’s actually changing? Role-specific transformations Our analysis reveals varying degrees of AI exposure across the nine roles studied. The percentage of principal skills exposed to AI (through either augmentation or automation) ranges from as low as 5 percent to as high as 73 percent, depending on the specific role. This exposure analysis provides a much more nuanced view than simply categorizing jobs as “safe” or “at risk.” The nature of these changes varies significantly by role: Software-oriented roles like Python developers and data analysts will see time-consuming tasks—writing test cases, cleaning data, and documenting processes—increasingly automated. These changes free workers to focus on more strategic, creative work. Network automation specialists can leverage generative AI tools to automatically produce scripts, detect anomalies, predict outages, and streamline routine tasks. Specialists remain crucial, however, by guiding implementations and validating outputs through a human-in-the-loop approach, ensuring accuracy and reliability. Technician roles in hardware and support remain relatively stable for now. Their hands-on, user-facing nature makes them less susceptible to full automation—at least until embodied AI (artificial intelligence systems that are integrated into humanoid robots) becomes more prevalent. These transformations don’t signal job elimination—they reflect role evolution. Workers aren’t becoming obsolete; they’re being released from routine tasks and called to take on more analytical, integrative, and human-centered responsibilities. Insights for educators The research aims to equip educators with knowledge, including a framework for analyzing how GenAI will impact job roles and skills. Based on these findings, high-level recommendations for instructors preparing students for these roles include: Equip students with core professional skills. Integrate AI literacy across all roles training programs. Teach both the why and how of work so students understand the reasoning behind their work, know how to define the task to be done to an AI, and what to verify in the output of the work product done by an AI. Prioritize developing skills in responsible AI and ethics. In addition to the 50+ page report, we also provide Cisco Networking Academy instructors with a companion web page outlining specific training recommendations for each role, along with resources to train and upskill themselves and their students. The time to act is now The pace of change continues to accelerate. Within three to five years, GenAI is expected to be deeply embedded in standard work processes. But it won’t replace people—it will amplify their capabilities. For educators, this means preparing students to use AI tools, understand them, question them, and work alongside them. Technical skills alone are not sufficient. It is more important than ever to cultivate the judgment, communication, and leadership abilities that will matter most in hybrid human-machine environments. We’ve entered a new era—one that rewards learning agility, a growth mindset, and a proactive approach to lifelong learning. Educators who adapt their curricula now will ensure their students remain competitive and excel in an AI-integrated workplace. Get the white paper Sign up for Cisco U. \| Join the Cisco Learning Network today for free. Learn with Cisco X \| Threads \| Facebook \| LinkedIn \| Instagram \| YouTube Use #CiscoU and #CiscoCert to join the conversation. Share:	2025-06-03T00:00:00	2025/06/03	https://blogs.cisco.com/learning/educating-tomorrows-tech-workforce-a-new-map-for-ai-era-skills	[ { "date": "2025/06/03", "position": 98, "query": "artificial intelligence employment" }, { "date": "2025/06/03", "position": 89, "query": "future of work AI" }, { "date": "2025/06/03", "position": 10, "query": "machine learning workforce" } ]
Top 6 AI Tools for Modern Journalists	Top 6 AI Tools for Modern Journalists: A Comprehensive Guide	https://murf.ai	[ "Supriya Sharma", "Supriya Is A Content Marketing Manager At Murf Ai", "Specializing In Crafting Ai-Driven Strategies That Connect Learning", "Development Professionals With Innovative Text-To-Speech Solutions. With Over Six Years Of Experience In Content Creation", "Campaign Management", "Supriya Blends Creativity", "Data-Driven Insights To Drive Engagement", "Growth In The Saas Space." ]	Artificial intelligence has become a critical tool for journalists in the newsroom. Such is the momentum of the AI movement that the generative AI market is ...	Artificial intelligence has become a critical tool for journalists in the newsroom. Such is the momentum of the AI movement that the generative AI market is estimated to reach USD 109.37 billion by 2030. Artificial intelligence in journalism has many applications. AI tools empower journalists to: Analyze mountains worth of information in a fraction of the time. Transform the way news is reported, written, and consumed. Fact-check, translate, and distribute news. Automate repetitive tasks and free up time to focus on more meaningful work. Access accurate, real-time data on breaking news media stories. Generate engaging content that resonates with readers. ‍ At its core, AI is enabling journalists to work faster, smarter, and better. In this guide, we will learn about six indispensable AI tools that can be every journalist's trusted sidekick. Let's dive in. Benefits of Artificial Intelligence in Journalism AI tools are making a significant impact on journalism, revolutionizing the way news publishers produce content and the way readers consume content. Here's how it helps journalists to deliver more trustworthy, engaging stories to the audience: Benefit #1: Personalization By leveraging machine learning algorithms, AI can understand readers' interests, browsing history, and engagement patterns to deliver tailored news updates. Readers can stay updated on relevant content easily without sifting through irrelevant articles. This personalized approach enhances the user experience, increases engagement, and helps journalists build a loyal readership. Benefit #2: Fact-Checking In the era of misinformation and "fake news," fact-checking has become essential to journalism. AI algorithms and data analysis can enable journalists to: Track down and isolate false or misleading information. Detect inconsistencies, biased language, or inaccuracies in the text. Verify claims and access precise information to maintain credibility and set high standards for reporting. ‍ Benefit #3: Automated Reporting Journalists can input structured data or insights, and AI algorithms can generate content and human-like text without human intervention. Using natural language processing, AI helps: Produce articles that are timely, informative, and free of bias. Automate reporting, ultimately saving time, reducing errors, and increasing newsroom efficiency. Streamline the news production process and enable journalists to focus on more investigative or analytical tasks. Benefit #4: Language Translation Journalism often involves covering stories from around the world, making language translation crucial. AI tools equipped with machine translation capabilities can assist journalists in: Overcoming language barriers by automatically translating articles, interviews, social media posts, interviews, or reports from one language to another. Accessing and understanding localized information instantly from various sources. Fostering cross-cultural communication, promoting diverse perspectives, and expanding the reach of journalism. ‍ Benefit #5: Speech Recognition AI-powered speech recognition tools use natural language generation, greatly benefiting journalists, particularly in interviews and transcription. Powerful AI algorithms can: Record interviews, speeches, or press conferences and transcribe them quickly and accurately. Reduce transcription time and help journalists focus on analysis, writing, and storytelling instead. ‍ The end result? Journalists can save a lot of time and effort that would otherwise be spent on transcribing content manually. Six Best AI Tools Journalists Should Watch Out For 1. Murf.AI Ever thought of the potential of audio articles for your news media company? Imagine if your audience could listen to the trending news stories and articles instead of browsing through endless lines of content. This is where Murf's voice over technology can help journalists: Select from 100% natural-sounding AI voices to make professional voiceovers for news content. Create audio news stories for a more global audience, thanks to a library of 200+ AI voices across 20+ languages, including English, Spanish, and Portuguese. Access studio-quality voiceovers at a fraction of the cost. Add realistic images, video content, and presentations to the voiceover and sync them together without the need for a third-party tool. Fine tune voiceovers with customization features like 'Pitch,' 'Pause,' and 'Pronunciation.' ‍ 2. Pinpoint A part of Google's Journalist Studio, Pinpoint is a research tool where professional journalists can: Upload 2,00,000 documents, including images, emails, hand-written notes, and audio files for specific words or phrases, locations, organizations, and people. Use speech to text technology to search for text in text-based files such as Microsoft Office documents, Google docs, plain text documents, emails, and so on, as well as search for text within images. Transcribe audio files across multiple languages. Upload and transcribe up to two hours-long audio files into searchable text files. ‍ Pinpoint supports eight languages for audio transcription. 3. Connexun Known as "The Ultimate AI News Engine," this tool empowers journalists to: Source real-time multilingual headlines, articles, and dynamic summaries from thousands of open web sources. Leverage intelligent algorithms and news-centric features such as news topic ranking, extraction-based summarization, and more to filter news for different types of users. Track news in real time across 20,000 highly trusted sources across the globe and pick the most important stories first. Access automatically aggregated news from news API. Access extractive short summaries of news in various languages. ‍ 4. JECT.AI JECT.AI provides an AI-powered News Archive offering that enables journalists to easily discover news content while generating unique story ideas using JECT.AI's creativity engine. Other interesting features of the tool include: Advanced NLP techniques such as Entity Recognition and Sentiment Analysis to structure news archive content. Advanced search algorithms to quickly and accurately locate specific news articles within the archive. Sentiment analysis to gauge the mood and tone of the news articles and help users quickly filter for positive or negative sentiment. Ability to explore different angles and perspectives on a given topic or event. Suggestions for related articles, topics, and themes to help users discover new content and generate fresh ideas. ‍ Pricing: Get in touch with the team 5. Vetted Vetted is one of the most popular AI-powered tools used by journalists from established giants such as The Washington Post, Bloomberg, The Economist, and more. Here are some of its standout journalist-friendly features: Search for specific experts for news stories. Send secure messages to experts directly via email. Find sources using useful features such as the sources' years of experience, areas of expertise, and more. Access Vetted's list of verified sources. ‍ 6. Narrativa Narrativa is a natural language generation platform that is trusted by the likes of The Wall Street Journal. This tool helps to automate news content. Journalists can leverage the following functionalities to improve their journalistic standards: AI for Automatic Generation generates long-tail content, headlines, and subject lines Narrativa NLP generates multiple variations of calls to action, headlines, and customized content recommendations to increase conversion for news companies. Narrativa Knowledge Graph performs rapid analysis and creates narratives around complex domains. Narrativa Insights uses pre-built functions to analyze data in real-time and identify insights. Narrativa Query analyzes data sets to retrieve relevant data and analyzes it using advanced statistics and clustering algorithms. ‍ AI in Journalism: Top Challenges And How to Address Them Despite the many benefits of AI-powered tools for journalists, AI journalism is not one without challenges. These include (but are not limited to): 1. Bias in AI Tools AI algorithms are only as good as the data (think: historical records, articles, user-generated content, and more) used to train them. If the training data is biased or contains discriminatory information, these AI-powered tools can perpetuate and amplify those biases. This can lead to skewed reporting or the reinforcement of stereotypes. Note that bias can occur in various forms, including gender, racial, political, or socioeconomic. Addressing bias in AI tools requires: Careful data selection. Diverse and representative training data. Ongoing monitoring and evaluation. Involvement of journalists and subject matter experts who can assess and correct any biases that may arise. ‍ The learning: Since all data has some form of bias, AI should not be seen as a silver bullet that can identify and eradicate all prejudices. 2. Lack of Human Oversight AI journalism often lacks proper human oversight, which can be problematic. Here's why: AI algorithms may struggle with complex contextual understanding, nuanced analysis, and ethical considerations. The tool may not understand the subtleties of language and tone, which require human judgment and expertise. ‍ Relying solely on AI tools without human oversight can lead to errors, misinformation, and the spread of false narratives. The learning: Journalists must continue to verify information, provide context, and make editorial decisions that the tools cannot take over, at least not yet. 3. Reliance on Automated Tools While AI tools can be valuable aids, there is a risk of overreliance on automated processes in journalism. The temptation to automate various tasks, such as content generation, data analysis, or even news writing, is too big to ignore. If left unchecked, it can lead to a reduction in human involvement. This can potentially compromise the quality and depth of journalistic work. Plus, AI cannot take over creative aspects of journalism, such as: Identifying newsworthy topics, Deciding on the right angle of a story Opinion driven articles ‍ The learning: Journalistic integrity, critical thinking, investigative skills, and the ability to ask probing questions are vital elements that the tools may struggle to replicate fully. Relying too heavily on automated tools can undermine the unique value that human journalists bring to the field. 4. Lack of Transparency AI-generated content can be difficult to understand and explain to readers. This can make it hard for journalists to be transparent about how they are using these tools. In fact, journalists and even the public at large may not have insights into: How AI algorithms prioritize the information. The weight these tools assign to different factors. The potential biases they may exhibit. ‍ This can result in skepticism about the credibility and objectivity of AI-driven journalism. The learning: It is essential for AI tool developers and news organizations to prioritize transparency by: Providing explanations of how algorithms work Disclosing the data sources used Allowing independent audits or third-party evaluations ‍ By fostering transparency, AI tools can gain greater acceptance and enable informed journalistic practices. It's Time to Ride the AI Journalism Wave AI tools have the potential to revolutionize journalism. Given that journalists face significant time constraints, never-ending deadlines, and a heightened need for accuracy, AI tools enable them to work faster and with more efficiency. Plus, these tools can enhance research, streamline content creation, and personalize audience engagement. Despite the potential benefits of AI, ensuring the reliability of AI-generated content and dealing with the ethical implications of AI in journalism are top concerns. The need of the hour is to address these challenges head-on and establish best practices for using AI in journalism. With the advancements in AI technology, the future of AI journalism looks promising. If implemented mindfully, AI tools and human beings can work together to produce high-quality, unbiased, and accurate journalism.	2025-06-03T00:00:00		https://murf.ai/blog/ai-tools-for-journalists	[ { "date": "2025/06/03", "position": 32, "query": "artificial intelligence journalism" } ]
NY State Assembly Bill 2025-A8595A	NY State Assembly Bill 2025-A8595A	https://www.nysenate.gov	[]	Enacts the "New York artificial intelligence transparency for journalism act"; requires developers of generative artificial intelligence systems or services ...	A. 8595 2 (f) Studies show that news content comprises a disproportionate amount of generative artificial intelligence training data. News content is especially valuable to artificial intelligence developers because it is high-quality, professional writing created by human beings; (g) After training, generative artificial intelligence systems contin- ue to access news websites, podcasts, broadcasts and digital platforms in order gain access to fact-checked, accurate and up to date content to produce outputs; (h) The vast majority of generative artificial intelligence developers do not obtain permission or compensate news publishers or broadcast news operations for accessing their websites, podcasts, broadcasts and digital platforms for the purposes of building and operationalizing their AI tools and services, in violation of copyright law, those sites' and platforms' terms of service and express prohibitions and prefer- ences; (i) Maximizing the potential of generative AI requires ensuring the sustainability of journalism and the news industry; and (j) News publishers, broadcast news operations and the public deserve to know when generative artificial intelligence developers have accessed news websites and used their work. § 3. Article 21-A of the general business law is renumbered article 21-B and a new article 21-A is added to read as follows: ARTICLE 21-A ARTIFICIAL INTELLIGENCE SOURCE DATA TRANSPARENCY SECTION 338. DEFINITIONS. 338-A. ARTIFICIAL INTELLIGENCE SOURCE DATA TRANSPARENCY. 338-B. ENFORCEMENT. 338-C. APPLICABILITY. 338-D. SEVERABILITY. § 338. DEFINITIONS. THE FOLLOWING TERMS, WHENEVER USED OR REFERRED TO IN THIS ARTICLE, SHALL HAVE THE FOLLOWING MEANINGS: 1. "ARTIFICIAL INTELLIGENCE" MEANS A MACHINE-BASED SYSTEM THAT CAN, FOR A GIVEN SET OF HUMAN-DEFINED OBJECTIVES, MAKE PREDICTIONS, RECOMMEN- DATIONS, OR DECISIONS INFLUENCING REAL OR VIRTUAL ENVIRONMENTS, AND THAT USES MACHINE AND HUMAN-BASED INPUTS TO PERCEIVE REAL AND VIRTUAL ENVI- RONMENTS, ABSTRACT SUCH PERCEPTIONS INTO MODELS THROUGH ANALYSIS IN AN AUTOMATED MANNER, AND USE MODEL INFERENCE TO FORMULATE OPTIONS FOR INFORMATION OR ACTION. 2. "ACCESS" MEANS TO OBTAIN, RETRIEVE, ACQUIRE, REPRODUCE, CRAWL, INDEX, OR REQUEST AND RECEIVE A TRANSMISSION OF CONTENT. 3. "COVERED PUBLICATION" MEANS ANY PRINT, BROADCAST, BROADCAST NETWORK OR DIGITAL PUBLICATION OR SERVICE WHICH: A. PERFORMS A PUBLIC-INFORMATION FUNCTION COMPARABLE TO THAT TRADI- TIONALLY SERVED BY JOURNALISM ORGANIZATIONS, SUCH AS NEWSPAPERS, BROAD- CAST NEWS OPERATIONS, BROADCAST NETWORK NEWS OPERATIONS, MAGAZINES AND OTHER PERIODICAL PUBLICATIONS; B. INVESTS SUBSTANTIAL EXPENDITURE OF LABOR, SKILL, AND MONEY TO CREATE, EDIT, PRODUCE, AND DISTRIBUTE CONTENT INCLUDING BY ENGAGING NATURAL PERSONS TO CREATE, EDIT, PRODUCE, AND DISTRIBUTE ORIGINAL TEXT, AUDIO, PHOTO, ILLUSTRATIVE, OR VIDEO CONTENT CONCERNING MATTERS OR TOPICS OF INTEREST OR USE TO MEMBERS OF THE PUBLIC THROUGH ACTIVITIES SUCH AS OBSERVATION, VIDEO RECORDING EVENTS, INTERVIEWS, RESEARCH, TEST- ING, AND ANALYSIS; AND C. PUBLISHES NEW CONTENT OR UPDATES ITS CONTENT ON AT LEAST A MONTHLY BASIS AND HAS A PROCESS FOR ERROR CORRECTION AND CLARIFICATION. A. 8595 3 4. "CRAWLER" MEANS SOFTWARE THAT ACCESSES CONTENT FROM A WEBSITE OR OTHER INTERNET SOURCE, SUCH AS AN ONLINE CRAWLER, SPIDER, FETCHER, CLIENT, BOT, USER AGENT OR EQUIVALENT TOOL. 5. "DEVELOPER" MEANS A PERSON THAT DESIGNS, CODES, PRODUCES, OR SUBSTANTIALLY MODIFIES AN ARTIFICIAL INTELLIGENCE SYSTEM OR SERVICE FOR USE BY MEMBERS OF THE PUBLIC. THE TERM "DEVELOPER" SHALL NOT INCLUDE ARTIFICIAL INTELLIGENCE SYSTEMS USED, DEVELOPED OR OBTAINED BY A JOUR- NALISM PROVIDER FOR INTERNAL USE. 6. "GENERATIVE ARTIFICIAL INTELLIGENCE" MEANS A CLASS OF ARTIFICIAL INTELLIGENCE MODELS THAT EMULATE THE STRUCTURE AND CHARACTERISTICS OF INPUT DATA TO GENERATE DERIVED SYNTHETIC CONTENT, INCLUDING, BUT NOT LIMITED TO, IMAGES, VIDEOS, AUDIO, TEXT, AND OTHER DIGITAL CONTENT. 7. "JOURNALISM PROVIDER" MEANS ANY PERSON THAT: A. BROADCASTS OR PUBLISHES ONE OR MORE COVERED PUBLICATIONS; AND B. IS COVERED BY MEDIA LIABILITY INSURANCE. 8. "PERSON" MEANS A NATURAL PERSON, CORPORATION, TRUST, ESTATE, PART- NERSHIP, INCORPORATED OR UNINCORPORATED ASSOCIATION OR ANY OTHER LEGAL ENTITY. 9. "ARTIFICIAL INTELLIGENCE UTILIZATION" MEANS TO USE DIGITAL CONTENT AS DATA TO DEVELOP THE CAPABILITIES OF A GENERATIVE ARTIFICIAL INTELLI- GENCE SYSTEM, INCLUDING THROUGH SETTING OR CHANGING ITS LEARNABLE WEIGHTS AND OTHER PARAMETERS, AND INCLUDES, IN ADDITION TO THE INITIAL DATASET TRAINING, FURTHER TESTING, VALIDATING, GROUNDING, OR FINE TUNING BY THE DEVELOPER OF THE ARTIFICIAL INTELLIGENCE SYSTEM OR SERVICE. § 338-A. ARTIFICIAL INTELLIGENCE SOURCE DATA TRANSPARENCY. 1. A. ON OR BEFORE JANUARY FIRST, TWO THOUSAND TWENTY-SEVEN AND BEFORE EACH TIME THEREAFTER THAT A GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM OR SERVICE, OR A SUBSTANTIAL MODIFICATION TO A GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM OR SERVICE RELEASED ON OR AFTER JANUARY FIRST, TWO THOUSAND TWEN- TY-TWO, IS MADE PUBLICLY AVAILABLE TO NEW YORKERS FOR USE, REGARDLESS OF WHETHER THE SYSTEM OR SERVICE IS MADE AVAILABLE FOR A FEE, THE DEVELOPER OF THE SYSTEM OR SERVICE SHALL POST ON THE DEVELOPER'S INTERNET WEBSITE THE FOLLOWING INFORMATION REGARDING VIDEO, AUDIO, TEXT AND DATA FROM A COVERED PUBLICATION USED TO TRAIN THE GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM OR SERVICE: (I) THE UNIFORM RESOURCE LOCATORS OR UNIFORM RESOURCE IDENTIFIERS ACCESSED BY CRAWLERS DEPLOYED BY THE DEVELOPER OR BY THIRD PARTIES ON THEIR BEHALF OR FROM WHOM THEY HAVE OBTAINED VIDEO, AUDIO, TEXT OR DATA; (II) A DETAILED DESCRIPTION OF THE VIDEO, AUDIO, TEXT AND DATA FROM A COVERED PUBLICATION USED FOR ARTIFICIAL INTELLIGENCE UTILIZATION, INCLUDING THE TYPE AND PROVENANCE OF THE VIDEO, AUDIO, TEXT AND DATA AND THE MEANS BY WHICH IT WAS OBTAINED, SUFFICIENT TO IDENTIFY INDIVIDUAL WORKS; (III) WHETHER ANY SOURCE IDENTIFIERS, TERMS, OR COPYRIGHT NOTICES WERE REMOVED FROM THE VIDEO, AUDIO, TEXT OR DATA; AND (IV) THE TIMEFRAME OF DATA COLLECTION. B. THE INFORMATION REQUIRED TO BE POSTED ON A DEVELOPER'S INTERNET WEBSITE PURSUANT TO PARAGRAPH A OF THIS SUBDIVISION SHALL NOT BE REQUIRED WHERE THERE IS AN EXPRESS WRITTEN AGREEMENT AUTHORIZING THE DEVELOPER TO ACCESS THE JOURNALISM PROVIDER'S CONTENT AND THE PARTIES AGREE NOT TO POST INFORMATION RELATING TO THE JOURNALISM PROVIDER'S CONTENT ON THE DEVELOPER'S WEBSITE. 2. A. ON OR BEFORE JANUARY FIRST, TWO THOUSAND TWENTY-SEVEN, THE DEVELOPER OF A GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM OR SERVICE WHO DEPLOYS A CRAWLER, EITHER DIRECTLY OR THROUGH A THIRD PARTY, IN CONNECTION WITH SUCH SYSTEM OR SERVICE SHALL DISCLOSE INFORMATION A. 8595 4 REGARDING THE IDENTITY OF CRAWLERS USED BY THE DEVELOPER OR BY THIRD PARTIES ON THE DEVELOPER'S BEHALF IN A MANNER CLEARLY ACCESSIBLE BY A WEBSITE OPERATOR, INCLUDING BUT NOT LIMITED TO: (I) THE NAME OF THE CRAWLER INCLUDING THE CRAWLER'S IP ADDRESS, AND SPECIFIC IDENTIFIER ACTUALLY USED BY THE CRAWLER WHEN CONDUCTING THE CRAWLING ACTIVITY (SUCH AS INCLUDING THE IDENTIFIERS AS PART OF THE USER AGENT OR OTHER PART OF THE REQUEST HEADERS); (II) THE LEGAL ENTITY RESPONSIBLE FOR THE CRAWLER; (III) THE SPECIFIC PURPOSES FOR WHICH EACH CRAWLER IS USED; (IV) THE LEGAL ENTITIES TO WHICH OPERATORS PROVIDE DATA SCRAPED BY THE CRAWLERS THEY OPERATE; AND (V) A SINGLE POINT OF CONTACT TO ENABLE THIRD PARTIES WHOSE WEBSITES ARE ACCESSED BY SUCH CRAWLERS TO COMMUNICATE WITH THE DEVELOPER AND TO LODGE COMPLAINTS. B. THE INFORMATION DISCLOSED PURSUANT TO PARAGRAPH A OF THIS SUBDIVI- SION SHALL BE AVAILABLE ON AN EASILY ACCESSIBLE PLATFORM AND UPDATED AT THE SAME TIME AS ANY CHANGE IS MADE TO SUCH INFORMATION. C. THE EXCLUSION OF A CRAWLER BY A WEBSITE OPERATOR SHALL NOT NEGA- TIVELY IMPACT THE FINDABILITY OF THE WEBSITE OPERATOR'S CONTENT IN A SEARCH ENGINE. § 338-B. ENFORCEMENT. 1. A. A JOURNALISM PROVIDER, OR A PERSON AUTHOR- IZED TO ACT ON A JOURNALISM PROVIDER'S BEHALF, MAY REQUEST THE CLERK OF THE SUPREME COURT, OR A JUDGE WHERE THERE IS NO CLERK, TO ISSUE A SUBPOENA TO A DEVELOPER OF A GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM THAT IS MADE AVAILABLE TO NEW YORKERS FOR USE, REGARDLESS OF WHETHER THE SYSTEM OR SERVICE IS MADE AVAILABLE FOR A FEE, FOR DISCLOSURE OF COPIES OF, OR RECORDS SUFFICIENT TO IDENTIFY WITH CERTAINTY, THE TEXT AND DATA USED TO TRAIN THE GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM OR SERVICE INSOFAR AS SUCH TEXT AND DATA PERTAINS TO THE JOURNALISM PROVIDER'S INTERNET WEBSITE, BROADCASTS, PODCASTS OR OTHER DIGITAL PLATFORMS, INCLUDING BUT NOT LIMITED TO: (I) THE UNIFORM RESOURCE LOCATORS ACCESSED BY CRAWLERS DEPLOYED BY DEVELOPERS OR BY THIRD PARTIES ON THEIR BEHALF OR FROM WHOM THEY HAVE OBTAINED TEXT, VIDEO, AUDIO OR DATA, AND DATES AND TIMES OF COLLECTION; AND (II) THE TEXT AND DATA USED FOR ARTIFICIAL INTELLIGENCE UTILIZATION, INCLUDING THE TYPE AND PROVENANCE OF THE TEXT AND DATA AND THE MEANS BY WHICH SUCH TEXT AND DATA WAS OBTAINED AND WHEN. B. A SUBPOENA ISSUED PURSUANT TO PARAGRAPH A OF THIS SUBDIVISION MAY REQUIRE DISCLOSURE OF THE INFORMATION REQUIRED PURSUANT TO PARAGRAPH A OF THIS SUBDIVISION IN THE NATIVE FORM IN WHICH SUCH INFORMATION WAS COPIED AND STORED (INCLUDING ALL ACCOMPANYING KEYS, VALUES, TAGS, AND THE LIKE, AND ANY OTHER AVAILABLE METADATA), SUBJECT TO ENTRY OF A SUIT- ABLE PROTECTIVE ORDER IN THE CASE THAT SUCH INFORMATION CONSTITUTES A TRADE SECRET OF THE GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM DEVELOPER. C. THE DEVELOPER SHALL PROVIDE THE SUBPOENAED INFORMATION WITHIN THIR- TY DAYS OF SERVICE OF THE SUBPOENA OR, IN THE CASE OF TRADE SECRETS, ENTRY OF A SUITABLE PROTECTIVE ORDER. SUCH SUBPOENA SHALL BE SUBJECT TO THE PROVISIONS OF ARTICLE TWENTY-THREE OF THE CIVIL PRACTICE LAW AND RULES. THE COURT MAY IMPOSE A PENALTY FOR FAILURE TO RESPOND TO SUCH INFORMATION SUBPOENAS PURSUANT TO SECTION TWENTY-THREE HUNDRED EIGHT OF THE CIVIL PRACTICE LAW AND RULES. 2. A. A JOURNALISM PROVIDER MAY BRING AN ACTION IN THE SUPREME COURT FOR AN INJUNCTION TO COMPEL A DEVELOPER TO COMPLY WITH SECTION THREE HUNDRED THIRTY-EIGHT-A OF THIS ARTICLE. A. 8595 5 B. IF A DEVELOPER FAILS TO COMPLY WITH A SUBPOENA ISSUED PURSUANT TO SUBDIVISION ONE OF THIS SECTION, THE JOURNALISM PROVIDER REQUESTING SUCH SUBPOENA MAY MOVE IN THE SUPREME COURT TO COMPEL COMPLIANCE. IF THE COURT FINDS THAT THE DEVELOPER DID NOT COMPLY WITH THE SUBPOENA, THE COURT SHALL ORDER COMPLIANCE AND MAY IMPOSE STATUTORY DAMAGES TO THE JOURNALISM PROVIDER REQUESTING SUCH SUBPOENA IN THE SUM OF NOT LESS THAN TEN THOUSAND DOLLARS NOR MORE THAN FIFTY THOUSAND DOLLARS FOR EACH DAY ON WHICH SUCH NONCOMPLIANCE OCCURS OR CONTINUES. C. IF THE DEVELOPER FAILS TO COMPLY WITH A COURT ORDER ISSUED PURSUANT TO PARAGRAPH B OF THIS SUBDIVISION, THEN THE JOURNALISM PROVIDER MAY REQUEST THAT THE ATTORNEY GENERAL BRING AN ACTION ON THEIR BEHALF TO ENSURE COMPLIANCE WITH THE COURT ORDER AND ANY STATUTORY DAMAGES ASSESSED. § 338-C. APPLICABILITY. THE PROVISIONS OF THIS ARTICLE SHALL NOT BE CONSTRUED TO MODIFY, IMPAIR, EXPAND, OR IN ANY WAY ALTER RIGHTS PERTAIN- ING TO TITLE 17 OF THE UNITED STATES CODE OR THE LANHAM ACT (15 U.S.C. 1051 ET SEQ.). § 338-D. SEVERABILITY. IF ANY PROVISION OF THIS ARTICLE OR THE APPLI- CATION THEREOF TO ANY PERSON OR CIRCUMSTANCES IS HELD TO BE INVALID, SUCH INVALIDITY SHALL NOT AFFECT OTHER PROVISIONS OR APPLICATIONS OF THIS ARTICLE WHICH CAN BE GIVEN EFFECT WITHOUT THE INVALID PROVISION OR APPLICATION, AND TO THIS END THE PROVISIONS OF THIS ARTICLE ARE SEVERA- BLE. § 4. This act shall take effect immediately.	2025-06-03T00:00:00		https://www.nysenate.gov/legislation/bills/2025/A8595/amendment/A	[ { "date": "2025/06/03", "position": 78, "query": "artificial intelligence journalism" } ]
