Who should I follow to learn about AI and the future of work?

If you want to understand **AI itself *and* what it means for jobs, careers, and society**, I’d follow a deliberately mixed group rather than one “AI guru”: - **Ethan Mollick** - practical, research-based thinking about how AI changes knowledge work, management, education, and entrepreneurship. His newsletter *One Useful Thing* is particularly good for staying current without getting lost in technical jargon. [Ethan Mollick – Management Department](https://mgmt.wharton.upenn.edu/profile/emollick/?utm_source=chatgpt.com) - **Erik Brynjolfsson** - follow for the economics of AI: productivity, wages, automation vs. augmentation, skills, and employment. He directs Stanford’s Digital Economy Lab, which publishes ongoing research on these questions. [Erik Brynjolfsson - Stanford Digital Economy Lab](https://digitaleconomy.stanford.edu/person/erik-brynjolfsson/?utm_source=chatgpt.com) - **Daron Acemoglu** - useful as a counterweight to overly optimistic AI narratives. His work examines automation, technological change, inequality, and how institutions shape who benefits from technology. - **Tom Mitchell** - a strong bridge between technical AI and questions about its impact on workers and society; he co-chaired the National Academies' study of AI and the future of work with Brynjolfsson. [Q&A | AI and the Future of Work with Erik Brynjolfsson and Tom Mitchell - Stanford Digital Economy Lab](https://digitaleconomy.stanford.edu/news/qa-ai-and-the-future-of-work-with-erik-brynjolfsson-and-tom-mitchell/?utm_source=chatgpt.com) - **Meredith Whittaker** - follow for the labor, social, privacy, and governance dimensions of AI, particularly perspectives that can be underrepresented in industry discussions. - **Anton Korinek** - particularly valuable if you want to understand the economics of increasingly capable AI, including possible effects on labor markets and inequality. - **Daniel Susskind** - excellent for the longer-term question of what happens to human work when machines become capable of performing more cognitive tasks. ### If you only want 3 I'd make it: 1. **Ethan Mollick** → *What is AI actually doing to how we work?* 2. **Erik Brynjolfsson** → *What does the evidence say about jobs and the economy?* 3. **Daron Acemoglu** → *What could go wrong, and how might the benefits be distributed?* That combination gives you **practical experience + empirical economics + skepticism about technological disruption**, rather than a single narrative. One particularly useful thing to follow is Stanford's **Digital Economy Lab**, rather than just individual personalities. Its current research covers AI agents, transformative AI, employment, skills, productivity, and the effects of AI on workers. [Home - Stanford Digital Economy Lab](https://digitaleconomy.stanford.edu/?utm_source=chatgpt.com) For example, its August 2026 research using payroll data through June 2026 found **no evidence of widespread economy-wide displacement**, while finding a substantial employment gap for younger workers in AI-exposed occupations. That's exactly the kind of evidence I'd prioritize over sweeping predictions about “AI taking everyone's jobs.” [Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence - Stanford Digital Economy Lab](https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/?utm_source=chatgpt.com)

