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.

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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 kinds of benefits that we all hope for.

Sam Ransbotham: I rode one of the automated cars out in Palo Alto at the last meeting where I saw you at. It very quickly got boring. Now I was fascinated for a few seconds and then bored very quickly. You touched on that would have been AGI a few years ago, or LLM stuff would have been AGI a few years ago, but it’s not anymore. How much of this is sort of a boil frog, we are getting used to these technologies, it takes even more to impress us? Is that part of it? Is it a measurement part? Is it a J-curve, which we’ll come back to in a second? All the above? None of the above?

Erik Brynjolfsson: All of the above. Part of it is, like you said, getting bored in the self-driving car; the first few minutes are kind of scary and thrilling, and then it’s exciting. And then within half an hour you’re like, “OK, I kind of get this.” These cars, they kind of drive like my grandmother, very carefully and slowly, which is, I guess, the right thing to do.

Sam Ransbotham: That’s why you left Boston maybe.

Erik Brynjolfsson: Exactly. Their accident rate is 90% lower than humans. I’m looking forward to the day that the cars are all like that. Right now they are a little slower point to point. Sometimes when I’m in a rush, I’ll actually get an Uber because I know that the driver is more willing to cut some corners, so to speak. But part of it is that we kind of take for granted things that initially were eye-popping.

I guess our brains are designed to get used to things. But the other two things you said, I think, are more important and more interesting. One is the measurement issue. Our economic metrics are just not designed to capture a lot of the benefits of AI, especially the benefits of free digital goods. So we’ve got a whole methodology for addressing that. Maybe we can talk about that later.

The other part is the J-curve. This is what we started the conversation about — there really is a gap and a difficulty in translating capabilities into real business change. It’s because there [are] so many complementary assets and investments that need to be made. Many of them have to be invented, and we don’t know what they are, these complementary co-inventions of new business processes, new skills, whole new business models and ways of working. They’re hard and we have entrepreneurs and managers and business school professors and consultants all working on figuring them out.

In earlier work I did with [University of Pennsylvania’s] Lorin Hitt and Prasanna (Sonny) Tambe and others, we found that the investments in those intangible assets are about 10 times bigger than the direct investments in computer technology. But since they’re largely intangible, largely invisible, we tend to not appreciate them as much, and we don’t measure them, and people don’t even realize that they’re there. But we’re trying to make them more visible in our research and create an appreciation that you have to make those investments as well.

Sam Ransbotham: Actually, the measurement thing is huge and difficult. I remember you and [Carnegie Mellon University’s] Avinash Collis were talking about the measurement of value from Wikipedia and from Facebook and these other technologies. All this stuff is just superhard to measure. And when we don’t measure stuff, we do a bad job, I think.

Let’s switch maybe to measurement because I think one of the cool stories about your “Canaries in the Coal Mine” study … I think your headline finding was that people ages 22 to 25 were the most exposed [and] lost about 16% of their employment. But you’re using ADP payroll data to find that. That’s a very interesting signal of information. Tell us about that study.

Erik Brynjolfsson: Like everybody, we were seeing all these headlines about AI eliminating jobs or other headlines [that] AI is creating jobs. In any given month, there [are] hundreds of thousands of people who gain or lose jobs. A half-decent journalist can easily find examples of either one and build a narrative around it, or just a man in the street they can talk to. To me at least, I don’t really know how to aggregate that. What’s the real story? So we wanted to get large-scale data.

When we looked at the current population survey, we didn’t really see much, and when we looked at the top line of ADP, which is the world’s largest payroll processor, they shared the data with us here at the Digital Economy Lab. We actually didn’t see much in the top line either. I was almost thinking, “OK, we’ll write a story about how this is all just a bunch of hype.” But then we dove in a little bit more deeply, and some of the subgroups who are having these very large effects, like you just mentioned, people who are ages 22 to 25, early-career workers, they had a noticeable fall in employment, but it really got striking when you subdivided it by how exposed they were to AI.