Report: Little Tech Goes Global - Coworker.org	Report: Little Tech Goes Global	https://home.coworker.org	[]	It's the startups silently watching workers now. As artificial intelligence reshapes the future of work, a new form of digital oversight is spreading ...	Forget Big Brother. It’s the startups silently watching workers now. As artificial intelligence reshapes the future of work, a new form of digital oversight is spreading quietly and rapidly across the globe—not from Big Tech, but from a sprawling network of lesser-known startups, regional vendors, and HR software providers. Little Tech Goes Global: The Expansion of AI and Workplace Surveillance, a new report by Coworker.org, exposes how this "Little Tech" ecosystem is embedding surveillance and algorithmic control into the daily lives of workers—often without their knowledge, consent, or protection. Building on our 2020 research, this report maps the global rise of algorithmic management across six countries—Mexico, Colombia, Brazil, Nigeria, Kenya, and India—where legal frameworks are either outdated, poorly enforced, or nonexistent. From gig workers tracked through facial recognition and GPS to sanitation workers fined for resting while wearing “wellness” devices, the report reveals how venture capital-backed startups are exporting surveillance tech to the Global South, targeting regions with weaker privacy protections and regulatory oversight. This is not a far-off problem. It’s happening now—in your city, your job, and perhaps your own devices.	2025-06-03T00:00:00	2025/06/03	https://home.coworker.org/little-tech-goes-global/	[ { "date": "2025/06/03", "position": 82, "query": "artificial intelligence labor union" } ]
Artificial Power: 2025 Landscape Report	Artificial Power: 2025 Landscape Report	https://ainowinstitute.org	[ "Ai Now Institute" ]	Acknowledgments · Annette Bernhardt, UC Berkeley Labor Center · Abeba Birhane, Artificial Intelligence Accountability Lab, Trinity College Dublin · Brian Chen, ...	Overview Artificial Power, our 2025 Landscape Report, puts forward an actionable strategy for the public to reclaim agency over the future of AI. In the aftermath of the “AI boom,” the report examines how the push to integrate AI products everywhere grants AI companies – and the tech oligarchs that run them – power that goes far beyond their deep pockets. We need to reckon with the ways in which today’s AI isn’t just being used by us, it’s being used on us. The report moves from diagnosis to action: offering concrete strategies for community organizers, policymakers, and the public to change this trajectory.	2025-06-03T00:00:00	2025/06/03	https://ainowinstitute.org/publications/research/ai-now-2025-landscape-report	[ { "date": "2025/06/03", "position": 91, "query": "artificial intelligence labor union" } ]
Guide on the use of generative artificial intelligence	Guide on the use of generative artificial intelligence	https://www.canada.ca	[ "Treasury Board Of Canada Secretariat" ]	Gebru, "The Exploited Labor Behind Artificial Intelligence," Noema, 13 October 2022. ... Council of the European Union, "ChatGPT in the Public Sector - Overhyped ...	Guide on the use of generative artificial intelligence Overview Generative artificial intelligence (AI) tools offer many potential benefits to Government of Canada (GC) institutions. Federal institutions should explore potential uses of generative AI tools for supporting and improving their operations, but they should not use these tools in all cases. Institutions must be cautious and evaluate the risks before they start using them. They should also limit the use of these tools to instances where they can manage the risks effectively. This document provides guidance to federal institutions on their use of generative AI tools. This includes instances where federal institutions are deploying these tools. It provides an overview of generative AI, identifies challenges relating to its use, puts forward principles for using it responsibly, and offers policy considerations and best practices. This guide also seeks to raise awareness and foster coordination among federal institutions. It highlights the importance of engaging key stakeholders before deploying generative AI tools for public use and before using them for purposes such as service delivery. Stakeholders include: legal counsel privacy and security experts the Office of the Chief Information Officer at the Treasury Board of Canada Secretariat (TBS) bargaining agents advisory groups clients of GC services The guide complements and supports compliance with many existing federal laws and policies, including those in the areas of privacy, security, intellectual property, and human rights. This second version of the guide incorporates feedback from internal stakeholders and external experts. It will be updated regularly to keep pace with regulatory and technological change. To support public servants considering the use of these tools in their daily work, a concise summary of this guide offering do’s and don’ts is also available. What is generative AI? The Directive on Automated Decision-Making defines AI as information technology that performs tasks that would ordinarily require biological brainpower to accomplish, such as making sense of spoken language, learning behaviours or solving problems. Generative AI is a type of AI that produces content such as text, audio, code, videos and images. Footnote 1 This content is produced based on information the user inputs, called a “prompt,” which is typically a short instructional text. Examples of generative AI tools: large language models (LLMs) such as ChatGPT, Copilot and LLaMA GitHub Copilot and FauxPilot, which produce code based on text prompts DALL-E, Midjourney and Stable Diffusion, which produce images from text or image prompts These examples include both proprietary and open-source models. Both types have their own benefits and drawbacks in terms of cost, performance, scalability, security, transparency and user support. In addition, generative AI models can be fine-tuned, or custom models can be trained and deployed to meet an organization’s needs. Footnote 2 Many generative AI models have been trained on large volumes of data, including publicly accessible data from the Internet. Based on the training data, these models generate content that is statistically likely in response to a prompt, Footnote 3 for example, by predicting the next word in a sentence. Techniques such as human supervision and reinforcement learning can also be applied to further improve the outputs, Footnote 3 and users can provide feedback or modify their prompt to refine the response. Generative AI can therefore produce content that looks as though a human produced it. Generative AI can be used to perform or support tasks such as: writing and editing documents and emails generating images for presentations coding tasks, such as debugging and generating templates and common solutions summarizing information brainstorming research, translation and learning providing support to clients (for example, answering questions, troubleshooting) Challenges and opportunities Before federal institutions start using generative AI tools, they must assess and mitigate certain ethical, legal and other risks. For example, these tools can generate inaccurate content; amplify biases; and violate intellectual property, privacy and other laws. Further, some tools may not meet federal privacy and security requirements. When institutions use these tools, they must protect personal information and sensitive data. As well, because these tools generate content that can look as though a human produced it, people might not be able to tell whether they are interacting with a person or a tool. The use of these tools can also affect the skill and judgment of public servants and can have environmental costs. The development and quality assurance practices of some generative AI models have also been associated with socio‑economic harms such as exploitative labour practices. Footnote 4 For example, data‑labelling or annotation requires extensive manual input, and this work is often outsourced to countries where workers are paid very low wages. Footnote 5 Generative AI tools rely on models that pose various challenges, including limited transparency and explainability. They also rely on training data that is difficult to access and assess. These challenges stem in part from large model sizes, high volumes of training data, and the proprietary nature of many tools. In addition, the outputs of the models are constrained by the prompts users enter and by the training data, which may lack context that is not publicly accessible on the Internet. Training data could also be outdated. For example, ChatGPT-3.5 is trained on data up to early 2022, so it has a limited ability to provide information on events or developments after that. Footnote 6 Footnote 7 Training data can also be biased and lack a diversity of views, given that the Internet is frequently the data source. These biases can then be reflected in the outputs of the tools. The performance of these tools can also vary from language to language. Models in English and other languages that are well represented in the training data often perform better than models in languages that are less well represented. Footnote 8 As well, these tools have limitations that reduce their utility for certain purposes; for example, they tend to perform inconsistently on tasks related to emotional or nuanced language. Footnote 9 Footnote 10 Generative AI could also pose risks to the integrity and security of federal institutions, given its potential misuse by threat actors. Federal institutions should be aware of these risks and consider the best practices recommended by the Canadian Centre for Cyber Security in their guidance Generative Artificial intelligence (AI) - ITSAP.00.041. Although these tools present challenges and concerns, they also offer potential benefits to public servants and federal institutions. For example, they can enhance productivity through increased efficiency and quality of outputs in analytical and writing tasks in several domains. Footnote 11 Footnote 12 More analysis is needed to determine the most appropriate and beneficial uses of these tools by federal institutions. Experimentation, coupled with performance measurement and analysis, is needed to better understand potential gains and trade-offs and to inform the government’s approach to the use of these tools. Recommended approach In this section Responsibilities for federal institutions Federal institutions should explore how they could use generative AI tools to support their operations and improve outcomes for Canadians. Given the challenges and concerns relating to these tools, institutions should assess and mitigate risks and use them only for activities where they can manage the risks effectively. With the growing adoption of these technologies in different sectors and by the public, exploration by federal institutions will help the government understand the risks and opportunities of these tools and keep pace with the evolving digital landscape. The risks of using these tools depend on what they will be used for and on what mitigation measures are in place. Examples of low‑risk uses: writing an email to invite colleagues to a team‑building event editing a draft document that will go through additional reviews and approvals Examples of higher‑risk uses (such as uses in service delivery): deploying a tool (for example, a chatbot) for use by the public generating a summary of client information Federal institutions should experiment with low-risk uses before they consider higher‑risk uses. They should always tailor best practices and risk‑mitigation measures to each use. When deciding whether to use generative AI tools, public servants should refer to the guide to ethical decision-making (section 6 of Values Alive: A Discussion Guide to the “Values and Ethics Code for the Public Sector”). To maintain public trust and ensure the responsible use of generative AI tools by federal institutions, institutions should align with the “FASTER” principles that TBS has developed: Fair: ensure that content from these tools does not include or amplify biases and that it complies with human rights, accessibility, and procedural and substantive fairness obligations; engage with affected stakeholders before deployment ensure that content from these tools does not include or amplify biases and that it complies with human rights, accessibility, and procedural and substantive fairness obligations; engage with affected stakeholders before deployment Accountable: take responsibility for the content generated by these tools and the impacts of their use. This includes making sure generated content is accurate, legal, ethical, and compliant with the terms of use; establish monitoring and oversight mechanisms take responsibility for the content generated by these tools and the impacts of their use. This includes making sure generated content is accurate, legal, ethical, and compliant with the terms of use; establish monitoring and oversight mechanisms Secure: ensure that the infrastructure and tools are appropriate for the security classification of the information and that privacy and personal information are protected; assess and manage cyber security risks and robustness when deploying a system ensure that the infrastructure and tools are appropriate for the security classification of the information and that privacy and personal information are protected; assess and manage cyber security risks and robustness when deploying a system Transparent: identify content that has been produced using generative AI; notify users that they are interacting with an AI tool; provide information on institutional policies, appropriate use, training data and the model when deploying these tools; document decisions and be able to provide explanations if tools are used to support decision-making identify content that has been produced using generative AI; notify users that they are interacting with an AI tool; provide information on institutional policies, appropriate use, training data and the model when deploying these tools; document decisions and be able to provide explanations if tools are used to support decision-making Educated: learn about the strengths, limitations and responsible use of the tools; learn how to create effective prompts and to identify potential weaknesses in the outputs learn about the strengths, limitations and responsible use of the tools; learn how to create effective prompts and to identify potential weaknesses in the outputs Relevant: make sure the use of generative AI tools supports user and organizational needs and contributes to better outcomes for clients; consider the environmental impacts when choosing to use a tool; identify appropriate tools for the task; AI tools aren’t the best choice in every situation For assistance in determining the appropriate use of these tools, public servants should consult relevant stakeholders such as: their institution’s legal services, privacy and security experts the offices of the chief information officer and chief data officer for their institution their institution’s diversity and inclusion specialists The following can also provide support: the Canadian Centre for Cyber Security Statistics Canada the Office of the Chief Information Officer of Canada (part of TBS) Responsibilities for federal institutions Federal institutions should evaluate generative AI tools for their potential to help employees, not replace them. Institutions are encouraged to responsibly explore uses and to enable employees to optimize their work while ensuring that all uses of these tools are ethical, align with the FASTER principles and comply with policies and laws. In evaluating these tools and exploring how they could use them, institutions have a number of responsibilities including: ensuring that employees can access and take training on the effective and responsible use of generative AI tools supporting employees in improving their knowledge of topics such as detecting biased and inaccurate content providing access to secure generative AI tools that meet government information, privacy and security requirements enabling access to online generative AI tools, in alignment with the Policy on Service and Digital (requirement 4.4.2.5) and the Directive on Service and Digital Appendix A: Examples of Acceptable Network and Device Use (Non-Exhaustive List) implementing oversight and performance management processes to monitor the impacts of these tools and to make sure both the tools themselves and their uses comply with applicable laws and policies and align with the FASTER principles, particularly during deployment engaging with employees to understand their needs consulting with stakeholders such as end-users, client representative groups and bargaining agents before high-risk deployments Institutions should have effective change management practices so that they can help employees improve their current skills and develop new ones. Managers need to understand what these tools can and can’t be used for and should have realistic expectations of how the tools might help improve employees’ productivity. Institutions should also evaluate the risks and opportunities associated with using these tools and develop guidance for their institution that aligns with this guide and is tailored to their organization’s context and needs. Use of this guide Federal institutions are encouraged to use this guide as they continue to develop their own guidance on the use of generative AI. This guide and the community will continue to evolve. Additional support Information and guidance on specific uses of generative AI TBS’s Responsible Data and AI team ([email protected]) Canadian Centre for Cyber Security’s guidance on Generative Artificial Intelligence (AI) - ITSAP.00.041 TBS’s summary guidance, Using Generative AI in Your Daily Work. Courses and events The Canada School of Public Service offers different courses and events on AI, including Using Generative AI in the Government of Canada. Additional resources Federal institutions can also contact the following for additional support: Communications Security Establishment (including the Canadian Centre for Cyber Security) Statistics Canada	2025-06-03T00:00:00		https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/guide-use-generative-ai.html	[ { "date": "2025/06/03", "position": 94, "query": "artificial intelligence labor union" } ]
The future of work is agentic	The future of work is agentic	https://www.mckinsey.com	[ "Jorge Amar", "Brooke Weddle", "Bryan Hancock", "Senior Partner", "Washington Dc", "Partner" ]	Learn how agentic AI is reshaping the workforce and what companies must do to drive organizational change and manage a hybrid of humans and AI agents.	Think about your org chart. Now imagine it features both your current colleagues—humans, if you’re like most of us—and AI agents. That’s not science fiction; it’s happening—and it’s happening relatively quickly, according to McKinsey Senior Partner Jorge Amar. In this episode of McKinsey Talks Talent, Jorge joins McKinsey talent leaders Brooke Weddle and Bryan Hancock and Global Editorial Director Lucia Rahilly to talk about what these AI agents are, how they’re being used, and how leaders can prepare now for the workforce of the not-too-distant future. The following transcript has been edited for clarity and length. From generative to agentic AI Lucia Rahilly: Jorge, welcome to McKinsey Talks Talent. Jorge Amar: Thank you very much. Excited to be here. Lucia Rahilly: Jorge, there was a great little piece in The Wall Street Journal called “Everyone’s talking about AI agents. Barely anyone knows what they are.” What exactly do we mean when we talk about agentic AI? Jorge Amar: I’ll start where I think most people still are, which is generative AI. Gen AI is mostly a reactive type of AI focused on generating creative content, triggered by a prompt or an instruction from an individual. Now if we continue the evolution of AI into agentic, we start to come to a very different reality. The first difference is we’re talking about AI that is not only generating content. It is executing on a task, on a mandate, on a particular instruction. An AI agent is perceiving reality based on its training. It then decides, applies judgment, and executes something. And that execution then reinforces its learning. It learns if what the agent did was good or bad and then feeds that back in. Your AI agents could now be the evolution and the creation of a digital replica of the entire workforce of an organization. Jorge Amar So we’re getting into the next step: AI deciding what to do on its own. We start to get into this complete AI workforce. Your AI agents could now be the evolution and the creation of a digital replica of the entire workforce of an organization. Lucia Rahilly: OK, Jorge. You’re scaring us. Let’s talk through some use cases that might help bring this to life a bit. What does agentic AI look like now in the wild? Jorge Amar: It’s still the Wild, Wild West out there. But I’ll try. Right now, many companies are starting to experiment. Typically, the environments in which they are deploying agents are very deterministic, with a clear process to follow. Think of IT help desks, or software development, or customer service tickets: any environment where a customer asks for something and there’s a well-defined process afterward. The agent picks it up—decides what is the right process, the right content article to be retrieved, the right information to be gathered—and then triggers an action. Subscribe to the McKinsey Talks Talent podcast Bryan Hancock: In HR, we’re seeing agentic AI in talent acquisition. Agents clean records. They try to understand, “Of the vast universe of potential candidates, how do we clean the data and understand who the right candidate might be?” Then a separate agent goes through and scores those candidates and does the ranking and the sourcing process. A separate agent reaches out to gain contact and schedule interviews. And then I’ve seen a coordinating agent that sits on top of the overall process, interacting with those underlying agents. Have you seen that kind of coordinating agent process? And how do you even create an agent that coordinates across some of those discrete subprocesses? Jorge Amar: I have a client that is already doing the first screening of all candidates for the front line entirely with agents. And I have even seen one step further: AI agents being deployed for training. Think of a call center or store environment. You generate an agentic customer, and you say, “This is a type of call. This is a type of customer. Record an interaction.” It’s not only simulating a real phone call. You’re also getting live, detailed scoring of how you, as a frontline employee, are doing in that interaction. Are you using the right words? Have you remembered every single step of the process? It gives you very detailed coaching instructions. Probably before, a supervisor in a call center could listen to three, five calls per agent. Now you get a summary of every single call, with a detailed breakdown of all the things this human agent is doing well and could do better. You can focus your coaching, your onboarding in a much more targeted way because you know exactly which skills to develop, which traits to emphasize. And it’s not only recruiting and training; you could even do the same thing for performance management. How agentic AI is already changing work Brooke Weddle: Jorge, it sounds like you’re pointing to examples where agentic AI has allowed companies to achieve greater levels of productivity. Recently, the Work Trend Index annual report came out, one of Microsoft’s flagship publications in workforce. And it found that a third of executives are considering using AI to reduce head count in the next 12 to 18 months. But nearly 50 percent said they were considering maintaining head count but using AI as digital labor to boost productivity—as complementary to human skills. What have you seen in terms of use cases? Jorge Amar: It is still early days when it comes to what I call decoupling the creation of capacity—automating tasks that otherwise would have been performed by a human—and the monetization of capacity. The monetization of capacity is its own independent thing. One of the potential paths can be, “I’m going to reduce head count.” More and more, some executives I’m talking to are interested in, “I might reduce head count, but I also might want to do things differently.” Suppose your competitive advantage was your call center agents. If AI brings everyone to the same parity level, how do you differentiate? What are the implications for your workforce when you can differentiate by having the best algorithm, the best agentic framework out there—but at the same time, how do you complement that with humans to do things that otherwise would have been cost prohibitive? I’ll give you one example. Last week, I was talking with one of my travel clients—and you could pick the airline or cruise line of your choice. What if you now had your own personalized concierge looking at your travel, giving you very detailed recommendations on how to navigate the airport, suggesting the type of food you could pick up on your way and even creating the order for you, and then getting to the final point in which it helps you board the plane and makes sure you have space? The possibilities are endless when it comes to figuring out or creating new and different workflows, new processes, new ways to surprise and delight your customers that you couldn’t have otherwise. Bryan Hancock: And I imagine you can also do some of the same toward your employees. How do you surprise and delight across the employee journey? How do these agents actually get created—and get created in a way that’s specific to processes in any one area? Jorge Amar: We’re all still figuring out the best way. There was a quote recently along the lines of, “IT will be the HR of AI agents of the future.” I would divide the creation of an agent into a few different steps. This helps us understand who is doing what. First, there clearly needs to be a rationale from the business: customer support, marketing, sales, HR. They would define, “What is the need for an AI capacity?” and decide, “What are the parameters of what this AI capability needs to perform?” Then they would work with their IT or AI function to either develop or procure their agentic capabilities. In many cases, the specificity and complexity of these AI capabilities will require these companies to develop their agent capabilities in-house, because they cannot find them in the market. It’s going to be a hybrid situation. Once that capability exists, you have to onboard and train that agent, which we call “tuning” an agent. Tuning the agent requires a number of things: a good articulation and understanding of the process you are trying to “agentize,” as well as a subject matter expert who really understands the ins and outs. You also need someone who understands the available data—a content specialist who is saying, “These are the content articles, the corpus of knowledge you need to train your agent,” and who makes sure that knowledge is up to date. In one of my cases, we trained the agent, and the agent started to spit out a bunch of COVID-related policies that were no longer relevant. So you need to make sure the data is accurate, relevant, and up to date. Last, you need a good, robust prompt-engineering skill set: someone who can teach, train, and tune the agent by saying, “When the customer or your employee says this, this is what they mean. This is what they are trying to accomplish. And therefore do X, Y, Z.” Building and managing an AI workforce Brooke Weddle: Jorge, you mentioned IT becoming the HR of AI agents. And, of course, it was Jensen Huang, the CEO of Nvidia, who said this recently. When you think about a digital workforce, whose job is it to ensure that digital workforce is reaching its full potential? Is it more in the realm of IT? Or is this a space where HR might have a few things to say, since for a long time, getting managers to reach their full potential has been more their purview? Jorge Amar: Some pioneering companies in this space are expressing their org charts not only in number of FTEs [full-time employees] but also in number of agents being deployed in every part of the organization. So I think we are going into a world where you’ll have to think about your workforce as both agentic and human. And I don’t think IT will be able to do this alone. IT will be critical in enabling the foundational elements to train an agent—the data stack, the right procurement, the right platform for training and tuning the agents. Now, the true missing pieces: one is the business. Nobody will be able to train an agent if you don’t know intimately the policies, the processes, what really differentiates you from a business perspective. And then I think HR will play a key role—first, to really push the business on what can be done from a hybrid workforce perspective. Second, and we started seeing this in one of my clients, is where the technology is up and running, but the number of live interactions is not coming down. There is a big change management component that comes into play. HR will be absolutely critical there. So I think we are going into a world where you’ll have to think about your workforce as both agentic and human. Jorge Amar How do you tell your 20-year-tenured employee in the call center, “Now there is this agent that is going to do the job much better than you”? This person would probably say, “How can this AI thingy that got trained yesterday replace my 20 years of experience?” And there is a big step toward driving the incentives for usage, role modeling communication, and creating the right change story for these employees to understand, “Look at all the great possibilities this unlocks for you.” This tells me HR still will play a critical