{"id":8602,"date":"2026-07-21T22:10:04","date_gmt":"2026-07-21T14:10:04","guid":{"rendered":"https:\/\/moresourcing.com\/creating-shared-prosperity-with-ai-stanford-digital-economy-labs-erik-brynjolfsson\/"},"modified":"2026-07-21T22:10:04","modified_gmt":"2026-07-21T14:10:04","slug":"creating-shared-prosperity-with-ai-stanford-digital-economy-labs-erik-brynjolfsson","status":"publish","type":"post","link":"https:\/\/moresourcing.com\/en\/creating-shared-prosperity-with-ai-stanford-digital-economy-labs-erik-brynjolfsson\/","title":{"rendered":"Stanford Digital Economy Lab\u2019s Erik Brynjolfsson"},"content":{"rendered":"<p><\/p>\n<div>\n<div class=\"article-left-col\">\n<section class=\"article-topics\">\n<h4 class=\"article-topics__title\">Topics<\/h4>\n<ul class=\"article-topics__list\">\n<li class=\"article-topics__item\">\n                <a href=\"https:\/\/sloanreview.mit.edu\/topic\/data-ai-machine-learning\/\">Data, AI, &amp; Machine Learning<\/a>\n            <\/li>\n<li class=\"article-topics__item\">\n                <a href=\"https:\/\/sloanreview.mit.edu\/topic\/ai-machine-learning\/\">AI &amp; Machine Learning<\/a>\n            <\/li>\n<\/ul>\n<\/section><\/div>\n<aside class=\"article-ad ad-300  ad-300x250 ad-desktop\">\n<\/aside>\n<aside class=\"article-ad ad-300  ad-300x250 ad-mobile\">\n<\/aside>\n<p>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 <cite>Me, Myself, and AI<\/cite>, the Stanford University economist explains why technology isn\u2019t the biggest barrier to progress \u2014 people, organizations, and institutions are. Drawing on new research into AI\u2019s impact on jobs, productivity, and economic growth, he argues that the future isn\u2019t 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.<\/p>\n<aside class=\"callout-info\">\n<img decoding=\"async\" alt=\"Erik Brynjolfsson\" src=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2026\/07\/Stanford-Digital-Economy-Labs-Erik-Brynjolfsson.jpg\"\/><img decoding=\"async\" src=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2026\/07\/Stanford-Digital-Economy-Labs-Erik-Brynjolfsson.jpg\" alt=\"Erik Brynjolfsson\"\/><\/p>\n<h4>Erik Brynjolfsson, Stanford Digital Economy Lab<\/h4>\n<p>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.<\/p>\n<p>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.<\/p>\n<\/aside>\n<p>Subscribe to <cite>Me, Myself, and AI<\/cite> on <a href=\"https:\/\/podcasts.apple.com\/us\/podcast\/me-myself-and-ai\/id1533115958\" target=\"_blank\" rel=\"noopener\">Apple Podcasts<\/a> or <a href=\"https:\/\/open.spotify.com\/show\/7ysPBcYtOPVgI6W5an6lup\" target=\"_blank\" rel=\"noopener\">Spotify<\/a>.<\/p>\n<h4>Transcript<\/h4>\n<p><strong>Allyson Ryder:<\/strong> Today\u2019s guest has a bold provocation: AI isn\u2019t being held back by the technology. It\u2019s being held back by us. Curious how? Find out now.<\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> I\u2019m Erik Brynjolfsson at Stanford, and you\u2019re listening to <cite>Me, Myself, and AI<\/cite>. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> Welcome to <cite>Me, Myself, and AI<\/cite>, a podcast from <cite>MIT Sloan Management Review<\/cite> exploring the future of artificial intelligence. I\u2019m Sam Ransbotham, professor of analytics at Boston College. I\u2019ve been researching data, analytics, and AI at <cite>MIT SMR<\/cite> 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.<\/p>\n<p>Today I\u2019m talking with Erik Brynjolfsson, who runs the Stanford Digital Economy Lab and studies how people use technology in general and now AI specifically. He\u2019s trying to think about how technology is affecting [the] economy and work. His book <cite>The Second Machine Age<\/cite> shaped a lot of this debate. And his team recently published \u201cCanaries in the Coal Mine?\u201d, a research paper about what\u2019s 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\u2019t considered. No pressure, Erik, but I\u2019m expecting the same today. <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> [It\u2019s] good to be here, Sam. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> Welcome to the show. Let\u2019s start with the Stanford Digital Economy Lab. Can you give us a quick view of what the lab does? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Sure. We study the digital economy. \u2026 I loved my time at MIT. I was there for over 25 years, but now I\u2019m out here in Silicon Valley, the epicenter of the AI revolution. We\u2019re 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\u2019t keeping up. My job is to try to close that gap. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> It\u2019s those pesky people. The technology moves fast. Organizations and people slow us down, I guess, is the summary there. <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> That\u2019s right. A lot of your work has highlighted that, too, and we\u2019re doing what we can to keep up. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> I was thinking [about] the <cite>Race Against the Machine<\/cite> book and \u2026 \u201cCanaries\u201d today. What connects all of it? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> \u201cRace against the machine\u201d \u2026 was the headline. The conclusion was that we should race with machines, not against machines. And that\u2019s what I\u2019ve continued to emphasize \u2014 there\u2019s an opportunity for humans and machines to work together. It\u2019s not automatic. There\u2019s 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. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> I like the choice part because I think it\u2019s so easy for us to slip into [the] vernacular of saying, \u201cAI does this, technology does that.\u201d I know you\u2019ve been against that. <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> That\u2019s exactly right. It\u2019s probably one of the most common questions I get: \u201cWhat\u2019s AI going to do to us? What\u2019s AI going to make happen next?\u201d I\u2019m like, \u201cWait a minute, the premise of your question is wrong. It\u2019s \u2018What are we going to do with AI?\u2019\u201d <\/p>\n<p>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\u2019s 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. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> Yeah, for better or worse. I think that\u2019ll probably come up a couple of times as we talk here. <\/p>\n<p>I was thinking about how much you got right in the early <cite>Race Against the Machine<\/cite> book. But that\u2019s not interesting as an academic. What do you think you got wrong? What were you surprised about? What\u2019s changed differently than you thought 16 years ago or 15 years ago? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Well, first off, since we\u2019re talking about what we got wrong, I should highlight that this is cowritten with Andy McAfee, along with <cite>The Second Machine Age<\/cite>. He\u2019s been a partner on a lot of my projects.  <\/p>\n<p>What did we get wrong? We were looking at some advances \u2014 I\u2019d highlight particularly in <cite>The Second Machine Age<\/cite>, we started that with a ride in Google\u2019s self-driving car. Actually, it was 2012, right after we wrote <cite>Race Against the Machine<\/cite>, we rode from Mountain View up to San Francisco. I\u2019ve got to say I thought, \u201cWow, self-driving cars are just around the corner,\u201d metaphorically, and I thought it would happen very quickly. It\u2019s 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\u2019ve had them for a while, and now they\u2019ve come down to Palo Alto as well, and you can ride them all the way on the highway. But that\u2019s 15 years later. <\/p>\n<p>It\u2019s 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\u2019s 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\u2019s 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\u2019re really quite good. So that happened faster. We got that wrong a little bit. <\/p>\n<p>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\u2019t expect it to be this much slower. At times it\u2019s almost like it\u2019s moving backward. So that\u2019s been pretty disappointing, that our political institutions, our businesses, organizations, they aren\u2019t 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\u2019re not really translating these capabilities into better business performance or the kinds of benefits that we all hope for. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> 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\u2019s 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\u2019ll come back to in a second? All the above? None of the above? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> 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\u2019s exciting. And then within half an hour you\u2019re like, \u201cOK, I kind of get this.\u201d These cars, they kind of drive like my grandmother, very carefully and slowly, which is, I guess, the right thing to do. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> That\u2019s why you left Boston maybe. <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Exactly. Their accident rate is 90% lower than humans. I\u2019m 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\u2019m in a rush, I\u2019ll 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. <\/p>\n<p>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\u2019ve got a whole methodology for addressing that. Maybe we can talk about that later.<\/p>\n<p>The other part is the J-curve. This is what we started the conversation about \u2014 there really is a gap and a difficulty in translating capabilities into real business change. It\u2019s because there [are] so many complementary assets and investments that need to be made. Many of them have to be invented, and we don\u2019t know what they are, these complementary co-inventions of new business processes, new skills, whole new business models and ways of working. They\u2019re hard and we have entrepreneurs and managers and business school professors and consultants all working on figuring them out.<\/p>\n<p>In earlier work I did with [University of Pennsylvania\u2019s] 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\u2019re largely intangible, largely invisible, we tend to not appreciate them as much, and we don\u2019t measure them, and people don\u2019t even realize that they\u2019re there. But we\u2019re trying to make them more visible in our research and create an appreciation that you have to make those investments as well. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> Actually, the measurement thing is huge and difficult. I remember you and [Carnegie Mellon University\u2019s] 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\u2019t measure stuff, we do a bad job, I think. <\/p>\n<p>Let\u2019s switch maybe to measurement because I think one of the cool stories about your \u201cCanaries in the Coal Mine\u201d study \u2026 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\u2019re using ADP payroll data to find that. That\u2019s a very interesting signal of information. Tell us about that study. <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> 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\u2019t really know how to aggregate that. What\u2019s the real story? So we wanted to get large-scale data. <\/p>\n<p>When we looked at the current population survey, we didn\u2019t really see much, and when we looked at the top line of ADP, which is the world\u2019s largest payroll processor, they shared the data with us here at the Digital Economy Lab. We actually didn\u2019t see much in the top line either. I was almost thinking, \u201cOK, we\u2019ll write a story about how this is all just a bunch of hype.\u201d 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. <\/p>\n[OpenAI\u2019s] Tyna Eloundou, [University of Pennsylvania\u2019s] 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\u2019re 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. <\/p>\n<p>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\u2019s more like a 16% to 17% employment decline relative to the other occupations. So it\u2019s really quite striking. We didn\u2019t see a fall in employment for the older workers, even in the exposed occupations. You really have to slice it this way. <\/p>\n<p>Even when we controlled for other factors, like we controlled for interest rates, we controlled for the tech industry \u2014 you can take the entire tech industry out if you want, or control for tech overhiring \u2014 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. <\/p>\n<p>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. <\/p>\n<p>And then, the most interesting result, the one I\u2019m most excited about, was that if you divide the way they\u2019re 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\u2019s kind of a double win: higher productivity and growing employment. For some reason that doesn\u2019t get picked up as much in [the] press when they write about our research, but I think it\u2019s really important that we found that AI could be associated with higher employment for certain kinds of workers. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> I think that ties back to how you use it, your decision-making, the choices that we all make in that. That doesn\u2019t surprise me, because if I think about a task and, going back to this aggregation of tasks you alluded to, no job, I\u2019m 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\u2019s going to leave those parts more valuable. <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Exactly. That\u2019s 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, \u201cReading medical images, that\u2019s what radiologists do. That\u2019s going to be replaced by AI.\u201d What he didn\u2019t factor in was there were 25 other tasks that radiologists do, and most of them were not affected by AI. <\/p>\n<p>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\u2019m not saying that always happens, but there [are] definitely cases \u2014 some people call it Jevons paradox \u2014 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. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> I think it\u2019s the frustrating thing. I think everyone would like an answer, which is \u201cHere\u2019s the clean answer,\u201d and what you\u2019ve come across here is just a gigantic \u201cIt depends,\u201d and it depends on an increasingly complicated set of stuff.<\/p>\n<aside class=\"article-ad ad-300  ad-300x600 ad-desktop\">\n<\/aside>\n<aside class=\"article-ad ad-300  ad-300x250 ad-mobile\">\n<\/aside>\n<p><strong>Erik Brynjolfsson:<\/strong> 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 \u2014 and I\u2019ll get a little wonky for your readers, but it\u2019ll be [a] useful time \u2014 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.<\/p>\n<p>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\u2019re not going to buy 10 times more apples. Maybe you buy a few more apples. But the demand curve\u2019s pretty steep. So as prices go down, you end up spending less. That\u2019s pretty intuitive. Most people think that all goods are like that.<\/p>\n<p>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\u2019s cheaper, and a lot of us fly around the country way too much. So there are goods and services like that. That\u2019s called elastic demand or a flat demand curve.<\/p>\n<p>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\u2019s really good news. It means that as we get more productive, more efficient, some things shrink, but other things grow. I think there\u2019s a little bit too much of a bias in the public conversation toward the side that\u2019s shrinking. One of the things I want to do with your podcast is get more people to think about that side that\u2019s growing. <\/p>\n<p>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\u2019re going to help themselves, and they\u2019re going to help the rest of the economy by inventing new things. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> Apples [are] a great example. Like you say, we don\u2019t consume them when we get more of them, but I don\u2019t know of any company that says, \u201cPretty much all the IT projects that we could ever think about, we\u2019re doing them.\u201d You know what I mean? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Yeah, exactly. It seems like there\u2019s almost infinite demand for solving all sorts of IT problems. When I look around the world, I don\u2019t 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\u2019t see us running out of opportunities to address those. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> The fantasy that we would run out of those types of problems, that\u2019s great, but I don\u2019t think that\u2019s going to happen in the next couple of years though, the next couple of centuries. Let\u2019s 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? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> This is another concept I think people find very useful, and that is whenever there\u2019s 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\u2019s 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\u2019re the reason we have higher living standards than our ancestors, a couple hundred years ago or even 50 years ago. <\/p>\n<p>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\u2019s enabling all sorts of new business processes, and it\u2019s not just the physical technologies. It\u2019s also these intangible assets that we talked about earlier. That\u2019s great. The thing is that these intangible assets take time to create. They\u2019re expensive. You have to invest in them. Whether you hire consultants or you do it yourselves, you have to reinvent how work gets done. <\/p>\n<p>During that costly period, you\u2019re spending more as you\u2019re reinventing your business processes, but output doesn\u2019t 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\u2019s 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\u2019s the upward part of the J-curve. <\/p>\n<p>What we\u2019re seeing with most general-purpose technologies is that there\u2019s an initial lull where productivity is low or even negative, and then later it takes off. Now it\u2019s hard to see exactly where we are for AI. We\u2019re in the middle of it. But if you look back at earlier history, if you look back at, say, electricity \u2014 I wrote [about] some of it in my Ph.D. dissertation \u2014 believe it or not, it took about 20 to 30 years of time where companies were trying to reinvent how factories were organized. <\/p>\n<p>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. <\/p>\n<p>When they did that, productivity started skyrocketing, like doubling and tripling productivity. But like I said, it took 30 years. I don\u2019t think AI is going to take 30 years. I\u2019m pretty sure it\u2019s 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\u2019re done. <\/p>\n<p>The reality, as you know, Sam, in your work and in my work, there\u2019s a lot of business process redesign that has to happen. We\u2019re in the middle of that, and I\u2019m 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\u2019ll be great, but we\u2019re still going to have a bit of a J-curve. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> I\u2019m thinking back to the electricity times. I\u2019m guessing they didn\u2019t have the quarterly stock report pressures and the need for instant results that perhaps people deal with now.<\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> The biggest thing, honestly, is that they didn\u2019t really have all of our institutions. Not to pat ourselves on the back, but business schools didn\u2019t exist. Consulting books [and] the whole science of industrial engineering and management, didn\u2019t exist. I went back and I read some of the books. I went to Harvard Business School\u2019s Baker Library, and they had these old books, and it was pretty primitive how they were thinking about things. <\/p>\n<p>Now there\u2019s 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\u2019re leaning in much more aggressively, and they\u2019re sharing best practice. <\/p>\n<p>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. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> 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\u2019re doing and how you\u2019re doing it helps. <\/p>\n<p>Let me kind of be antagonistic a little bit. I wrote something at <cite>MIT Sloan Management Review<\/cite> a while back about <a href=\"https:\/\/sloanreview.mit.edu\/article\/rethink-ai-objectives\/\">rethinking AI objectives<\/a>. You wrote something about <a href=\"https:\/\/digitaleconomy.stanford.edu\/news\/the-turing-trap-the-promise-peril-of-human-like-artificial-intelligence\/\" target=\"_blank\">the Turing Trap<\/a>. 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\u2019re not measuring the right things. What are you thinking about that, short term versus long term? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Well, there\u2019s definitely a short term, long term; [those are] your objectives. I was talking to a CFO a couple months ago, and she told me, \u201cHey, we really need to measure the benefits of AI, the ROI.\u201d And I said, \u201cThat\u2019s great. You should be measuring it. Tell me about it.\u201d And she said, \u201cSo that\u2019s why I\u2019m demanding that every division tell me how much head count reduction they\u2019re getting from AI.\u201d I was like, \u201cWait a minute. I\u2019m glad you\u2019re measuring, but isn\u2019t that a little bit simple-minded, because, yes, there\u2019s nothing wrong with cutting costs, but it\u2019s really missing the bigger opportunity of doing new things, allowing your workers to create new kinds of value.\u201d <\/p>\n<p>There\u2019s a CFO getting paid millions of dollars, and the reason they\u2019re making big money is that they\u2019ve got to think more creatively about value creation, not just having machines do what the humans are already doing and replacing them. That\u2019s the classic Turing test: Can you make a machine imitate a human perfectly?