Silicon Valley Girl: AI, Tech and Career Growth - Most Replayed Moment: Stanford's AI Economist Names the Jobs That Won't Exist in 5 Years | Erik Brynjolfsson

Episode Date: August 21, 2026

What if the highest-paying jobs today are the onesdisappearing first?Erik Brynjolfsson is Stanford's leading AI economist,director of the Stanford Digital Economy Lab, and one ofthe most cited voices on how technology reshapes theworkforce. He's spent decades studying which jobs getdestroyed, which get created, and who wins in everymajor shift.In this Moment, Erik Brynjolfsson names the specificprofessions being replaced right now, why radiologistsare thriving while paralegals are losing ground, and theone skill he believes every worker needs to become theCEO of their own AI agents.Listen to the full episode here!Spotify: https://open.spotify.com/episode/2mqN4S2Q48iHMyBKSBFey8?si=vqmIay61RLGyZl9RWoHzVwApple: https://podcasts.apple.com/il/podcast/silicon-valley-girl-ai-tech-and-career-growth/id1819090545?i=1000777720945Watch the full episode on YouTube: https://youtu.be/72duHF7iZiU?si=YSY7h6rPhR9-A4yW

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Is there any data that shows that you need to become a generalist or an entrepreneur within your workspace? Because we're talking about this, but is there something that's proving that? You know, generalist and specialist 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, you know, 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 to find. So I hope all of your listeners are, you know, 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 a lot 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 does it. But let's be realistic. These agents sometimes they hallucinate, they mess up. Or what often happens is, you know, you think you ask the right question, and the agent does, and you look back 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 a skill that more and more people. people are going to have to have. Yeah. How do you learn that? Just start deploying agents for your work? That's a great way. So everybody should start deploying if they haven't already.

But, you know, when I teach at Stanford, you know, we do a lot of things by the Socratic method. You know, my students don't love it when I cold call on them, but I ask them to, you know, think on their feet and define the problem. They do homework and they have to like 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 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. You know, so I think if you only have technical skills, you're going to miss on understanding the problem. If you only have, you know, people skills or, you know, 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? Because you also have to have this academic knowledge because otherwise, how do you know that this is correct or an incorrect answer? I think so, yeah. I mean, you know, some people, you know, they have this idea, there's rigor on one end, you know, 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, mens et manos in 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, but 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. I have a job. there. From what you're saying, how relevant is education to what's happening. It is 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, 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 Engineering. that pays 95K year. No, unfortunately, that's one that's very much in the bull's eye 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. 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.

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 because, you know, they had like this pyramid. Most companies have this pyramid, like a law firm, software engineering. 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 those middle managers going to come from and where the senior people are going to come from? And too many companies are being short-sighted about that. I think InfoSys is doing it right and saying, you know, 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 economists call it. Because for every company individually, maybe it's privately okay to just like save the cost, 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 emphasis 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. Sorry, that's another one that I'm not really seeing a lot of value. 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, 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.

What a list. Look, I don't want a sugarcoat it. My job's not here to 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 in our canaries data look that 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 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 uh to get talked about or or these jobs you just mentioned um every society has succeeded by leaning in to dynamism, to re-education, 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 freeze the old way of doing things. That hasn't worked for a country. It wouldn't work for a 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. and we don't not have in place the resources and the investment to help with the transition.

Yeah, 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 going to 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 have been in most of a century. From 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 management,

the transition, you're going to 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 that gets 350K.

Radiologist. Okay, this is a good one. Talked about Tony. I love radiologists because this is such an iconic story. You know, 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 them. AI can do that. However, we now have more radiologists than ever. If there's almost a shortage of radiologists, they're 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 people. tasks that they do and so when you make one part more efficient it actually increases the demand for the other parts and the 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 cost me $2,000 like an MRI I'm like now I'm gonna do it cost $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 And there's a lot of people who could use 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 jobs 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 after a 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 gonna get there, but if we do, then, you know, learning how to appreciate it, 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 a little contrarian 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 total 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, if you go to wine tasting here in Napa, like, you know, you can, like, learn to, like, recognize new kinds of tastes. 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 semi-recent, industrial revolution. It didn't happen as fast. Apart from 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... 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. Apart from speed, what else is different this time? Well, the main thing, 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 first, 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. 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, you had 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 compounded, 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, 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 tidal wave of change. So people make money these days because they have this scarce 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. 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, your 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 are 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, Xander, he likes to play chess and I asked him, do you play against machines or humans? Well, humans, of course. It's no fun to play

against machines. And, you know, there was this Nick's basketball game last night that millions of people watched. I don't think it would have been nearly as fun if it was 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. 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, you've turned out that, oh, my God, you know, Space Data Center as well, maybe that can work. I don't know. And so we have an ecosystem here. I had a, had a brunch with a VC this morning, and she was telling me that, you know, all of her payoff is just from like five or ten percent 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 willing to 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 Reid Hoffman on this before. And he told me something really valuable when you ask 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 told me this funny little example that really crystallized it for me. He said, imagine that you have like an ordinary person from 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 short circuit. 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 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. Two and five Canadians will hear the words, you have cancer. That's why every step and dollar raised matters. On September 19th, join thousands in Toronto for the Princess Margaret Cancer Foundation walk. Challenge yourself, friends, and family to walk 21.

kilometers in support of life-saving research. Together, we can carry the fire and help create a world free from the fear of cancer. Register today at pmcf walk.ca.ca. Behind.