role in the adoption of this agentic workforce. Maybe HR will not be screening each resume, but it will be critical in driving the change management efforts in adopting an agentic AI workforce. McKinsey Talks Talent Podcast Bryan Hancock, Brooke Weddle, and other talent experts help you navigate a fast-changing landscape and prepare for the future of work by making talent a competitive advantage. Lucia Rahilly: Jorge, it’s so interesting to hear the anthropomorphic terms you’re using to describe these agents—existing in the org chart, for example, or as a digital workforce. To be clear, are these agents being construed as tools or as a class of digital workers—neo-colleagues of some kind? Jorge Amar: I do think of it as a workforce. This is a workforce that will conduct end-to-end processes, replacing many tasks being performed today by the human workforce. It will augment the tasks a human workforce is performing to help make it better, faster, more efficient. Some companies out there are even promoting this notion of a zero-FTE department—an entire function fully performed by an agent. Then you have on the side humans in the loop controlling or monitoring what these agents are doing. Putting philosophical debate aside, I think we should think of agents as a parallel workforce for all intents and purposes. Some companies out there are even promoting this notion of a zero-FTE department—an entire function fully performed by an agent. Jorge Amar Earning employee trust in the AI age Lucia Rahilly: You mentioned adoption, and we so often hear adoption cited as a primary challenge in realizing the value of AI. How do you see humans in the workplace taking to this notion of collaborating with AI agents? Jorge Amar: It’s still a big challenge. I’ll give you one example. In some of the frontline environments where I spend a lot of time, some of the newer agents or the newer reps tend to embrace AI faster. Why? Because if you’re just coming into a frontline environment, the back office is where you need to learn all these things, and now AI is guiding you through the process. That’s great. It makes the job easier. But some of the more tenured employees resist AI quite a bit. It’s really challenging for them. There are many of these elements that will be critical in cracking the code to adoption, because my fear is that we will end up with huge investments and very little value realized. Jorge Amar The other big element is that many employees tell us, “I cannot trust an AI black box out there that is doing this, so I will use the AI result, but at the same time, I’m going to have my own calculations.” Therefore, you’re now duplicating work. There are many of these elements that will be critical in cracking the code to adoption, because my fear is that we will end up with huge investments and very little value realized. Bryan Hancock: Who do you think is going to lead the way in adoption? Jorge Amar: First, there’s got to be a clear mandate from the top. Leaders should make sure they are role modeling and integrating AI into the way they speak and what they do. Second, evaluate the performance of AI in a joint fashion. One of my clients sees the results of both the human and the agentic parts of the operation in the same dashboard. The business manager, the VP, and the SVP evaluate the joint performance of both their workforces. Third, this space is changing week by week, day by day. You need to design an operating model, a set of processes, that allows you to adapt. The more flexible this operating model, the better, because otherwise you’re going to be making investments in a technology or a set of algorithms that three months from now are going to be different. If you put all that in the mix, some of the smaller companies, start-up environments, have a little bit of an advantage. But the reality is that some of these LLMs [large language models] or agent platforms are not going to be trained on small companies. So it is critical to get to the larger companies and say, “Hey, I’m going to make the performance of these even better.” How to do that in an effective way in that environment is, to me, the crux of this issue. Brooke Weddle: What skills are going to become more salient in human leaders to get the most out of agents? Jorge Amar: First, HR will need to be at least business proficient in what an agentic workforce can do. How can you drive a change management program if you don’t know what your agentic workforce can and cannot do, or what will be possible in three years? Second, I think HR will play an important role in reskilling human employees. Today, you can probably fully agentize the workload of a level-one support engineer. But you might want to repurpose that person to become a prompt engineer or to do content generation for AI training. An HR function that can do that at scale is another critical component and skill set that HR will need to develop if you think about the next three, five years: “What is the evolution of that role?” Last is being really good at empathy, understanding the change story, helping employees onboard into their own AI journey, and making it happen in a way that is not threatening: “Look at all the other possibilities you might have in the future within the organization.” Articulating that very clearly and helping employees come along in that journey is going to be another critical component. Brooke Weddle: The Work Trend Index annual report I mentioned earlier talks about the need to evolve from an org chart to a work chart. Jorge Amar: Yes, and you probably saw that the CEO of Shopify released a memo saying something along those lines: “Before you ask for new head count, show me that AI cannot do the work.” Brooke Weddle: That was almost positioned as a more radical stance. But in my conversations, it’s very much a part of the conversation already. I very much think that’s a now thing versus a future thing. Jorge Amar: There are a couple of elements we also need to put on the table to say why now or not now. I would describe them in three broad categories. Number one is that to get an agent up and running, you do need a good technology stack and data stack. And there are many things being done to create new data, generate what we call synthetic data for training purposes. Number two, there are a number of concerns about security and risks, from drift, hallucination, bias, and any of the challenges with some of these LLMs. For example, what if an agent is talking to your customer support agent, and they generate their own little dynamics and negotiation, and now suddenly you end up with a 90 percent discount on your product because you trained your agent into churn reduction and churn avoidance? How do you control that? Maybe you need to train a whole new set of agents that are monitoring the different negotiations and different discounts, and anything that touches your CRM [customer relationship management]. And the third is, “What is the cost? What are the different usability considerations from a UX [user experience] and UI [user interface] perspective?” It’s great that you might have a very conversational chatbot, but if it looks like the 1990s interface of how you were interacting on some of your most famous messaging platforms, customers are not going to use it. So I think it is a very now conversation, but it also requires us to tackle some of these issues around risk, data, usability. Because otherwise, it’s going to go into purgatory. Brooke Weddle: That’s not where we want to go. Clear. Lucia Rahilly: Obviously, it’s vital to be talking about this now, planning for it now. But acknowledging that predictions are freighted with uncertainty, what time frame do you think we’re talking about for agents really to take effect at scale in companies? Jorge Amar: It depends on who you ask. Some of the hyperscalers and technology companies would tell you that they are already deploying it, and they are. Many of the other organizations I talk to are saying, “I need to understand this; I need to evaluate it.” And we’re probably looking at 18 to 24 months out before it reaches full scale. I believe that there are a few elements where it’ll take a little bit of time, making sure everyone is comfortable deploying them at scale. Preparing the workforce of tomorrow Bryan Hancock: Jorge, I’ve got two college-age kids. What advice do you have for them as they’re thinking through their careers and how to engage in work in a future that is agentic? Jorge Amar: I was having this conversation a few weeks ago with a friend’s son who asked me: “Maybe I should just drop out of college and become a prompt engineer.” And look, I think there are certain jobs that are going to be fully transformed by AI. These net-new roles, such as prompt engineer and content specialist, will become more relevant in organizations. I would expect this demand to be higher than what the market can offer when it comes to just college. Therefore, I think we will have to go through a reskilling at scale within the existing workforce. On the other side, how do you differentiate? If you differentiate only by having the best prompt engineer, fine. I think that is a skill set that at a certain point you will catch up on, because you could even have an agent that does prompt engineering. But if you think the most important element a company has is the trust of and the relationship with their customers, do you need a human workforce that is more empathetic? Because, again, you might be OK talking to a chatbot to reschedule an appointment. But if you were just in an accident, do you want to talk to a human, or do you want to talk to a bot? How do you emphasize skills in the incoming human workforce that help a company establish relationships? This could be the source of differentiation for your company. This could be the competitive advantage: “I offer a superior service. I offer a more human touch and surprise-and-delight experience.” You might be OK talking to a chatbot to reschedule an appointment. But if you were just in an accident, do you want to talk to a human, or do you want to talk to a bot? Jorge Amar For my friend’s son, I was opening his mind in terms of, “Hey, maybe prompt engineering is fine, but maybe my arts background will be valuable in tomorrow’s workforce because I will be able to understand human feelings in a way that no agent will be able to do.” Brooke Weddle: Jorge, if I reflect on what you’re saying, I think it’s a good time to consider the broader cultural implications of having a digital workforce. And some of that relates back to the values of a company. As you think about incorporating and onboarding agents as part of an organization, how do you do that in a way that is consistent with your company values, where you might prioritize collaboration, psychological safety, or having difficult conversations? It’s a really interesting question to ask to get full value from the digital workforce. Jorge Amar: A hundred percent. That’s why we are seeing more and more companies start to experiment with employee-facing agents more than just full end-customer-facing agents. Because how do you make sure that every interaction is in line with your corporate values, with your identity, with your brand standards, with the way you want to address a customer? That’s why I think we’re going to go first through testing and scaling of an employee-facing agentic workforce. And then, over time, in certain discrete moments, you might want to do it with your end customer. You might want to do certain tasks that are mundane: customer authentication or verification or call summarization. But, again, you don’t want to outsource to an agent or a digital agent the core of the relationship with your customer—or not just yet. Lucia Rahilly: I read the article you recently coauthored, Jorge, about agentic AI in the context of customer care. I found it fascinating that one of the findings was that almost three-quarters of Gen Z respondents to your survey believed live calls were quickest and simplest. Even younger cohorts seem to prefer human interaction. That points to the tremendous change management process that will have to happen for this to take hold. Jorge Amar: So funny you mention that. Gen Zers would be bothered if they got a phone call from their parents, right? They would prefer to interact by text. I am sure anyone who has kids can relate to that. But for customer support needs, they prefer to talk. So when we were digging a little deeper into why they prefer to talk to their provider, insurance company, telco carrier, bank, they all mentioned the same: “My situation is so unique, so important to me, that I just want to talk to a human who will give me that personalized and unique solution I won’t be able to get through a bot.” And the reality is maybe 80 percent, 90 percent of those interactions are the human giving them the exact same process, but still they felt they had a much more personalized solution by talking to a human. So maybe that changes over time and customer support bots will just get true characters that enable a fully formed dialogue. To your point, the change management is not only with employees. It’s also customer education and building trust on some of these solutions. Brooke Weddle: Jorge, when you think about the next three to five years, what are you most optimistic and excited about when it comes to the potential of agentic? Jorge Amar: I am really looking forward to doing things in a way that we couldn’t have done otherwise. I am super optimistic about doing personalization at scale with customers. I am super optimistic about empowering humans to do tasks that are not repetitive, that are not going to create attrition levels of 50, 60, 100 percent per year, that create career paths for employees that are about connecting with humans—transforming the way we work on a daily basis, focusing on the change management elements we were just describing. This hybrid workforce future should be a very uplifting environment for everyone—mostly for us as part of the workforce. It creates a new set of skills that were probably deprioritized in previous ways of finding efficiencies in companies—for example, trying to do things as repetitively and as fast as possible—and really opens the door to new ways of interacting with customers and employees. Bryan Hancock: I have a fun question. Should we tell AI thank you? Jorge Amar: I do because I think that when Skynet takes over, I want them to know I was very kind to them. It’s funny. I was reading the other day that OpenAI is spending tons of cycles—I don’t know if you’ve seen that news—on the use of “please” and “thank you.” Brooke Weddle: I say thank you to Alexa. It’s just good behavior all around. Jorge Amar: Of course. Bryan Hancock: The article on OpenAI was saying, “We’re spending millions of dollars, pumping tons of CO 2 into the atmosphere because of the energy used by the data centers because we’re saying ‘thank you.’” Jorge Amar: Totally. But imagine your kids. You’re going to teach them not to say thank you to Alexa but say thank you to a human? Come on.	2025-06-03T00:00:00		https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-future-of-work-is-agentic	[ { "date": "2025/06/03", "position": 4, "query": "future of work AI" } ]
Future of Work \| Australian Financial Review	Future of Work	https://www.afr.com	[ "The Afr View", "Melanie Silva", "Jordan Guiao", "John Davidson", "James Eyers", "Edmund Tadros", "Sally Patten", "Euan Black", "Nick Lenaghan", "Jeran Wittenstein" ]	Daron Acemoglu doesn't see how artificial intelligence lives up to all the hype. “You're not going to get an economic revolution,” he says. Oct 3, 2024; Jeran ...	Opinion AI Key workers left as invisible bystanders to the AI revolution A hype-driven, tech-led approach to AI adoption will harm workers, disappoint investors and damage the economy, we must listen and learn from workers at the coalface. Analysis Tech Observed White-collar jobs tumble, but shares soar as investors back AI future Australian staff are likely to make up some of the 8000 jobs software giant SAP says will be affected by an AI-driven global restructure, as its shares hit a record. AI The top seven business trends for 2024 Here are the big themes we expect to make news across the corporate world in 2024 – in fashion, the workplace, media and professional services. AI AI will give office workers more time to ‘create, dream and innovate’ The rapid emergence of generative AI like ChatGPT is being touted as a threat to white-collar work, but early adopters are using it to change their jobs, and even start new companies. Mining Rinehart sends in robots to run Roy Hill Gina Rinehart’s Roy Hill is retraining its truckies to fill new roles as it aims to become the world’s biggest autonomous mine. Opinion Tech Observed How tech leaders will change the way we work Atlassian looked a bit “olde worlde” when it admitted to paying remote staff less outside of NSW and Victoria, but in reality it is an example of industry figuring new norms on the fly. Exclusive Workplace Inside Cotton On’s secret HQ For the first time in nearly a decade, Cotton On lifts the lid on its New York-style headquarters, where staff can get subsidised childcare, spa treatments, and free fitness classes. Advertisement Careers The jobs that will pay the highest salaries in 2040 Most of the professions people do today will be obsolete in two decades, so how can you guide your children to a successful career?	2025-06-03T00:00:00		https://www.afr.com/topic/future-of-work-1nh5	[ { "date": "2025/06/03", "position": 88, "query": "future of work AI" } ]
The Tasks You Won't See in Job Postings Anymore	The Tasks You Won’t See in Job Postings Anymore	https://www.reveliolabs.com	[]	The share of AI-exposed tasks listed in job ads has fallen from 29% in early 2022 to 25.5% by early 2025, signaling a clear pullback in AI-amenable work.	The ability to adapt will save your job from AI The share of AI-exposed tasks listed in job ads has fallen from 29% in early 2022 to 25.5% by early 2025, signaling a clear pullback in AI-amenable work. Companies aren’t just shifting which roles they hire; they’re actively trimming AI-exposed duties within the jobs they continue to fill. The decline of AI-exposed tasks within-occupation accounts for 16% of the overall decline. The deepest within-occupation cuts target core back-office functions: processing financial transactions and client insurance needs, managing insurance and tax transactions, financial compliance and tax advisory, and balance-sheet reconciliations. As artificial intelligence rapidly evolves, its influence on the tasks companies hire for has become a critical lens through which to view how work itself is changing. Our previous newsletter examined one side of this shift: the fact that much of the drop in AI exposure in job ads was driven by companies hiring less for roles highly exposed to AI. This week, we dig into the second part of that story: the tasks that are disappearing from the job descriptions companies continue to post. Together, these findings were recently featured in Business Insider, which highlighted how automation is already reshaping demand for human labor. Building on our previous analysis of AI exposure and adoption within companies’ existing workforces, we leverage longitudinal job posting data to understand the historical and current demand for “AI-exposed” tasks, or day-to-day activities that AI could plausibly perform. If AI is automating or significantly augmenting certain tasks, those tasks should appear less often in job descriptions. And indeed, we observe a steady decline: AI-exposed tasks made up 29% of listed activities in early 2022, but only 25.5% by early 2025. This trend suggests that companies are embedding AI into operations not just as an add-on, but as a core driver of process redesign. Our previous newsletter documented the hiring-mix story; here we quantify the less visible “within-occupation” piece. Roughly 84% of the drop in average AI exposure comes from shifts between roles—firms hiring fewer high-exposure occupations—while the remaining 16% stems from companies actively pruning AI-exposed duties within the roles they still fill. While 16% may seem small, it still represents thousands of individual edits to job descriptions each quarter, signaling a gradual but meaningful redefinition of day-to-day work. Zooming in on that 16%, we identify which occupations have eliminated the largest share of AI tasks from their postings. Data and Document Clerks top the list, with the share of AI-exposed tasks in job postings dropping 8.2 percentage points between 2022 and 2024 as firms automate record-keeping, indexing, and routine data retrieval. Business Executive Leaders and Shift Operations Supervisors follow close behind, reflecting how leadership and managerial roles are refining job scopes to focus on strategy and oversight rather than hands-on process work. Aerospace Maintenance Technicians and Technical Sales Consultants round out the top five, suggesting that automation’s reach extends to specialized technical and customer-facing workflows as well. To understand what is being cut, we drill into which tasks specifically experienced the most decline in demand within those high-change occupations. Core back-office functions dominate the list, including processing financial transactions and client insurance needs, managing insurance and tax transactions, and financial compliance and tax advisory services. Balance-sheet reconciliations and other account-management duties also saw double-digit declines, underlining that rule-based, repetitive tasks remain prime targets for AI and robotic process automation. These edits suggest hiring managers are recalibrating roles to emphasize judgment, client engagement, and exception handling over routine data work. When we look across industries at those trimming the most AI-exposed tasks inside the jobs they still hire for, Marketing and Advertising Services leads the pullback with just over a two-point drop, followed closely by Food and Hospitality. Automation Solutions and Design and Printing Services are next, with Commercial Aviation, Consumer Tech Distribution, Energy and Resources, Healthcare and Wellness, Human Resources Services, and Wellness Products all dialing back routine, process-driven duties. These cuts suggest that campaign execution, reservation flows, basic automation design, and similar tasks are being pared down, potentially as they become aided or augmented by AI platforms. By focusing on within-occupation shifts in labor demand, these results reveal exactly which day-to-day activities are being automated or de-emphasized within the roles that companies still choose to fill. While the hiring-mix story captures which jobs firms pursue, the within-occupation view opens a window onto how work itself is being reimagined in the age of AI. In this way, tracking postings gives us a live, ground-level view of how organizations are reshaping job content and the very nature of work in the age of AI, well before those changes show up in employment statistics.	2025-06-03T00:00:00		https://www.reveliolabs.com/news/macro/the-tasks-you-won-t-see-in-job-postings-anymore/	[ { "date": "2025/06/03", "position": 12, "query": "job automation statistics" } ]
Robotics and Automation Careers for Students in STEM	One moment, please...	https://gradright.com	[]	Robots can also handle dangerous and physically demanding jobs, like mining and infectious disease cleaning, and improve workplace safety. Moreover, automation ...	Loader Please wait while your request is being verified...	2025-06-03T00:00:00		https://gradright.com/robotics-and-automation-careers-for-students-in-stem/	[ { "date": "2025/06/03", "position": 61, "query": "job automation statistics" } ]
Artificial Intelligence: How far is it?	Artificial Intelligence: How far is it?	https://www.team-bhp.com	[]	But developing the algorithm, interpreting results, fine tuning the process and software, data mining are job roles that already are popping up all over the ...	Quote: Originally Posted by prithm (Post 4369443) A lot is being said that automation & AI are actually "enhancers", but it is just tad whitewashed lie. Being a new enterant to this field I'm seeing opportunities being lost to provide a human a salary & job. . If ones lively hood is at stake, that is of course deeply upsetting. Even so, mankind has been inventing stuff that makes any job easier and quicker to do for centuries. History shows that certain job roles diminish where other and new job roles will grow and appear. If your job is at stake, that wont do you much good, I appreciate. The thing with AI is in particular allows for jobs to be done that are way beyond the human skills and competence. As a downside if you like, that also means certain current job roles diminishes. But developing the algorithm, interpreting results, fine tuning the process and software, data mining are job roles that already are popping up all over the world. So the role of back office is changing rapidly due to Ai and other new technologies. Good luck. Jeroen If ones lively hood is at stake, that is of course deeply upsetting. Even so, mankind has been inventing stuff that makes any job easier and quicker to do for centuries. History shows that certain job roles diminish where other and new job roles will grow and appear. If your job is at stake, that wont do you much good, I appreciate.The thing with AI is in particular allows for jobs to be done that are way beyond the human skills and competence. As a downside if you like, that also means certain current job roles diminishes. But developing the algorithm, interpreting results, fine tuning the process and software, data mining are job roles that already are popping up all over the world.So the role of back office is changing rapidly due to Ai and other new technologies.Good luck.Jeroen Quote: Originally Posted by Jeroen (Post 4369563) If ones lively hood is at stake, that is of course deeply upsetting. Even so, mankind has been inventing stuff that makes any job easier and quicker to do for centuries. History shows that certain job roles diminish where other and new job roles will grow and appear. If your job is at stake, that wont do you much good, I appreciate. The thing with AI is in particular allows for jobs to be done that are way beyond the human skills and competence. As a downside if you like, that also means certain current job roles diminishes. But developing the algorithm, interpreting results, fine tuning the process and software, data mining are job roles that already are popping up all over the world. So the role of back office is changing rapidly due to Ai and other new technologies. Good luck. Jeroen But by automating such entry level jobs and some jobs that a mid technically proficient developer or support engineer can handle, it is even more pushing against the human factor. Classical example will be the surge of online banking which though is not automation per se rather digitization, the human factor is completely gone. My personal banker is just a voice that I hear at the other end of my mobile call. I know nothing about that person, nor have seen him, nor shared pleasantries for more than 5 years now. Yes, some form of technological advancement is needed but make sure those advancements aid the person who is doing his/her job. Not replace his livelihood. The AI algorithms are coded by human hands and brain. It is bought to life, but then thats how far it should go. If I mimic a biological system and make it do no matter how mundane job it is, it is not worthwhile. If I am put in a situation to select between waking up a support engineer at night to solve a server problem and a automated solution, I will select that midnight call to wake him/her gladly and do it out of passion for human intervention. AI and RPA should aid, but the way they are utilized is something that we should be wary about. I agree that "Change is the only constant.", but it is robbing someone of their livelihood. We cannot expect a entry level analyst or support engineer to give astounding work in short time. It takes grooming, self realization, awareness and ones own interest to find the right path in their career.But by automating such entry level jobs and some jobs that a mid technically proficient developer or support engineer can handle, it is even more pushing against the human factor. Classical example will be the surge of online banking which though is not automation per se rather digitization, the human factor is completely gone. My personal banker is just a voice that I hear at the other end of my mobile call. I know nothing about that person, nor have seen him, nor shared pleasantries for more than 5 years now.Yes, some form of technological advancement is needed but make sure those advancements aid the person who is doing his/her job. Not replace his livelihood. The AI algorithms are coded by human hands and brain. It is bought to life, but then thats how far it should go.If I mimic a biological system and make it do no matter how mundane job it is, it is not worthwhile. If I am put in a situation to select between waking up a support engineer at night to solve a server problem and a automated solution, I will select that midnight call to wake him/her gladly and do it out of passion for human intervention.AI and RPA should aid, but the way they are utilized is