**If you want to learn about AI and the future of work, follow a mix of researchers, entrepreneurs, educators, and ethicists.** This ensures you get both technical depth and practical insights into how AI is reshaping industries, jobs, and society. ## 🌟 Key Thought Leaders to Follow Name| Role| Why Follow| Platform Highlights ---|---|---|--- **Andrew Ng**| Founder of DeepLearning.AI, Coursera| Leading AI educator, focuses on democratizing AI skills and agentic workflows| LinkedIn, X [30 Top AI Thought Leaders To Watch](https://www.linkedin.com/pulse/30-top-ai-thought-leaders-watch-akhilesh-singh-pmp-caio-caiso-f9nxc)[Top 10 AI Influencers to Follow on LinkedIn in 2026 | GenAI.Works](https://genai.works/insights/top-10-ai-influencers-to-follow-on-linkedin-in-2026) **Fei‑Fei Li**| Director, Stanford HAI| Human-centered AI, inclusion in AI development| LinkedIn, Stanford HAI [30 Top AI Thought Leaders To Watch](https://www.linkedin.com/pulse/30-top-ai-thought-leaders-watch-akhilesh-singh-pmp-caio-caiso-f9nxc)[Top 10 AI Influencers to Follow on LinkedIn in 2026 | GenAI.Works](https://genai.works/insights/top-10-ai-influencers-to-follow-on-linkedin-in-2026) **Demis Hassabis**| CEO, Google DeepMind| Multimodal AI, scientific breakthroughs like AlphaFold| X, LinkedIn [Top 10 AI Influencers of 2026 - KDnuggets](https://www.kdnuggets.com/top-10-ai-influencers-of-2026)[Top 10 AI Influencers to Follow on LinkedIn in 2026 | GenAI.Works](https://genai.works/insights/top-10-ai-influencers-to-follow-on-linkedin-in-2026) **Yann LeCun**| Chief AI Scientist, Meta| Deep learning pioneer, outspoken on AGI debates| X, LinkedIn [Top 50 People in AI to Follow in 2026 (X, LinkedIn, Websites)](https://www.aitraining2u.com/blog/top-50-ai-people-to-follow-2026.html)[Top 10 AI Influencers to Follow on LinkedIn in 2026 | GenAI.Works](https://genai.works/insights/top-10-ai-influencers-to-follow-on-linkedin-in-2026) **Timnit Gebru**| Founder, DAIR| Ethical AI, transparency, fairness in algorithms| LinkedIn, DAIR publications [30 Top AI Thought Leaders To Watch](https://www.linkedin.com/pulse/30-top-ai-thought-leaders-watch-akhilesh-singh-pmp-caio-caiso-f9nxc) **Ethan Mollick**| Wharton Professor| Practical insights on AI in business and education| LinkedIn, Co-Intelligence blog [Top 10 AI Influencers to Follow on LinkedIn in 2026 | GenAI.Works](https://genai.works/insights/top-10-ai-influencers-to-follow-on-linkedin-in-2026) **Kai‑Fu Lee**| CEO, Sinovation Ventures| AI investing, East-West innovation bridge| LinkedIn, books like _AI Superpowers_ [30 Top AI Thought Leaders To Watch](https://www.linkedin.com/pulse/30-top-ai-thought-leaders-watch-akhilesh-singh-pmp-caio-caiso-f9nxc) **Linda A. Hill**| Harvard Business School| Leadership in AI adoption, co-creation in the workplace| TIME articles, LinkedIn [The Leaders AI Innovation Needs](https://time.com/article/2026/09/08/the-leaders-ai-innovation-needs/) **Susan Etlinger**| Tech researcher & advisor| Data ethics, responsible AI adoption| TED Talks, LinkedIn [Top 10: AI Influencers | AI Magazine](https://aimagazine.com/top10/top-10-ai-influencers-2026) **Stuart Russell**| UC Berkeley Professor| AI safety, alignment with human values| Academic publications, talks [30 Top AI Thought Leaders To Watch](https://www.linkedin.com/pulse/30-top-ai-thought-leaders-watch-akhilesh-singh-pmp-caio-caiso-f9nxc) ## 🔑 Why These Voices Matter * **Educators(Ng, Li, Mollick):** Help you understand AI’s impact on skills and workforce training. * **Researchers(LeCun, Hassabis, Russell):** Provide cutting-edge technical insights. * **Ethicists(Gebru, Etlinger):** Keep you grounded in responsible AI use. * **Business Leaders (Lee, Hill):** Show how AI is transforming organizations and leadership. ## ⚠️ Risks & Considerations * **Echo chambers:** Following only tech CEOs may bias you toward hype. Balance with ethicists and academics. * **Rapid change:** AI developments shift weekly; prioritize voices who post regularly and critically. * **Workplace impact:** Many leaders emphasize augmentation, not replacement. Be wary of simplistic “AI will take all jobs” narratives. 👉 To get the most value, I’d suggest curating your feed across **LinkedIn for professional insights** and **X(Twitter) for real-time debates**. Would you like me to create a **short starter list of LinkedIn profiles** so you can follow them directly?