<\/p>\n<p>That\u2019s 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\u2019s 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? <\/p>\n<p>These are all things that won\u2019t 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\u2019re 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\u2019t happen in weeks, but when you do achieve it, you have something that lasts for years. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> There\u2019s 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\u2019m 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\u2019t 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\u2019ve got a science article that said something like 90% of the most important new AI models are coming out of industry. We\u2019ve 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\u2019s there. All right, I\u2019m worried. Is that the right place for that stuff? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Part of the reason it\u2019s coming out of industry is that it\u2019s 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\u2019re speaking. Anthropic and OpenAI are filing to go public. Google has a nice cash engine they can pour into this as does Meta. <\/p>\n<p>So that\u2019s what\u2019s driving that. At the same time, what I tell my academic colleagues is your comparative advantage isn\u2019t [in] spending more money on computers. It\u2019s in thinking creatively. There [are] some brilliant ideas that happen just from sitting in your office and thinking deep thoughts. It\u2019s still a good strategy.<\/p>\n<p>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, \u201cThis is the hardware I use.\u201d And for those kinds of fundamental breakthroughs, it\u2019s not always a matter of having thousands of GPUs. <\/p>\n<p>I think academics need to lean into their comparative advantage. That said, it\u2019s fair to say that more and more of the frontier research is happening through a really expensive approach. In a way it\u2019s a testament to how valuable AI is. One of the reasons industry didn\u2019t do it before was not just that it was expensive, but it just didn\u2019t have that big a payoff. <\/p>\n<p>I\u2019ll share a story. The first AI company I started, believe it or not, [was] in the late 1980s. That\u2019s 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\u2019t 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\u2019s a testament to the success of the field that industry is investing as much as they are. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> 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\u2019s 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. <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> But on that, I think it\u2019s fair. You want to have both. I mean, it\u2019s the job of academia, it\u2019s the job of the federal government to invest before it\u2019s commercially viable. So much of the long-term benefits of R&amp;D [are] not apparent, and there\u2019s 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\u2019t 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. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> That\u2019s the basic applied cut. We don\u2019t have to fund the applied stuff because the market will take care of that. And [for] the basic stuff, the invisible hand doesn\u2019t work quite as well in those, or it works slower than everybody would like. <\/p>\n<p>I think you\u2019re going to have a bunch of academics listen to this because they\u2019re 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\u2019m seeing in academia are, let\u2019s say, consulting projects for companies. What are these basic types of ideas that people should be pursuing? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Well, there\u2019s a lot of them in lots of different areas, and I think I don\u2019t want to prescribe them because I think in a way part of the magic is letting people pursue their own ideas even if there\u2019s nobody on the outside that sees the value of them. <\/p>\n<p>That said, I\u2019ll give you my own list. I\u2019m focused on the economic side. I think that right now the biggest gap is that we don\u2019t 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\u2019t also transform and we develop new kinds of organizations to manage work. But we haven\u2019t 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? <\/p>\n<p>I\u2019m super optimistic about the potential of technology to boost productivity. I have a bet, a long bet on that with [Northwestern University\u2019s] Robert Gordon, and I\u2019ve written about how I expect productivity to grow. At the same time, I\u2019m pretty worried that if we don\u2019t 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\u2019t figure out an economic system that balances not just wealth creation but also shared prosperity.<\/p>\n<p><strong>Sam Ransbotham:<\/strong> I was looking up some of your background before talking, to refresh, and I saw the phrase <em>mindful optimist<\/em>, 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? <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Let me take a minute to define <em>mindful optimist<\/em>, because I have a very specific meaning for that. There\u2019s 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\u2019re like, \u201cHey, Erik, don\u2019t worry. It\u2019s worked out in the past. It\u2019ll work out in the future. Just chill.