Stanford's AI Economist: The Next 10 Years Will Be the Best AND the Worst in History | Erik Brynjolfsson

There are a bunch of jobs, millions of jobs that are going to disappear. How soon? Already, it's already happening. This is Eric. Stanford economist who saw AI coming before almost anyone. He spent 30 years measuring what technology does to jobs. And he says, we've just turned the corner. But what happens next depends on what we do right now. I think it's going to be even bigger than most people realize. The industrial revolution allowed machines to augment muscle power. Now we're doing the same thing for our brains, our minds. If intelligence is automated, what is left for humans to make money with? The next decade, if we play our cards right, will be the best decade in human history by far. Or this could be like one of the worst 10 years ever. What can someone like me do? I think what you really need to do is-- You just had this lap paper called Canaries in the Cold Mind. That shows that AI has already wiped out 16% of entry-level jobs. But only for people under 25. Can we talk about that? Sure. It's not just under 25. It's also specifically in the most exposed occupations. You can rank all the occupations in the economy by whether AI can affect them. So we did that. And we looked at the most exposed occupations. And that's the number you just quoted. About 16% less employment for young people up to age 25. But it's also worth noting that in the other end of the spectrum, the least exposed occupations, like home health aids, there's actually growing employment. Also for older workers, growing employment. And perhaps most interestingly, for people using AI to augment what they're doing versus automate what they're doing, we could kind of look at the kinds of prompts that we're using. That also, those people also did significantly better. So I don't want to sugarcote it. The core folks who are using AI to automate their jobs in places like coding and call centers that are highly exposed, there was double digit declines and employment. And since we published that paper, we've continued to track it and the effects just getting bigger every month. What are these most exposed fields? So coding is obviously dead center, call centers, parts of sales, marketing. It's actually, we find that the most useful way to do it is look at tasks as opposed to entire occupations. So every job is a bundle of tasks. Like Jeff Hinton, the famous deep learning researcher, talked about radiologists being replaced. But radiologists actually do 26 distinct tasks we've recorded. One of them is reading medical images. That one's getting done by machines. But they also sometimes conduct physical exams. They review lab data. They coordinate care with other physicians. Those are not nearly as affected by LLMs. If you look at all the occupations in the economy, there's not a single one where LLMs just run the table and you can do everything. In each case, there's parts of the job that LLMs can help with, writing memos, doing emails, looking at labs. There's others where LLMs can't help. They don't lift a box or drive a car, at least not yet. For those most endangered fields, how much is AI doing in terms of tasks? Is it close to 80% or? Well, that's the other thing. Within tasks is varying quite a bit as well. So in coding, it's happened so fast with agents. I teach my course at Stanford. Even last year, the students all did projects. And they presented at the end of the class, typically like PowerPoint presentations. This year, every single student, every single project, they have to have running code. Because whether or not they were a coder before or not, everybody's a coder now. Everybody's a coder now. That's a good message for your listeners. If you use tools like Reply, or Cursor, or Cloud Code, you can just have an idea. You describe it and Cloud Code, or Reply, will help create it. So they all presented, actually just Friday, we had our final presentations. 20 teams presented it. So that's something where it's doing a lot. Call centers, I did a paper on call centers. And when I wrote that a couple of years ago, with Lindsay Raymond and Danielle Lee, we found that the LLMs were mainly helping the human agents answer questions. And the human always did the actual discussion with the person calling in. Now we're working with the same company and a big percentage of the questions are being directly answered by the agent, by the AI agent, I should say. So they're employing less people? Not clear, actually. That's another really interesting thing is that it sort of seems intuitive that when AI can do a task, you need fewer people, but that's actually not always true. In some cases, with farmers and other categories, you do see falling employment. And I mentioned with the coders, we fall falling employment. But in other cases, when a person becomes more productive and AI does parts of their job, that actually leads companies to hire more of them. And if I can get a little bit wonky, I'm going to explain a little economics here. Please. So the way I think of it is through the lens of what we call demand curves, which is a downward sloping curve. So if you compare price on the vertical axis and quantity on the horizontal axis, then lower prices lead to more quantity. We all kind of have intuition that if you cut the price, more people can buy. But the steepness varies a lot. It matters a lot. If it's very, very steep, then a lower price leads to only a small increase in quantity. So you end up earning less money. But sometimes demand curves are very flat. Economists call that an elastic demand curve. And then a small decrease in price leads to a big increase in quantity. Like when jet engines made air travel cheaper, it didn't mean that we spent less on air travel. You and I and lots of other people fly a lot more than people did 50 years ago, because flying is just so much cheaper than it used to be. And it ended up spending more than you did before. Roughly half the economy is in categories where you have falling spending as the price goes down. But the more interesting part is the half of the economy where lower prices lead to more spending. And that's a really important message, I think, is that as AI makes things more efficient, it's definitely destroying jobs and eliminating income in some places, but it's also creating opportunities and lots of other ones. That creation part is where I'm focusing my energy. That's what my course at Stanford is about. I have a master class that teaches people how to lean in to that creation part of the economy. I mentioned some of the changes in employment, a little bit in productivity, but it's really not a dramatic change yet. We're watching it carefully to see whether or not it will start taking off more. There's a real contrast. We also create something called the