Stanford's AI Economist Names the Jobs That Won't Exist in 5 Years | Erik Brynjolfsson

Private employees added 122,000 new jobs in May. 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? - 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. It didn't really give you wish 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 ask the right question and the agent does and you look back, 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. So every way you should start deploying it, they haven't already. But when I teach at Stanford, we do 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 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 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. - 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're seeing? - I hope so. - Yeah. - I have a job there. - Remember what you're seeing? - Yeah. - How relevant is education? - It's changing, honestly, it's changing quite a bit and I think, you know, 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, you know, for doing whatever. I think those are going to disappear. They become less valuable because AI tools will do them. - I'm going to name some jobs. Well, good paying jobs. Would you tell people to spend years becoming them or no, junior software engineer that pays 95K, you know? - No, unfortunately, that's one that's very much in the bull's eye of being replaced. I mean, 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 sort of 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 learn 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, you know, 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, you know, they had like this pyramid. Most companies have this pyramid, like, you know, a law firm, software engine. We get a bunch of junior people and then some of them work 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 junior, those middle managers going to come from and where are the senior people going to come from? And too many companies are being shortsighted about that. I think Infosys is doing it right and saying, you know, we're still going to hire those 'cause 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? - Yeah, 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 like save the cost and not hire the junior people, but as a society, you need to have those people have jobs and learn the skills. So we need to, you know, I'm glad Infosys is doing it on their own, but we also need to come up with some societal solutions. You know, for me, I think part of that is public investment in education and training. - Mid-level marketing manager, 115, okay. - Sorry, that's another one that I'm not really seeing. - Uh-huh. - 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, there are more immune, you know, 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. There are a bunch of jobs, millions of jobs that are going to disappear. - 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 on to 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. - It's just the speed at which it's happening these days is much, much faster. - Totally fair. We don't not have in place the resources and the investment to help with the transition. - And especially where we're still figuring out. - 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 the 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. And 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 a radiologist, 'cause this is such an iconic story. Jeff Hinton, back in 2017, he looked at what deep learning could do, read medical images. And he famously said, he's one of the smartest guys, I wanna give him credit, but he got this one really wrong. He famously said, we should stop hiring radiologists. It's over for the AI can do that. However, we now have more radiologists than ever. If there's almost a shortage of radiologists that are being well paid, why is that? Well, it's a couple of things. First off, reading medical images is only part of a reality. audiologist job. They have these 25 other tasks that they do. And so when you make one part more efficient, it actually increases the demand for the other parts. And a 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. It costs $200. Yeah, I'll go, I'll go have it checked 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. 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. Yeah. Are you going to sing for us a little bit? Maybe. You know, that actually is is a great thing for universities to do. And so it's kind, 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 it all 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. Like 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 know, you can learn to like 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. All the previous revolutions. And we've had the recent one. Oh, so recent industrial revolution. Didn't happen as fast. Apart from speaking. You're taking the long view. I like how you call the industrial revolution a recent one. Yeah. Well, it's one of them in the greater scheme of in 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. You know, so Andy McAfee 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. And the reason for that is the industrial revolution allowed machines to augment muscle power. So instead of, you know, humans or cows, you know, providing muscle power, yet steam engines. And it just unleashed 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 like 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, our 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. It's, you know, communicating ideas. Mine 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 were talking before. We're not really prepared for the size of this, this tidal wave of change. So people make money these days because they have this scarce resource, that resource, which is intelligence, if intelligence is automated, what is left for humans to make money with? 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 prize 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. What 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 we'll 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 watch. I don't think it would have been nearly as fun if there's a bunch of machines playing each other. So in the future, we will value things that are certified human that are, you know, 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. That said, 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 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, 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 had brunch with a VC this morning. And she was telling me that, you know, all of her payoff is just from like five 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 it. That's our superpower. You know, I know you had read Hoffman on this before. And he told me something really valuable. You asked this question, but what will 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 it. 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 figure something out and they would they would find a way to win and that's what humans are good at doing. If you want to stay ahead in the AI era follow Silicon Valley Girl podcast on your favorite platform new episode every week on AI careers and how to not get left behind.