Stanford Digital Economy Lab's Recent LinkedIn Posts

I predict that US productivity growth will come in at about 2.7% for 2025. That is nearly double the average of the previous 10 years. There are many factors at work, but part of the story may be that businesses are finally beginning to reap some of AI's benefits. That's exactly what we're studying at the Stanford Digital Economy Lab and what my company, Workhelix, is focused on accelerating. I discuss the latest evidence in my column in the Financial Times this morning. See

Voters are already anxious about the economic impact of AI, and the effects will surely grow in the coming months and years. Yasmin Khorram and Cheyenne Haslett explain what's happening, with some data from our Canaries in the Coal Mine paper.

Does a higher minimum wage lead to more robots? Our latest research says: Yes. I’m excited to share a new NBER working paper, "Minimum Wages and Rise of the Robots," co-authored with J. Frank Li, Javier Miranda, Rob Seamans, and Andrew Wang. Using 30 years of U.S. Census microdata (1992–2021), we found that when labor costs go up, firms often look to technology to fill the gap. Key findings: - The Impact: A 10% increase in the minimum wage leads to a roughly 8% jump in robot adoption relative to the mean. - The Location: This effect is especially visible in manufacturing firms located near state borders where wage floors differ. - The Why: Higher wages change the math for firms, making capital-labor substitution more attractive in routinized settings. Technological change doesn't happen in a vacuum—policy helps set the pace. Read the full paper here: https://lnkd.in/gWJdQSTN

In a remarkably short time, AI has moved from something most people associated with science fiction or specialized research to something that is starting to expand what individuals can create and build. What’s changing isn’t just the tools. It’s the scale at which a single individual can operate. With the right combinations of human judgment and machine capability, people can now take on projects that would have required entire teams not long ago. In our research, the biggest gains come from these combinations—not just doing existing work more efficiently, but creating entirely new tasks and new ways of generating value. That shift is still unfolding. But it points to a different kind of opportunity: individuals who learn how to work effectively with these systems can move faster, experiment more, and operate with far greater leverage. We’re still early. I’ve been working on something that explores this shift and how to navigate it. If you’d like to stay in the loop: https://lnkd.in/gaiSQYCR

Check out the new Stanford Digital Economy Lab website. It's my favorite place insights about the the effects of AI and digital technologies on the economy. If you follow the Stanford Digital Economy Lab here on linked in, you'll get regular updates about our research.

The Digitalist Papers Volume 2 has launched! This year's volume turns to the economics of Transformative AI—technology that could usher in economic change comparable to the Industrial Revolution but with far greater speed and scope. Read the essays at DigitalistPapers.com! Featuring the contributions of Daniel Susskind, Ajay Agrawal, Erik Brynjolfsson, Anton Korinek, Alex 'Sandy' Pentland, Lisa Abraham, Susan Athey, David Autor, Avital Balwit, Yoshua Bengio, Nicolas Berggruen, Nick Bostrom, Sarah Friar, Joshua Gans, Nathan Gardels, Alvin Wang Graylin, Steve Jurvetson, Joshua Kavner, Prof. Alexander Lipton, Lee Lockwood, Ioana Marinescu, Jason Matheny, Alvin Moon, Daniela Rus, Eric Schmidt, Fiona Scott Morton, Betsey Stevenson, Joseph E. Stiglitz, Neil Thompson, Gabriel Unger, Maxim Ventura Bolet, and Anna Yelizarova!

Since Bharat Chandar, Ruyu Chen and I first posted our "Canaries in the Coal Mine?" paper, there has been an explosion of research seeking to assess the relationship between AI and employment, especially for early career workers in highly exposed occupations, where we found some of the most striking effects. While several studies have confirmed our initial findings, others have raised questions in two areas in particular. 1. Can the sharp increase in interest rates in 2022 explain the employment effects we observed better than AI-exposure? 2. Is the overall timing of the employment effects consistent with AI-exposure? We've written a note to address these questions. In brief, we find that: 1. While interest rates affect overall employment, existing evidence does NOT suggest they are a good explanation for the disproportionate decline in entry-level hiring in AI-exposed occupations.    2. We do find suggestive evidence that when you include the broadest set of controls (firm-time fixed-effects), the timing of the employment decline in AI-exposed occupations becomes significant only in 2024; some of the earlier declines are likely due to a combination of factors, not just AI. Here's our note, "Canaries, Interest Rates and Timing", with more details:

Since my op-ed in the Financial Times was published over the weekend (https:// https://lnkd.in/gMvd-j9f), there’s been a growing debate about whether we’re beginning to see evidence that AI is boosting productivity. First, let me be clear that the aggregate productivity data by itself is far from definitive. Even with the new revisions, there is certainly a lot of noise in US productivity numbers. No doubt lots of other factors, beyond AI, are also at work. That said, my growing confidence that AI is powering higher productivity draws on evidence from a variety of sources: 1. The stunning capabilities of AI. If anything, I think the impressive improvements in machine learning and generative AI over the past decade are still underrated. We are in the early stages of a massive economic transformation: https://lnkd.in/gUcPFMyE 2. A growing number of micro studies document double-digit productivity gains in specific applications. Alex Imas has a great catalog in his blog post: https://lnkd.in/gGVKhDuS 3. My discussions with power users who use AI for coding, customer service, research, and other applications, as well as more and more business executives, convince me that the facts on the ground are (finally) changing. 4. Data from our Canaries in the Coal Mine paper show employment changes in occupations most affected by AI: https://lnkd.in/gF-PX59p 5. And now, inklings in the aggregate productivity and employment data are also telling the same story: https://lnkd.in/gAz7uq-k These are all consistent with the hypothesis that AI is beginning to have a positive impact on productivity. The FT put a more definitive headline on my recent piece than I would have liked, but my bet (yes, I really made a bet: https://longbets.org/868/ ) is that we're likely to see more and more evidence as time goes on, barring some other shocks (e.g. macro mismanagement, trade wars, etc). As each quarter goes by and we see more data, I continue to update my views. No doubt, I'm currently out of sync with a lot of mainstream economists on this topic, but that’s ok by me!

We're hiring! Come work with the amazing team at the Stanford Digital Economy Lab There's no better place to be a postdoc interested in AI and the Economy:

US productivity grew by a robust 2.8% for all of 2025 (as well as in q4 of 2025) a smidge higher than the 2.7% that I predicted three weeks ago in my Financial Times article. Is this partly due to AI? It's too soon to be sure - these numbers are notoriously volatile. But the old line "we see AI everywhere but in the productivity statistics" may need to be retired. More importantly, I see large productivity gains -often double digits - in specific applications in many companies I visit. As those successes proliferate, I expect clearer evidence to be visible in the macro data.

Since Bharat Chandar, Ruyu Chen and I first posted our "Canaries in the Coal Mine?" paper, there has been an explosion of research seeking to assess the relationship between AI and employment, especially for early career workers in highly exposed occupations, where we found some of the most striking effects. While several studies have confirmed our initial findings, others have raised questions in two areas in particular. 1. Can the sharp increase in interest rates in 2022 explain the employment effects we observed better than AI-exposure? 2. Is the overall timing of the employment effects consistent with AI-exposure? We've written a note to address these questions. In brief, we find that: 1. While interest rates affect overall employment, existing evidence does NOT suggest they are a good explanation for the disproportionate decline in entry-level hiring in AI-exposed occupations.    2. We do find suggestive evidence that when you include the broadest set of controls (firm-time fixed-effects), the timing of the employment decline in AI-exposed occupations becomes significant only in 2024; some of the earlier declines are likely due to a combination of factors, not just AI. Here's our note, "Canaries, Interest Rates and Timing", with more details: https://lnkd.in/gyxqfmkf

The New York Times piece today by Thomas Byrne Edsall highlights a concern I share: “If we stay on the current path, the risk of extreme concentration — both economic and political — is very real.” In work with Zoe Hitzig, we ask why AI may shift the balance between dispersed knowledge and centralized control. Hayek’s classic insight was that much economically relevant knowledge is dispersed, local, and often tacit. That is one reason decentralized markets have historically outperformed central planning. But AI can change those “knowledge physics” by making more of that knowledge codifiable, transferable, and usable at scale. Our argument focuses on the potential of transformative AI (TAI) to codify judgment, heuristics, and know-how that once stayed embedded in people, teams, and local settings. TAI can shift decision-making toward whoever controls the models, data, and compute. That creates a genuine risk of greater concentration: larger firms, less local autonomy. In addition, greater concentration of economic power often translates into greater concentration of political power. (It's important to understand that greater concentration of power is NOT inevitable. And it is certainly not what we advocate.) A key policy question is whether we use AI mainly to substitute for people or to augment them. As I argued in The Turing Trap, we can and should build institutions and technologies that preserve human agency and decentralized power.