AI Index, the Stanford AI Index, which tracks some of the raw capabilities. Like all these benchmark tests, like how well can it do on a math test or read a document. And on those, it's doing really well. So the raw capabilities are skyrocketing, but the economic impact is pretty muted right now. That gap between the capabilities and what's actually happening is a big opportunity, I think. Over the next few years, businesses are going to kind of close that. That's why I teach the master class. I also have a startup called Work Helix, where we're very focused on teaching companies how to use those amazing capabilities to boost productivity, profits, sales. It's not happening as much yet as it should be, but over the next few years, I think we'll see a lot more. So when you say it is not happening, does that mean that companies use it in a way that creates an AI slop or things they can't use? Why is it even happening? So part of it is they're creating AI slop. Or they're using it in things that aren't that important. I was at one company. They did a big hackathon where everybody was making stuff. And they were so excited. The winning one was this person who used LLMs to make lunch menus. And I was like, oh, that's kind of fun. But is that really the core value of your company to have better lunch menus? So they need to connect it to real business problems. And it takes a while to figure out what those opportunities are and then execute well. Not to be fair, this happens every time there's a powerful new technology. Like I studied in my PhD work. I studied how electricity rolled out 100 years ago in American factories. Believe it or not, it took about 30 years between when they first introduced electric motors in American factories. And when you saw significant productivity gains, people like Paul David looked at the production records. Now, three years, that's insane. That's a lot. That's insane. But that's true. That's what the data show. How long will it take us with AI? It's going to be a lot faster. But it's not going to be overnight. I was just visiting DeepMind about 10 days ago. And they were telling me, yeah, in London. And they were telling me how-- oh, my god. With the next 18 months, 24 months, we have all these capabilities. And I believe-- well, I don't know. I mean, they're the experts on the capabilities. But I say it's going to take a lot longer for that to translate into business value. Because you need to change your business processes. You need to re-skill your workforce. Sometimes you need to invent new products and services. Takes a while. I'm sure it's not going to be 30 years like it was with electricity or 50 years with this team engine. I mean, some of these early technologies took a long time. This time, I think it's going to be more like three to five years. I think we're already actually seeing some inklings of it turning up. I actually made a bet with one of my economist friends, Bob Gordon. But he's kind of an AI skeptic. And he said, look, AI is overblown. And I said, I'm on the other side of that. I think AI is anything, believe it or not, I think it's underhyped. So we made a friendly wager that by the end of the 2020s-- by the year 2030, we actually made this bed the beginning of the 2020s. Productivity is going to be significantly higher than what the Bureau of Labor Statistics is predicting. So I think the official government statistics way low walling what's gonna happen. And that's going to be great news. If we can get this higher productivity, it's going to help with the budget deficit. It's going to be help with poverty. It's going to help us with health care. We're going to have a lot more wealth than we would otherwise have. I'm already a little bit ahead in that bet. And I think that the best is going to happen in the next three or four years. - And you have this report with ADP that private employees added 122,000 new jobs in me. What kind of jobs are they? Are they connected with AI? For people who are watching who are like, "Okay, I'm very technical." - What is my next step? Do you, is there any data that shows that you need to become a generalist or an entrepreneur within your workspace? 'Cause we're talking about this, but is there something that's proving that? - You know, generalist, especially, is one lens. I actually have a different way of thinking about it. So when I look at it, I think almost every project can be divided into three parts. There's defining the question. There's executing it once you've got it defined. And then there's evaluating it. Did it really give you what you wanted? How do you need to change things? And through most of history, people did all three parts. There wasn't anyone else, right? But now AI agents are getting really good at that middle one, executing. Once you've got it defined. So I hope all of your listeners are playing around with cloud code or these other tools. And they'll see that once you ask the right question, these tools will execute and generate software. So in the near future, and today it's already happening for folks at Work Helix and all of our clients, most people their job will be managing agents, not just one agent, but like a whole fleet of agents. Each person will be kind of like the CEO of a bunch of agents. And their job is going to be at the first and third parts, that is asking the right questions and evaluating, which is a lot of what a CEO does, right? And if you can think about, okay, what's the right question? Like the FTEs, what are the problems that really need to be solved? That adds a lot of value. And then once you can scope it out, now the agent doesn't. But let's be realistic. These agents sometimes they hallucinate, they mess up. Or what often happens is, you think you asked the right question and the agent does and you look back and say, oh, I guess you did what I literally asked, but that's not really what I meant. And then you iterate and you go back and you change the question. So that's the evaluation part. That's the future of work, I think, is figuring out how to ask questions and evaluate and how the agents do a lot of the execution. And I think it can be learned. I think it can be taught. I think it's the skill that more and more people are gonna have to have. - Yeah, how do you learn that? Just start deploying agents for your work? - That's a great way. (laughs) So, everybody should start deploying if they haven't already. But when I teach at Stanford, a lot of things by the stocratic method, my students don't love it when I call call on them, but I ask them to think on their feet and define the problem. They do homework and they have to figure out how to scope something. So it's not just, okay, let me write the problem for you and you just carry out the steps, kind of like a cookbook. That's the old way of learning. The new way of learning is you give them a much more unstructured set of issues and they figure out, okay, what's the core question here? And like anything, you practice, you get better at it and you get to be pretty good at it. - The art of understanding the problem and understanding which answer is correct. - That's exactly it. And it takes a special mix of skills. So I think if you only have technical skills, you're gonna miss on understanding the problem. If you only have people skills or domain knowledge, you may not understand where the technology can help. But if you combine the two, that's where you really add the most value. - So basically becoming a generalist, right? 