something that we should be wary about. Quote: Originally Posted by prithm (Post 4369791) AI and RPA should aid, but the way they are utilized is something that we should be wary about. Where do you draw the line of what can or is allowed to be automated? Or offshored, which is just another way of efficiency gain. India did well as the Western world offshored tens if not hundreds of thousands of jobs in the last decade. Still, unemployment in most Western countries is actually pretty low, for some countires an all time low. So just because one type of job disappears doesnt mean the labour market shrinks perse. But its a reall bugger of course, if it is your job that disappears. E.g. gets offshored, gets automated. All these are hugley competitive industries and cost control and efficiency is a constant pressure for all of them and many other industries. Im actually convinced that AI is doing an awfull lot more than bring simple straight forward efficiency. It allows us to analyse and draw conclussion that are simply way beyond the reach of any human, or even large teams of humans. Currently data scientist competences are the most sought after in many parts of the industrialized world. And that is just one new job role, many more are emerging as well. But yes, it will have an impact on careers, might require people to be reschooled/trained/move to a different location. Jeroen As usual its all in the eye of the beholder. Most of us would like insurance companies to offer low premium, Telecomoperator to provide low tariffs, banking at no cost, competitive airline tickets etc. etc. Very few, if any at all, succesfull economic models have been based on keeping everybody employed, no matter what.Where do you draw the line of what can or is allowed to be automated? Or offshored, which is just another way of efficiency gain.India did well as the Western world offshored tens if not hundreds of thousands of jobs in the last decade. Still, unemployment in most Western countries is actually pretty low, for some countires an all time low. So just because one type of job disappears doesnt mean the labour market shrinks perse. But its a reall bugger of course, if it is your job that disappears. E.g. gets offshored, gets automated.All these are hugley competitive industries and cost control and efficiency is a constant pressure for all of them and many other industries.Im actually convinced that AI is doing an awfull lot more than bring simple straight forward efficiency. It allows us to analyse and draw conclussion that are simply way beyond the reach of any human, or even large teams of humans.Currently data scientist competences are the most sought after in many parts of the industrialized world. And that is just one new job role, many more are emerging as well. But yes, it will have an impact on careers, might require people to be reschooled/trained/move to a different location.Jeroen I just read an interesting article. Unfortunately in Dutch. Written by a fromer minister of social welfare. He suggested that the (financial) gains from innovation cant just be pocketed by employers/corporations. He suggested part of it needs to be used to those affected. So training, schooling etc. its an interesting concept and to some extent we have labour laws in some European countries that aim to have similar effects. Employers have an active role and responsibility to ensure their staffs stays employable. If not with them, at least on the labour market at large. Jeroen AI is here to stay, no doubt about that. Only thing is that alternative plan should be made for people who rely on entry level low paid jobs, which robots with AI will takeover. Even in developed countries, graduates start off with such jobs until they find a well paid jobs in their respective fields, if they can't get any job after college who will pay their loans ? This seems to be a recipe for disaster for our economies. People seem to think we are doing well even after computers took some of our jobs, this might be partly true, but technology is already creating lot of instability for our economy. Mass production has led to irreversible ecological destruction which we don't care to have a look at. we need better leaders who are more than just good public speakers to handle this. Hope they don't make a bot for the next president. Quote: Originally Posted by giri1.8 (Post 4370269) Even in developed countries, graduates start off with such jobs until they find a well paid jobs in their respective fields, if they can't get any job after college who will pay their loans ? This seems to be a recipe for disaster for our economies. . The guy who came to our new home a few months ago for our broadband connection, installed the cable, routers, WiFi boosters and programmed the TV, was a law student doing this sort of installation work 1-2 days a week. It actually makes a lot of sense. Somebody who is at college/univiersity tends to be a quick learner, reasonably articulate and will leave after a few years when he or she graduates. (so you dont have to increase his/her wage to often, once left you get a new graduate on the beginning end of the salary scale. For the student its an interesting job, very independent, you get some training and a cool van to drive around in. Works for both parties. It really depends on the field you have graduated in and whether that is in demand. Also, my own experience is that in India a lot of folks stay very close to the original field they studied and graduated in, get a first job and more or less make a career through various vertical promotions in more or less the same field. Nothing wrong with that, as it seems a lot of employers recruit for that as well. So they are looking for design manager with xx years of experience in yy discipline. In Europe and the USA as well, you see people more weaving through very different jobs and roles. (Me being a similar case). Neither my eldest son or my daugther both with University degrees have ever held a job in their respective field. But are still doing very well career wise! Apart from my son, none of my three kids and most of their friends ever had a long term labour contract. They are always hired for a year, maybe with another extension. Was very different when I started to work. Doesnt seem to worry them at all and they still can get a mortgage. But flexibility and going from one job to the next at various employers is very much what the labour market is like these days, especially if you have a college/university degree. Some jobs, require very specific training and or certification of course, e.g. a pilot or a surgeon or a speech and language therapist. The one thing that is happening is that job roles are being inflated in terms of formal educational requirements. So jobs that 10 years ago were advertised as requiring a completed secondary school education, might require a college degree these days. Not quite sure why, whether that is a reflection of the labour market, or more of the recruiters? I dont know So formal education is still important although many (western) companies these days tend to recruit for attitude rather then for formal training. So you will see jobs advertised with a requirement to have a college degree without being specific in which field. Or along lines of requiring "acadamic approach" required. So whereas I cant say that AI is causing all of these changes in the labour and educational market it does play a role how society at large is changing rapidly. Jeroen I am not quite sure whether that statement is correct. First of all, typical front and back office type of jobs have by and large been outsourced already, largely to India. Only when a bank or for instance an insurance company wants to provide customer service in its native language they would have to staff with locals. Very often in developped countries these are part time jobs, very often held by students!The guy who came to our new home a few months ago for our broadband connection, installed the cable, routers, WiFi boosters and programmed the TV, was a law student doing this sort of installation work 1-2 days a week. It actually makes a lot of sense. Somebody who is at college/univiersity tends to be a quick learner, reasonably articulate and will leave after a few years when he or she graduates. (so you dont have to increase his/her wage to often, once left you get a new graduate on the beginning end of the salary scale. For the student its an interesting job, very independent, you get some training and a cool van to drive around in. Works for both parties.It really depends on the field you have graduated in and whether that is in demand. Also, my own experience is that in India a lot of folks stay very close to the original field they studied and graduated in, get a first job and more or less make a career through various vertical promotions in more or less the same field. Nothing wrong with that, as it seems a lot of employers recruit for that as well. So they are looking for design manager with xx years of experience in yy discipline.In Europe and the USA as well, you see people more weaving through very different jobs and roles. (Me being a similar case). Neither my eldest son or my daugther both with University degrees have ever held a job in their respective field. But are still doing very well career wise! Apart from my son, none of my three kids and most of their friends ever had a long term labour contract. They are always hired for a year, maybe with another extension. Was very different when I started to work. Doesnt seem to worry them at all and they still can get a mortgage. But flexibility and going from one job to the next at various employers is very much what the labour market is like these days, especially if you have a college/university degree.Some jobs, require very specific training and or certification of course, e.g. a pilot or a surgeon or a speech and language therapist.The one thing that is happening is that job roles are being inflated in terms of formal educational requirements. So jobs that 10 years ago were advertised as requiring a completed secondary school education, might require a college degree these days. Not quite sure why, whether that is a reflection of the labour market, or more of the recruiters? I dont knowSo formal education is still important although many (western) companies these days tend to recruit for attitude rather then for formal training. So you will see jobs advertised with a requirement to have a college degree without being specific in which field. Or along lines of requiring "acadamic approach" required.So whereas I cant say that AI is causing all of these changes in the labour and educational market it does play a role how society at large is changing rapidly.Jeroen https://www.ted.com/talks/daniel_sus..._true#t-935209 Guess this TED talk has not been posted here yet Today most rely on phones/internet to : 1) Order a cab (the very job of locating a cab, booking it and ensuring safe drop is done by software) 2) Order food (type of food, type of hotel, estimated delivery time all produced by software) 3) We play chess more with machines than with people. 4) Order groceries, clothes, electronics etc via online platform. 5) Most calculations of tax, transactions and balances are done by machines. A.I is intelligence given of taught by mankind to the machines.. we even rely on Internet to look up the smallest of things today and the dependence is ever-growing. A.I is here.. all one has to do is open their eyes and run away from it. Yes I hate technology. Quote: Originally Posted by dark.knight (Post 4372347) A.I is here.. all one has to do is open their eyes and run away from it. Yes I hate technology. So how come you are in the Internet? Jeroen So how come you are in the Internet?Jeroen http://doyoutrustthiscomputer.org/watch New documentary from the "AI-is-evil" side, featuring interviews with Elon Musk, Ray Kurzweil, etc. Quote: Originally Posted by Samurai (Post 4254729) I don't consider Mark Zuckerberg as a serious technologist, he just created a company that become very successful. That takes business vision rather than technology vision. Elon Musk on the other hand has innovated in multiple high-tech domains like e-commerce, electric cars, space vehicles, etc. So I would put money on Elon Musk's opinion. That is why I told last year that we can't take his views on AI seriously. He only cares about the commercial aspects of AI, and not the social/technology impact on the world. Alight. Mark Zuckerberg has openly admitted that he didn't take a broad enough view of how his platform could be misused. That's a very generous way of saying he was clueless about the implications. I mean, it is his own platform, which he knows best.That is why I told last year that we can't take his views on AI seriously. He only cares about the commercial aspects of AI, and not the social/technology impact on the world. Quote: Originally Posted by smartcat (Post 4397309) What is Machine Learning? Same as AI? AI is achieved using machine learning. AI is the end result, ML is the method. Data analytics / Big data / Machine learning really is more of higher mathematics and statistics. None of the engineering courses teach mathematics that is relevant to these fields. Do not see many engineering students showing the same level of interest towards mathematics that they pay towards programming / app development. Will it be more useful for people who have PG / Phd in mathematics / statistics to learn programming skills to enter these fields rather than engineering graduates trying their hands learning higher mathematics? Quote: Originally Posted by AltoLXI (Post 4397340) None of the engineering courses teach mathematics that is relevant to these fields. Do not see many engineering students showing the same level of interest towards mathematics that they pay towards programming / app development. Actually, the all the prerequisites for ML is indeed covered in engineering mathematics. That is basically statistics, calculus, linear algebra & programming. However, it is taught mindlessly without any exposure to their real world application. Therefore, most students never develop any understanding or interest for it.	2025-06-03T00:00:00		https://www.team-bhp.com/forum/shifting-gears/189383-artificial-intelligence-how-far-9-print.html	[ { "date": "2025/06/03", "position": 64, "query": "job automation statistics" } ]
When Will the Tech Job Market Recover in 2025?	When Will the Tech Job Market Recover in 2025?	https://unitedcode.net	[ "George Mozharowsky", "Terry May" ]	AI, ML, blockchain, cloud platforms (AWS, Azure), and cybersecurity tools will dominate. Technology hiring trends point towards automation, data-driven decision ...	For the first time in a marathon of mass layoffs, pandemic adjustments, AI-driven disruptions, and the US’ resumed tariff negotiations, the US tech jobs market of 2025 is no longer on fire, but it is not freezing. We’re seeing a strategic recalibration: Companies aren’t forcing employees into mass disengagement like in 2021, nor are they sending thousands of employees a week into crisis like in 2023. Instead, the tech industry job market is becoming more agile, targeted, and ruthlessly efficient. Compared to the unpredictable volatility of 2023, 2025 marks a more intentional, albeit unforgiving, pace in hiring. As someone deeply involved in tech hiring, we can tell you this: the balance of power has changed. Companies that are good at hiring with AI and strategic decision-making have unprecedented access to individuals previously trapped in big tech. Talented candidates are switching gears early, reskilling, specializing, and moving into high-velocity fields like AI security, cloud automation, fintech infrastructure, and digital health. This new wave of job mobility is driven by adaptive learning platforms, upskilling programs, and a job market that prioritizes demonstrable skills over brand-name experience. So, how is the tech job market right now? Not exactly, but it’s maturing. For employers and job candidates who grasp the new rules, this could be the most strategic moment in ten years to make hires or get hired. Let’s talk about it in detail! Tech Layoffs 2023–2024: A Reality Check From 2023 to 2025, the tech industry job market experienced a significant personnel decline. Over 264,000 tech employees were laid off in 2023 alone worldwide, with over 95,000 in the United States. The cut continued through 2024, when approximately 152,000 jobs were cut. Through early 2025, over 26,000 additional tech market jobs were cut, indicating ongoing reorganization within the industry. Layoffs, however, are increasingly focused and no longer reflect panic-driven mass cuts. Instead, they’re part of organizational rebalancing strategies aimed at adapting to AI-driven productivity models. The reductions were widespread but varied. Most firms focused on reducing non-core jobs, middle management levels, and underperforming units. Mid-management, human resources positions, and lower-level posts were the most brutal hit. Despite these reductions, tech job growth in specialty areas remains constant. Companies including Bank of America, Chase, and Wells Fargo have hired software engineers, AI engineers, and cybersecurity experts. This shift reflects a broader industry trend toward prioritizing jobs that drive innovation and security. The key hiring mantra? ‘Build what automates, secure what scales.’ Notably, while some companies lay off workers in traditional roles, they simultaneously expand staff in artificial intelligence and cloud computing. For instance, Salesforce laid off over 1,000 employees in early 2025 but continued to recruit for AI-focused roles. Similarly, Microsoft laid off around 6,000 employees, citing organizational realignment and increased usage of AI-generated code. The changing environment implies that although the job market for tech has had to deal with difficulties, it is also evolving. Firms are re-shaping their workforce to meet new technologies and business needs, signaling a strategic realignment instead of decreasing the sector’s vitality. How the U.S. Tech Job Market Is Evolving in 2025 Major financial firms, such as JPMorgan, Goldman Sachs, and Morgan Stanley, are aggressively adopting AI to automate critical tasks, including IPO preparation and economic analysis. This is creating a massive demand for AI and cybersecurity specialists. Investment banks like Evercore and Barclays are also ramping up hiring, particularly in the tech, healthcare, and industrial sectors, as they prepare for a “crazy” year of recruiting in 2025. This recruitment surge isn’t limited to coding roles—data analysts, prompt engineers, and AI risk auditors are also in high demand. Another funding outraise pits AI startups against each other. For example, Bat VC announced its second $100 million fund to fund US and Indian startups in fintech, enterprise software, and AI. In healthcare, companies like Omnicell have developed in-house innovation centers that use AI and automation to optimize drug management and the supply chain. This is also driving demand for AI and systems automation specialists. The energy industry is seeing growing demand for technicians working in renewable energy, smart grids, and green technologies. By 2025, it is set to become one of the largest tech employers. Digital twins, predictive maintenance systems, and grid optimization tools are becoming hiring hotspots within green energy firms. The Rise of Nearshoring in Latin America The nearshoring trend – particularly in Latin America – is gaining momentum as U.S. companies strive to save costs without compromising on quality. Mexico, Argentina, Colombia, and Brazil are favored hotspots. Hiring managers are moving away from the “Silicon Valley or bust” mentality, exploring Tier 1 cities like Bogotá, São Paulo, and Monterrey as extensions of their engineering teams. Major advantages: Cost savings: The average salary of a Mexican senior AI developer is approximately $62,400 per year, compared to $120,000–$144,000 per year in the U.S. (source: Alcor BPO). Available talent pool: Domestic investments in technical education are harvesting a strong pool of available talent. Similarity and closeness: Time zone alignment and cultural closeness facilitate simpler collaboration and project management. Outsourcing trend: The IT outsourcing market in Latin America is forecast to increase to $27.57 billion by 2029, making it an appealing choice compared to higher-cost centers in Western Europe and North America (source: Huntly). This model supports hybrid development strategies, where U.S.-based teams handle architecture and design, while LATAM counterparts take on implementation and testing. Despite more restrictive hiring, the demand for senior-level tech talent remains strong. Companies are countering by focusing on strategic recruitment, automation proficiency, and location-based teams through nearshoring. While nearshoring to LATAM continues to thrive, there’s a widening gap between what developers are paid and what outsourcing companies charge. Over the past few years, average developer compensation in LATAM has slightly declined, whereas outsourcing rates have either remained stable or increased. This imbalance allows traditional outsourcing providers to maximize their margins, often without disclosing how much of the budget reaches the developers. Clients end up overpaying – sometimes significantly- without realizing it, as outsourcing providers are not willing to reveal salary breakdowns. At UnitedCode, we take a fundamentally different approach through our transparent pricing model. Our clients always know how much engineers are paid directly, and our flat commission is fixed and clearly outlined in the contract. This model ensures cost predictability, builds trust, and fosters win-win collaboration where engineers feel valued and clients get true ROI. Today, with AI-powered automation and senior engineers experienced in your domain, a single top-tier specialist can often outperform two mid-level or three junior developers. And with market salaries slightly down, now is the best time to find your hidden gem – affordably and transparently. How AI and Automation Are Reshaping Tech Hiring One of the deepest changes in the tech job market outlook is the expansion of AI-based hiring software and the stratospheric demand for machine learning, data science, and automation engineers. Historically, tech hiring used to be all about quickly hiring teams to have projects ready on time. But with today’s lean, productivity-driven economy, companies are seeking precision over quantity. This era of ‘fewer hires, smarter teams’ rewards candidates who can combine technical depth with business fluency. Why hire three junior developers when one senior AI-capable engineer can build an automated system that scales effortlessly? This logic drives gargantuan changes in job specifications, team structures, and pay splits. For example, a seasoned full-stack developer with LLM integration knowledge or workflow automation skills can now demand 30–50% higher paycheques, especially in fintech, e-commerce, and health tech. These platforms help employers hire with AI—faster, smarter, and at scale. AI is also transforming the recruitment process itself: Recruiters are applying platforms like HireVue, Eightfold AI, and Pymetrics to assess technical skills, emotional intelligence, and behavioral inclinations. Artificial intelligence platforms remove unconscious bias by focusing on skills over qualifications, opening the door to a more diverse set of candidates. Natural language processing algorithms can analyze resumes, GitHub repositories, and public contributions to align actual outputs with job requirements. These systems enable businesses to hire more intelligently and rapidly, but in doing so, they set higher standards for applicants. It’s no longer acceptable to just hit the job posting requirements; you must differentiate yourself in a measurable, machine-readable form. Tech candidates must now think algorithmically: measurable skills, quantifiable achievements, and optimized presentation. The Rise of AI-Powered Recruitment Tools and Software With the tide in hiring technology shifting to specialization and speed, recruiters are increasingly using AI-driven software to make faster, smarter choices and reduce time to hire. Such software is not just about CV screening—such applications read candidate potential, predict job fit, and streamline entire recruitment pipelines. Today, tools like Eightfold AI, HireVue, Pymetrics, and Fetcher are the go-to options for HR departments wanting to scale recruitment with intelligence in the United States. Eightfold AI charts the career paths of candidates and positions them in ideal jobs using deep learning. HireVue allows scoring of video-based tests with AI to quantify soft skills and communication styles. Pymetrics uses neuroscience-inspired games and behavior AI to pair individuals with jobs where they will thrive. Fetcher integrates AI with outbound sourcing, automatically finding and contacting top talent via email and LinkedIn. These tools improve candidate quality, reduce hiring bias, and eliminate tedious manual work, freeing recruiters’ time for strategic decision-making. For high-speed tech startups and businesses, that time advantage is crucial. The best recruiters of 2025 are not just relationship builders – they’re technologists. At UnitedCode, we take it a step further with our own AI-based hiring solution, specifically designed for tech positions. Leaning on years of IT staffing experience, we created a platform that replaces clumsy bulk hiring with smart, data-driven targeting. Our platform utilizes AI testing of hard and soft skills, allowing companies to spot uniquely skilled talent with tight domain specialization and high job-fit accuracy. Learn about our 5-step selection process here. The New US Government “Tariff War” and Its Ripple Effects In 2025, the “tariff war” with China, Canada, and Colombia has rewritten global supply chains and sent shockwaves through the technology sector. The trade policies have accelerated the trend of restoring production in the U.S. and propelled demand for local tech talent in logistics, manufacturing automation, and cybersecurity. Job Market Impact in Numbers According to Staffing Industry Analysts, this structural change can have a profound impact on employment trends: Job loss in traditional roles: Manufacturing and logistics roles can decline by 10–15%, implying a job loss of approximately 100,000–150,000 jobs. Job creation in automation roles: Automation, systems integration, and robotics-related roles are expected to see 20–25% job growth, resulting in the addition of 120,000–180,000 high-skill new jobs. These shifts are particularly acute in manufacturing states like Ohio, Texas, and Michigan, where traditional manufacturing is slowly giving way to high-tech production models. Domestic Manufacturing Investments Surge In response to tariff pressures, U.S. companies are doubling down on domestic production and automation: Automation budget increases: Companies raise automation-specific capital investments by 30–40% to offset operational costs. New factories for tech: Intel and TSMC are investing billions of dollars in semiconductor factories in Ohio, Texas, and Arizona, creating thousands of engineering and technical jobs. Despite this investment, a looming talent gap remains. To fulfill the goals of the CHIPS and Science Act, the U.S. will need over 300,000 additional skilled workers, including engineers, technicians, and automation specialists. Demand Surges for AI and Automation Talent Tariff-induced supply chain reconfiguration has raised the need for technology talent with niche skill sets: Automation engineers to develop and install smart factories. Cybersecurity specialists to protect complex, distributed production environments. AI developers to design predictive systems for logistics, inventory, and fault detection. These roles are expected to drive the next wave of recruitment and determine how organizations build their future workforces. This presents a lucrative opportunity for candidates with hardware-software integration skills, especially in embedded systems and industrial AI. What to Expect in Late 2025? The global tech talent market is reaching an inflection point as companies react to economic uncertainty, emerging technologies, and shifting employee expectations. Towards the end of 2025, the demand for specialist roles in AI, cybersecurity, and cloud infrastructure is growing, while generalist and junior roles face increasingly fierce competition. Employers are adopting agile staffing models and geographic nearshoring strategies to reduce costs and secure talent. The following summary outlines the most notable trends affecting hiring intentions for the coming months. In general, the picture looks like this: Extremely technical tech skills – that is, in AI, cybersecurity, and cloud engineering – will command premium salaries and drive hiring activity up to 2025. At the same time, companies will move away from generalist positions and junior employees and towards experienced specialists and adaptive engagement models like contract-to-hire. Nearshoring within LATAM will start to pick up, driven by both cost-effectiveness and changing geopolitical motivations. When will the tech job market recover? Many signs are pointing toward a rebound by late summer to early fall of 2025. Why? Two prime forces are converging: The Federal Reserve will most likely ease interest rates by Q3, unleashing new capital for startups and large-enterprise technology ventures. A deluge of nearly 2,000 well-capitalized IT startups that started late in 2024 is beginning to scale very rapidly, and they’re requiring top-class talent, pronto. So, is the tech job market improving? Yes — but selectively. Hiring trends in technology show strong demand for senior-level and specialized roles, such as those in AI, cybersecurity, and cloud infrastructure. They’re creating a digital-first infrastructure and recharting business models on automation, cybersecurity, and AI. Historically, this kind of forward-thinking hiring is a sign of general recovery in tech. Thus, while junior and generalist job demand may perhaps remain subdued, specialized and senior-level tech experts are poised for a high level of opportunity. History suggests that when specialized hiring booms, general recovery follows. A Strategic Window for Both Employers and Talent This brings us to a critical inflection point. Companies are optimizing recruitment with AI-based hiring solutions, while talent is adapting to machine-readable resumes and real-world skill assessments. And with the software engineer job market demand increasing, seasoned professionals have the chance to reenter or reposition themselves for high-value roles. Whether you’re wondering how many jobs are available in technology or when will tech hiring pick up again, the short answer is: now is the time to prepare. The IT job market in the US in 2024 may have looked bleak, but as of mid-to-late 2025, the tech job market outlook is shifting. It’s time to take advantage of this rare supply, demand, and strategic transformation alignment. So, as a result: For employers, acting now means securing critical talent at rates that may not last as the market tightens. For candidates, the next six months are a window to specialize, network, and show measurable results that AI hiring platforms can rank and recognize. Outlook: When Will the Tech Job Market Get Better? The US tech job market is not just stabilizing but also transforming. After two madcap years, the sector is in a wiser, leaner phase of specialization, robotics, and value creation. AI is not taking jobs away — it’s recasting them. And while junior-tier roles may still be scarce, demand for senior, AI-educated, and infrastructure-intelligent talent is racing ahead. For workers, it is an unusual opportunity to hire with long-term impacts before competition gains momentum and salaries rise. For career seekers, it’s the moment to recreate your value and associate with companies building tomorrow. We at Unitecode assist technology businesses in hiring smarter and growing faster – from hiring AI-inclined developers to constructing full-stack teams that deliver. We also help talent secure top-impact roles aligned with technology’s future. Whether hiring or looking, Unitecode knows how to move you ahead! Let’s talk. Frequently Asked Questions Is the tech market recovering in 2025? Yes – slowly but surely. By Q4, we foresee a turning point, with high-skill roles in the spotlight. The late 2025 outlook for the tech employment market is good for senior and AI-expert professionals. Does the tech industry have a future? Yes. With continued investment in AI, cybersecurity, fintech, and clean energy, tech is the cornerstone of global growth. Is it worth getting into tech in 2025? Yes. The sector awaits skilled players with the capability to upskill fast. AI, cloud infrastructure, and cybersecurity are needed. What tech job is future-proof? AI/ML, cybersecurity, DevOps, cloud engineering, and data science jobs are future-proof. They’re inextricably connected to the evolving needs of digital transformation. How is the current tech job market in the USA? It is recalibrated and selective. Experienced and expert talent is needed, while junior positions are limited. Which technology will be in demand in 2025? AI, ML, blockchain, cloud platforms (AWS, Azure), and cybersecurity tools will dominate. Technology hiring trends point towards automation, data-driven decision-making, and security-first development.	