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

📌 AI tools are everywhere — but Genspark is the one I turned into a system that actually runs my business. Just hit $250M ARR in 12 months. New users can trial Pro-tier Deep Research free with the Get Started bonus. Come build yours ↓ https://www.genspark.ai/?utm_source=yt&utm_campaign=SiliconValleyGirl @GensparkProduct Erik Brynjolfsson is the Stanford economist, Director of the Stanford Digital Economy Lab. He's spent 30 years measuring what technology actually does to jobs, wrote "The Second Machine Age", and just published "Canaries in the Coal Mine" — the paper showing AI has already cut employment 16% for workers under 25 in the most exposed jobs. In this episode he breaks down which jobs are in the bullseye and which are quietly growing, why "everyone is a coder now," the demand-curve reason more efficiency can actually mean MORE hiring, and the one skill (asking the right questions and managing a fleet of agents) that makes you valuable over the next 3-5 years. *Timestamps:* 00:00 — Intro 00:49 — The 16% of entry-level jobs AI has already erased 02:03 — The exact jobs getting hit first (and the ones quietly growing) 03:05 — Why every one of his Stanford students now has to ship real code 04:27 — The plane-ticket rule that decides whether your job survives 07:32 — The company that "won" its AI hackathon with better lunch menus 09:20 — His money bet with a skeptic economist: "AI is underhyped" 10:18 — Ad 12:47 — Why your next job is running a fleet of AI agents 16:45 — Junior engineer, marketing manager, paralegal: what he tells them 17:09 — How to become senior when the junior jobs are gone 21:56 — The radiologist Hinton said was doomed (now there's a shortage) 28:08 — If AI is smarter than you, the 4 things you still get paid for 31:33 — Reid Hoffman's chess story and the one human superpower 33:58 — What to do if you don't have a podcast or a startup 44:37 — What Erik would pay to keep AI for one month (his real number) 45:18 — How to start if you only use AI like a search bar 50:46 — The best decade in history, or the worst: his answer 🎙 GUEST — Erik Brynjolfsson (Stanford economist, Director of the Stanford Digital Economy Lab) X: https://x.com/erikbryn Linkedin: https://www.linkedin.com/in/erikbrynjolfsson/ Stanford Digital Economy Lab: https://digitaleconomy.stanford.edu Links: 📩 Follow my Newsletter (Future Proof): https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=youtube&utm_medium=video&utm_campaign=futureproof-sub&utm_content=Erik-Brynjolfsson 🔗 Instagram: https://www.instagram.com/siliconvalleygirl/ ⁠⁠⁠𝕏 : ⁠⁠https://x.com/siliconvalleymm⁠ 📌 My Companies & Products: https://Marinamogilko.co #genspark #WorkWithGenspark #siliconvalleygirl

Erik BrynjolfssonguestMarina Mogilkohost
Jul 21, 202653mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 0:49

    Intro

    1. EB

      There are a bunch of jobs, millions of jobs that are gonna disappear.

    2. MM

      How soon?

    3. EB

      Already. It's already happening.

    4. MM

      This is Erik, 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.

    5. EB

      I think it's gonna 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.

    6. MM

      If intelligence is automated, what is left for humans to make money with?

    7. EB

      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.

    8. MM

      What can someone like me do?

    9. EB

      I, I think what you really need to do is-

  2. 0:49 – 2:03

    The 16% of entry-level jobs AI has already erased

    1. MM

      You just had this lab paper called Canaries in the Coal Mine-

    2. EB

      Right

    3. MM

      ... that shows that AI has already wiped out 16% of entry-level jobs, but only for people under 25. Can we talk about that?

    4. EB

      Sure. It's not just under people 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 aides-

    5. MM

      Mm-hmm

    6. EB

      ... 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.

    7. MM

      Mm.

    8. EB

      So I don't wanna sugarcoat 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 in employment. And since we published that paper-

    9. MM

      Wow

    10. EB

      ... we've continued to track it, and the effect's just getting bigger every month.

  3. 2:03 – 3:05

    The exact jobs getting hit first (and the ones quietly growing)

    1. MM

      What are these most exposed fields?