'Cause you also have to have-- - A special committee generalist. - You also have to have this academic knowledge 'cause otherwise, how do you know that this is correct or an incorrect answer? - I think so, yeah. I mean, some people, they have this idea, there's rigor on one end, theory on one end and there's relevance or practicality on the other end. That's not the way I think about it. I think of these two as being very synergistic and if you combine rigor with relevance, that's the motto of MIT where I used to work, men's at Monos and Latin, mind and hand. That's where you get the biggest value by combining those two things together. - And a lot of people have a dream of going to Stanford for maybe they're, I don't know, 12 years old. I had this dream when I was a kid. Do you think it will still be a valid dream in 10 years from what you see? - I hope so. - Yeah. - And I have a job there. (laughing) - What are you seeing? How relevant is education? - It's changing, honestly, it's changing quite a bit and I think the kinds of courses where they're just kind of teaching a cookbook, this is how you invert a matrix, this is the step-by-step process for doing whatever. I think those are gonna disappear, they become less valuable 'cause AI tools will do them. - I'm gonna name some jobs, well, good paying jobs. Would you tell people to spend years becoming them or junior software engineer that pays 95K you? - No, unfortunately that's one that's very much in the bullseye of being replaced. Especially because you said the junior part, we see that in the data, they're disappearing. If you had said senior, I would be much more positive. - Where are they going when they're disappearing? What happens to them? - I mean the jobs are the people. - People. - They need to find something else to do. So one of the things they do is they learn to do more of the senior stuff. So I was working with Infosys, one of the big companies, and they said they're actually hiring as many junior people as before, but instead of having them do this routine work that the LLMs and the agents can do, they're actually having them spend a lot more time training and learning the big picture project management stuff. They used to kind of lures that by osmosis, just by hanging around and hoping that it would rub off on them. Now they're explicitly teaching them, sometimes using AI as a tool. So it's a different mindset. Most companies, to be frank, are not that forward looking and I think they're going to be hurt. 'Cause they had this pyramid. Most companies have this pyramid, like a law firm, software engine. We got a bunch of junior people and then some of them worked their way up and become middle management and senior. Now if you get rid of the base of the pyramid it becomes like a diamond. Then where are those middle managers going to come from? And where are the senior people going to come from? And too many companies are being short-sighted about that. I think Infosys is doing it right and saying, "We're still going to hire those "because we need the people with more taste and experience." - And that's the question that a lot of people are having these days, how do I become senior if there is no position where I can be a junior for a few months at least? - I'll tell you something. It's a societal problem. It's a bit of a prisoner's dilemma, I think, or a coordination problem economist call it because for every company individually, maybe it's privately okay to just save the cost from on higher the junior people, but as a society, you need to have those people have jobs and learn the skills. So we need to, I'm glad Infosys is doing it on their own, but we also need to come up with some societal solutions. For me, I think part of that is public investment in education and training. - Mid-level marketing manager, 115. - Sorry, that's another one that I'm not really seeing. We see a lot of LMS being able to do that. Now, to be fair in each of these jobs, there's bits and pieces of them that are more immune, some of the project management, the taste part, but the core part of the job is kind of in the bull's eye. - Okay, paralegal. - Oh my God, it's even worse. (laughing) - What a list. - Look, I don't want to sugarcoat it. My job's not here until I paint a happy story. - How soon? - Already, it's already happening in our Canary's data. Look, that's, again, that's only half the story. The bigger story is all the new jobs being created. Technology has always been destroying jobs. It's always been creating jobs. And, you know, while we have this job destruction on one side, we're having a new creation. And no society has ever succeeded by trying to hang onto the old jobs, you know, the coal miners or whatever that sometimes to get talked about or these jobs you just mentioned. Every society has succeeded by leaning in to dynamism, to reeducation, to training, and to embracing that kind of flexibility. There's a real instinct among politicians, among union leaders, among workers, sometimes to try to just like, oh, you know, just just freeze the old way of doing things. That has no work for our country. It wouldn't work for our company. It doesn't work as an individual. - Mm-hmm. It's just the speed at which it's happening these days is much, much faster. - Totally fair. And we don't not have in place the resources and the investment to help with the transition. - And it's really, yeah, where we're still figuring out. - No, no, no. I mean, look, we've seen this movie before, unfortunately, with globalization and free trade. And I have to confess as an economist, I'm one of the people who said, hey, free trade is great. It's gonna make the pie bigger. Yes, there'll be some disruption, there'll be winners and losers, but with a bigger pie, we can make basically everyone better off. Well, we did the first part, we did the free trade, but we didn't do the second part where we helped out the people who were hurt. And now there's this huge backlash, like a tidal wave of anti-globalization, anti-free trade, tariffs are like the highest they've been in most of a century. It's from an economist's perspective, it's a catastrophe. But in a way, we brought it upon ourselves by not being careful