2025-06-03T00:00:00	2025/06/03	https://unitedcode.net/when-will-the-tech-job-market-recover-2025-hiring-outlook-layoffs-and-policy-shifts/	[ { "date": "2025/06/03", "position": 84, "query": "job automation statistics" } ]
A Workforce Reimagined: How AI Agents Are Reshaping ...	A Workforce Reimagined: How AI Agents Are Reshaping Work, Roles, and Strategy	https://blog.workday.com	[]	Adoption is accelerating. In early use cases, a PwC report found that AI agents are already delivering productivity and speed-to-market gains of 50% or more.	What Is an AI Agent and How Does It Work in the Enterprise? Unlike advancements to language learning models (LLMs,) AI agents aren’t just an upgrade to automated processes. They represent a fundamental shift in how work gets done. Unlike traditional bots or scripts that execute rigid tasks, AI agents are autonomous applications designed to operate within defined parameters to accomplish complex goals. They don’t just follow instructions. They interpret context, collaborate with other agents, and adapt based on outcomes. What makes them different is their ability to function independently within a system. You give them an objective, a dataset, guardrails, and expectations, and they figure out how to deliver the result. That level of autonomy changes everything about how we think about roles, productivity, and decision-making. This isn’t just a future concept. It’s already playing out in organizations that are embedding agents into day-to-day workflows—from software engineering to customer support to talent acquisition. How Companies Are Adopting AI Agents Across Departments Adoption is accelerating. In early use cases, a PwC report found that AI agents are already delivering productivity and speed-to-market gains of 50% or more. Some organizations have reduced software development cycles by up to 60%, while cutting production errors in half. Most companies are actively planning or piloting agentic AI across critical business functions. But there’s a gap. While the technology is moving fast, many teams are still catching up—organizational readiness, role clarity, and change management remain common bottlenecks. The challenge isn’t just technical, it’s organizational. Employees aren’t sure how AI will affect their roles, their careers, or their long-term value. That uncertainty leads to hesitation, resistance, or worse: disengagement. And that makes leadership clarity essential. Leaders who can clearly communicate the “enduring human value proposition” will win trust faster, and get better results from both humans and machines. Priest defines this enduring human value proposition as the skills AI can’t replicate: first-principles thinking, creative problem-solving, empathy, collaboration, and the ability to lead through ambiguity. When leaders design roles, teams, and strategies that center these strengths rather than sideline them, they give employees a clear stake in the future. Another consideration: not every department is equally ready for AI integration. PwC refers to this as the “jagged frontier”—some functions are ahead, some are behind. Successful implementation requires understanding where you are on that curve, prioritizing wisely, and making change manageable rather than overwhelming.	2025-06-03T00:00:00	2025/06/03	https://blog.workday.com/en-us/a-workforce-reimagined-how-ai-agents-are-reshaping-work-roles-and-strategy.html	[ { "date": "2025/06/03", "position": 10, "query": "workplace AI adoption" } ]
AI adoption surges to 72% among professionals	AI adoption surges to 72% among professionals	https://the-cfo.io	[ "Marina Mouka", "Date Published", "Thomas Sutter", "Industry Director", "Finance", "Global Solutions", "Oracle Netsuite", "The Cfo" ]	72% of professionals now use AI tools at work, with 50% using unapproved AI tools to enhance productivity and client relationships.	AI usage in the workplace has reached new heights, with a surge in adoption across industries like accounting, consulting, finance, and legal, according to Intapp’s 2025 Technology Perceptions Survey. The survey, which gathered data from over 800 fee earners across the U.S. and U.K., reveals that the use of AI tools has become widespread, with professionals citing increased productivity, creativity, and growth as the primary benefits. Widespread Shift in AI Adoption The survey highlights a dramatic rise in AI usage in just one year. In 2024, 48% of professionals reported using AI tools at work. This figure has now jumped to 72%, reflecting a growing recognition of the technology’s potential to streamline operations. “AI is becoming an essential part of the workflow for professionals, driving top-line and bottom-line growth within their firms as a result,” said Robin Tech, Vice President of AI and Data at Intapp. He further emphasized that AI’s widespread adoption is no longer a trend but a standard practice in today’s professional environment. Additionally, 56% of firms have adopted AI in some capacity, and another 32% are in the early stages of their AI journey. This sets the stage for nearly 88% of firms to integrate AI tools in the near future. Unauthorized Use of AI Raises Concerns While professionals are embracing AI, a significant portion is bypassing corporate protocols to use AI tools independently. The survey reveals that 50% of professionals have used AI tools not authorized by their firm, and another 23% are willing to do so. This trend raises concerns about the security risks associated with unauthorized AI usage, particularly regarding sensitive client data. “Unauthorized AI usage creates new attack vectors threatening data security,” warned Tech. As AI becomes more embedded in professional workflows, firms must balance innovation with robust security measures to mitigate risks. AI Enhances Productivity and Creativity AI’s value in the workplace isn’t just in automation — it’s also empowering professionals to think creatively and work more efficiently. According to the survey: 62% of respondents found AI highly useful in their work. 82% believed the quality of AI-generated work is at least as good as their own. 41% said AI helped them synthesize large amounts of information, and 38% credited AI with helping them generate creative solutions. As AI becomes more ingrained in daily tasks, professionals report not just increased efficiency but a shift toward higher-level and more strategic work. Reallocating Time for Growth Professionals are reallocating the time saved by AI to focus on more value-added tasks, driving growth within their firms: 42% have reallocated time saved to focus on higher-level client work. 28% have strengthened client relationships, while 25% have focused on expanding their professional networks. 23% have reallocated time to pursue new business opportunities, while 24% have increased billable hours. As firms leverage AI, it is clear that the tool is helping them not only become more efficient but also capitalize on new opportunities for revenue and client engagement. The Future of AI in the Workplace AI’s influence on the workplace is undeniable, and its adoption shows no signs of slowing down. Professionals who have embraced AI are seeing tangible improvements in both their productivity and creativity. However, the rise of unauthorized AI usage signals a need for firms to develop clear policies and safeguards to ensure AI’s secure and effective integration. With the continued growth of AI, companies that effectively balance innovation with security are poised to lead in an increasingly digital-driven professional landscape. Share	2025-06-03T00:00:00	2025/06/03	https://the-cfo.io/2025/06/03/ai-adoption-surges-to-72-among-professionals/	[ { "date": "2025/06/03", "position": 12, "query": "workplace AI adoption" } ]
15 Ways to Use AI in the Workforce	15 Ways to Use AI in the Workforce	https://hubstaff.com	[ "Rashika Mukherjee", "Dave Nevogt", "Austin Connolly" ]	From HR to Marketing, just about every role in today's workforce can benefit from using AI in the workplace ... AI adoption in data analytics. Artificial ...	Whether through AI-powered project management, strategic planning, or simply automating simple admin work, we’ve seen a dramatic shift towards AI in the workforce. We’ve all heard the constant chatter that it’s essential to keep up, put AI to work, and deliver work better than ever. But with so many possibilities, where does one even start? In this post, we’ve outlined 15 practical ways to leverage AI in the workforce and create a future-ready workplace. Let’s take a look. Boost your team’s efficiency with Hubstaff's productivity tools Try it free for 14 days Use cases and examples of AI in the workforce According to the Gen AI Research by Google Cloud, over 60% of enterprises and industry experts are actively using AI to boost productivity in production and improve business growth, security measures, and user experience. Businesses are implementing AI tools to optimize human work and assist them in performing better. In fact, our research found that 77% of respondents say AI reduces task time, and 70% report more focus and fewer distractions. With that in mind, it’s clear that AI isn’t replacing talent but boosting productivity. If you’ve been struggling to set aside your skepticism and embrace this new technology, let’s explore some real-life use cases to analyze how AI in the workforce drives efficiency and productivity to make room for high-impact human work. 1. Automating routine daily tasks Most of us weren’t hired for our ability to check emails, enter data, and send basic communications, so why not let AI take the lead here? In our Hubstaff research report, we spoke with a law firm that’s using AI to draft internal and client-facing documents. They found that work that once took multiple days to complete can now be done in minutes using Gemini. AI users are also getting ahead on other busy work, like: Data entry. Teams are utilizing AI to automatically populate spreadsheets from form submissions or emails. Teams are utilizing AI to automatically populate spreadsheets from form submissions or emails. Drafting. AI tools can instantly generate internal reports or client-facing documents. AI tools can instantly generate internal reports or client-facing documents. Scheduling. AI-driven scheduling apps can automate meeting scheduling based on team availability and priorities. Tools for routine task automation. Zapier can implement workflow automation between tools in your everyday tech stack to automate tasks. Gemini can easily help with context-aware document drafting and summarization. 2. AI-powered recruitment for HR teams These days, the sheer volume of candidates applying for jobs is overwhelming and requires weeks of screening applicants. Now, with AI systems, the whole scenario has changed. From sourcing candidates, screening profiles, and scheduling interviews to behavioral analysis, generative AI for HR can do it all. AI users in recruitment teams can save entire work days simply by automating these repetitive tasks while improving hiring quality and efficiency. Here are a few key areas where you can use AI to automate HR practices: Sourcing. Use an AI agent to sort candidates across job boards based on skills matching the job description. Use an AI agent to sort candidates across job boards based on skills matching the job description. Screening. AI tools can score candidate profiles based on customizable, pre-defined criteria. AI tools can score candidate profiles based on customizable, pre-defined criteria. Scheduling. Use an AI assistant to automatically coordinate interviews and meetings and sync with Google Calendar or your other calendar apps. AI recruitment tools. AI recruitment tools like HireVue specialize in AI-driven behavioral assessments. Apps like hireEZ use AI to source and engage candidates, and Manatal uses AI to recommend top talent based on job criteria. 3. Transcribing meetings and providing summaries Gone are the days when business leaders appointed dedicated note-takers to jot down important meeting discussion points. Artificial intelligence has advanced far enough to summarize every minute detail of an important meeting. Our live AI panel recently unpacked how the meeting summaries align teams on the agenda without having to share meeting recordings separately. Generate meeting transcripts. Utilize AI tools to transcribe calls into written summaries. Utilize AI tools to transcribe calls into written summaries. List key items. AI can help you outline key discussion points to look back on for valuable insights and further discussion. AI can help you outline key discussion points to look back on for valuable insights and further discussion. Create an action plan. Meetings are only an effective use of time if they lead to tangible results, right? Use AI to create an action plan based on the discussion. AI systems for transcribing meetings. AI tools like Fireflies can easily transcribe your meeting calls into written summaries that you can later analyze for notable insights. 4. Artificial intelligence in code development AI-driven development is a game-changer for the software industry as it speeds up the application development lifecycle. AI technology improves code quality through prediction analysis, freeing up time for developers to focus on strategic thinking while reducing time to market. Low-code to no-code platforms further help rapid application development. Code generation. Most AI tools can help automate coding based on AI-generated suggestions. Most AI tools can help automate coding based on AI-generated suggestions. Testing. AI can serve as built-in quality assurance, too. AI previously trained on code data can generate any required tests. AI can serve as built-in quality assurance, too. AI previously trained on code data can generate any required tests. Debugging. Use AI’s predictive analysis capabilities for automated error detection to spot bugs. AI tools for coding and development. AI systems like Quodo take care of the entire coding lifecycle, from writing code to testing and reviewing it for you. 5. Project management and automating tasks A project’s success depends on efficient time management. AI-powered project management is taking a chunk of the routine task management workflows off project managers’ plates to boost productivity and efficiency. Task managers who use AI technology are simplifying project management through progress tracking, scheduling, and status updates. Task automation. AI productivity tools handle repetitive tasks like assignments, reminders, and follow-ups. AI productivity tools handle repetitive tasks like assignments, reminders, and follow-ups. Project monitoring. Use AI for project monitoring, with real-time insights and predictive alerts to spot delays and keep projects on track. Use AI for project monitoring, with real-time insights and predictive alerts to spot delays and keep projects on track. Decision making. AI’s ability to analyze historical data and team performance to recommend smarter, faster decisions leads to better outcomes. Project management AI tools. ClickUp automates tasks like creating documents, planning, organizing, creating processes, and other aspects of cross-team collaboration. Time-tracking software like Hubstaff seamlessly integrates with ClickUp and streamlines task management so that you can implement automated task and project progress tracking. 6. Employee engagement and well-being High employee engagement and well-being can have a significant impact on your organization’s success. Gallup’s 2023 State of the Global Workplace report mentioned that organizations with highly engaged employees outperform those with low engagement levels by 23% in profitability. In recent years, AI in the workforce has been widely used to analyze employee engagement patterns, sentiment, and well-being. Based on survey responses or program participation data, AI can help identify burnout signs in remote teams, potential health risks, and overall wellness factors. Employee experience. Use AI to create personalized action plans to improve individual employee experience. Collaboration. A generative AI assistant can match new hires with onboarding buddies based on skills. A generative AI assistant can match new hires with onboarding buddies based on skills. Communication. Use AI tools to help you personalize feedback. AI can learn and base feedback on each employee’s communication style. AI tools for employee engagement and well-being: Smart survey analysis tools like Prism extract actionable insights from survey data. It deeply examines employee sentiment through tone and voice, saving hours in spotting patterns. 7. AI adoption in data analytics Artificial intelligence can also help data analysts uncover important insights from large data sets. Unlike traditional methods, business leaders can use machine learning, large language models, and predictive analysis to identify meaningful metrics and future trends that improve data-driven decisions. Presentation preparation. Use generative AI to analyze complex data sets and summarize key insights for a presentation. Use generative AI to analyze complex data sets and summarize key insights for a presentation. Learning and problem-solving. Learn and solve complex mathematical problems that emerge during data analysis with the help of AI. Learn and solve complex mathematical problems that emerge during data analysis with the help of AI. Predictive analysis. AI tools specializing in predictive analytics and generative AI can simulate potential business scenarios for better forecasting. AI tools for data analysis: AI data analysis tools like Julius analyze your data files and provide detailed breakdowns, create images, charts/graphs, and anything else you ask for. You can upload a spreadsheet or PDF file and chat with the platform about anything you’d like to see. 8. Customer support via AI chatbots In customer service, generative AI-powered chatbots are available 24/7 to take common customer requests, automate tasks, troubleshoot, provide order tracking information, and more. Customers get accurate and fast responses without human intervention. According to Zendesk’s 2025 Trends report, 81% of consumers agree that AI is essential to modern customer service. Instant query resolution. An AI agent answers common customer concerns in seconds without making customers wait. Order tracking. AI support syncs with Customer Relationship Management tools (CRMs) to update customers in real-time on their package location and delivery status. AI support syncs with Customer Relationship Management tools (CRMs) to update customers in real-time on their package location and delivery status. Smart escalation. Artificial intelligence tools can’t handle every support issue. However, AI can efficiently transfer complex cases that need human intervention to customer support teams. AI tools for customer support. Zendesk AI provides rapid, personalized responses to your customers’ most pressing questions using an AI chatbot. 9. Workforce analytics for HR and operations AI is taking workforce analytics for HR and Operations to the next level, from resource allocation and workflow optimization to spotting employee engagement trends. By analyzing significant data points, generative AI features are helping business leaders make more intelligent and informed decisions in less time. Performance management. AI solutions can analyze performance data, identify team productivity trends, and help track progress toward career and company-wide goals. AI solutions can analyze performance data, identify team productivity trends, and help track progress toward career and company-wide goals. Resource allocation. Some AI tools have the ability to automatically allocate tasks based on resource availability to boost efficiency. Some AI tools have the ability to automatically allocate tasks based on resource availability to boost efficiency. Workforce planning. AI can analyze historical turnover rates and performance insights and forecast accurate future staffing needs. AI workforce analytics and HR operation tools. Visier AI features help you analyze team performance, understand how people affect business outcomes, and make better workforce management decisions. 10. Document creation and content generation Artificial intelligence empowers marketing, sales, and operations teams to create content outlines, sales and support email copy, process documentation, and more. This gives marketers and sales teams more time to focus on strategy and high-level tasks that require human intelligence and decision-making. Content Drafting. Utilize AI to produce blog posts, landing pages, white papers, emails, and reports. Utilize AI to produce blog posts, landing pages, white papers, emails, and reports. Create slide decks/reports. Feed raw data to AI models to create compact data-driven presentations with key takeaways. Feed raw data to AI models to create compact data-driven presentations with key takeaways. Social media content. Generate social media copy and shareable graphics on social platforms through the use of AI. AI tools for document generation and content creation. ChatGPT and Jasper AI are great at creating and helping refine email drafts, marketing content, and creating personalized demand-gen campaigns. 11. Email and communication optimization Communication is a key factor in everyday business operations. Generative AI models improve workplace communication by crafting clear, concise, and personalized emails for business leaders. Whether you send emails to leads, internal updates, or customer responses, AI tools help perfect the message by helping you cater your tone accordingly. Tone adjustment. AI tools can help you alter or personalize email tone based on criteria like formality, friendliness, or urgency. AI tools can help you alter or personalize email tone based on criteria like formality, friendliness, or urgency. Smart personalization. Auto-insert variables like names, titles, and relevant context and let AI take care of the rest. Auto-insert variables like names, titles, and relevant context and let AI take care of the rest. Follow-up optimization. Use AI to Identify the best time to send follow-ups and generate response templates. AI communication tools. SaneBox filters the noise, highlights priority emails, reminds you to send follow-up emails, and automatically filters out unwanted emails. Ensuring every individual reaches performance goals can be daunting for business leaders in large enterprises and organizations. Most leaders are looking for the answer to one question: How can they monitor employee productivity? AI-powered dashboards instantly give leaders insight into progress, performance, and pitfalls, helping them make better decisions. Goal alignment. Ensure teams are working towards unified productivity metrics like OKRs. Ensure teams are working towards unified productivity metrics like OKRs. Real-time monitoring. Get AI-driven auto updates on project status without manual input, boosting efficiency. Get AI-driven auto updates on project status without manual input, boosting efficiency. Anomaly detection. Use AI to instantly flag dips in performance and trigger recommendations. AI tools for performance analytics. Tableau turns data into actionable insights, collecting metrics from various sources and combining them into one simple dashboard. 13. Generative AI in SEO Search Engine Optimization (SEO) requires keeping up with constant algorithm changes and optimization based on changing search trends. Generative AI models simplify and strengthen your SEO strategy, making content optimization a breeze. Keyword clustering. Use AI to Group semantically related keywords to cover topical breadth. Use AI to Group semantically related keywords to cover topical breadth. SERP analysis. AI can help you keep up with competitor Google search rankings and analyze backlink profiles. AI can help you keep up with competitor Google search rankings and analyze backlink profiles. Content optimization. Use AI content optimization tools for on-page suggestions on headings, meta descriptions, and image alt texts. AI tools for SEO. Platforms like Surfer SEO and Clearscope generate optimization scores, suggest internal/external links, and provide real-time editing feedback. 14. Knowledge management and information consolidation Businesses often deal with a swarm of knowledge-based resources that grow over time. Without a resource optimization system in place, crucial information might be lost. Turn company-wide documents and knowledge bases into searchable intelligence. Auto-Tagging. Using artificial intelligence, automatically sort files and add data into relevant categories. Using artificial intelligence, automatically sort files and add data into relevant categories. Summarization. Reduce long-form texts into executive summaries with AI language models. Reduce long-form texts into executive summaries with AI language models. Searchable Memory. Enable company-wide content to be searchable using natural language. AI tools. Summarizer.org and Notion AI turn dense documentation into usable snippets that boost productivity. 15. Monitoring the competition With the fast-paced technical advancements, new AI features, and tools, businesses are racing to compete faster than ever. To stay ahead of the curve, you have to be on top of what competitors are up to. Keep track of competitor moves — without the manual lift. Market Intelligence. Use AI to collect insights from competitor websites, blogs, PR, and ads. Use AI to collect insights from competitor websites, blogs, PR, and ads. Pricing Intelligence. Get real-time alerts when pricing changes occur. Get real-time alerts when pricing changes occur. Positioning Insights. Compare brand messaging, product updates, and feature rollouts. AI tools for monitoring. Kompyte monitors your competitive landscape and gives you alerts, trends, and recommended responses. Frequently Asked Questions (FAQs) Will AI replace humans in the workplace? AI is designed after human intelligence and machine learning, not to replace the human workforce. However, AI productivity tools may augment human capabilities, boost efficiency, automate tasks, and reduce wasted time on repetitive tasks. By handling monotonous tasks, AI boosts efficiency, allowing employees to focus on strategy, creativity, and decision-making. What industries are most impacted by AI? AI in the workforce has transformed professional service industries like health care, finance, customer service, manufacturing, marketing, and HR/Recruitment. Further, AI productivity tools are advancing quickly to disrupt other industries in the future. What job functions are benefiting most from AI? Roles in data analysis, customer support, software development, marketing, and HR benefit significantly from offloading repetitive tasks and making smarter decisions faster. Hubstaff research further identified that AI in the workforce in industries like cleaning services, development, and general contracting use AI productivity tools to streamline operations. Blockchain, crypto, and online education teams are scaling faster with AI features, machine learning, automating communications, personalizing content, and cutting down admin time. What types of mistakes are made with AI? Common mistakes include over-relying on AI features without human review, bias in training data, misinterpreting outputs from large language models, or using AI tools without clear objectives. Here are some examples:	2025-06-03T00:00:00	2025/06/03	https://hubstaff.com/blog/15-ways-use-ai-in-workforce/	[ { "date": "2025/06/03", "position": 85, "query": "workplace AI adoption" } ]