[OpenAI’s] Tyna Eloundou, [University of Pennsylvania’s] Daniel Rock, and others have created a taxonomy where they rank all of the tasks in the economy, 18,000 distinct tasks by how much they’re exposed to LLMs. Could an LLM help you write a memo? For sure. Could an LLM help you lift a box? Not really. If you rank every task like that and then aggregate them up to occupations, you basically have a score of whether or not this occupation is going to be affected by LLMs.

We looked at about 750 occupations. We divided them into quintiles. The most exposed quintile, a little over a hundred occupations in this youngest age group, had at first about a 12% to 13% decline. Now it’s more like a 16% to 17% employment decline relative to the other occupations. So it’s really quite striking. We didn’t see a fall in employment for the older workers, even in the exposed occupations. You really have to slice it this way.

Even when we controlled for other factors, like we controlled for interest rates, we controlled for the tech industry — you can take the entire tech industry out if you want, or control for tech overhiring — you can control for remote work, you can control for education, none of those things knocked out the core result. We continued to have this very noticeable decline.

I can say another thing, which is that, as I mentioned, the older workers were doing OK. There are a couple of other groups that were doing OK. One is the people in the not exposed occupations. So if you look at the other end of the spectrum, like home health aides, where AI is not affecting them nearly as much, they actually had growing employment, even for the young workers.

And then, the most interesting result, the one I’m most excited about, was that if you divide the way they’re using AI into mainly automating and eliminating work versus augmenting and creating new skills, new opportunities, doing new things that you never did before, the automating group had falling employment, and the augmenting group, the ones who were learning new skills, had growing employment. So it’s kind of a double win: higher productivity and growing employment. For some reason that doesn’t get picked up as much in [the] press when they write about our research, but I think it’s really important that we found that AI could be associated with higher employment for certain kinds of workers.

Sam Ransbotham: I think that ties back to how you use it, your decision-making, the choices that we all make in that. That doesn’t surprise me, because if I think about a task and, going back to this aggregation of tasks you alluded to, no job, I’m sure, runs a hundred percent down the list of tasks and a hundred percent down the list of non-automatable tasks. The composition of that task is going to leave parts of those jobs more valuable. That’s going to leave those parts more valuable.

Erik Brynjolfsson: Exactly. That’s another really important point: You can take every occupation; [each has] a bundle of tasks. In O*Net, a typical occupation has 20 to 30 tasks. A radiologist has 26 distinct tasks. [Computer scientist] Geoffrey Hinton famously said, “Reading medical images, that’s what radiologists do. That’s going to be replaced by AI.” What he didn’t factor in was there were 25 other tasks that radiologists do, and most of them were not affected by AI.

The net effect was that as reading images got cheaper and better because of AI, it actually increased the value of radiologists for doing those other tasks, and net employment grew for radiologists. I’m not saying that always happens, but there [are] definitely cases — some people call it Jevons paradox — where making something cheaper increases the demand for it, and you end up having more employment rather than less, even as it gets more efficient.

Sam Ransbotham: I think it’s the frustrating thing. I think everyone would like an answer, which is “Here’s the clean answer,” and what you’ve come across here is just a gigantic “It depends,” and it depends on an increasingly complicated set of stuff.

Erik Brynjolfsson: Well, let me try and make it a little simpler. It is somewhat more complicated than the simple story of you always eliminate jobs, or for that matter, it always creates jobs. But as an economist, a really useful tool for me — and I’ll get a little wonky for your readers, but it’ll be [a] useful time — is you can think of a demand curve. When price falls, quantity increases. Demand curves are downward sloping. Most of us intuitively know [when] you make something cheaper, people will buy more of it.

But what really matters is how steep that demand curve [is]. For some products, like apples, even if you made them 10 times cheaper, you’re not going to buy 10 times more apples. Maybe you buy a few more apples. But the demand curve’s pretty steep. So as prices go down, you end up spending less. That’s pretty intuitive. Most people think that all goods are like that.

It turns out that not all goods are like that. There are lots of goods where as the price goes down, even just a little bit, the quantity goes up a lot, like jet travel. It used to be that very few people would fly in planes across the country, but now it’s cheaper, and a lot of us fly around the country way too much. So there are goods and services like that. That’s called elastic demand or a flat demand curve.