enough to point out you need to compensate and retrain people. If you just unleash all this disruption without a plan for managing the transition, you're gonna get a backlash. And what's happening with AI, I think, is 10 times bigger. We're already seeing a backlash. I urge my friends in the tech industry, political leaders to work on smoothing that transition. You can't ignore it. Totally. Let's wrap up with a radiologist, I guess, 350K. A radiologist, okay, this is a good one. So this is-- - I'm not radiologist, 'cause this is such an iconic story. Jeff Hinton back in like 2017, he looked at what deep learning could do, read medical images, and he famously said, he's one of the smartest guys I want to give him credit, but he got this one really wrong. He famously said, you know, we should stop hiring radiologists. It's over for the AI can do that. However, we now have more radiologists than ever. There's almost a shortage of radiologists are being well paid. Why is that? Well, it's a couple of things. First off, reading medical images is only part of a radiologist job. They have these 25 other tasks that they do. When you make one part more efficient, it actually increases the demand for the other parts. Related part of it is that the elasticity of demand for medical images is very high. What that means is that as you make it more efficient, you actually have more demand. Yeah, like if I have a little bit of a sore shoulder and it costs me $2,000, like an MRI, I'm like, ah, now I'm going to do it. Cost $200, yeah, I'll go have a check out. And so what we've seen is that making things more efficient led to more demand. And there's a lot of people who could do more medical care. So I think that's one of the areas where, in general, we're going to have growth is in medical care. AI is going to make it more efficient. But that doesn't necessarily mean we'll spend less, we'll spend more. And, you know, I actually think that's good news because it means more people are going to be helped and we're going to have, you know, maybe twice as much spending, but four times as much cures, four times as much benefits. So it's a good job. It's, I think it's been a good job, yeah, and it probably will be for a while. Okay, this is the part that actually worries me a little. Everything Eric just walked through, which job shrink, which ones grow and the whole economy is shifting under our feet is a lot to sit with. And the thing people always ask me at your conversation like this, okay, but what do I actually do? Where do I even start? That's literally what my newsletter is for every week. I take what I learn from podcasts like this one, from my own experiments with different agents and models and turn it into the real moves. What to learn? What to build? How to end up on the right side of the ship. My newsletter is called Future Proof. It's free and it's very, very practical. The link is in the description, subscribe and start deploying AI in your life. You know, ironically, I think a lot of the liberal arts become more valuable, philosophy, like even art appreciation. You know, in a future world where we have abundance and I don't know for sure we're going to get there, but if we do, then, you know, learning how to appreciate art and music, you said you were a singer earlier. Are you going to sing for us a little bit? Maybe. You know, that actually is a great thing for universities to do. And so it's kind of, you know, it's a little contrary in view, but I think one of the things that universities should think about doing is going back to the way they were like a few hundred years ago. You know, a lot of universities really started off as being liberal arts, philosophy, religion, art, music and history. That stuff, I think, is going to always be important. That makes no sense. That's developing taste, basically. Developing taste, exactly. And, you know, I mostly took like nerdy math courses, but I'm so glad I took some music appreciation courses and I honestly like can hear music differently. You literally hear things that you wouldn't otherwise hear before you took the course. And you can taste things. You go to wine tasting here in Napa, like, you know, you can learn to recognize new kinds of taste. You can see things in art that you didn't see before. It's like opening up your eyes. Okay. Now let's talk about this AI revolution as an economist, right? You've studied all the previous revolutions. And we've had the recent one, all of them, my recent industrial revolution. Didn't happen as fast. Apart from the speed. You're taking the long view. I like how you call the industrial revolution a recent one. Yeah. Well, it's one of the greater scheme of the greater scheme and in terms of impact. So I think the one that we can talk about when we try to compare to AI is industrial revolution. It's a greater comparison. Yeah. But that happened much slower. Exactly. Apart from speed, what else is different this time? Well, the main thing. So Andy MacCafee and I wrote this book called The Second Machine Age, which everyone should go out and buy and read. The second machine age explains all this and the basic idea is that the furt, the industrial revolution was this first amazing transition in our world. Up until then, most people, their living standards just barely moved. Their parents, grandparents, great grandparents, they all lived close to poverty. That was just life. And you know, the average family didn't change. With the industrial revolution, we started seeing economic growth skyrocket. Well, so right now we're like 30 to 50 times richer than our ancestors a couple hundred years ago. The reason for that is the industrial revolution allowed machines to augment muscle power. So instead of humans or cows, providing muscle power, you had steam engines. And to just unleash this an amazing explosion of productivity growth, a couple percent per year, which may not sound like much, but when you compound it, it's like I said 30 to 50 times richer. That was a real, it was kind of like a singularity, the first singularity where we transitioned from stagnant growth to much faster growth. The current era is what we call the second machine age because now we're doing the same thing for our brains or minds. We're augmenting them. And in my view, that's going to be at least as big. It's going to be bigger. It's going to be faster. It's going to affect a much bigger share of the economy. Most workers in the United States and other advanced economies are doing cognitive work. Like, you know, most of what your job is is not like lifting boxes. You know, communicating ideas, mind too. And even, you know, even people who are doing a lot of physical work, they're also usually doing a lot of cognitive work as well. So AI is going to be even bigger than the industrial revolution. It's clearly happening a lot faster. And