To successfully navigate the intersection of artificial intelligence and the future of work, it helps to follow a mix of **economists, tech researchers, workplace futurists, and hands-on practitioners**. They bridge the gap between technical capability and real-world economic and career impact. ### Key Thinkers and Researchers * **Ethan Mollick:** Associate Professor at the Wharton School of the University of Pennsylvania. He writes extensively on practical AI use in education and the workplace, offering actionable insights for how organizations and individuals can adapt. * **Kai-Fu Lee:** Venture capitalist, AI expert, and author of _AI Superpowers_. He focuses heavily on how automation and AI will reshape global labor markets, specific job categories, and economic structures. * **Daron Acemoglu:** MIT Economist whose research frequently explores how technology shapes productivity, wages, and employment. He offers a grounded, macro-economic counterweight to tech-utopian hype. * **Sal Khan:** Founder and CEO of Khan Academy. He is a premier voice on how generative AI is transforming education, skill-building, and the foundational learning required for the future workforce. ### Tech Leaders & Practitioners * **Andrew Ng:** Founder of DeepLearning.AI and Landing AI. Alongside his technical contributions, he is one of the world's most prolific educators, offering accessible frameworks (like _AI for Everyone_) to help professionals understand how AI applies to their industries. [Preparing for the Future of Work with AI: A Comprehensive Guide](https://bytebridge.medium.com/preparing-for-the-future-of-work-with-ai-a-comprehensive-guide-649f6158edb7#:~:text=Platforms%20like%20Coursera%2C%20edX%2C%20Udacity%2C%20and%20Udemy,AI%20fundamentals%20and%20broader%20digital%20skills.%20Adaptability.) * **Sam Altman & Demis Hassabis:** Leaders of OpenAI and Google DeepMind respectively. Following their updates and essays provides a direct window into the technological capabilities that will define tomorrow's workplace tools. ### Platforms and Structured Learning Hubs If you are looking for continuous updates and practical skill development rather than just commentary: * **Google AI & DeepLearning.AI:** Excellent for structured learning paths, offering beginner-to-advanced resources on prompt engineering, productivity tools, and AI literacy. [How to Learn Artificial Intelligence: A Beginners Step-by-Step Guide](https://www.goodwill.org/blog/general/how-to-learn-artificial-intelligence-a-beginners-step-by-step-guide/#:~:text=Beginner%2Dfriendly%20AI%20tools%20include%20writing%20assistants%2C%20summarization,coding%20experience%20to%20learn%20artificial%20intelligence%3F%20No.) * **Substack & Professional Newsletters:** Look for curators tracking the "future of work," such as _One Useful Thing_ (Ethan Mollick) or various industry-specific tech newsletters that break down weekly automation trends. What specific industry or role are you looking to future-proof with AI?

To truly understand how artificial intelligence is reshaping careers, productivity, and organizational structures, you need a mix of voices: macro-economists, hands-on automation practitioners, enterprise leaders, and foundational tech researchers. Here are the top people and channels to follow, categorized by their specific focus on AI and the future of work: 1\. The Automation & Enterprise Transformation Experts * * * __Pascal Bornet_ :_ Known globally as a leading voice on AI, business automation, and leadership. He writes extensively on how human work is augmenting alongside intelligent automation. Follow him on [Pascal Bornet's LinkedIn](https://www.linkedin.com/pulse/50-top-ai-influencers-follow-2026-jean-ng--vyotc). * * * __Ethan Mollick_ :_ A professor at Wharton who treats his classroom and newsletter as a live laboratory for generative AI in work, education, and management. His practical experiments on productivity are essential reading. * * * __Daron Acemoglu_ :_ An MIT economist whose work focuses heavily on technology, labor, and inequality. If you want a rigorous, data-driven macro view of whether AI will actually create or destroy jobs long-term, his research and commentary are gold standards. [Top 20 AI Influencers on LinkedIn 2026 | Raj V. posted on the](https://www.linkedin.com/posts/raj-vuyyuru_top-ai-influencers-list-2026-activity-7459912786970746880-Q3pm)[50 Top AI Influencers to Follow in 2026 - LinkedIn](https://www.linkedin.com/pulse/50-top-ai-influencers-follow-2026-jean-ng--vyotc) 2\. Practical Workflow & AI Implementation Guides * * * __Ruben Hassid_ :_ Excellent for tactical, day-to-day prompt engineering and practical LLM workflows (like using Claude or ChatGPT) to speed up professional tasks. Check out his insights on [Raj Vuyyuru's LinkedIn Post](https://www.linkedin.com/posts/raj-vuyyuru_top-ai-influencers-list-2026-activity-7459912786970746880-Q3pm). * * * __Allie K. Miller_ :_ One of the most followed non-technical AI voices online. She regularly breaks down how everyday professionals across various industries can practically apply generative AI to future-proof their careers. [LinkedIn·Raj V.] 3\. Deep Tech Pioneers & Industry Visionaries * * * __Andrew Ng_ :_ Founder of DeepLearning.AI and former head of Google Brain. He frequently speaks and writes on how AI will transform industries and why continuous upskilling is critical for the workforce. Find his updates via [Andrew Ng on Feedspot](https://x.feedspot.com/artificial_intelligence_twitter_influencers/). * * * __Kai-Fu Lee_ :_ CEO of 01.AI and author of _AI Superpowers_ and _AI 2041_. He offers a unique global perspective on how AI will restructure economic powerhouses, education, and labor markets globally. [Top 100 AI Influencers in 2026 (Artificial Intelligence)](https://x.feedspot.com/artificial_intelligence_twitter_influencers/) 4\. Critical & Philosophical Analysts * * * __Gary Marcus_ :_ A cognitive scientist and frequent critic of AI hype. Following him provides a healthy, reality-checking counter-narrative to corporate tech optimism, helping you distinguish what AI can actually do today versus what is pure speculation. [LinkedIn] If you'd like, let me know: * Are you looking for insights on a **specific industry** (like marketing, law, coding, or healthcare)? * Do you prefer **newsletters, podcasts, or social media feeds**? I can narrow down this list to the exact resources that fit your goals.

A good AI-and-work information diet needs more than product news: follow people who study AI capability, workplace adoption, labor economics, management, and policy. Start with 5–7 sources from different lenses rather than relying on any single futurist - AI’s effects are likely to be uneven by occupation, industry, and task, not one universal “jobs vanish” story.[Workforce policy for the age of AI - Brookings Institution](https://www.brookings.edu/articles/workforce-policy-for-the-age-of-ai/) ## Best people to follow | Person | Best for | What you’ll get | |---|---|---| | **Ethan Mollick** | Practical AI at work and education | Clear, evidence-minded explanations of how people and organizations can use generative AI. His *One Useful Thing* newsletter is especially useful for knowledge workers, managers, and educators. [Best AI Newsletters to Subscribe To in 2026: 16 Picks Ranked | Blog](https://www.readless.app/blog/best-ai-newsletters-to-subscribe) | | **Erik Brynjolfsson** | Economics of AI and productivity | Research-based perspective on how digital technologies affect productivity, wages, organizational design, and inequality. Look for his Stanford Digital Economy Lab work. | | **Daron Acemoglu** | Skeptical labor-economics lens | Essential counterweight to hype. He focuses on whether AI will actually be economically useful at scale, who captures the gains, and how technology design affects workers. | | **David Autor** | Jobs, tasks, wages, and labor markets | A leading labor economist for understanding why automation changes *tasks* within jobs - not simply “replaces jobs” - and why effects differ across workers. | | **Daniela Rus** | Robotics and embodied AI | Helpful for separating changes to digital knowledge work from changes to physical work, manufacturing, logistics, and service roles. | | **Fei-Fei Li** | Human-centered AI and governance | Strong perspective on designing and governing AI around human outcomes, especially relevant to work, institutions, and public policy. | | **Andrew Ng** | Broad AI literacy | Follow for approachable technical and business context through *The Batch* and DeepLearning.AI. It helps you understand the technology beneath workplace claims without becoming an ML specialist. [Best AI Newsletters to Subscribe To in 2026: 16 Picks Ranked | Blog](https://www.readless.app/blog/best-ai-newsletters-to-subscribe) | | **Jack Clark** | Frontier AI, policy, and industry signals | His *Import AI* newsletter is a valuable source for AI research and policy developments, with a more technically informed and policy-aware lens. [Best AI Newsletters to Subscribe To in 2026: 16 Picks