Whenever you have situations like that, you actually have growing demand for something, and you end up having more spending. At least half the goods and services in the economy are on that side of the curve. That’s really good news. It means that as we get more productive, more efficient, some things shrink, but other things grow. I think there’s a little bit too much of a bias in the public conversation toward the side that’s shrinking. One of the things I want to do with your podcast is get more people to think about that side that’s growing.

I teach a master class [in] which we basically show people how to identify and discover these new opportunities for wealth creation, how you can use AI to do new sorts of things to create entrepreneurial ventures. The more we do that, I think the better off people are going to be. They’re going to help themselves, and they’re going to help the rest of the economy by inventing new things.

Sam Ransbotham: Apples [are] a great example. Like you say, we don’t consume them when we get more of them, but I don’t know of any company that says, “Pretty much all the IT projects that we could ever think about, we’re doing them.” You know what I mean?

Erik Brynjolfsson: Yeah, exactly. It seems like there’s almost infinite demand for solving all sorts of IT problems. When I look around the world, I don’t see a shortage of problems. Poverty, the environment, health care especially, these are all things where we could use a lot more resources, and I don’t see us running out of opportunities to address those.

Sam Ransbotham: The fantasy that we would run out of those types of problems, that’s great, but I don’t think that’s going to happen in the next couple of years though, the next couple of centuries. Let’s go back to this J-curve idea. First explain a little bit about what is the J-curve, and how should people think about that?

Erik Brynjolfsson: This is another concept I think people find very useful, and that is whenever there’s an important powerful technology, we economists call them general-purpose technologies. We used to just say GPT, but my AI friends stole that acronym. But AI is both. It is a generative pre-trained transformer, like GPT-5, but it’s also a general-purpose technology like the steam engine, electricity, the internal combustion engine, and cars. Those are the things that actually drove most economic growth, most productivity. They’re the reason we have higher living standards than our ancestors, a couple hundred years ago or even 50 years ago.

AI is the most important general-purpose technology of our era, maybe of all of history. But the thing about these general-purpose technologies is that the value really comes from the complementary assets. With electricity, it enabled light bulbs and electric motors and air conditioning and ultimately computers and a lot of other things. With AI, it’s enabling all sorts of new business processes, and it’s not just the physical technologies. It’s also these intangible assets that we talked about earlier. That’s great. The thing is that these intangible assets take time to create. They’re expensive. You have to invest in them. Whether you hire consultants or you do it yourselves, you have to reinvent how work gets done.

During that costly period, you’re spending more as you’re reinventing your business processes, but output doesn’t instantly go up. Mathematically that means more input, no increase in output, [and] productivity, at least as it is conventionally measured, goes down. So that’s the downward part of the J-curve. Then later you start harvesting that. You start using these new business processes to create more output. Now output is going up. And that’s the upward part of the J-curve.

What we’re seeing with most general-purpose technologies is that there’s an initial lull where productivity is low or even negative, and then later it takes off. Now it’s hard to see exactly where we are for AI. We’re in the middle of it. But if you look back at earlier history, if you look back at, say, electricity — I wrote [about] some of it in my Ph.D. dissertation — believe it or not, it took about 20 to 30 years of time where companies were trying to reinvent how factories were organized.

They were installing electric motors. At first they did not get much productivity. [Economist] Paul David and others documented this very carefully. For literally up to 30 years there was essentially no productivity gain in factories as they were electrifying, which is just remarkable, but, eventually, they completely redesigned how factories were organized. Instead of being clustered around a central motor like a steam engine, they had separate motors for each piece of equipment, and they distributed the work, and they laid out the arrangement based on the flow of materials.

When they did that, productivity started skyrocketing, like doubling and tripling productivity. But like I said, it took 30 years. I don’t think AI is going to take 30 years. I’m pretty sure it’s already beginning to happen. But at the same time, it takes longer than some of my technology friends, some of my AI friends expect. They think that as soon as you invent the AI, you’re done.