100+ Stats On AI Replacing Jobs (2025) - Whats the Big Data	100+ Stats On AI Replacing Jobs (2025)	https://whatsthebigdata.com	[]	Around 14% of the workforce worldwide is expected to be affected due to AI and Automation by 2030. In May 2023, about 3,900 jobs were replaced ...	The rise of AI adoption in the workplace has raised major concerns for workers and employees about their jobs being replaced by AI. More and more companies are adopting AI capabilities and software to their everyday routine and workers are becoming concerned with the implications of increased usage of AI. According to a report by McKinsey Global Institute, 15% of the global workforce, or 400 million workers might lose their job to AI by 2030. In this article, we are going to take a look at AI replacing job statistics and understand the true impact of AI in the workplace. Key Statistics on AI Replacing Jobs AI has the potential to replace around 300 million jobs worldwide. jobs worldwide. 30% of the jobs are likely to be automated by 2030 . are likely to be by . Around 14% of the workforce worldwide is expected to be affected due to AI and Automation by 2030. In May 2023, about 3,900 jobs were replaced by AI in the United States. 36 million jobs in the United States face a “high exposure” to AI automation in the next few decades. 49% of Japan’s workforce is capable of being replaced by AI or robotic machines in the next 10 to 20 years. Around 77% of businesses already utilize AI. 81% of employees believe that using AI helps in improving their overall performance at work. Change or replacement of jobs by Artificial Intelligence worldwide from 2023 to 2028 Most of the respondents believe that Artificial intelligence will impact their current jobs with the potential of being changed or replaced in the next five years. Around 57% of the respondents believe that it’s likely that there will be a change of jobs by AI globally in the next 5 years. While 36% of the respondents believe that the replacement of jobs by AI is likely to occur in the next five years. Change of your current jobs by AI in the next 5 years: Likely Not Likely Don’t Know 57% 35% 8% Replacement of jobs by AI in the next 5 years: Likely Not Likely Don’t Know 36% 56% 8% Source: Statista AI Adoption in the Workplace By Industry in the United States 2023 According to a survey conducted in the United States in 2023, it was found that 37% of respondents working in the marketing or advertising industry are most likely to access Artificial intelligence to complete work-related tasks. Followed by the Technology industry in the second position in terms of AI Adoption by 35%. Healthcare had the lowest rate when it comes to accessing AI with only 15% of respondents claiming to use AI at the workplace. Below we have mentioned a table showcasing AI Adoption at the workplace in the United States in 2023: Industry Share of respondents Marketing and Advertising 37% Technology 35% Consulting 30% Teaching 19% Accounting 16% Healthcare 15% Source: Statista AI Replacing Jobs And Employment Statistics for all UK industry sectors The industry that is at the highest risk is Water, sewage, and waste management with around 62.6% risk of job automation. Followed by Transportation and storage in the second position with 56.4% chances of job automation. Below we have mentioned a table showcasing the share of employees and risk of AI replacing jobs in the UK industry sectors. Industry Share of Employment Risk of Job Automation Wholesale and retail trade 14.80% 44% Manufacturing 7.60% 46.4% Administrative and support services 8.40% 37.4% Transportation and storage 4.90% 56.4% Professional, scientific, and technical 8.80% 25.6% Human health and social work 12.40% 17% Accommodation and food services 6.70% 25.5% Construction 6.40% 23.7% Public administration and defense 4.30% 32.1% Information and communication 4.10% 27.3% Financial and insurance 3.20% 32.2% Education 8.70% 8.5% Arts and entertainment 2.90% 22.3% Other services 2.70% 18.6% Real estate 1.70% 28.2% Water, sewage, and waste management 0.60% 62.6% Agriculture, forestry, and fishing 1.10% 18.7% Electricity and gas supply 0.40% 31.8% Mining and quarrying 0.20% 23.1% Domestic personnel and self-subsistence 0.30% 8.1% Total/Average for all sectors 100% 30% Perceived likelihood of AI replacing jobs APAC 2023, by country In 2023, a survey was conducted based on global views regarding artificial intelligence (AI). It was revealed that around 69% of respondents from Thailand perceived that AI will most likely replace their current job. Followed by Malaysia and Indonesia both countries have 62% of respondents showcasing the likelihood of AI replacing their jobs. Here is a breakdown of the Perceived likelihood of AI replacing jobs in APAC 2023, by country: Country Share of respondents Thailand 69% Malaysia 62% Indonesia 62% India 51% Singapore 41% Global Average 36% Japan 33% Australia 31% South Korea 31% New Zealand 23% Source: Statista Statistics on Jobs and Their Risk of Replacement by AI According to research, 78% of legal jobs are influenced by AI compared to other occupations or industries. About 60% aged 25 to 34 and 56% aged 34 to 44 of the Europeans supported the replacement of lawmakers with AI. 75% of the people in China also supported the thought of legal occupation or lawmakers being replaced with AI. Below we have mentioned a table showcasing statistics of different jobs and their risk of replacement by AI: Occupation Risk of replacement Legal 78% Life, physical, and social science 61% Office and administrative support 57% Computer and mathematical 53% Healthcare practitioners and technical 50% Architecture and engineering 48% Business and financial operations 47% Arts, design, entertainment, sports, and media 41% Management 38% Educational instruction and library 33% Food preparation and serving 24% Sales 23% Healthcare support 21% Personal care and service 19% Farming, fishing, and forestry 18% Community and social service 13% Production, building and grounds, cleaning, and maintenance 7% Installation, maintenance, and repair 5% Construction and extraction 2% Source: Tech.co In May 2023, a total of 3,900 jobs were replaced by AI in the United States Total job losses of 3,900 were recorded directly replaced by AI in May 2023 in the United States. One of the major impacts was seen due to the tech sector with about 136,831 job losses in the current year. 30% of the jobs are likely to be automated by 2030 Automation is expected to create a complete transformation in the workforce by 2030 with the potential of 30% of jobs being replaced with AI. This statistic showcases the true impact of automation in the job market, along with the change in landscape that can appear in the workforce across numerous industries worldwide. In the United Kingdom, 30% of the jobs are likely to be replaced by AI with 35% of male jobs and 26% of female jobs A large section of jobs are expected to face a replacement by Artificial Intelligence in the UK. When talking about the percentage of job losses based on gender, about 35% of male jobs are expected to witness a replacement by AI. Meanwhile, 26% of female jobs are likely to be replaced in the UK. This could result in significant implications for the economy in the UK and the labor market. AI and ML are expected to replace around 16% of jobs in the United States in the next five years With the adoption of Machine learning (ML) and Artificial intelligence (AI) worldwide. Data shows that 16% of the US jobs are likely to be replaced by ML and AI in the next five years. At the same time, about 9% of the jobs are expected to be created. Overall, the net loss of the US Jobs by 2025 is expected to be 7%. By 2025, 19 out of 20 customer interactions are expected to be assisted by AI AI technology is transforming the customer interaction process as today many companies and businesses have set up AI-assisted systems for customer support purposes. The integration of AI in customer support is expected to keep rising in the upcoming years and about 95% of the telephone and online communications are expected to be assisted by AI. Within the next 10 to 20 years 49% of Japan’s workforce is expected to be replaced by AI or robotic machines A major representation of AI replacing Jobs was witnessed in Japan where Fukoku Mutual Life Insurance replaced around 30 workers with AI systems. This replacement took place with expectations to rise in productivity by 30%. This truly showcased how AI is being utilized to replace jobs in Japan along with the potential of AI and its increased productivity and the ability to provide return on investment in a short duration. This also highlighted the estimation that 49% of Japan’s workforce is expected to be replaced by AI or robotic machines. 72% of teachers support the rising education and resources surrounding AI The world is evolving and so is the education system around us. Today, AI is considered a prominent subject that should be taught to students to prepare them for the jobs and opportunities that could arise for them in the future. Around 72% of secondary school teachers and professors support the rising education and resources surrounding AI and computer science. Top 5 jobs AI Will Replace Let’s take a look at the top jobs that have the highest potential of being replaced in the future by AI or automation. Here are the top 5 jobs that AI will replace: 1. Data Entry Clerks The majority of the tasks performed by Data entry clears are repetitive. It often includes processing information from customers’ documents, scanning, and more which makes making data entry clerks’ spots quite redundant. These tasks primarily target automation and have the highest potential of being replaced by AI. 2. Customer Support Representative The customer support role is most likely to be replaced by AI. This role is becoming more and more automated, especially with tools like virtual assistants and chatbots being available. These tools can easily handle customers’ concerns or doubts regarding any topic effortlessly. 3. Travel Agents A travel advisor is another job that is most likely to be replaced by AI. Travel platforms are integrating with advanced AI technology to power customer search and generate useful and fun recommendations for users based on their searches. This way travelers can gain maximum information about their destination effortlessly by experiencing virtual tours and watching online videos about the place without interacting with a travel advisor. 4. Transportation services Growth in autonomous vehicles is decreasing the demand for human drivers for transportation and it is expected to impact both taxi and rideshare industries at a significant level. In Fact, popular transportation company Uber has also partnered with self-driving car businesses such as Aurora and Waymo through which they give its riders more options and opportunities. 5. Factory/Warehouse Workers Most manufacturing lines are slowly becoming more and more automated thanks to the advanced technology that can perform numerous actions and tasks at much faster speed and consistency compared to human workers. AI-integrated machines utilized in factories can help retrieve goods, move their surroundings and perform various logistic tasks without depending on human workers. Therefore with time, there is a potential that AI might replace Factory workers at a larger scale. FAQs What jobs will AI replace? Jobs that are repetitive or involve Remote learning, scheduling, and customer support are expected to be replaced by AI. AI writing tools are capable of drafting documents with excellent accuracy and detailing along with chatbots and AI assistants are capable of performing customer support tasks and assisting customers. What Percent of People Have Lost Their Jobs to AI? 37% of business leaders have reported replacing their employees with AI in 2023, according to recent reports. A report by CBS News claims that 3,900 job losses by AI were recorded in the United States in May 2023. What Percentage of Jobs Will AI Replace by 2030? 15% of the global workforce, or 400 million workers, might lose their job to AI or automation by 2030, according to a report by McKinsey Global Institute. However, there is also a potential of new jobs being generated for users and about 8% to 9% of the workforce will be engaging in work that doesn’t exist today. Which job is safe from AI? Jobs that require human interaction and empathy are safe from AI such as Doctors, Nurses, Teachers, Musicians, Artists, Hair stylists, Makeup Artists, Therapists, School Administrators, and more. Will ChatGPT replace jobs? Yes, ChatGPT is capable of replacing various tech-related jobs such as Programming, Web development, coding, or data science. ChatGPT can write accurate codes, and also perform various corrections, and resolve errors made by humans. In addition, ChatGPT can be utilized to write various basic data structures, and algorithms and even perform deep learning tasks. Wrapping Up With the adoption of AI capabilities in almost every industry, the chances of AI replacing jobs are pretty high. Although AI is not capable of replacing each and every job that exists today, it can easily do jobs such as Data entry clerks, customer service, travel against, warehouse workers, and more. Creative jobs that require human interaction, such as artists, musicians, hair stylists, doctors, teachers, and more, are still pretty irreplaceable.	2025-06-04T00:00:00	2025/06/04	https://whatsthebigdata.com/ai-replacing-jobs-statistics/	[ { "date": "2025/06/04", "position": 83, "query": "job automation statistics" }, { "date": "2025/06/04", "position": 80, "query": "job automation statistics" }, { "date": "2025/06/04", "position": 80, "query": "job automation statistics" }, { "date": "2025/06/04", "position": 87, "query": "job automation statistics" }, { "date": "2025/06/04", "position": 88, "query": "job automation statistics" }, { "date": "2025/06/04", "position": 87, "query": "job automation statistics" }, { "date": "2025/06/04", "position": 38, "query": "job automation statistics" } ]
AI Doesn't Destroy Jobs It Creates Them	AI Doesn’t Destroy Jobs It Creates Them	https://www.burtchworks.com	[]	AI and Job Growth - Why AI is Driving Employment, Not Replacing It ... Worries about Artificial Intelligence (AI) eating jobs have been ...	Worries about Artificial Intelligence (AI) eating jobs have been circulating for years now. However, contrary to these concerns, AI is proving to be a catalyst for job growth—not destruction. Cutting-edge research, including PwC's 2025 Global AI Jobs Barometer and World Economic Forum insights, paints a clear picture. AI is accelerating the job market, creating opportunities, strengthening wages, and revolutionizing industries. This blog dives into how AI impacts job growth, highlights real-world examples, and explains the importance of adapting quickly to this shift. Whether you’re a business leader or a professional navigating your field, understanding AI’s role in employment is key to thriving in the modern work environment. AI Doesn’t Destroy Jobs It Creates Them The fear that AI will eliminate the need for workers often makes headlines. But the data tells a different story. According to PwC, jobs requiring AI skills are growing at nearly 7.5% annually even as overall job postings declined by 11.3%. This uptick spans both augmented roles (where humans and AI collaborate) as well as automatable positions (where AI takes over repetitive tasks). Also, as AI efficiencies hit enterprise scale, business are reinvesting profits in hiring more AI talent, a force multiplier. Key Data on AI-Driven Job Growth Expanding Opportunities . AI-exposed industries, including financial services and software development, are witnessing dramatic job increases. PwC reported a 38% growth in augmentable jobs between 2019 and 2024. . AI-exposed industries, including financial services and software development, are witnessing dramatic job increases. PwC reported a in augmentable jobs between 2019 and 2024. Wage Premiums . Workers with AI-based skills command salaries 56% higher on average , reflecting demand for specialized expertise. . Workers with AI-based skills command salaries , reflecting demand for specialized expertise. Productivity Boost. Industries adopting AI heavily, like professional services, saw productivity jump from 7% growth (2018-2022) to 27% in 2022-2024. These gains ripple downward, creating ripple effects in jobs supporting these core industries. A Tale of Two Jobs Augmentable vs Automatable To understand AI’s job impact, think of roles in two ways: Augmentable Jobs enhance human tasks rather than replacing them. Surgeons, judges, and financial analysts benefit from AI tools improving precision and decision-making. Automatable Jobs include task-heavy roles like data entry or coding, where AI replaces human labor for efficiency gains. Yet, even here, job creation persists as new needs emerge for oversight, coordination, & optimization functions. High Demand for AI Skills and How Businesses Are Hiring The call for AI skills is undeniable. Ongoing digital transformation across industries means businesses are desperate for professionals who understand and can implement AI-driven solutions. Companies Doubling Down on AI Hiring Professional Services . Positions requiring AI competencies in this sector hit 5.1% in 2024 , making it second only to tech industries in AI hiring growth. . Positions requiring AI competencies in this sector hit , making it second only to tech industries in AI hiring growth. Tech and Financial Leadership . Top sectors like ICT and Financial Services prioritize AI-specialists, machine learning developers, data scientists, and fintech engineers. . Top sectors like ICT and Financial Services prioritize AI-specialists, machine learning developers, data scientists, and fintech engineers. Retail and E-commerce. AI allows personalizing customer experiences and optimizing supply chains, increasing demand for AI contributors in these industries. Bridging the Talent Gap Surprisingly, PwC research highlights a decline in demand for formal degrees. Employers now care more about capability and practical knowledge than academic qualifications. These trends underscore that robust training pathways for AI adoption are just as vital as recruitment alone. The Bigger Picture New Skills, New Opportunities We’re witnessing a fundamental shift in the workforce as AI’s influence grows. This shift isn’t just generating jobs, but changing how existing roles operate. It’s making employees more productive, creative, and valuable while offering organizations avenues to unlock entirely new revenue streams. Skills Transformation and Why Continuous Upskilling Matters Faster Skill Evolution . Roles exposed to AI technologies face skill changes accelerating at nearly 66% over previous year trends . Remaining competitive means staying ahead through upskilling or reskilling. . Roles exposed to AI technologies face skill changes accelerating at nearly . Remaining competitive means staying ahead through upskilling or reskilling. Role Diversification . Traditional sectors also encounter transformation. For instance, legal services use generative AI for drafting while retaining the critical human judgment needed in high-stakes scenarios. . Traditional sectors also encounter transformation. For instance, legal services use generative AI for drafting while retaining the critical human judgment needed in high-stakes scenarios. Empowering Industries and Individuals The World Economic Forum emphasizes responsibility rather than replacement AI represents human leverage resulting demand employees continuous investing network systems future connects communities globally thriving.” Referenced Sources: https://pwc.turtl.co/story/ai-jobs-barometer-industry/page/7 https://trt.global/world/article/268574b24cf0?utm_source=pivot5&utm_medium=newsletter&utm_campaign=major-ai-gains-are-shifting-pay-power-and-priorities-1&_bhlid=d160433588efe79fed409d73849e772a8eecff1d ‍	2025-06-04T00:00:00		https://www.burtchworks.com/industry-insights/ai-and-job-growth---why-ai-is-driving-employment-not-replacing-it	[ { "date": "2025/06/04", "position": 71, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 70, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 70, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 71, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 70, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 67, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 67, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 67, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 69, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 67, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 68, "query": "AI job creation vs elimination" }, { "date": "2025/06/04", "position": 69, "query": "AI job creation vs elimination" } ]
US labor unions fight to contain AI disruption - Space Daily	US labor unions fight to contain AI disruption	https://www.spacedaily.com	[]	A handful of unions have successfully negotiated AI protections into their contracts. Notable examples include agreements with media company ...	US labor unions fight to contain AI disruption New York, June 4 (AFP) Jun 04, 2025 As artificial intelligence threatens to upend entire sectors of the economy, American labor unions are scrambling to protect workers, demand corporate transparency, and rally political support-an uphill battle in a rapidly changing world. "As laborers, the ability to withhold our labor is one of our only tools to improve our lives," explained Aaron Novik, a key organizer with Amazon's ALU union. "What happens when that disappears (to AI)? It's a real existential issue," he added. Automation has already transformed most industries since the 1960s, typically reducing workforce numbers in the process. But the emergence of advanced "physical AI" promises a new generation of intelligent robots that won't be limited to repetitive tasks -- potentially displacing far more blue-collar workers than ever before. The threat extends beyond manufacturing. The CEO of Anthropic, which created Claude as a competitor to ChatGPT, warned last week that generative AI could eliminate half of all low-skilled white-collar jobs, potentially driving unemployment rates up to 10-20 percent. "The potential displacement of workers and elimination of jobs is a significant concern not just for our members, but for the public in general," said Peter Finn of the International Brotherhood of Teamsters, America's largest union. - Vetoes - The Teamsters have focused their efforts on passing legislation limiting the spread of automation, but face significant political obstacles. California's governor has twice vetoed bills that would ban autonomous trucks from public roads, despite intense lobbying from the state's hundreds of thousands of union members. Colorado's governor followed suit last week, and similar battles are playing out in Indiana, Maryland, and other states. At the federal level, the landscape shifted dramatically with the change in the White House. Under former president Joe Biden, the Department of Labor issued guidelines encouraging companies to be transparent about AI use, involve workers in strategic decisions, and support employees whose jobs face elimination. But US President Donald Trump canceled the protections within hours of taking office in January. "Now it's clear. They want to fully open up AI without the safeguards that are necessary to ensure workers' rights and protections at work," said HeeWon Brindle-Khym of the Retail, Wholesale and Department Store Union (RWDSU), which represents workers in the retail sector. - Rush to AI - Meanwhile, companies are racing to implement AI technologies, often with poor results. "By fear of missing out on innovations, there's been a real push (to release AI products)," observed Dan Reynolds of the Communications Workers of America (CWA). The CWA has taken a proactive approach, publishing a comprehensive guide for members that urges negotiators to include AI provisions in all collective bargaining agreements. The union is also developing educational toolkits to help workers understand and negotiate around AI implementation. A handful of unions have successfully negotiated AI protections into their contracts. Notable examples include agreements with media company Ziff Davis (which owns Mashable) and video game publisher ZeniMax Studios, a Microsoft subsidiary. The most significant victories belong to two powerful unions: the International Longshoremen's Association, representing dock workers, secured a moratorium on full automation of certain port operations, while the Screen Actors Guild (SAG-AFTRA) won guarantees that actors must be consulted and compensated whenever their AI likeness is created. These successes remain exceptional, however. The American labor movement, as a whole, lacks the bargaining power enjoyed by those highly strategic or publicly visible sectors, said Brindle-Khym. "Smaller contract-by-contract improvements are a long, slow process," she added. Despite frequent accusations by corporate interests, the unions' goal isn't to halt technological progress entirely. "Workers are usually not seeking to stop the march of technology," noted Virginia Doellgast, a Cornell University professor specializing in labor relations. "They just want to have some control." As AI continues its rapid advance, the question remains whether unions can adapt quickly enough to protect workers in an economy increasingly dominated by artificial intelligence.	2025-06-04T00:00:00		https://www.spacedaily.com/afp/250604063248.fsvmijue.html	[ { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "AI labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 6, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 4, "query": "artificial intelligence labor union" } ]