    2. EB

      So, um, coding is obviously dead center, call centers, parts of sales, marketing. It's actually w- we find that the most w- useful way to do it is look at tasks as opposed to entire occupations, so every job-

    3. MM

      Mm

    4. EB

      ... is a bundle of tasks. Like, um, you know, Jeff Hinton, the famous, uh, uh, deep learning researcher, talked about radiologists being replaced.

    5. MM

      Yeah.

    6. EB

      But, you know, radiologists actually do 26 distinct tasks we've recorded. One of them is reading medical images. That one's getting, you know, 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, uh, LLMs just run the table and can do everything. In each case, there's parts of the job that LLMs can help with, writing memos, you know, doing emails, looking at labs. Um, there's others where, uh, LLMs can't help. You know, they don't lift a box or drive a car-

    7. MM

      Yeah

    8. EB

      ... at least not yet.

  4. 3:05 – 4:27

    Why every one of his Stanford students now has to ship real code

    1. MM

      For those, uh, most endangered fields, how much is AI doing in terms of tasks? Is it, like, close to 80% or?

    2. EB

      Well, it... You know, so every... That's the other thing. Within tasks it's, it's varying quite a bit as well. So in coding it's, it's happened so fast with agents, and, like, am 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-

    3. MM

      Mm

    4. EB

      ... whether or not they were a coder before or not-

    5. MM

      Doesn't matter

    6. EB

      ... everybody's a coder now. Everybody's a coder now.

    7. MM

      Mm.

    8. EB

      That's a good message for your, for your listeners. If you use tools like Replit or Cursor or Cloud Code, you can just have an idea, you describe it, and Cloud Code or, or Replit 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, it's doing a lot. Uh, call centers, uh, I did a paper on call centers, and when I wrote that a couple of years ago, uh, with Lindsay Raymond and Danielle Li, um, 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-

    9. MM

      Mm

    10. EB

      ... AI agent, I should say. [laughs]

    11. MM

      Mm-hmm. So they're employing less people?

  5. 4:27 – 7:32

    The plane-ticket rule that decides whether your job survives

    1. EB

      Not clear, actually.

    2. MM

      Oh.

    3. EB

      Um, that's another really interesting thing, is that, um, you know, 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, like with farmers and other, uh, categories, you do see falling employment, and I mentioned with, with the coders we fall- falling employment.

    4. MM

      Mm.

    5. EB

      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 gonna explain a little economics here. Um-

    6. MM

      Please, yeah

    7. EB

      ... so, so the way I think of it is, 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, um, lower prices lead to more quantity. We all kind of have intuition that if you cut the price, more people can buy.

    8. MM

      Yeah.

    9. EB

      But the steepness varies a lot, and 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.

    10. MM

      Mm-hmm.

    11. EB

      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.

    12. MM

      Mm.

    13. EB

      Like when, uh, 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.

    14. MM

      Mm.

    15. EB

      And you end up spending more than you did before.

    16. MM

      Mm-hmm.

    17. EB

      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 in lots of other ones. That creation part is where I'm focusing my energy.

    18. MM

      Mm-hmm.

    19. EB

      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. Um, we're mark- watching it carefully to see whether or not, um, it will start taking off more.

    20. MM

      Mm.

    21. EB

      I mean, 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, you know, all these, uh, benchmark tests, like how well can it do on a math test or, uh, read a document. And, 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 gonna kinda close that. That's why I teach the master class. I also have a startup called WorkHelix, 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

  6. 7:32 – 9:20

    The company that "won" its AI hackathon with better lunch menus

    1. EB

      see a lot more.

    2. MM

      So when, when you say it is not happening, does that mean that companies use it in a way that creates, I don't know, AI slop or things they can't use?

    3. EB

      Yes.

    4. MM

      Why is it even happening?