that's the good news. The bad news is like we're talking before, we're not really prepared for the size of this tidal wave of change. So people make money these days because they have this scarce resource, resource, which is intelligence. Oh my God. That's the trillion dollar question. And I don't think there's a clean answer, but you're totally right. Like, people like me, I kind of fries intelligence because, you know, it's helped me make a lot of money and it's kind of where I get my status from. But AI is going to, you know, have intelligence on demand. So one thing that's going to be more valuable is initiative or agency. My closing class, my students will remember me saying, I think whenever they hear the words AI, they should think of amplifying intention, not artificial intelligence. Because what it does is it takes your agency or intention and it amplifies it. If you don't have any, it doesn't do much for you. But if you've got a plan, this can totally amplify it. So the people in the future, the ones with a lot of high agency. The second thing I think that will be increasingly important is human connection. You know, when AI was able to defeat humans at chess, that was not the end of chess playing for humans. People today play chess more than they did before. My son, Zander, he likes to play chess and I asked him, do you play against machines or humans? He said, well, humans, of course, it's no fun to play against machines. And you know, there's this Nick's basketball game last night that millions of people watched. I don't think it would have been nearly as fun if there was a bunch of machines playing each other. So in the future, we will value things that are certified human, that are authentic, that real people are creating. I think that's another big area. A third area that, at least for a little window, will be valuable is just like physical work. I mean, AI is getting very good at cognitive work. And if you are a plumber or carpenter, if you have, you know, particular skills, that's something that turns out is harder for machines to do. Instead, I think the window is closing on that one. And then the fourth category, I would say, is all the things I haven't thought of. Every time in history that we have tried to think of what the future holds, we've always way underestimated. If you and I were having this conversation 200 years ago, we'd be like, well, all the farmers, you know, they're going to disappear at 90% of people are farmers. I'm pretty sure we wouldn't have thought of, you know, podcaster or, you know, all the other jobs that exist today. And there will be new ones that are invented and created and it's not necessarily my job to invent those. You know whose job it is? It's your viewers. It's entrepreneurs. And here in Silicon Valley, people are constantly trying out new ideas. A lot of them are really dumb, honestly. And some of the really dumb ideas turn out to be brilliant later who you've turned out that, oh my god, you know, space data centers, well, maybe that could work. I don't know. And so we have an ecosystem here. I had a brunch with a VC this morning and she was telling me that, you know, all of her pay off is just from like 5 or 10% of her investments or less. And the other ones, you know, they don't pan out. And thank God we've got an ecosystem where people like her are willing to take those gambles and the entrepreneurs will take those gambles and they try out things and America is leading the world in this kind of innovation of inventing new things. And I'm looking forward to seeing what they invent next. Yeah, we're always good with coming up with new things, new bottlenecks and things to solve. That's the definition of a human. Yeah, that's our superpower. You know, I know you had read Hoffman on this before and he told me something really valuable and you asked this question about what will humans do? And he said, human superpower is improvisation. And you know, you define the problem really well and the machine can do it. But if there's something unexpected that comes up, you know, then the human figures out how to do it. Actually, if you have time, he had told me this funny little example that really crystallized it for me. He said, "Imagine that you have like an ordinary person for my class had to play chess against the world's best chess computer. And the game was in 30 days. And, you know, whoever wins, you know, great that the loser dies. He said that he wasn't sure, but he thought there'd be a decent chance that the human would win. Not because the human could play chess better, but let's face it, if that human was life or death, they would probably figure out some way to show some sort of, you know, maybe there'd be a virus in there, maybe there'd be a lightning bolt that day, you know, something water would spill in the wrong way. And they would just, they'd figure something out. And they would, they would find a way to win. And that's what humans are good at doing. How do you see resource distribution when it's not companies hiring humans? What is it? I'm super worried about this. You know, you heard me earlier say that I'm optimistic about growth. And I think we're going to have higher productivity growth. A lot more wealth creation. I'm concerned that that's going to be very concentrated, more concentrated than it is right now. It's not an inevitability. We have choices going forward. And one of the things I want people to think about is what kind of values we have and what kind of future we want to create. I would like to see a world where we not only have prosperity, but shared prosperity. But one scenario that worries me is AI will automate a lot of work, a lot of jobs, and people will be entrepreneurial. But if it becomes too focused in just a few companies or one big government-owned entity, then all the wealth and power gets concentrated. And we need to plan for a future where lots of people can participate and where everybody has a stake in the society. I don't think either of those paths is inevitable. But I do worry that we are right now on a bit of a path towards that growing concentration of economic wealth. And therefore, political power. And we need to be mindful of that. Or someone who doesn't have a podcast like how can they make sure they participate by stocks? Well, literally one of the reasons I created it. No, no, I think, well, stocks is a bit, but I think what you really need to do is create the value. And that's why I created the master class. That's why I teach in my Stanford class is how can you use AI to create new goods and services? Not to be a rule follower who just does stuff because you're going to be replaced by a machine if you do that. But how can you be one of those people who asks the right questions? How can you use AI to create new products and services? And in a world where there's more