The reality, as you know, Sam, in your work and in my work, there’s a lot of business process redesign that has to happen. We’re in the middle of that, and I’m doing what we can to try to speed that up. My company Workhelix is very involved in showing companies how to identify the opportunities for value creation. If we can speed that up, it’ll be great, but we’re still going to have a bit of a J-curve.

Sam Ransbotham: I’m thinking back to the electricity times. I’m guessing they didn’t have the quarterly stock report pressures and the need for instant results that perhaps people deal with now.

Erik Brynjolfsson: The biggest thing, honestly, is that they didn’t really have all of our institutions. Not to pat ourselves on the back, but business schools didn’t exist. Consulting books [and] the whole science of industrial engineering and management, didn’t exist. I went back and I read some of the books. I went to Harvard Business School’s Baker Library, and they had these old books, and it was pretty primitive how they were thinking about things.

Now there’s a much bigger ecosystem for helping companies redesign their business processes. It still takes time and redesign, [and reskilling] their workforce. But my sense is managers are much more conscious of it right now, and they’re leaning in much more aggressively, and they’re sharing best practice.

At Workhelix, we have a tool that will scan through all of the opportunities and share them with the workers and look at who the superusers are who are really crushing it, and then compare them to the average worker. So it just speeds up the learning by probably 10x compared to what people would have done before they had these kinds of tools.

Sam Ransbotham: I really like that because that again ties in measurement and some other things. The annual performance review is really not helpful anymore. The idea that you could get [a] better measurement about what you’re doing and how you’re doing it helps.

Let me kind of be antagonistic a little bit. I wrote something at MIT Sloan Management Review a while back about rethinking AI objectives. You wrote something about the Turing Trap. I think the first inclination that people have is to pass the Turing test, to make the machine do what the human can do. And that leads us to incremental improvements of existing processes. It seems like measurement could accidentally feed into that as well if we’re not measuring the right things. What are you thinking about that, short term versus long term?

Erik Brynjolfsson: Well, there’s definitely a short term, long term; [those are] your objectives. I was talking to a CFO a couple months ago, and she told me, “Hey, we really need to measure the benefits of AI, the ROI.” And I said, “That’s great. You should be measuring it. Tell me about it.” And she said, “So that’s why I’m demanding that every division tell me how much head count reduction they’re getting from AI.” I was like, “Wait a minute. I’m glad you’re measuring, but isn’t that a little bit simple-minded, because, yes, there’s nothing wrong with cutting costs, but it’s really missing the bigger opportunity of doing new things, allowing your workers to create new kinds of value.”

There’s a CFO getting paid millions of dollars, and the reason they’re making big money is that they’ve got to think more creatively about value creation, not just having machines do what the humans are already doing and replacing them. That’s the classic Turing test: Can you make a machine imitate a human perfectly?

That’s a very narrow way of thinking about it. I think Alan Turing was a brilliant guy, but I think that mindset has done a lot of damage to how we use AI. I call it the Turing Trap because I think that it’s a trap to only focus on using AI to replace or imitate workers. We should also think about how AI can augment and allow us to do new things. But that requires new measures, like, how do you create new products and services? How do you have higher customer satisfaction? How do you improve quality, both the quality of the products but even the quality of the work life for the people working?

These are all things that won’t show up in your narrow replacement mindset, so this is something that also takes a longer time, like you said. Short-termism tends to focus on just cutting costs and what you’re already doing. But the ones that have the most competitive advantage, the ones that have the most lasting benefits, are the ones that think of going beyond that and doing new things, new business processes, new products and services. It doesn’t happen in weeks, but when you do achieve it, you have something that lasts for years.

Sam Ransbotham: There’s plenty of places where machines outperform us. The easiest way to tell if this is a machine or a person and pass that Turing test is that I’m not going to be able to do math very fast. Our objectives are not exactly the right standard, I guess. You mentioned that in the [beginning of] electrification people didn’t have the institutions around that. Neil Thompson [and] the MIT Initiative on the Digital Economy, [which] you were obviously involved in before you moved to Stanford, they’ve got a science article that said something like 90% of the most important new AI models are coming out of industry. We’ve had this massive switch from academia and public funding [producing] technology benefits to most of it coming out of technology companies and startups. You mentioned Palo Alto and the epicenter that’s there. All right, I’m worried. Is that the right place for that stuff?