In a remarkably short time, AI has moved from something most people associated with science fiction or specialized research to something that is starting to expand what individuals can create and build. What’s changing isn’t just the tools. It’s the scale at which a single individual can operate. With the right combinations of human judgment and machine capability, people can now take on projects that would have required entire teams not long ago. In our research, the biggest gains come from these combinations—not just doing existing work more efficiently, but creating entirely new tasks and new ways of generating value. That shift is still unfolding. But it points to a different kind of opportunity: individuals who learn how to work effectively with these systems can move faster, experiment more, and operate with far greater leverage. We’re still early. I’ve been working on something that explores this shift and how to navigate it. If you’d like to stay in the loop: https://lnkd.in/gaiSQYCR

Will the most important job of the future be the CQO?

As you can tell from the photo, we had some fun on this Stanford Institute for Economic Policy Research panel discussion about AI and the economy. And yes, we also discussed some serious topics, from productivity growth and economic disruption to catastrophic risk and the need for better metrics.

240 years ago, our society was in the throes of a massive technological and political transformation. The Federalist Papers helped chart a new course. The Digitalist Papers (https://lnkd.in/gc93GQfW) is our effort to provide an analysis and roadmap for today's AI-driven challenges and opportunities.

For those who weren't able to make our seminar today: our new podcast, 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨: 𝘏𝘰𝘸 𝘋𝘪𝘥 𝘞𝘦 𝘎𝘦𝘵 𝘏𝘦𝘳𝘦?, is here! Tom Mitchell, Founders University Professor at Carnegie Mellon University and Lab Digital Fellow, is tracing the history of machine learning through conversations with the one-of-a-kind minds who built it. Two episodes are available now. The first is Tom's CMU lecture, "The History of Machine Learning." The second is a conversation with Geoffrey Hinton, winner of the 2024 Nobel Prize in Physics for his work with neural networks. Available on YouTube, Spotify, Apple Podcasts, and more: https://lnkd.in/gqGubP9u

If you stopped by the DEL website today, you may have noticed our new look. But it's not just a nice new coat of paint. The digital landscape has changed a great deal since 2020, especially with AI advancing faster and farther than most of us predicted. To continue our mission of shaping a future where technology drives human well-being and shared prosperity, we need to stay ahead of these shifts. So we've reorganized our research into four primary areas to make sure we're looking in the right places to ensure a better digital future for everyone: - The Economics of Transformative AI - New Measures of the Economy - Digital Platforms and Society - AI Agents We do also like the nice new paint (and how it highlights our brilliant research team). If you haven't yet, we'd love for you to check out the new site: https://lnkd.in/gQYZiEB

“We are winning the innovation race, but losing the diffusion and adoption races.” - Condoleezza Rice, Lab Digital Fellow and Tad and Dianne Taube Director of the Hoover Institution, Stanford University At "𝗖𝗮𝘁𝗮𝗹𝘆𝘇𝗶𝗻𝗴 𝗘𝗮𝗿𝗹𝘆-𝗖𝗮𝗿𝗲𝗲𝗿 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗘𝗿𝗮," Rice, Lab Director Erik Brynjolfsson, and leaders from across sectors are exploring how to protect access to meaningful employment and career growth for those just starting out. In collaboration with Jobs for the Future (JFF), Stanford Center on Longevity, and Stanford Accelerator for Learning

This was a fun conversation. We talked about some of my research on AI's effect of employment and on productivity, as well as what we can do to improve things. They also had Gemini ask me a question, but I didn't use an AI to help with my answers.

"Pilot Purgatory" is not a fun place. One of the reasons I co-founded Workhelix was to help companies escape. Nelson Griggs and Dr. Athina Kanioura have cracked the code and share some great insights on this panel.

As usual, Andrew McAfee nails it.

It was such an honor to have a chance to share my work with the amazing folks at Uppsala University. Mikolaj Norek does a nice job summarizing the key points in my lecture.