entrepreneurship and value creation, then I think we continue to have widely dispersed economic power. But if everybody is just following instructions, then we're going to have that concentration of wealth. So that's the number one thing. Another thing, look, I think we do have to look at different kinds of redistribution. It's not my first choice, but we need to have it as a backup plan. That if we have a lot of concentration of wealth, then we need to have things like universal basic income and progressive income taxes, wealth taxes. I know a lot of my Silicon Valley friends are going to yell at me for that. But I think that you don't want to have all the wealth and power to concentrate it. It's not in anyone's interest, including the billionaires. People come after them with pitchforks. And so we want to have a world where everyone can participate. And in the end, people create more value. You know, I've visited some of these developing countries or parts of Latin America, where wealthy people live in gated communities with these walls and they have like machine guns and they have their own schools, their own doctors and private police forces. No, it's not fun for anybody. I had a friend she lived in Brazil and she said she and all of her rich friends were in prison. I said, wouldn't it not in prison? She said, no, prison of our own creation. I sit behind these walls. And when I go out, I have guards on either side of me because it's just like the society is not safe for me. Totally. And I don't think anybody wants to live in a world like that. He is just interesting. When we talk about this problem, it feels like it's up to those large corporations, governments and on the individual level. Yes, you could become an entrepreneur, but it's not like everyone is entrepreneurial. Let me push back on that a little bit. Honestly, I think a lot more people could be entrepreneurial than they are right now. You know, a few hundred years ago, most people were kind of farmer entrepreneurs, they were in the same thing. And then we created these societies with big corporations where people became kind of like hogs and create a lot of wealth. But I think we make potentially, I'm not for sure, but I think we could try to go back to a world where a lot of us are initiative, our agency became more important. And you know, I really think using these tools like we show in the master class is exactly what you want to do. Is figure out how do you I think almost everybody has some area where they see problems that other people don't see where they understand some needs and opportunities. And you know, you can just take a Saturday afternoon and just brainstorm with a sheet of paper or with one of the LMS helping you. All the types of things you might be able to create and try some of them out. And the neat thing is that it's so low cost to give it a try. If it doesn't work, then you try something else. And for most of it, it's kind of fun. Honestly, I think it's more fun creating new things than it is just following instructions. So I would encourage probably every one of your listeners to at least give it a try. Yeah, that makes total sense. That's what the purpose of this channel is, honestly, to inspire people to you. Yeah, you were doing it. And we need more people like you. We need more people listening to the show to give it a shot and have it work. And it'll not only be good for them, it'll be good for all the people. If you want more conversations like this with the people who can see where the economy is going before the rest of us and what they'd actually do about it, subscribe to Silicon Valley girl for more. What about the whole concept? Because I studied economics and you know, we're all studied market economies. Do you think we're going to switch to this new AI economy where money loses value? When you think about this like in 10 years, what do you think it's going to be? It could be. It could be different. You know, we need an economist who can think through what the economics of the future is. I'm trying to help play that role. You know, Adam Smith helped define the market economy and John Maynard Keynes helped update it in the early 20th century. I think for the 21st century, we're going to need some new economic rules to understand it. AI agents, we're going to have billions or trillions of them. We're going to have a lot of routine work done automatically. The kinds of things that worked in the old market economy won't necessarily work going forward. I mean, one way I think about it as I learned in my PhD program is you can think of a market as a big information processor. It takes all this information about prices and quantities and aggregates it and allocates resources. You can also think of an organization like a big company as an information processor. Both of them are information processors based on 20th century technology. Now we're going to have a millionfold more powerful information processes in AI. It would be a miracle if those two institutions just stayed the way they are. I'm pretty sure they're going to change exactly how I'm not sure. You asked about money, particularly. I think it's very likely that we will have a world where our basic needs, you know, the base of Maslow's hierarchy will be taken care of and will be able to just like you gave me some water here for free. It didn't charge it for me. Thank you. You know, it'll be like that for most goods and services. It'll be just like, why would you charge something for something that can just be made by robots for free? Now, there will still be things that are scarce. One obvious thing is status because it's kind of zero sum. It's like a hierarchy. You know, or there'd be a few physical things like, you know, I want to go to the far side of Pluto or something. You know, that would be still be expensive. But a lot of basic needs would be taken care of and then we'll have to figure out, you know, what the economy is. What are new status hierarchies are? Some people will, you know, get status from being great entrepreneurs. Some will be from getting lots of citations and academic literature. Some will be great snowboarders or video gamers or, you know, movie stars. There will be lots of different ways you can get status. And I think for better or worse, we humans are kind of wired for that. And the real job of the future economy is to steer all that status competition into something productive. You know, be like Einstein or be like a pastor and cure some diseases rather than zero sum status that doesn't really help anybody. Totally. Do you think GDP is going to explode in five years? Depends how you measure it. So traditional GDP is getting to be a worse and worse measure of what's really happening. I do think welfare and productivity is going to explode and probably conventional GDP will capture a big part of it. But