Ranked | Blog](https://www.readless.app/blog/best-ai-newsletters-to-subscribe) | | **Josh Bersin** | HR, talent, skills, and enterprise adoption | Best if you lead teams, work in HR, or want practical views on hiring, reskilling, organizational design, and AI-enabled work. He publishes frequently on the future of work and talent management. [Josh Bersin | AI and the Future of Work Podcast - LinkedIn](https://www.linkedin.com/pulse/ai-frees-us-have-new-ideas-newsletter-66-peoplereign-98wje) | | **Ravin Jesuthasan** | Job redesign and workforce strategy | Particularly useful for the practical question, “How do we redesign work around AI?” rather than merely forecasting whether jobs disappear. | | **Amy Edmondson** | Leadership and psychological safety | AI adoption is organizational, not just technical. Her work helps explain why employees need psychological safety to experiment, report errors, and adapt responsibly. | | **Kate Crawford** | AI’s social and material costs | Follow for critical work on power, labor, data extraction, infrastructure, and the human costs hidden behind “automation” narratives. | ## Institutions worth tracking People are useful, but institutional research often gives you more rigorous data and fewer hot takes. - **Stanford Institute for Human-Centered AI (HAI):** Research, policy discussion, and the annual AI Index for broad indicators on AI development and adoption. - **MIT Work of the Future:** Research on how technology changes job quality, labor markets, worker voice, and training. - **MIT Sloan / Initiative on the Digital Economy:** Strong for productivity, firm adoption, and management implications. - **OECD:** Cross-country data and policy work on AI, skills, employment, and regulation. - **International Labour Organization (ILO):** Useful for global worker and job-quality effects, including gender and occupational differences. - **Brookings Institution:** Good for readable research and policy analysis; its recent work stresses that AI exposure is not the same thing as viable automation and that the central question is how AI changes the value of human expertise.[Workforce policy for the age of AI - Brookings Institution](https://www.brookings.edu/articles/workforce-policy-for-the-age-of-ai/) - **Anthropic Economic Index / economic research:** Worth monitoring as a source of usage data, but read it alongside independent economists because company data reflects one product’s users and incentives. - **OpenAI Economic Research:** Another useful primary source for observed AI use at work. Its recent research highlights that workers may use AI for activities outside their traditional occupational tasks and incorporate those activities into recurring workflows.[How workers are unlocking new ways of working - OpenAI](https://openai.com/index/unlocking-new-ways-of-working/) ## A balanced starter set If you only want a manageable list, follow these six: 1. **Ethan Mollick** for day-to-day implications and experiments. 2. **Erik Brynjolfsson** for productivity and organizational economics. 3. **Daron Acemoglu** for a rigorous skeptical check. 4. **David Autor** for labor-market and task-level analysis. 5. **Josh Bersin** for enterprise talent and workplace practice. 6. **Jack Clark** for frontier-AI and policy context. Then add **Kate Crawford** if you care about labor rights, power, surveillance, and distributional consequences - or **Andrew Ng** if you want to build enough technical fluency to judge claims yourself. ## How to filter what you read When someone makes an AI-and-work prediction, ask: - Are they discussing **tasks**, jobs, occupations, or employment levels? These are not interchangeable. - Is the claim based on observed workplace use, a survey, a model, or a scenario? - Does it distinguish **technical possibility** from adoption that is profitable, reliable, legal, and accepted by workers and customers? - Who benefits: workers, firms, consumers, or owners of capital? - Does it account for complementary changes - training, workflow redesign, new management practices, data quality, and human review? That filter matters because the emerging evidence points more toward substantial task and workflow redesign than an immediate, uniform disappearance of jobs. One recent analysis similarly argues that labor-market effects will vary sharply, and that AI exposure alone does not prove that automation is commercially viable.[Workforce policy for the age of AI - Brookings Institution](https://www.brookings.edu/articles/workforce-policy-for-the-age-of-ai/)