    5. EB

      So, so part of it is they're j- yeah, they're creating AI slop, or they're using it in things that aren't that important. I, I was at one company, they did a, a big hackathon where everybody was making stuff, and they were so excited. You know, the winning one was this person who used, uh, uh, LLMs to make lunch menus. And I was like, "Oh, that's kind of fun," but is that really, like, the core value of your company to have-

    6. MM

      Yeah

    7. EB

      ... better lunch menus? So they need to connect it to real business problems, and it, it takes a while to figure out what those opportunities are and then execute well. Now, 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 be- between when they first introduced electric motors in American factories and when you saw, you know, significant productivity gains. People-

    8. MM

      Mm

    9. EB

      ... like Paul David looked at the production records. Now, 30 years, that's insane.

    10. MM

      That's a lot. Yeah.

    11. EB

      That's insane, but that's true. That's what the data show.

    12. MM

      How long will it take us with AI?

    13. EB

      It's gonna be a lot faster.

    14. MM

      Okay.

    15. EB

      Uh, but it's not gonna be overnight. I was just visiting DeepMind, uh, about 10 days ago, and-

    16. MM

      In London?

    17. EB

      ... they were telling me... Yeah, in London.

    18. MM

      Mm-hmm.

    19. EB

      And they were telling me, um, how, "Oh my God," you know, s- within the next, you know, 18 months, 24 months, we're gonna have all these capabilities. And I, I believe them. I don't know. I mean, you know, they're, they're the experts on the capabilities. But I say it's gonna take a lot longer for that to translate into business value, 'cause you need to change your business processes. You need to reskill your workforce. Sometimes you need to invent new, uh, products and services. Takes a while. Uh, I, I'm, I'm sure it's not gonna be 30 years like it was with electricity or, you know, 50 years with the steam engine. I mean, some of these earlier technologies took a long

  7. 9:20 – 10:18

    His money bet with a skeptic economist: "AI is underhyped"

    1. EB

      time. This time I think it's gonna be, you know, 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, uh, one of my economist friends, Bob Gordon, but he's kind of an AI skeptic.

    2. MM

      Mm.

    3. EB

      And he said, "Look, you know, AI is overblown." And I, and I said, "I'm on the other side of that. I, I think AI is, anything, believe it or not, I think it's underhyped. I think it's gonna be even bigger than most people realize." So we made a friendly wager that by the end of the 2020s, by the year 2030, we actually made this bet at the beginning of the 20, uh, 20s, productivity's gonna be significantly higher than what the Bureau of Labor Statistics is predicting. So I, I think the official government statistics are way lowballing what's gonna happen. A- and that's gonna be great news. If we can get this higher productivity, it's gonna help with the budget deficit. It's gonna be, help with poverty. It's gonna help us with healthcare. Uh, we're gonna have a lot more, uh, wealth than we would otherwise have. Um, I'm already a little bit ahead in that bet. [laughs]

    4. MM

      Uh-huh.

    5. EB

      And I think that the best is going to happen in the next three or

  8. 10:18 – 12:47

    Ad

    1. EB

      four years.

    2. MM

      Erik just made a bet that the real payoff from AI shows up in the next two, three years, and it goes to whoever actually puts it to work. And that's actually a perfect place to pause for a second. Lately, I care less about which model is best and more about turning AI into a system that actually helps me run the business. So I've been testing Genspark with exactly that in mind. Every model is already inside it, GPT, Claude, Gemini, plus the image and video ones like Nana Banana, Veo, and Kling. So I'm not picking favorites or paying for five different subscriptions. Genspark crossed $250M ARR in 12 months, and honestly, the models are just the starting point. The real value is what you build on top of them. Take a simple example. My team lives in spreadsheets, so I opened AI sheets and typed, "Rank my last 30 Silicon Valley Girl episodes by views, tag each one by topic, and tell me which three topics outperform." A few seconds later, I had every episode ranked, sorted by topic, and three clear winners to make more of. That's exactly what Erik's betting on, put AI to work on real decisions and act on them. That's where it turned into a system. I run that breakdown every week, so instead of retyping the prompt, I saved it as a skill. Now it's a reusable tool my whole team runs with one click, and nobody rebuilt it from scratch. It feels less like an AI tool and more like an operating system for your business. And there is also design. I took our current Future Proof newsletter logo and turned it into a banner in a few clicks. None of this replaces anyone on my team, but what it does, it takes the repetitive work off our plates so we get our hours back for the parts that matter. I'm still finding new workflows to turn into skills. So if you try just one thing, make it this. Pick one task you repeat every week and turn it into a skill. And if you're new, you can trial Pro-tier Deep Research and AI web app building through the Get Started bonus. The link is in the description. And you have this, uh, report with ADP that private employees added 122,000 new jobs in May.