the thing is that GDP measured all things that are bought and sold in the economy. So when something has zero price with few exceptions, it has zero weight in GDP. And think of all the free goods we have like Wikipedia, YouTube, you know, most users of chat, GPT are free. That doesn't show up in GDP. But it shows in valuations of those companies. Well, a little bit. That's not really GDP either. Yeah. But yeah, so they think it goes somewhere. It's not like no. It goes to well-being, but it doesn't necessarily show up in any measure of GDP. A little bit of it is an electricity. I wonder if those people who own those stocks and money. The ones that we can do those separately. But let's just look at like a Wikipedia. Something is totally free. Like that doesn't show up in any stock value. But the average person we've measured this values Wikipedia, you know, way more than psychedipedia Britannica. They value it at like $10 a month if I have to go back and check the numbers. So there's, you know, billions of dollars being created and there's lots of other free things like that. And some of it does show up in advertising stock and elsewhere. But I've studied this and most of it is just invisible in GDP. So we need a new measure. Happiness like Nordic countries, whether you have free education, free healthcare, they measure happiness. It would measure it. So some of it shows up. up in happiness. And that really is the ultimate measure. And so there's one measure, there's these happiness measures where they ask people on scale of one to 10, how happy you are. And yeah, you know, my country Denmark usually does pretty well. So that's party, but you know, that's pretty coarse like one to 10, like are you a 6.2 or 6.3? I mean, it's kind of. So we've developed a new measure we call it GDPB. And the D stands for benefits. And what we do is for every good we ask, you know, even if you're getting it for free, how much would I have to pay you to stop using it? If I paid you $50, would you stop using Wikipedia for the next month? Some people say yes. Some people say no. What if I paid you $2? How about Chatchy PT? How about Google search? How about email? And so we've done this for 600 goods and services. And we now have kind of a ranking of how much consumer surplus, how much value people are getting from all these goods. And it's staggering. There's trillions of dollars from free goods that are not otherwise being measured in our economy. I think for the 21st century, we need to lean more on tools like GDPB and be able to understand where the real value is. So we're in the process of rolling this out in such a way that we'll still have traditional GDP, which is what we spend the money. But increasingly, we want to start paying attention to GDPB, which is where you're getting the value. And those are two different things. There may be things you spend zero on and you get a lot of value. There may be things you spend a lot of money on and you're not getting a lot of value. There are two different things. Roughly, how much value is created by Wikipedia versus Chatchy PT versus bacon and eggs? We do it almost. Just like for LMS, we did this and we just published this. So for LMS, like chatbots, the amount of value just in the past nine months has gone up by like 70%. And that's partly because people value each LMS more than they did nine months ago. It's also partly because more and more people are using it. And so we're just getting, these are creating a huge increase in welfare in the economy. Is it 125 a month? I think the number that people, it varies. So here's the thing. It's like different people have different values. So our approach allows it to be heterogeneous. So there's some people who value it $125 a month or even $1,000 a month. There's other people who value it at $10 or zero. So you get a whole demand curve of them. And the total area under that is the value created. You know, a few people who value it a lot add some of it. And then a lot of people who value it a little bit add some and you get the total value is the sum of all those. What's the number for you? How much would you pay to know? To not take AI this month. Oh my god. It's almost, I mean, for me, it's tens of thousands, you know, somebody because it's my life. Like it's, I use it every day. I use it every night until too late at night. You know, I'm working with cloud co-work and testing at different research ideas. I use it for fun when I plan things. Anytime I land in a new city, I have it give me advice on which restaurants to go to. It's just so integrated into my life. It would be like tearing off my left arm. Is there a use case that can be very inspiring for people who haven't tried using AI deeply enough if they only use it like search? Here's a kind of meta way of doing it. Sit down with it and ask it how I can use it in my life. But if they haven't used it enough, I don't think there's like enough. No, no, no, no, you have, you have, you have the conversation. So what you do is you, is you ask chatchy pt or cloud say, hey, tell me how you can be useful to me and ask me questions. You can literally say, keep asking me questions. Interview me. And it'll say, okay, you know, what's your job? You know, do you have kids? You know, whatever. What are some of the problems you worried on last week? And it'll have a conversation with you. And then I've done this, by the way. It'll come up with like 10 recommended things that you can be using it for. What would you never delegate to AI? What would I never delegate to AI? There's nothing like that. I know. I can't. It doesn't even pop into my head. I said, no, I could see through that. You know, delegate entirely, you know, there's some really like life or death decisions. I use it before I go to the doctor and it gives me some thing questions to ask. But at the end of the day, I still want to have a real human make the call. And you know, they're just not good enough that they have the issues. And I think it's, it's usually a partnership. Like so I almost never 100% delegate something to AI. It for me, it's always co-working and collaboration where I'll interact with the AI and it will give me some ideas and then I'll overrule some and I'll agree with some. And it's kind of a partner. How much more productive have you become in the last few years? I think I've become a lot more productive. I'm not sure it would show up in official GDP statistics. But I feel like the, the amount of papers maybe you can, can you try that? A little bit. I think it's also like the quality. I'm working on some more interesting problems that I probably wouldn't have. Yeah. And my citations have gone up, but that's just because I think I just say the word AI and people, you know, cite me. You know, from my daily work, I feel like I'm being much more productive. I like, like give you a little