Erik Brynjolfsson: Part of the reason it’s coming out of industry is that it’s gotten to be incredibly expensive to build these very large models. There are these scaling laws for LLMs where as you make a model bigger, if [it has] more data, more compute, more parameters, you get a predictable improvement in performance. So they went from spending millions to tens of millions, hundreds of millions, billions, now tens of billions, even hundreds of billions of dollars on training these models. No university can afford that. No nonprofit organization can afford that. So the companies are raising huge amounts of money as we’re speaking. Anthropic and OpenAI are filing to go public. Google has a nice cash engine they can pour into this as does Meta.

So that’s what’s driving that. At the same time, what I tell my academic colleagues is your comparative advantage isn’t [in] spending more money on computers. It’s in thinking creatively. There [are] some brilliant ideas that happen just from sitting in your office and thinking deep thoughts. It’s still a good strategy.

I had a conversation with Geoffrey Hinton a while back, and I asked him what kinds of hardware he uses to make his insights and discoveries. He pointed to his laptop and tapped it and said, “This is the hardware I use.” And for those kinds of fundamental breakthroughs, it’s not always a matter of having thousands of GPUs.

I think academics need to lean into their comparative advantage. That said, it’s fair to say that more and more of the frontier research is happening through a really expensive approach. In a way it’s a testament to how valuable AI is. One of the reasons industry didn’t do it before was not just that it was expensive, but it just didn’t have that big a payoff.

I’ll share a story. The first AI company I started, believe it or not, [was] in the late 1980s. That’s how old I am. I started an expert systems company with Todd Loofbourrow. Expert systems are these rule-based systems. We created a little bit of value for some banks and HR organizations, but [at] the end of the day, it was nothing like the value that companies are creating today. That brand of AI just wasn’t all that valuable, I have to be honest. It [is] only now that we have these very powerful neural networks that you really have commercial incentives that are unlike anything we had before. So in a way it’s a testament to the success of the field that industry is investing as much as they are.

Sam Ransbotham: I like that point. The U.S. government, I think, put like a billion and a half dollars or so into non-defense research spending last year, and that’s roughly what Google is putting in. You know, the [National Institutes of Health] and [National Science Foundation] budgets are small. As you point out, we are not going to be able to outspend those.

Erik Brynjolfsson: But on that, I think it’s fair. You want to have both. I mean, it’s the job of academia, it’s the job of the federal government to invest before it’s commercially viable. So much of the long-term benefits of R&D [are] not apparent, and there’s not a commercial incentive to invest. The internet, early space travel, so many fundamental breakthroughs in biology that have extended our lives, they happened in universities and government laboratories because they were not commercially viable, but then later people built on them. You can’t always tell in advance which ones are going to pay off. But if you do the fundamental research, the track record shows that eventually some percentage of them are going to really make a difference in our living standards.

Sam Ransbotham: That’s the basic applied cut. We don’t have to fund the applied stuff because the market will take care of that. And [for] the basic stuff, the invisible hand doesn’t work quite as well in those, or it works slower than everybody would like.

I think you’re going to have a bunch of academics listen to this because they’re curious what you say. What should people be looking at? What are the kinds of things that fall into that category? Maybe a critique is that a lot of the studies that I’m seeing in academia are, let’s say, consulting projects for companies. What are these basic types of ideas that people should be pursuing?

Erik Brynjolfsson: Well, there’s a lot of them in lots of different areas, and I think I don’t want to prescribe them because I think in a way part of the magic is letting people pursue their own ideas even if there’s nobody on the outside that sees the value of them.

That said, I’ll give you my own list. I’m focused on the economic side. I think that right now the biggest gap is that we don’t have a good understanding of how AI is going to transform the economy. We are creating incredibly powerful information processing entities. As an economist, you can think of a market as an information processing entity. You can think of firms and organizations as information processing entities. If you have a thousandfold or a millionfold improvement in AI, it would be a miracle if firms and markets didn’t also transform and we develop new kinds of organizations to manage work. But we haven’t figured those out yet. So [a] big part of my work is trying to understand, what does the economy look like? [What are the] firms, markets, and maybe some new kinds of entities as AI gets more powerful, as it aggregates information, as it processes information? And then how are we going to manage the concentration of wealth and power that could result?