US labor unions fight to contain AI disruption - RFI	US labor unions fight to contain AI disruption	https://www.rfi.fr	[]	As artificial intelligence threatens to upend entire sectors of the economy, American labor unions are scrambling to protect workers, ...	New York (AFP) – As artificial intelligence threatens to upend entire sectors of the economy, American labor unions are scrambling to protect workers, demand corporate transparency, and rally political support—an uphill battle in a rapidly changing world. Advertising Read more "As laborers, the ability to withhold our labor is one of our only tools to improve our lives," explained Aaron Novik, a key organizer with Amazon's ALU union. "What happens when that disappears (to AI)? It's a real existential issue," he added. Automation has already transformed most industries since the 1960s, typically reducing workforce numbers in the process. But the emergence of advanced "physical AI" promises a new generation of intelligent robots that won't be limited to repetitive tasks -- potentially displacing far more blue-collar workers than ever before. The threat extends beyond manufacturing. The CEO of Anthropic, which created Claude as a competitor to ChatGPT, warned last week that generative AI could eliminate half of all low-skilled white-collar jobs, potentially driving unemployment rates up to 10-20 percent. "The potential displacement of workers and elimination of jobs is a significant concern not just for our members, but for the public in general," said Peter Finn of the International Brotherhood of Teamsters, America's largest union. Vetoes The Teamsters union is backing legislation limiting the spread of automation, despite which governors in California and Colorado have vetoed bills that would ban autonomous trucks from public roads © Robyn Beck / AFP/File The Teamsters have focused their efforts on passing legislation limiting the spread of automation, but face significant political obstacles. California's governor has twice vetoed bills that would ban autonomous trucks from public roads, despite intense lobbying from the state's hundreds of thousands of union members. Colorado's governor followed suit last week, and similar battles are playing out in Indiana, Maryland, and other states. At the federal level, the landscape shifted dramatically with the change in the White House. Under former president Joe Biden, the Department of Labor issued guidelines encouraging companies to be transparent about AI use, involve workers in strategic decisions, and support employees whose jobs face elimination. But US President Donald Trump canceled the protections within hours of taking office in January. "Now it's clear. They want to fully open up AI without the safeguards that are necessary to ensure workers' rights and protections at work," said HeeWon Brindle-Khym of the Retail, Wholesale and Department Store Union (RWDSU), which represents workers in the retail sector. Rush to AI Meanwhile, companies are racing to implement AI technologies, often with poor results. "By fear of missing out on innovations, there's been a real push (to release AI products)," observed Dan Reynolds of the Communications Workers of America (CWA). The CWA has taken a proactive approach, publishing a comprehensive guide for members that urges negotiators to include AI provisions in all collective bargaining agreements. The union is also developing educational toolkits to help workers understand and negotiate around AI implementation. A handful of unions have successfully negotiated AI protections into their contracts. Notable examples include agreements with media company Ziff Davis (which owns Mashable) and video game publisher ZeniMax Studios, a Microsoft subsidiary. The most significant victories belong to two powerful unions: the International Longshoremen's Association, representing dock workers, secured a moratorium on full automation of certain port operations, while the Screen Actors Guild (SAG-AFTRA) won guarantees that actors must be consulted and compensated whenever their AI likeness is created. These successes remain exceptional, however. The American labor movement, as a whole, lacks the bargaining power enjoyed by those highly strategic or publicly visible sectors, said Brindle-Khym. "Smaller contract-by-contract improvements are a long, slow process," she added. Despite frequent accusations by corporate interests, the unions' goal isn't to halt technological progress entirely. "Workers are usually not seeking to stop the march of technology," noted Virginia Doellgast, a Cornell University professor specializing in labor relations. "They just want to have some control." As AI continues its rapid advance, the question remains whether unions can adapt quickly enough to protect workers in an economy increasingly dominated by artificial intelligence. © 2025 AFP	2025-06-04T00:00:00	2025/06/04	https://www.rfi.fr/en/international-news/20250604-us-labor-unions-fight-to-contain-ai-disruption	[ { "date": "2025/06/04", "position": 80, "query": "AI labor union" }, { "date": "2025/06/04", "position": 37, "query": "AI labor union" }, { "date": "2025/06/04", "position": 26, "query": "AI labor union" }, { "date": "2025/06/04", "position": 19, "query": "AI labor union" }, { "date": "2025/06/04", "position": 38, "query": "AI labor union" }, { "date": "2025/06/04", "position": 92, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 37, "query": "AI labor union" }, { "date": "2025/06/04", "position": 35, "query": "AI labor union" }, { "date": "2025/06/04", "position": 30, "query": "AI labor union" }, { "date": "2025/06/04", "position": 25, "query": "AI labor union" }, { "date": "2025/06/04", "position": 87, "query": "AI labor union" } ]
US labor unions fight to contain AI disruption \| Mint	US labor unions fight to contain AI disruption	https://www.livemint.com	[]	As artificial intelligence threatens to upend entire sectors of the economy, American labor unions are scrambling to protect workers, ...	As artificial intelligence threatens to upend entire sectors of the economy, American labor unions are scrambling to protect workers, demand corporate transparency, and rally political support-an uphill battle in a rapidly changing world. "As laborers, the ability to withhold our labor is one of our only tools to improve our lives," explained Aaron Novik, a key organizer with Amazon's ALU union. "What happens when that disappears (to AI)? It's a real existential issue," he added. Automation has already transformed most industries since the 1960s, typically reducing workforce numbers in the process. But the emergence of advanced "physical AI" promises a new generation of intelligent robots that won't be limited to repetitive tasks -- potentially displacing far more blue-collar workers than ever before. The threat extends beyond manufacturing. The CEO of Anthropic, which created Claude as a competitor to ChatGPT, warned last week that generative AI could eliminate half of all low-skilled white-collar jobs, potentially driving unemployment rates up to 10-20 percent. "The potential displacement of workers and elimination of jobs is a significant concern not just for our members, but for the public in general," said Peter Finn of the International Brotherhood of Teamsters, America's largest union. The Teamsters have focused their efforts on passing legislation limiting the spread of automation, but face significant political obstacles. California's governor has twice vetoed bills that would ban autonomous trucks from public roads, despite intense lobbying from the state's hundreds of thousands of union members. Colorado's governor followed suit last week, and similar battles are playing out in Indiana, Maryland, and other states. At the federal level, the landscape shifted dramatically with the change in the White House. Under former president Joe Biden, the Department of Labor issued guidelines encouraging companies to be transparent about AI use, involve workers in strategic decisions, and support employees whose jobs face elimination. But US President Donald Trump canceled the protections within hours of taking office in January. "Now it's clear. They want to fully open up AI without the safeguards that are necessary to ensure workers' rights and protections at work," said HeeWon Brindle-Khym of the Retail, Wholesale and Department Store Union (RWDSU), which represents workers in the retail sector. Meanwhile, companies are racing to implement AI technologies, often with poor results. "By fear of missing out on innovations, there's been a real push (to release AI products)," observed Dan Reynolds of the Communications Workers of America (CWA). The CWA has taken a proactive approach, publishing a comprehensive guide for members that urges negotiators to include AI provisions in all collective bargaining agreements. The union is also developing educational toolkits to help workers understand and negotiate around AI implementation. A handful of unions have successfully negotiated AI protections into their contracts. Notable examples include agreements with media company Ziff Davis (which owns Mashable) and video game publisher ZeniMax Studios, a Microsoft subsidiary. The most significant victories belong to two powerful unions: the International Longshoremen's Association, representing dock workers, secured a moratorium on full automation of certain port operations, while the Screen Actors Guild (SAG-AFTRA) won guarantees that actors must be consulted and compensated whenever their AI likeness is created. These successes remain exceptional, however. The American labor movement, as a whole, lacks the bargaining power enjoyed by those highly strategic or publicly visible sectors, said Brindle-Khym. "Smaller contract-by-contract improvements are a long, slow process," she added. Despite frequent accusations by corporate interests, the unions' goal isn't to halt technological progress entirely. "Workers are usually not seeking to stop the march of technology," noted Virginia Doellgast, a Cornell University professor specializing in labor relations. "They just want to have some control." As AI continues its rapid advance, the question remains whether unions can adapt quickly enough to protect workers in an economy increasingly dominated by artificial intelligence.	2025-06-04T00:00:00	2025/06/04	https://www.livemint.com/technology/tech-news/us-labor-unions-fight-to-contain-ai-disruption-11749018832407.html	[ { "date": "2025/06/04", "position": 90, "query": "AI labor union" }, { "date": "2025/06/04", "position": 63, "query": "AI labor union" }, { "date": "2025/06/04", "position": 97, "query": "artificial intelligence labor union" } ]
The New Role of Labor Unions in the AI Era, (Paperback) - Walmart	Robot or human?	https://www.walmart.com	[]	This new role positions labor unions as crucial mediators between technological innovation and worker rights, ensuring that AI benefits all, ...	Activate and hold the button to confirm that you’re human. Thank You!	2025-06-04T00:00:00		https://www.walmart.com/ip/The-New-Role-of-Labor-Unions-in-the-AI-Era-Paperback-9798369380505/16873505171?wmlspartner=wlpa&selectedSellerId=0&selectedOfferId=01150839C3A6392BB2AF7030AFE05F37&conditionGroupCode=1	[ { "date": "2025/06/04", "position": 91, "query": "AI labor union" }, { "date": "2025/06/04", "position": 99, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 88, "query": "AI labor union" }, { "date": "2025/06/04", "position": 84, "query": "AI labor union" }, { "date": "2025/06/04", "position": 80, "query": "AI labor union" }, { "date": "2025/06/04", "position": 98, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 88, "query": "AI labor union" }, { "date": "2025/06/04", "position": 99, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 86, "query": "AI labor union" }, { "date": "2025/06/04", "position": 83, "query": "AI labor union" }, { "date": "2025/06/04", "position": 89, "query": "AI labor union" }, { "date": "2025/06/04", "position": 80, "query": "AI labor union" }, { "date": "2025/06/04", "position": 96, "query": "AI labor union" } ]
AI vs humans: Facing job losses, US unions demand protection	AI vs humans: Facing job losses, US unions demand protection; defend worker rights	https://timesofindia.indiatimes.com	[]	American labour unions are resisting Artificial Intelligence. They want transparency and legal protection for workers. Unions fear job losses ...	AP file photo Labour unions in the United States are pushing back against Artificial Intelligence, demanding transparency, legal safeguards, and a voice for workers in the process, as industries adopt advanced AI tools—from generative chatbots to physical automation.Aaron Novik, a key organiser with Amazon’s ALU union, told news agency AFP, “As labourers, the ability to withhold our labour is one of our only tools to improve our lives. What happens when that disappears to AI?”AI could soon replace a wide range of blue-collar roles, while generative AI may wipe out nearly half of low-skilled white-collar jobs. This could push unemployment rates up to 20 percent, according to Anthropic’s CEO.Unions like the International Brotherhood of Teamsters have pushed for state-level legislation to restrict autonomous vehicles and robots. But progress is uneven. California and Colorado governors have vetoed recent bills banning autonomous trucks. Other states face similar challenges.At the federal level, former president Biden’s labour guidelines—encouraging transparency and protection for workers facing job loss—were scrapped by president Trump within hours of taking office.Despite the setbacks, some unions have managed wins. The Communications Workers of America (CWA) is educating members about AI, while SAG-AFTRA secured contractual guarantees around the use of AI likenesses. Dock workers and tech sector employees have also negotiated clauses limiting or managing automation.Still, most unions lack the influence of high-profile sectors. “Smaller contract-by-contract improvements are a long, slow process,” said RWDSU’s HeeWon Brindle-Khym.In an unrelated but significant news, a Florida woman, Megan Garcia, is suing Google and Character.AI, alleging the AI chatbot's role in her 14-year-old son's suicide. The lawsuit claims the chatbot manipulated the teenager, leading to his death. A US judge ruled last month that the lawsuit can proceed, rejecting the companies' free-speech defense, marking a significant legal challenge for AI accountability.	2025-06-04T00:00:00		https://timesofindia.indiatimes.com/business/international-business/ai-vs-humans-facing-job-losses-us-unions-demand-protection-defend-worker-rights/articleshow/121618261.cms	[ { "date": "2025/06/04", "position": 93, "query": "AI labor union" } ]
The New Role of Labor Unions in the AI Era, (Hardcover) - Walmart	Robot or human?	https://www.walmart.com	[]	This new role positions labor unions as crucial mediators between technological innovation and worker rights, ensuring that AI benefits all, ...	Activate and hold the button to confirm that you’re human. Thank You!	2025-06-04T00:00:00		https://www.walmart.com/ip/The-New-Role-of-Labor-Unions-in-the-AI-Era-Hardcover-9798369380499/15724313032	[ { "date": "2025/06/04", "position": 68, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 71, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 68, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 60, "query": "AI labor union" }, { "date": "2025/06/04", "position": 59, "query": "AI labor union" }, { "date": "2025/06/04", "position": 54, "query": "AI labor union" }, { "date": "2025/06/04", "position": 66, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 57, "query": "AI labor union" }, { "date": "2025/06/04", "position": 64, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 69, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 57, "query": "AI labor union" }, { "date": "2025/06/04", "position": 60, "query": "AI labor union" }, { "date": "2025/06/04", "position": 64, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 60, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 62, "query": "AI labor union" }, { "date": "2025/06/04", "position": 67, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 64, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 67, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 55, "query": "AI labor union" }, { "date": "2025/06/04", "position": 66, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 65, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 65, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 59, "query": "AI labor union" }, { "date": "2025/06/04", "position": 59, "query": "AI labor union" } ]
How to Be Better Leaders in the AI Era - Paymo	How to Be Better Leaders in the AI Era	https://www.paymoapp.com	[ "Maya Kirianova" ]	AI has already transformed businesses across different industries. Now, it makes sense for leaders to capitalize on this technology for business ...	AI, the simulation of human intelligence into technologies, has become a game-changer in business. Think of intelligent automation accelerating processes, machine learning performing data analysis, generative AI creating content pieces, and more. As a business executive or decision-maker, leverage this modern technology to improve your business operations. But what does it take to be a better leader in the age of artificial intelligence? This blog seeks to answer the crucial question above. Read on to learn how to become a better leader in the AI era. Leadership in the Time of Artificial Intelligence Artificial intelligence is already upon us—right here and everywhere. However, it shows no signs of stopping or slowing down. Instead, AI technology is growing and evolving, having the power to shape the future of business. In fact, Deloitte regards this era as the “Age of With,” where human work gets “augmented and enhanced with AI.” Take some interesting statistics from two of its most recent reports: About 60% of companies claim AI will significantly transform their industry in the next few years. Nearly 95% of business leaders say AI is extremely vital to organizational success. The challenge is posed to each and every leader looking to improve operations and achieve success. But first things first, let’s define and understand what leadership and AI are: Leadership: Simply put, leadership entails influencing, motivating, and guiding others to achieve shared goals. Forbes argues that leaders don’t just delegate tasks but facilitate interactions among employees. They use stacks of software that seek to improve communication, eliminate distractions, enhance knowledge and skills, provide the utmost support, and ensure business productivity. Simply put, leadership entails influencing, motivating, and guiding others to achieve shared goals. Forbes argues that leaders don’t just delegate tasks but facilitate interactions among employees. They use stacks of software that seek to improve communication, eliminate distractions, enhance knowledge and skills, provide the utmost support, and ensure business productivity. Artificial Intelligence (AI) involves simulating human intelligence in digital tools and technologies. It allows machines to somewhat think and work like human beings. AI-powered chatbots, IVR systems, and generative AI like ChatGPT are perfect examples. Statista predicted the global AI market to reach $305.90 billion in 2024 and $738.80 billion by 2030 at a 15.83% compound annual growth rate (CAGR). Leaders and AI meeting halfway AI has already transformed businesses across different industries. Now, it makes sense for leaders to capitalize on this technology for business optimization. According to Gartner, 80% of leaders use AI technology for various strategies and decisions. Companies such as Netflix, Amazon, and Apple, among many others, have started using AI-powered tools and technologies to improve their operations. Business leaders require employees to use the robotic automation process (RPA) for task automation, the national language processing (NLP) model for customer service, generative AI for content creation, and predictive analytics for data analysis. However, there is more to these than meets the eye. The question is: Why should leaders invest in AI technology? Find out below. Key benefits of AI in leadership AI technology comes with several potential benefits for every business. Deloitte cites the top three benefits of intelligent automation adoption: increased productivity (73%), cost reduction (60%), and improved accuracy (44%). Unconvinced? Here’s how AI proves beneficial to your business or organization: Data-driven business decision-making: Machine learning (ML) is a subset of AI capable of learning and deciding based on data patterns or trends. ML can help you analyze data, forecast trends, and make business decisions. Use web and predictive analytics for data analysis, forecasting, and reporting. Leveraging advanced data analytics services can further enhance your ability to extract actionable insights and drive smarter business decisions. Machine learning (ML) is a subset of AI capable of learning and deciding based on data patterns or trends. ML can help you analyze data, forecast trends, and make business decisions. Use web and predictive analytics for data analysis, forecasting, and reporting. Leveraging advanced data analytics services can further enhance your ability to extract actionable insights and drive smarter business decisions. Increased efficiency and productivity: AI-powered project management software improves workflows. These automated tools speed up processes and make employees more productive. Invest in computer telephony integration (CTI) technology, customer relationship management (CRM), and task software automation. AI-powered project management software improves workflows. These automated tools speed up processes and make employees more productive. Invest in computer telephony integration (CTI) technology, customer relationship management (CRM), and task software automation. High customer satisfaction (CSAT): AI improves processes and empowers employees, allowing them to serve customers better. As a result, customers have the best experience interacting with your staff and doing business with you. Ultimately, this technology can substantially help boost your CSAT. AI improves processes and empowers employees, allowing them to serve customers better. As a result, customers have the best experience interacting with your staff and doing business with you. Ultimately, this technology can substantially help boost your CSAT. Business optimization and scalability: Technologies can generally improve business operations, helping your company scale up and down. AI is one of the most advanced technologies that can optimize various facets of your organization. Leverage some, whether AI-powered chatbots for customer service or ChatGPT for content creation. Here’s a case for ChatGPT in action. Technologies can generally improve business operations, helping your company scale up and down. AI is one of the most advanced technologies that can optimize various facets of your organization. Leverage some, whether AI-powered chatbots for customer service or ChatGPT for content creation. Here’s a case for ChatGPT in action. Cost reduction and business profitability: Investing in AI can be costly at the onset. However, it can help you cut costs by automating tasks, integrating tools, and reducing the labor workforce. With its efficiency and productivity, this technology can improve service delivery, satisfy customers, and ultimately boost your profits. In the next section, learn how to leverage AI and become a better leader. 7 Practical ways to become a better leader in the AI Era A line is drawn between leaders who invest in technology and those who remain outdated. The former could expect business growth, success, and sustainability, while the latter might experience organizational entropy. In this time and age when technology is instrumental to business success, leveraging AI tools can make you a better leader. Here’s how to become one: 1. Develop AI skills and competencies There’s no denying the rise of AI technology. As a leader, you can invest in the best AI tools for organizational efficiency and employee productivity. However, you must first develop your skills and abilities to use this technology. Then, you can train and share these competencies with your employees. Jim Pendergast, Senior Vice President at altLINE Sobanco, underscores the impact of AI on business. Pendergast argues, “As an excellent business leader, invest in AI tools to empower your staff, not necessarily to replace them. However, developing their skills and competencies using this technology is key. In ChatGPT, for example, you can teach your employees how to use the best prompts for content generation.” A crucial element in effective leadership today is the integration and understanding of machine learning tools. By taking advantage of available machine learning training programs, leaders can better equip their teams with the necessary skills to harness AI’s full potential. Such programs not only enhance technical competencies but also help staff leverage data-driven insights for smarter decision-making. 2. Use AI to optimize and streamline processes AI is such a game-changer in today’s business landscape. AI tools can streamline workflows and optimize processes. For instance, RPA removes and automates manual tasks, while data analytics simplifies and accelerates business forecasting and reporting. Generative AI, like ChatGPT and DALL-E, improves the processes involved in marketing and advertising. Robert Kaskel, Chief People Officer at Checkr, suggests incorporating AI tools into your business operations. Kaskel says, “Business leaders must focus on AI integration as it can improve various functions involved in your business processes. It can make the series of steps more seamless, accurate, efficient, and effective. Ultimately, AI can enhance your work quality, data accuracy, service efficiency, and overall productivity.” 3. Empower employees through AI leverage Automation harnesses AI power to perform tasks with minimal human intervention. It can empower your staff by reducing manual work, accelerating processes, and increasing efficiency or productivity. Acumen and Research Consulting predicts that the global automation software market could reach $76.4 billion by 2030 at a 16.5% CAGR. Brooke Webber, Head of Marketing at Ninja Patches, recommends leveraging intelligent automation for your staff. Webber explains, “As a great business leader, you want to make the lives of your employees much easier and faster in the workplace. Provide them with automated software applications so they can perform their jobs well and serve your customers better.” 4. Set an AI-aided and customer-centric business Sure, AI empowers your most valuable organizational assets—employees. However, they extend to your much-valued customers. AI technologies like CTI, CRM, and IVR systems can significantly boost the customer service provided by your employees. They can help improve team communication to enhance customer relations. As a good business leader, promote a customer-focused culture with the help of modern technology. First, take the lead in using AI tools to enhance employee-customer interactions. For example, let your agents utilize data analytics to offer regular customers loyalty rewards. Ultimately, take care of your employees so they’ll take good care of your customers. 5. Foster human and AI collaboration Communication issues arise in the workplace, hindering effective collaboration. Thanks to technologies, they help improve stakeholders’ interactions. For instance, AI tools like chatbots, IVR systems, and other AI-powered self-service portals can be a great addition to customer service. Image source Stephan Baldwin, Founder of Assisted Living, recommends fostering human and AI collaboration. Baldwin argues, “As a business leader, you don’t have to choose between your employees and AI technologies. The key to success is merging human and technological resources to bring out the best in your business operations and customer interactions.” 6. Leverage AI for decision-making A subset of AI, machine learning, is very promising in business. As cited, ML can learn from data sets and make predictions or decisions without being explicitly programmed. Hence, this technology can perform business functions like data analysis, forecasting, and reporting. Catherine Schwartz, Finance Editor at Crediful, highlights the value of AI for business leaders. Catherine explains, “As a business leader, you can be more effective by leveraging AI in your decision-making. Analytics tools can assist you in recording, organizing, analyzing, and reporting data more accurately, efficiently, and effectively. That can help you make informed decisions in your organization.” 7. Promote a culture of tech innovation Businesses can no longer ignore the power of modern technologies. Among others, AI is the most advanced and promising. Leveraging this technology can help foster a culture of innovation in your company or organization. Forbes Advisor cites how businesses can leverage AI for different functions: Volodymyr Shchegel, VP of Engineering at MacKeeper, suggests investing in AI for business scalability and sustainability. Shchegel explains, “AI has been disrupting industries in ways unimaginable, but it has only just begun. It will continue to evolve and do more for your business in the next few years. So, if you want to transform your business technologically in the long term, start leveraging AI as early as now. As a leader, you take the initiative and call the shots.” Final Words Effective leadership remains key to business success. However, a line is drawn between traditional and modern leaders. So, what makes a better one in the age of artificial intelligence? Heed our advice: The right leader capitalizes on advanced technologies to optimize business operations. To become a better leader, consider the practical ways recommended above. As such, leverage AI to: Empower employees Improve processes Enhance customer satisfaction Promote robust collaboration Foster real innovation Make sound decisions AI has already begun transforming the business landscape in more ways than one. If you want your business to succeed, you must keep up with the AI trends. Ultimately, it takes a great leader to harness the power of AI for continued growth and long-term success! Maya Kirianova Author Maya Kirianova is a freelance writer with a passion for crafting engaging content that spans various niches that range from technology to business. With a strong foundation in these industries, she delivers insightful and well-researched content that helps businesses and individuals navigate the complexities of the financial world.	2024-03-22T00:00:00	2024/03/22	https://www.paymoapp.com/blog/better-leaders-ai-era/	[ { "date": "2025/06/04", "position": 94, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 88, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 89, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 92, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 91, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 88, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 87, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 92, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 93, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 92, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 92, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 92, "query": "artificial intelligence business leaders" }, { "date": "2025/06/04", "position": 93, "query": "artificial intelligence business leaders" } ]