more concrete example. You know, as a professor, like a, a, a, a, a, pretty common routine is meet with grad students. We'll talk about a research project. I'll say, Hey, why don't you do this? You know, look at this data and see what the answer is. And then they come back. We meet like once a week and they show me what they found. And I'm like, Oh, that's interesting. Well, this part doesn't make sense. Why don't you go back and double check that or let's explore this. And we kind of had this weekly cycle. And we, you know, we move forward and after like, you know, 10 weeks or 20 weeks, we, you know, we figure out what the answer is or we think we do and we write a paper. Now that cycle is like almost instant. I will sit with cloud co work and I'll ask them say, Well, where do the data show? And you know, five or 10 minutes later, it'll pull up the data. And I'll say, Oh, wait, that doesn't seem right. You know, you should double check this part. And then we'll go back and I have a similar kind of conversation in some ways it's worse than grad students, but no offense to my wonderful grad students. In some ways, it's better. Like it's much faster and it's sometimes contract on different kinds of data. You have to know about it. Strengths and weaknesses. But that cycle time is just so much faster. Yeah, it's fascinating with the speed, but also something that I'm noticing myself. Yes, I'm becoming more productive. Yes, the speed is faster. But there hasn't been this change. I know almost dramatic from I was just talking about this with my peers like, for example, like COVID happened, right? That dramatically changed all eyes with AI. We're talking about this dramatic change for some people. Yes, it's around because they've been laid off, but we will never know if that's AI or not because a lot of companies just use AI as a word. But we haven't cured cancer yet. No, self driving is yes, it's cool, but it's in San Francisco and it's still like it's rolling out with this regulation. When do you think we're going to see something that's going to be mind blowing for all of us and we're going to say, oh, wow, this is where I see the impact. I think over the next three to five years, people are going to see more and more mind blowing things. There's little ones already happening. There are some breakthroughs in medicine and there are some, you know, you mentioned like cars and companies are beginning to use it. I agree 100% though that it hasn't nearly had the economic impact or the impact on work that you might expect given the magnitude of the technology. And that's back to the J curve idea. It just takes longer than the technologist think, but it is happening. It is coming and by 2030, I don't think there'll be any question that this is transformative of the economy, but these things happen step by step. So where somewhere do you think we're down here in the J curve? I think we're turning the corner. That's what we created the takeoff tracker. If you go to the AI economic indicators at Stanford, you know, we have these metrics and every month we're updating them and there's a few of them like we have these different categories, no evidence, mild evidence, strong evidence. And you know, there's one or two that shows strong evidence. There's three or four that show mild evidence and all the rest show no evidence yet, but I'm pretty confident. Well, we'll see is every month we're going to sort of be moving more and more into the milder, the strong evidence category. And then it might start happening really suddenly. You know, there's this thing that we say in the second machine age, my book is the thing about exponentials is that things happen slowly and then suddenly. And we're just entering the suddenly part. We aren't in the suddenly part yet, but we're getting there. Wow. Okay. This makes me very excited, a little bit scared. That's the right thing. I'm excited and scared to know. Look, if you're not both excited and scared, you're missing at least half the story. Yeah. Okay. My last question. If my daughter, she's four or five years old, just certain five asks me tomorrow. Yeah. What is my life in a look like in 30 years? Yeah. What would you? Nobody knows. 30 years. No. I think it's going to be hard enough either even five or 10 years. Look, I think the next decade, if we play our cards right, will be the best decade in human history by far. There'll be more wealth creation than ever before. We're going to have noticeable improvements in longevity. I mentioned I was over at Google and I was talking to I'm sorry, at DeepMind, I was talking to Demis Hesabis. He thinks that they'll start curing a majority of diseases within 10 years. I hope it's the right. That sounds ambitious, but your daughter will see that. So that's the good news. I also think there's a future that could this could be like one of the worst 10 years ever. I have to be honest that there's the potential for catastrophic risk. Viruses being created in the lab and released AIs taking over social media and manipulating people from vast centralization of power. We already see AI powered drones hunting down people. These are so tragic. It just to be in these drones, like chasing a soldier. It's like, oh my god. It doesn't matter which side of the war I'm on. I kind of sympathize with the human being chased by the drone. So all those things are also possible. The thing I would say is that we have a tremendous amount of agency. And so we should think less about what will happen to us and what AI will do and more about what we want to use AI for. AI is a tool and a message I keep hammering over and over is that when tools become more powerful, that means by definition we have more agency. We have more power to change the world. So we need to really think, you know, be philosophers and think about our values. What kind of world we want to shape and don't take it for granted that AI is just going to steer us one way or the other. We still have the agency right now and we should be steering that technology towards one of those more beneficial futures and being damn careful to avoid those catastrophic futures. They I totally think they're possible. The dooms are not wrong, but there's a real risk there. They are wrong if they think those are inevitable because we'll have choices. So I've been working with, you know, the labs and with politicians to do what we can to share a shape us towards that future of shared prosperity. Fingers crossed, we're going to land on the positive scenario. Now I keep telling my daughters that they won't have as many problems as I have. I mean, like, they will have different ones, but the ones that I'm having, they probably not going to happen. That's probably true. Thank you so much, Eric. This was so insightful. Oh my God. I was such fun talking to you. Thank you for having me. Thank you so much.