I’m super optimistic about the potential of technology to boost productivity. I have a bet, a long bet on that with [Northwestern University’s] Robert Gordon, and I’ve written about how I expect productivity to grow. At the same time, I’m pretty worried that if we don’t manage it right, it could lead to a great more concentration of wealth, and that tends to lead to a concentration of power. And that could hurt our freedoms if we don’t figure out an economic system that balances not just wealth creation but also shared prosperity.

Sam Ransbotham: I was looking up some of your background before talking, to refresh, and I saw the phrase mindful optimist, and it kind of hit me. So what is it going to take to get us to this positive version of the future versus the negative version of the future?

Erik Brynjolfsson: Let me take a minute to define mindful optimist, because I have a very specific meaning for that. There’s a lot of people out here in Silicon Valley who are optimists, but I think too many of them are kind of blind optimists. They’re like, “Hey, Erik, don’t worry. It’s worked out in the past. It’ll work out in the future. Just chill.” There are also a lot of people who are what I call blind pessimists. They’re like, “Oh my god, we’re doomed. There’s nothing we can do.” I think both those groups make the same mistake.

It’s the one we talked about at the top of the program, which is they think of AI as something that’s going to do things to us as opposed to us having the agency to control the future. A mindful optimist is somebody who sees a future and then works toward doing it, is mindful about creating it, doesn’t just assume it’s going to automatically happen.

They’re not like kids at Christmas [who] expect presents to magically appear the next morning, but instead they’re more like kids who see a tree, and they see some boards, and they think, “Hey, we could build a tree house there.” Then they’re like, “Well, it’s not going to happen by itself. Let’s figure out how to make it.” And then they make that optimistic reality happen through their hard work and through their imagination. That’s the kind of optimism that I would like to see. I don’t see enough of it. I see way too much just passivity.

I gave the closing lecture to my class here at Stanford last Friday, and one of the things I told them was every time they hear … “AI” they should think of amplified intention, that AI is the greatest amplifier of intention ever. Whatever they want to achieve, AI can amplify it, but it’s not going to happen without that agency, without that intention.

So that’s a message I want to have for the broader world: “Let’s think about what our values are, what kind of economy we want to shape, what kind of shared prosperity we want to create.” And I mean that for everybody, not just the technologists and sending that message to them, but also to policy makers, also to citizens, to workers, to managers. Let’s think about really actively using this.

Over the next 10 years, I expect the world is going to radically change. But there’s no one inevitable future. [When] you cross the border between, say, [the] United States and Mexico or other countries, you can see that different institutions lead to very different outcomes, different levels of wealth and democracy, and that’s the same for the future. We can live in a lot of different possible futures. One of the things I’m doing here at the Digital Economy Lab is a lot of research to understand which paths are more likely to lead to those beneficial outcomes.

Sam Ransbotham: I think that’s a great way to end here. We [do not] have easy answers in either direction. The gains are real, the harm is real, and which one wins is a choice that we’re making now and something that we have control of.

Erik Brynjolfsson: More than ever. One of the interesting things is this is the time for philosophers and for humanities and for economists and people who think about what kinds of values we want to instantiate in the world. With this greater power comes greater responsibility and more potential to change the world. So we really need to think consciously about what kind of world we want to shape going forward. I don’t think there’s been enough attention to that, but the Digital Economy Lab is in part focused on that.

Sam Ransbotham: Thanks for bringing the numbers and the nuance. We’ll see you in Boston this summer.

Erik Brynjolfsson: Absolutely. Looking forward to it, Sam. Great talking to you.

Allison Ryder: Thanks, everyone, for listening today. We’re off for summer break, and we’ll be back in the fall with an exciting lineup of speakers from Instacart, GoFundMe, Dropbox, and Honeywell. Please join us then.

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