Creating Shared Prosperity With AI: Stanford Digital Economy Lab's Erik Brynjolfsson

Erik Brynjolfsson has a challenge for anyone worried about artificial intelligence: Stop asking what AI will do to us, and start asking what we will do with AI. In this episode of Me, Myself, and AI , the Stanford University economist explains why technology isn’t the biggest barrier to progress — people, organizations, and institutions are. Drawing on new research into AI’s impact on jobs, productivity, and economic growth, he argues that the future isn’t predetermined: It will be shaped by the choices we make today. This is a timely conversation about human agency, shared prosperity, and why the most important AI breakthroughs may have less to do with technology than with how we use it. Erik Brynjolfsson, Stanford Digital Economy Lab Erik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and senior fellow at the Stanford Institute for Human-Centered AI, and director of the Stanford Digital Economy Lab. He is also the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research, professor by courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a research associate at the National Bureau of Economic Research. A best-selling author, Brynjolfsson focuses his research on examining the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. Subscribe to Me, Myself, and AI on Apple Podcasts or Spotify . Transcript Allyson Ryder: Today’s guest has a bold provocation: AI isn’t being held back by the technology. It’s being held back by us. Curious how? Find out now. Erik Brynjolfsson: I’m Erik Brynjolfsson at Stanford, and you’re listening to Me, Myself, and AI . Sam Ransbotham: Welcome to Me, Myself, and AI , a podcast from MIT Sloan Management Review exploring the future of artificial intelligence. I’m Sam Ransbotham, professor of analytics at Boston College. I’ve been researching data, analytics, and AI at MIT SMR since 2014, with research articles, annual industry reports, case studies, and now 13 seasons of podcast episodes. In each episode, corporate leaders, cutting-edge researchers, and AI policy makers join us to break down what separates AI hype from AI success. Today I’m talking with Erik Brynjolfsson, who runs the Stanford Digital Economy Lab and studies how people use technology in general and now AI specifically. He’s trying to think about how technology is affecting [the] economy and work. His book The Second Machine Age shaped a lot of this debate. And his team recently published “Canaries in the Coal Mine?”, a research paper about what’s happening to entry-level workers. Erik and I bump [into] each other a few times a year at the National Bureau of Economic Research, and he always brings up something I hadn’t considered. No pressure, Erik, but I’m expecting the same today. Erik Brynjolfsson: [It’s] good to be here, Sam. Sam Ransbotham: Welcome to the show. Let’s start with the Stanford Digital Economy Lab. Can you give us a quick view of what the lab does? Erik Brynjolfsson: Sure. We study the digital economy. … I loved my time at MIT. I was there for over 25 years, but now I’m out here in Silicon Valley, the epicenter of the AI revolution. We’re focusing on how AI and other digital technologies are changing the economy. Kind of the premise of the lab and of my work, my career, is that technology is advancing very rapidly. The capabilities are amazing. At the same time, our economic understanding is not advancing nearly fast enough. Our economic institutions, skills, organizations, they aren’t keeping up. My job is to try to close that gap. Sam Ransbotham: It’s those pesky people. The technology moves fast. Organizations and people slow us down, I guess, is the summary there. Erik Brynjolfsson: That’s right. A lot of your work has highlighted that, too, and we’re doing what we can to keep up. Sam Ransbotham: I was thinking [about] the Race Against the Machine book and … “Canaries” today. What connects all of it? Erik Brynjolfsson: “Race against the machine” … was the headline. The conclusion was that we should race with machines, not against machines. And that’s what I’ve continued to emphasize — there’s an opportunity for humans and machines to work together. It’s not automatic. There’s a lot of choices that we need to make, but the technology is enabling all sorts of new possibilities. Trying to invent and discover those is a big part of what we humans need to do. Sam Ransbotham: I like the choice part because I think it’s so easy for us to slip into [the] vernacular of saying, “AI does this, technology does that.” I know you’ve been against that. Erik Brynjolfsson: That’s exactly right. It’s probably one of the most common questions I get: “What’s AI going to do to us? What’s AI going to make happen next?” I’m like, “Wait a minute, the premise of your question is wrong. It’s ‘What are we going to do with AI?’” This is an incredibly powerful tool, arguably the most powerful tool that we humans have ever had. Almost by definition, that means we have more ability to change the world than we ever had before. So let’s think about how we want to use that. It turns out that our values, our choices matter more now than they did in the past because we do have this ability to make a really big dent in the universe, for better or worse. Sam Ransbotham: Yeah, for better or worse. I think that’ll probably come up a couple of times as we talk here. I was thinking about how much you got right in the early Race Against the Machine book. But that’s not interesting as an academic. What do you think you got wrong? What were you surprised about? What’s changed differently than you thought 16 years ago or 15 years ago? Erik Brynjolfsson: Well, first off, since we’re talking about what we got wrong, I should highlight that this is cowritten with Andy McAfee, along with The Second Machine Age . He’s been a partner on a lot of my projects. What did we get wrong? We were looking at some advances — I’d highlight particularly in The Second Machine Age , we started that with a ride in Google’s self-driving car. Actually, it was 2012, right after we wrote Race Against the Machine , we rode from Mountain View up to San Francisco. I’ve got to say I thought, “Wow, self-driving cars are just around the corner,” metaphorically, and I thought it would happen very quickly. It’s obviously taken longer. I do ride around in self-driving cars a fair amount here in the San Francisco Bay Area, up in the city. They’ve had them for a while, and now they’ve come down to Palo Alto as well, and you can ride them all the way on the highway. But that’s 15 years later. It’s taken a while, and I think I was overoptimistic about that. That said, I think I underestimated how fast the technology would advance in other ways. The way you and I can talk to [large language models], whether it’s ChatGPT or Gemini or Claude, and have them do work, I think I would not have expected that to have happened that quickly. I mean basically I would have considered this [artificial general intelligence] if you had asked me in 2012 or 2015. So that’s pretty cool that you can get really good advice from them. We all know they still hallucinate and make mistakes, but so do we humans. On average, they’re really quite good. So that happened faster. We got that wrong a little bit. Then the most disappointing part is, look, I knew that we were just talking about how human institutions change more slowly than technology. But oh my God, I didn’t expect it to be this much slower. At times it’s almost like it’s moving backward. So that’s been pretty disappointing, that our political institutions, our businesses, organizations, they aren’t keeping up. Productivity is not growing any faster than it was at the beginning of this AI revolution, maybe a smidge if you kind of squint. But we’re not really translating these capabilities into better business performance or the kind