    3. EB

      Yeah.

    4. MM

      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- Uh, 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

  9. 12:47 – 16:45

    Why your next job is running a fleet of AI agents

    1. MM

      that?

    2. EB

      You know, generalist specialist is, is one lens. I, 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?

    3. MM

      Mm-hmm.

    4. EB

      Um, 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, 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, at WorkHelix 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.

    5. MM

      Mm-hmm.

    6. EB

      Each person will be kind of like the CEO of a bunch of agents, and their job is gonna 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?

    7. MM

      Yeah.

    8. EB

      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 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."

    9. MM

      Hmm.

    10. EB

      And then you iterate, and you go back, and you change the question. So that's the evaluation part. Um, that's the future of work, I think, is figuring out how to ask questions and evaluate and have the agents do a lot of the execution. And I think it can be learned. I think it can be taught. Um, I think it's a skill that more and more people are gonna have to have.

    11. MM

      Yeah. How do you learn that? Just start deploying agents for your work?

    12. EB

      That's a great way. [laughs]

    13. MM

      [laughs]

    14. EB

      So everybody should start deploying if they haven't already. Um, but you know, when I teach at, at Stanford, you know, we do a lot of things by the Socratic method. You know, my students, uh, 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. Uh, they do homework, and they, they have to, like, figure out, um, 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-

    15. MM

      Hmm

    16. EB

      ... okay, what's the core question here?

    17. MM

      Hmm.

    18. EB

      And you c- you know, like anything, you practice, you get better at it, and you get to, get to be pretty good at it.

    19. MM

      The art of understanding the problem and understanding which answer is correct.

    20. EB

      That's exactly it.

    21. MM

      This is... Yeah.

    22. EB

      And it takes a special mix of skills, you know? So I think if you only have technical skills, you're gonna miss on understanding the problem. If you only have, you know, uh, people skills or, you know, uh, 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.

    23. MM

      Yeah, so basically becoming a generalist, right? 'Cause you, you also have to have-

    24. EB

      A special kind of generalist. Yeah

    25. MM

      ... you also have to have this academic knowledge 'cause otherwise, how, how do you know that this is a correct or an incorrect answer?

    26. EB

      I think so. Yeah. I, 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, that if you can combine rigor with relevance, that's the motto of MIT, where I, I used to work, Mens et manus in Latin, uh, mind and hand. Um, that's where you get the biggest value, by combining those two things together.

    27. MM

      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's... It will still be a valid dream in 10 years from what you're seeing?

    28. EB

      I hope so.

    29. MM

      Yeah. [laughs]

    30. EB

      I, I have a job there. [laughs]

  10. 16:45 – 17:09

    Junior engineer, marketing manager, paralegal: what he tells them

    1. EB

      will do them.

    2. MM

      I'm gonna name some jobs, well, good-paying jobs.

    3. EB

      Right.

    4. MM

      Would you tell people to spend years s- becoming them, or no? Junior software engineer that pays 95K a year now.

    5. EB

      No. [laughs] No. Unfortunately, that's one that's very much in the bullseye of being-

    6. MM

      Uh-huh

    7. EB

      ... replaced. I mean, 'cause especially s- 'cause you said the junior part.

    8. MM

      Mm-hmm.

    9. EB

      We see that in the data. They're disappearing. Um, if you'd said senior, I would be much more positive.

    10. MM

      Mm.

  11. 17:09 – 21:56

    How to become senior when the junior jobs are gone

    1. MM

      Do... Where are they going? When they're disappearing, what happens to them?

    2. EB

      You mean the jobs or the people?

    3. MM

      Pe- people.

    4. EB

      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, uh, Infosys, one of the big companies.