Our CEO, Tod Loofbourrow, has served on the Jobs for the Future (JFF) board for the past 15 years, and as Chair for 5 years. It’s a powerful example of how leadership at ViralGains extends beyond our business and into meaningful community impact. Which is why we’re excited to introduce our new series, ViralGains Changemakers! Rooted in our core value to "Embrace and Drive Change", this series spotlights a cause close to someone on #TeamVG, and the impact each organization is making. Throughout Tod’s career in AI and technology, he’s seen both the magic of innovation and the real ways it can transform and displace careers. That’s what led him to joining JFF as a board member and later Chair, to help grow their employer practice and ensure that courses after high school train people for the jobs of the future. JFF works with educators and employers to help more people build family sustaining careers, especially as technology reshapes the future of work. And by 2033, their mission is to help employers and educators build effective pathways to quality jobs for 75 million Americans facing barriers to economic advancement. For Tod, one of his favorite moments was visiting a JFF client, a flight academy in Arizona, and hearing students share how they went from tough beginnings to training for careers as commercial airline pilots. Seeing their work ethic, resilience, and adaptability up close was a powerful reminder of what access to the right opportunity and focused education can unlock. Interested in learning more? Registration is now open for JFF’s 2000 person Horizons Summit, July 13th - 14th in Washington DC. Register here: https://lnkd.in/ec7hcX3Z

Tom Mitchell‘s podcast with the other Giants of machine learning is amazing. Highly recommended!

Kevin Fox provides a terrific summary of GDP-B, a new way of measuring economic well-being

Few things in life are as much fun as talking about AI with Daniela Rus and Azeem Azhar...

Workers, especially younger ones, could use some advice right about now. As more companies cite #AI adoption as the reason for laying off workers, fears of an AI jobs apocalypse are growing. The 2026 SIEPR Economic Summit featured an in-depth look at AI's impacts on the #economy generally and on the workforce specifically. Watch fellow as SIEPR director Neale Mahoney elicits key insights from Erik Brynjolfsson, director of the Stanford Digital Economy Lab and SIEPR senior fellow; Erika McEntarfer, former commissioner of the Bureau of Labor Statistics and a SIEPR distinguished policy fellow; and Chad Jones, professor at Stanford University Graduate School of Business and SIEPR senior fellow. #ArtificialIntelligence #FutureOfWork #GenerativeAI #FutureofAI #AIandJobs Watch the full video: https://lnkd.in/g_Zv4-Pi

"It is a leadership question." That's Ethan Mollick's take on why so many organizations are still moving slowly on AI and our founder Andrew McAfee agrees. Andy is launching a new weekly series, "This Week in Putting AI to Work," rounding up the most important AI-in-the-workplace developments each week. The first edition is out. 👇 If you're trying to move your organization forward on AI, this one's for you.

CES is always a great way to get the pulse of new technologies. Here's a great post about it from Karen Fang Grant

According to the Bureau of Labor Statistics, the U.S. economy unexpectedly lost 92,000 jobs in February. Not good. https://lnkd.in/g2Ni8rBE

Christos Makridis and I have a new article published by The Brookings Institution on "Counting AI: A blueprint to integrate AI investment and use data into U.S. national statistics". Christos has a great summary below. What do you think?

🎙️ New episode of Machine Learning: How Did We Get Here? Tom Mitchell of Carnegie Mellon University sits down with Yann LeCun, Executive Chairman of Advanced Machine Intelligence Labs, Jacob T. Schwartz Professor of Computer Science at New York University, and co-winner of the 2018 ACM Turing Award for his work in neural network learning. Yann takes us from his days as a postdoc working with Geoffrey Hinton, through his days as Chief AI Scientist at Facebook/Meta. His simultaneous roles as a Professor at NYU and Chief AI Scientist at a large AI provider give Yann a unique perspective on how technological advances and commercial forces combined to get us to today's state of the art. Rate and follow/subscribe for more episodes, a new conversation with a pioneer of machine learning released every Monday! 🎧 Listen on Spotify: https://lnkd.in/gSzJXhEP 🎧 Listen on Apple Podcasts: https://lnkd.in/gqrUTT7A 🎥 Watch on YouTube: https://lnkd.in/gA5_6gGw

Stort tack IVA!

The Lab Seminar Series continues tomorrow with Joel B. of METR! Joel's talk presents the evidence on the productivity paradox in AI coding, shows the bottlenecks in deployment, and outlines the next steps for understanding AI’s productivity impacts. This free seminar is available online for everyone, and in-person for members of the Stanford community. Register here: https://lnkd.in/gxB_Hmmm