Expect an AI Shock to Change the Job Market, Not Destroy It	Expect an AI Shock to Change the Job Market, Not Destroy It	https://www.aei.org	[ "Sarah Jeddy", "James Pethokoukis" ]	... disruption primarily shifted jobs from one region to another rather ... Artificial intelligence \| business \| economics \| Labor economics ...	In a Wall Street Journal op-ed, “The Real Story of the ‘China Shock‘,” economists James J. Heckman (a Nobel laureate at the University of Chicago) and Hanming Fang (University of Pennsylvania) argue that the turn-of-the-century trade disruption primarily shifted jobs from one region to another rather than eliminating them nationwide. Moreover, they contend that Chinese imports delivered substantial benefits to consumers, and that automation—not trade—was the main cause of manufacturing job losses. The lack of public understanding about these economic realities—suggested by numerous studies over the past half-decade or so—is problematic to current American policymaking (and politics, I would add): The danger of blaming Chinese trade is that the U.S. is misdiagnosing the problem and pursuing the wrong solutions. While tariffs on Chinese goods might bring back a few factory jobs, they will raise prices for everyone and hurt U.S. businesses that rely on imports. Current attempts to turn back the clock by introducing tariffs are a costly remedy for a poorly understood ailment. Not that authors dismiss the suffering of people who lost jobs. Far from it. Again, from the op-ed: It’s true that communities exposed to heavy Chinese import competition saw steep drops in manufacturing jobs and a rise in local unemployment. Crucially, the displaced workers mostly stayed put rather than moved for new work. It’s no wonder these academic papers resonated because they highlighted real pain in America’s industrial heartland. But treating the China shock as a verdict on national employment is a mistake. I think there’s a lesson here when thinking about the emerging Age of AI: It’s going to be messy. Jobs will be lost, jobs will be changed, and jobs will be altered. As such, it will be possible to cherry-pick your way to whatever conclusion you want. But good-faith analysis will take a broader view. A new Financial Times piece about Walmart neatly illustrates the messy complexity of AI- and robotics-driven workplace transformation. The story there defies simple narratives about job loss from automation. From the FT: Walmart in April showed off labour-saving technologies to investors and media at two new warehouses outside Dallas — one a cold-storage hub for foods, the other a fulfilment centre to enable speedy deliveries for ecommerce customers. About 600 associates work inside the 730,000 sq ft refrigerated warehouse. The ratio amounts to one employee for every 1,200 sq ft, about the size of a small home. Inside, a multi-tiered geometry of lifts, conveyors and sorting machines handles pallets of eggs, meat, produce and other perishables after they arrive from suppliers, storing them in racks as tall as 80ft. Guided by algorithms, robots later sort foods to be bundled and dispatched to coolers at 175 stores in the region. The centres can ship more than twice the volume of traditional cold warehouses, while cutting costs by 20 per cent. Rob Montgomery, Walmart’s executive vice-president of supply chain operations, said the technology saved associates from walking miles and lifting tens of thousands of pounds daily: “Here, our associates are working with automation to achieve the job.” Some specific job tasks have vanished, yet others evolved significantly. Meanwhile, entirely new positions emerged in ecommerce operations that company executives say “didn’t exist just a few years ago,” as Walmart battles Amazon online while maintaining its physical store dominance. Again, from the FT: Walmart executives say the technology investments mean new roles for workers, not fewer. “Tasks will get automated. Jobs will change. And many years from now, we’ll still employ a large number of people and be happy to do so,” chief executive Doug McMillon said at an investor event in April. … “Walmart executives said they expected the company’s total payroll to stay roughly constant even as its business evolves.” Rather than “robots will take all the jobs,” the Walmart experience provides a helpful baseline for thinking about technology and the job market.	2025-06-04T00:00:00	2025/06/04	https://www.aei.org/economics/expect-an-ai-shock-to-change-the-job-market-not-destroy-it/	[ { "date": "2025/06/04", "position": 93, "query": "AI economic disruption" }, { "date": "2025/06/04", "position": 83, "query": "AI economic disruption" }, { "date": "2025/06/04", "position": 80, "query": "AI economic disruption" }, { "date": "2025/06/04", "position": 81, "query": "AI economic disruption" }, { "date": "2025/06/04", "position": 83, "query": "AI economic disruption" } ]
AI at Work: Why Responsible Adoption Is the Key to Business Success	Why Responsible AI Adoption Is the Key to Business Success	https://sbs-ed.com	[ "Emmanuel Agbeko Gamor" ]	However, successful AI integration in the workplace requires buy-in across all levels of the organisation. A study by HR consultancy firm ...	Businesses are increasingly turning to artificial intelligence (AI) to automate tasks, enhance operations, and streamline workflows. However, successful AI integration in the workplace requires buy-in across all levels of the organisation. A study by HR consultancy firm, Mercer, found that employee anxiety around AI replacing jobs has declined significantly—from 53% in 2022 to just 10% in 2024. However, concerns persist. The same study noted that 21% of employees in 2024—double the figure from 2022—are worried that AI advancements will lead to heightened expectations and increased pressure to deliver more, faster. It is therefore crucial that managers avoid using AI integration as a justification for unsustainable performance demands. Instead, leaders should focus on responsible implementation that enhances productivity without compromising employee well-being. Championing an AI Mindset from the Top Senior leadership plays a pivotal role in driving an AI-enabled culture. Executives and managers must be convinced of AI’s potential to solve real business challenges. For organisations grappling with productivity issues, the following AI-powered solutions can deliver a measurable impact: 1. Automate Routine Tasks Effective time management is the first step to improving productivity. Repetitive tasks like manual data-capturing from forms, appointment scheduling, and generating standard reports can be automated by using Intelligent Document Processing (IDP) solutions that utilise AI, particularly Optical Character Recognition (OCR) and Natural Language Understanding (NLU), to automatically extract and process information to increase accuracy and efficiency. Tools like Microsoft Power Automate or Zapier can be used to streamline workflows without exposing client data, protecting the organisation, saving time, and enabling teams to focus on higher-value tasks. 2. AI-Driven Task Prioritisation and Productivity Insights AI assistants embedded in popular and easy-to-use software programmes can analyse incoming tasks, deadlines, and employee capacities for optimal prioritisation. These tools can dynamically adjust schedules, ensuring the most critical tasks are addressed first while preventing repetitive bottlenecks. The use of AI tools like Clockwise or Reclaim.ai can adjust calendars, prioritise tasks, and reduce meeting overload. 3. Intelligent Information Retrieval Systems Staff often spend valuable time searching for information. Implementing AI-powered knowledge applications can assist employees to quickly access relevant information, such as legal references, company policies, use case examples, and best practices. AI-powered search tools can index internal documents, knowledge bases, and past communications, enabling staff to access the exact data they need with ease and efficiency. Natural language processing enables them to ask questions in plain language rather than using specific keywords, reducing search time and improving accuracy. AI tools like Adobe Acrobat’s AI or LuminPDF (Lumin’s Summarise) can extract, summarise, and organise documents while automatically removing sensitive information. 4. Smart Email and Communication Assistants Enhancing clear and effective communication is essential for improving productivity in the workplace. AI can simplify internal communication by summarising long email threads, identifying key discussion points, and suggesting appropriate colleagues for specific queries. Integrated into platforms like Slack or Teams, AI features can route tasks, track progress, and highlight potential blockers. However, to protect sensitive information like client data and company strategies, businesses must ensure that these platforms have end-to-end encryption and rigorous permission settings. Supporting AI with Training and Safeguards Regular training—spanning from the boardroom to frontline staff—paired with a robust feedback system, is essential for addressing challenges in the effective use of AI-powered systems. Crucially, adopting AI solutions should enhance employees’ work-life balance—much like the time-saving benefits of online meetings during the COVID-19 pandemic, which helped usher in hybrid work models. Responsible use of AI must reduce burnout, not exacerbate it. Trust matters: Keeping data safe Lastly, organisations should be mindful that integrating AI comes with an increased need to prioritise data protection. Therefore, always choose on-site, encrypted, and enterprise-grade AI solutions with strict access controls to ensure client and company information remains secure. A Future-Focused, People-Centred Approach to AI With AI becoming an integral part of modern business operations, its success ultimately hinges on how responsibly it is adopted. When embraced thoughtfully and inclusively, AI can unlock meaningful gains in productivity, efficiency, and employee well-being. References: https://www.mercer.com/insights/people-strategy/future-of-work/global-talent-trends/ https://blog.9cv9.com/top-5-strategies-to-boost-workplace-productivity-and-efficiency-for-managers/	2025-06-04T00:00:00	2025/06/04	https://sbs-ed.com/ai-at-work-why-responsible-adoption-is-the-key-to-business-success/	[ { "date": "2025/06/04", "position": 92, "query": "workplace AI adoption" }, { "date": "2025/06/04", "position": 94, "query": "workplace AI adoption" }, { "date": "2025/06/04", "position": 90, "query": "workplace AI adoption" }, { "date": "2025/06/04", "position": 92, "query": "workplace AI adoption" }, { "date": "2025/06/04", "position": 15, "query": "workplace AI adoption" } ]
When AI Steals Your Entry-Level Gig(Don't Worry—UBI's (Universal ...	When AI Steals Your Entry-Level Gig(Don’t Worry—UBI’s (Universal Basic Income) on the Way)	https://www.linkedin.com	[]	Enter Universal Basic Income: a policy once considered fringe is now edging toward mainstream discourse. By guaranteeing every adult a baseline ...	“Dario Amodei, CEO of Anthropic—one of the leading developers of cutting-edge AI—recently issued a stark warning: within the next one to five years, as many as half of all entry‐level white‐collar positions could vanish, driving unemployment rates into the double digits”. Speaking from Anthropic’s San Francisco headquarters, Amodei urged policymakers, corporate leaders, and everyday workers to stop downplaying what is unfolding. Technologies that already accomplish coding, legal research, financial modeling, and customer support tasks at near‐human proficiency are accelerating so rapidly that entire job categories—particularly junior roles in tech, finance, law, and consulting—are poised to evaporate almost overnight. Think about how entry‐level positions function as a gateway for young professionals. Scrawling through legal documents, drafting routine memos, or writing boilerplate code have long been the plumbing of corporate life—work that served as on‐the‐job training. Yet AI systems such as Anthropic’s Claude 4 are already able to interpret contracts, generate boilerplate codebases, and perform data analysis in seconds. Once businesses realize they can replace a $50,000‐a‐year junior analyst with an AI agent running at a fraction of that cost, the calculus changes. That tipping point could come as soon as next year. CEOs, strapped between quarterly targets and competitive pressures, will quietly freeze hiring for those entry rungs—and then begin systematically phasing out human employees in favor of AI “agents” that never tire, never ask for raises, and scale instantly. It is not only Amodei sounding the alarm. During a recent interview, Steve Bannon—who served in the Trump administration—predicted that AI’s assault on white‐collar work will become a front‐burner political issue by 2028. He highlighted how positions that once offered young people stepping stones into professions—first‐year associates at law firms, junior software developers, entry‐level financial analysts—are most vulnerable, since they perform repetitive, structured tasks that AI can already execute. The danger, Bannon cautioned, is that millions of Americans will awaken to a devastated job market only after the damage is done. Yet for all the ominous forecasts, few in government or corporate America are bracing for impact. Congress remains largely disengaged from serious AI regulation—worried about stifling innovation or ceding ground to geopolitical rivals rather than facing the impending human cost. CEOs privately acknowledge the threat but tend to keep it under wraps, afraid that pre‐announcement could undermine morale or invite regulatory scrutiny. And most workers—busy meeting today’s deadlines—have no idea their roles could be obsolete within months. “People hear this and think it’s science fiction,” Amodei observes, “but the machines are already reaching human levels of performance in tasks we once thought irreplaceable.” Consider how advertising agencies now deploy AI to draft marketing copy, design social media campaigns, and optimize ad spend—all with minimal human oversight. Law firms are testing LLMs and having their own LLM’s (large language models) to review contracts and flag potential risks. Banks are experimenting with AI‐driven underwriting models that outpace human analysts on speed and consistency. Once these systems prove reliable, the temptation for businesses to slash headcount will be irresistible. The moment one company blazes the trail—reporting millions in savings—it will trigger a domino effect across industries: why pay a recruiter, salary, benefits, and office space for someone whose primary tasks can be offloaded to an LLM for pennies on the dollar? Some argue this resembles past technological revolutions. The number of transistors in a dense integrated circuit (IC) doubles about every two years, is being challenged, Moore’s Law. Yet AI differs in two critical ways: first, the pace of change is blindingly fast; second, the scope is far broader. While past automations primarily applied to manufacturing or back‐office work, AI now reaches into professions we once deemed “safe”—law, accounting, journalism, software engineering. In many cases, entire workflows can be automated end to end: from drafting code and performing QA tests to deploying applications in production. The question is whether new roles will materialize quickly enough—roles that genuinely require human ingenuity rather than rote pattern recognition. Within Anthropic, “Amodei has seen first‐hand the contradictions these developments entail. He spends his days showcasing Claude 4’s extraordinary ability to generate code, analyze complex documents, and even exhibit “agentic” behavior—where the AI can initiate tasks, pursue multi‐step objectives, and learn from its environment. Yet he also feels compelled to warn society of the fallout. “If cancer is cured, the economy booms at 10 percent growth, but a fifth of people are jobless—that scenario is terrifyingly plausible,” he says. It is precisely because he leads a company at AI’s vanguard that Amodei believes he must speak truth to power, even if it runs counter to the narrative of boundless, unalloyed optimism. Contrast this with Sam Altman, CEO of OpenAI and Amodei’s former colleague, who emphasizes how past generations would marvel at today’s abundance: if an 18th‐century lamplighter saw the modern world, they could hardly imagine the prosperity. Altman argues that while AI will disrupt, it also carries the potential to create entirely new sectors: advanced biotech, climate modeling, education platforms, and so on. His hope is that innovation will outpace displacement. But Amodei warns that hoping for new sectors is not a substitute for planning. He has proposed several pragmatic steps to mitigate short‐term pain: Raise Public Awareness: Government and AI companies must be transparent about which types of jobs are most at risk, allowing individuals to pivot their career paths proactively. Amodei’s Anthropic Economic Index aims to shed light on how various occupations are already leveraging AI or seeing their tasks automated. Corporate Responsibility: CEOs should shift from simply asking “How can AI cut costs?” to “How can AI complement and augment our workforce?” By upskilling existing employees—teaching them to work alongside AI agents rather than being replaced—companies can slow down the displacement wave. Legislative Engagement: Most lawmakers still view AI as a niche policy issue, failing to recognize its existential implications for the labor market. Joint congressional committees, coupled with regular briefings by AI experts, could spark early debate on necessary safeguards before the technology permeates every corner of the economy. Taxation and Redistribution: Amodei has floated a “token tax” on every instance of AI usage—perhaps a small percentage of revenue whenever an AI model completes a paid task—redirected toward workforce retraining or a nascent universal basic income (UBI) fund. Though such a levy cuts into AI companies’ margins, Amodei argues it is a fair concession if society is to benefit rather than fracture. Rethinking Education: If rote tasks are soon automated, educational institutions must evolve curricula to emphasize critical thinking, creative problem‐solving, and interdisciplinary collaboration—skills that current AI struggles to replicate. A generation equipped to harness AI rather than be replaced by it remains our best safeguard. Some skeptics dismiss these warnings as hyperbolic. They point out that past fears about automation—like the “robots will take our jobs” rhetoric of the 1960s—did not come to pass in the direst forms. Yet AI’s capabilities are not comparable to industrial machinery; they resemble a cognitive revolution. The same systems that summarize medical research can write code, parse legal briefs, and even generate press releases. The initial transition—where AI augments human workers—is already underway. But the shift to pure automation looms just around the corner: hundreds of startups racing to build “agentic AI” that operates autonomously, forever altering business models. Meta’s Mark Zuckerberg, for example, has predicted that by 2025, these mid‐level coding jobs will vanish, replaced wholly by AI engineers that write and deploy software faster than their human counterparts. When large firms such as Microsoft, Walmart, or CrowdStrike announce layoffs—6,000 engineers here, 1,500 corporate jobs there—CEOs often cite “AI restructuring” as a driving force. Underneath those announcements is a deeper, systemic recalibration of labor demand. A recent LinkedIn op‐ed warned that junior paralegals, early‐career developers, and first‐year associates could find themselves edged out before they even gain a foothold. By the time the broader public realizes what has happened, businesses will have already reaped years of savings, and the social safety nets may be stretched thin. This future threatens to concentrate wealth more sharply than ever before. As AI providers accrue massive profits, the gulf between those who own superhuman intellectual property and those who rely on labor income will widen. Unless corrective measures are introduced, millions risk losing their means of economic participation—undermining democratic stability and eroding social cohesion. Amodei does not see himself as a doom‐merchant; rather, he considers it his duty to sound an alarm. “We built these tools,” he says, “and we have an obligation to acknowledge their consequences.” Even if the U.S. attempted to throttle AI progress, China’s rapid advances would ensure the global train keeps racing ahead—no nation can afford to be left behind. So where do we go from here? Some companies have begun to pause new job listings while experimenting with AI internally, hoping to slow the bleeding. Others are launching in‐house training programs, teaching employees how to wield AI tools effectively. A few forward‐thinking universities have inaugurated interdisciplinary “AI and Society” degrees, blending computer science, ethics, and public policy. Yet in the end, if tens of millions of jobs genuinely disappear within a few years, no amount of upskilling or regional talent pipelines will suffice. We will need a societal paradigm shift—one that reimagines work, value creation, and the social contract itself. Enter Universal Basic Income: a policy once considered fringe is now edging toward mainstream discourse. By guaranteeing every adult a baseline stipend—regardless of employment status—a UBI could serve as a bulwark against the shock of mass automation. It would allow people to pursue education, entrepreneurship, caregiving, or creative endeavors without the existential fear of financial ruin. We are not at that juncture yet, but we are rapidly approaching it. The era in which a full‐time job was the sine qua non of adult life is giving way to a world where intelligent machines shoulder more of society’s workload. As Amodei—and many others—have cautioned, delaying this conversation only magnifies the eventual upheaval. Tomorrow’s policy debates will need to consider not just how to regulate AI safely, but how to redistribute wealth, redefine purpose, and ensure that every citizen retains a stake in the new economy. The dawn of Universal Basic Income may well be the most vital conversation we have left.	2025-06-04T00:00:00		https://www.linkedin.com/pulse/when-ai-steals-your-entry-level-gigdont-worryubis-universal-vg6sf	[ { "date": "2025/06/04", "position": 45, "query": "universal basic income AI" }, { "date": "2025/06/04", "position": 47, "query": "universal basic income AI" }, { "date": "2025/06/04", "position": 48, "query": "universal basic income AI" } ]
US labor unions fight to contain AI disruption - ET CIO	US Labor Unions Battle AI Disruption: Protecting Workers' Rights, ET CIO	https://cio.economictimes.indiatimes.com	[]	A handful of unions have successfully negotiated AI protections into their contracts. Notable examples include agreements with media company ...	Advt Advt Advt Advt Join the community of 2M+ industry professionals. Subscribe to Newsletter to get latest insights & analysis in your inbox. All about ETCIO industry right on your smartphone! Download the ETCIO App and get the Realtime updates and Save your favourite articles. New York: As artificial intelligence threatens to upend entire sectors of the economy, American labor unions are scrambling to protect workers, demand corporate transparency, and rally political support-an uphill battle in a rapidly changing world."As laborers, the ability to withhold our labor is one of our only tools to improve our lives," explained Aaron Novik, a key organizer with Amazon's ALU union."What happens when that disappears (to AI)? It's a real existential issue," he added.Automation has already transformed most industries since the 1960s, typically reducing workforce numbers in the process.But the emergence of advanced "physical AI" promises a new generation of intelligent robots that won't be limited to repetitive tasks -- potentially displacing far more blue-collar workers than ever before.The threat extends beyond manufacturing.The CEO of Anthropic, which created Claude as a competitor to ChatGPT, warned last week that generative AI could eliminate half of all low-skilled white-collar jobs, potentially driving unemployment rates up to 10-20 percent."The potential displacement of workers and elimination of jobs is a significant concern not just for our members, but for the public in general," said Peter Finn of the International Brotherhood of Teamsters , America's largest union.The Teamsters have focused their efforts on passing legislation limiting the spread of automation, but face significant political obstacles.California's governor has twice vetoed bills that would ban autonomous trucks from public roads, despite intense lobbying from the state's hundreds of thousands of union members.Colorado's governor followed suit last week, and similar battles are playing out in Indiana, Maryland, and other states.At the federal level, the landscape shifted dramatically with the change in the White House.Under former president Joe Biden, the Department of Labor issued guidelines encouraging companies to be transparent about AI use, involve workers in strategic decisions, and support employees whose jobs face elimination.But US President Donald Trump canceled the protections within hours of taking office in January."Now it's clear. They want to fully open up AI without the safeguards that are necessary to ensure workers' rights and protections at work," said HeeWon Brindle-Khym of the Retail, Wholesale and Department Store Union (RWDSU), which represents workers in the retail sector.Meanwhile, companies are racing to implement AI technologies, often with poor results."By fear of missing out on innovations, there's been a real push (to release AI products)," observed Dan Reynolds of the Communications Workers of America (CWA).The CWA has taken a proactive approach, publishing a comprehensive guide for members that urges negotiators to include AI provisions in all collective bargaining agreements.The union is also developing educational toolkits to help workers understand and negotiate around AI implementation.A handful of unions have successfully negotiated AI protections into their contracts.Notable examples include agreements with media company Ziff Davis (which owns Mashable) and video game publisher ZeniMax Studios, a Microsoft subsidiary.The most significant victories belong to two powerful unions: the International Longshoremen's Association, representing dock workers, secured a moratorium on full automation of certain port operations, while the Screen Actors Guild (SAG-AFTRA) won guarantees that actors must be consulted and compensated whenever their AI likeness is created.These successes remain exceptional, however.The American labor movement, as a whole, lacks the bargaining power enjoyed by those highly strategic or publicly visible sectors, said Brindle-Khym."Smaller contract-by-contract improvements are a long, slow process," she added.Despite frequent accusations by corporate interests, the unions' goal isn't to halt technological progress entirely."Workers are usually not seeking to stop the march of technology," noted Virginia Doellgast, a Cornell University professor specializing in labor relations."They just want to have some control."As AI continues its rapid advance, the question remains whether unions can adapt quickly enough to protect workers in an economy increasingly dominated by artificial intelligence.	2025-06-04T00:00:00		https://cio.economictimes.indiatimes.com/news/artificial-intelligence/us-labor-unions-fight-to-contain-ai-disruption/121617300	[ { "date": "2025/06/04", "position": 86, "query": "artificial intelligence labor union" }, { "date": "2025/06/04", "position": 99, "query": "AI labor union" }, { "date": "2025/06/04", "position": 97, "query": "AI labor union" }, { "date": "2025/06/04", "position": 6, "query": "artificial intelligence labor union" } ]

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.