    5. MM

      Mm-hmm.

    6. EB

      And they said they're actually hiring as many junior people as before, but instead of having them do the 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.

    7. MM

      Hmm.

    8. EB

      They used to kind of learn that by osmosis just by hanging around and, and hoping that it would rub off on them. Now they're explicitly teaching them, sometimes using AI as a, as a tool. So, um, you know, it, it, it's, it's a different mindset. Most companies, to be frank, are not that forward-looking, and I think they're gonna be hurt 'cause, you know, they had, like, this pyramid. Most companies have this pyramid, like, you know, 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, a diamond.

    9. MM

      Hmm.

    10. EB

      Then, um, where are those junior... those middle cl- middle managers going to come from-

    11. MM

      Yeah

    12. EB

      ... and where are the senior people gonna 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 gonna hire those 'cause we need the, the people with more taste and experience."

    13. MM

      And that's, uh, the question that a lot of, um- 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?"

    14. EB

      Yeah. I'll tell you something. It's, it's a societal problem. It's a bit of a prisoner's dilemma, I think, um, or a coordination problem economists call it. Because for every company individually, maybe it's pr- you know, 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.

    15. MM

      Yeah.

    16. EB

      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, is public investment in education and training.

    17. MM

      Mid-level marketing manager, 115. Okay.

    18. EB

      Oh, sorry, that's another [laughs] one that I'm, I'm, I'm not really seeing-

    19. MM

      Mm-hmm

    20. EB

      ... a lot of value. We see a, 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 bullseye.

    21. MM

      Okay. Paralegal, 60.

    22. EB

      Oh my God, it's even worse. [laughs]

    23. MM

      [laughs] What a list.

    24. EB

      But look, I don't wanna sugarcoat it. My job's not here to, to like-

    25. MM

      Mm-hmm

    26. EB

      ... paint a happy story. There are a bunch of jobs, millions of jobs, that are gonna disappear.

    27. MM

      How soon?

    28. EB

      Already. It's already happening-

    29. MM

      Mm

    30. EB

      ... 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.

  12. 21:56 – 28:08

    The radiologist Hinton said was doomed (now there's a shortage)

    1. EB

      it.

    2. MM

      Totally. Let's, uh, wrap up with a radiologist that gets 350K a year.

    3. EB

      Radiologist. Okay, this is a good one.

    4. MM

      Talks about-

    5. EB

      So this... I love radiologist 'cause this is such an iconic story.

    6. MM

      Mm-hmm.

    7. EB

      You know, Geoff Hinton, uh, back in, like, 2017, he looked at what deep learning could do, read medical images, and he famously said... He's, he's one of the smartest guys. I wanna 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. There's almost a shortage of radiologists.

    8. MM

      Mm.

    9. EB

      They're being well-paid. Why is that? Well, it's, it's a couple of things. First off, reading medical images is only part of a radiologist's 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 the related part of it is that the, uh, elasticity of demand for medical images is very high. What that means is that as you make it more efficient-

    10. MM

      Mm-hmm

    11. EB

      ... you actually have more demand.

    12. MM

      More people want to-

    13. EB

      Yeah. Like, if I have a little bit of a sore shoulder and it costs me $2,000 to get an MRI, I'm like, "Ah, nah, I'm not gonna do it."

    14. MM

      Yeah.

    15. EB

      Cost $200? Yeah.

    16. MM

      Please, yeah.

    17. EB

      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-

    18. MM

      Mm

    19. EB

      ... and, um, there's a lot of people who could use more medical care.

    20. MM

      Totally.

    21. EB

      So I think that's one of the areas where, in general, we're gonna have growth is in medical care. AI's gonna 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 'cause it means more people are gonna be helped and we're gonna have, you know, maybe twice as much spending, but four times as much cures, four times as much, uh, benefits.

    22. MM

      So it's a good job.

    23. EB

      It's, I think it's, it's, it's been a good job, yeah.

    24. MM

      Okay.

    25. EB

      And it, and it probably will be for a while.

    26. MM

      Okay, this is the part that actually worries me a little. Everything Erik just walked through, which jobs shrink, which ones grow, and the whole economy 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.