\u201d There are also a lot of people who are what I call blind pessimists. They\u2019re like, \u201cOh my god, we\u2019re doomed. There\u2019s nothing we can do.\u201d I think both those groups make the same mistake. <\/p>\n<p>It\u2019s the one we talked about at the top of the program, which is they think of AI as something that\u2019s 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\u2019t just assume it\u2019s going to automatically happen. <\/p>\n<p>They\u2019re not like kids at Christmas [who] expect presents to magically appear the next morning, but instead they\u2019re more like kids who see a tree, and they see some boards, and they think, \u201cHey, we could build a tree house there.\u201d Then they\u2019re like, \u201cWell, it\u2019s not going to happen by itself. Let\u2019s figure out how to make it.\u201d And then they make that optimistic reality happen through their hard work and through their imagination. That\u2019s the kind of optimism that I would like to see. I don\u2019t see enough of it. I see way too much just passivity. <\/p>\n<p>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 \u2026 \u201cAI\u201d they should think of <em>amplified intention<\/em>, that AI is the greatest amplifier of intention ever. Whatever they want to achieve, AI can amplify it, but it\u2019s not going to happen without that agency, without that intention. <\/p>\n<p>So that\u2019s a message I want to have for the broader world: \u201cLet\u2019s think about what our values are, what kind of economy we want to shape, what kind of shared prosperity we want to create.\u201d 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\u2019s think about really actively using this. <\/p>\n<p>Over the next 10 years, I expect the world is going to radically change. But there\u2019s 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\u2019s the same for the future. We can live in a lot of different possible futures. One of the things I\u2019m 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. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> I think that\u2019s 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\u2019re making now and something that we have control of. <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> 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\u2019t think there\u2019s been enough attention to that, but the Digital Economy Lab is in part focused on that. <\/p>\n<p><strong>Sam Ransbotham:<\/strong> Thanks for bringing the numbers and the nuance. We\u2019ll see you in Boston this summer. <\/p>\n<p><strong>Erik Brynjolfsson:<\/strong> Absolutely. Looking forward to it, Sam. Great talking to you. <\/p>\n<p><strong>Allison Ryder:<\/strong> Thanks, everyone, for listening today. We\u2019re off for summer break, and we\u2019ll be back in the fall with an exciting lineup of speakers from Instacart, GoFundMe, Dropbox, and Honeywell. Please join us then.<\/p>\n<p>In the meantime, it helps our show a lot if you leave us an Apple Podcasts review or a Spotify rating. If our show has really impacted your life and work, please drop us a line. You can reach us at <a href=\"https:\/\/sloanreview.mit.edu\/audio\/creating-shared-prosperity-with-ai-stanford-digital-economy-labs-erik-brynjolfsson\/mailto:smr-podcast@mit.edu\" target=\"_blank\">smr-podcast@mit.edu<\/a>. We\u2019d love to hear from you. <\/p>\n<aside class=\"article-ad ad-300  ad-300x250 ad-desktop\">\n<\/aside>\n<aside class=\"article-ad ad-300  ad-300x250 ad-mobile\">\n<\/aside>\n<p class=\"mmai-trademark\">\n        ME, MYSELF, AND AI<sup>\u00ae<\/sup> is a federally registered trademark of Massachusetts Institute of Technology. All rights reserved.\n    <\/p>\n<div class=\"article-authors\" id=\"article-authors\">\n<h4 class=\"article-authors__title\">About the Production Team<\/h4>\n<div class=\"article-authors__bio\">\n<p><cite>Me, Myself, and AI<\/cite> is a podcast produced by <cite>MIT Sloan Management Review<\/cite> and hosted by Sam Ransbotham. It is engineered by David Lishansky and produced by Allison Ryder.<\/p>\n<p><a href=\"https:\/\/sloanreview.mit.edu\/sam-ransbotham\/\">Sam Ransbotham<\/a> is a professor in the information systems department at the Carroll School of Management at Boston College, as well as guest editor for <cite>MIT Sloan Management Review<\/cite>\u2019s Artificial Intelligence and Business Strategy Big Ideas initiative.<\/p>\n<\/div><\/div>\n<\/p><\/div>\n<p>#Stanford #Digital #Economy #Labs #Erik #Brynjolfsson<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Topics Data, AI, &amp; Machine Learning AI &amp; Machine Learning 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\u2019t the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":8603,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[9],"tags":[],"class_list":["post-8602","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-management"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.7.1 (Yoast SEO v25.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Stanford Digital Economy Lab\u2019s Erik Brynjolfsson - MORE SOURCING LTD<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/moresourcing.com\/en\/creating-shared-prosperity-with-ai-stanford-digital-economy-labs-erik-brynjolfsson\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Stanford Digital Economy Lab\u2019s Erik Brynjolfsson\" \/>\n<meta property=\"og:description\" content=\"Topics Data, AI, &amp; Machine Learning AI &amp; Machine Learning 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. 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