    27. EB

      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 art and music... You said you were a, a singer earlier?

    28. MM

      Yeah.

    29. EB

      Are you gonna sing for us a little bit?

    30. MM

      Maybe. [laughs]

  13. 28:08 – 31:33

    If AI is smarter than you, the 4 things you still get paid for

    1. MM

      So people make money these days because they have this scarce resource, uh, resource, which is intelligence. If intelligence is automated, what is left for humans to make money with?

    2. EB

      Oh my God, that's the trillion-dollar question.

    3. MM

      [laughs]

    4. EB

      And I don't think there's a clean answer, but you're totally right. Like, people like me, I kind of prize intelligence 'cause, you know, uh, it's helped me make a lot of money, and it, it's kind of where I get my, my status from. But AI is going to, um, you know, have intelligence on demand. So one thing that's gonna be more valuable is initiative or agency. Uh, what my closing class, m- my students, uh, will remember me saying, I think whenever they hear the words AI, they should think of amplifying intention, not artificial intelligence.

    5. MM

      Hmm. Mm-hmm.

    6. EB

      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, um, when AI was able to defeat humans at chess, that was not the end of chess playing for humans. Uh, people today play chess more than they did before. My son, uh, Xander, he likes to play chess, and I ask him, "Do you play against machines or humans?" He goes, "Well, humans, of course. It's no fun to play against machines."

    7. MM

      No.

    8. EB

      And, you know, there was this, uh, Knicks basketball game last night that, uh, millions of people watched. I don't think it would've been nearly as fun if it was a bunch of machines playing each other.

    9. MM

      Hmm.

    10. EB

      So in the future, we will value things that are certified human, that are, you know, authentic, uh, that real people are creating. I think that's another big area. A, a third area that, at least for a little window, will be valuable is, is just, like, physical work. I mean, AI is getting very good at cognitive work, and if you are a plumber, a carpenter, if you have, you know, uh, particular skills, uh, that's something that turns out is harder for machines to do. That said, I think the window's closing on that one. Um, and then the fourth category I would say is all the things I haven't thought of. [laughs]

    11. MM

      [laughs]

    12. EB

      Um, 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 gonna disappear. 90% of people are farmers." I'm pretty sure we wouldn't have thought of, you know, podcaster or-

    13. MM

      Totally

    14. EB

      ... you know, all, all the other jobs that exist today. And there will be new ones that are invented and created and, and, and it's not necessarily my job to invent those. You know whose job it is? It's your viewers. It's, 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 can work. I, I don't know.

    15. MM

      Hmm.

    16. EB

      Um, and so we have an ecosystem here. I had a, I had, uh, a- Uh, brunch with a, 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 are willing to take those gambles, and they try out things, and, uh, America is leading the world in this kind of innovation-

    17. MM

      Mm-hmm

    18. EB

      ... of inventing new things. And, uh, I'm looking forward to seeing what they invent next.

    19. MM

      Yeah. We're, we're always co- good with coming up with new things, new bottlenecks, and things to solve. That's the definition of [laughs] a human.

  14. 31:33 – 33:58

    Reid Hoffman's chess story and the one human superpower

    1. EB

      Yeah. It... That's, that's our superpower. You know, I had... I know you had Reid Hoffman on this before.

    2. MM

      Yeah.

    3. EB

      And he told me something really valuable. You asked this question about what will, what will humans do, and he said, "A human's superpower is impro- improvisation." And, um, you know, you, 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.

    4. MM

      Yeah.

    5. EB

      Actually, uh, if, if you have time, I'll... He had told me this f- funny little, little, uh, 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, the, the loser dies." Um, 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 it.

    6. MM

      Something, yeah. [laughs]

    7. EB

      You know, maybe there'd be a virus in there.

    8. MM

      [laughs]

    9. EB

      You know, maybe there'd be a, a lightning bolt that day. You know, something... Water would spill in the wrong way.

    10. MM

      Yeah.

    11. EB

      And they would just... They'd figure something out, and they would-

    12. MM

      Totally

    13. EB

      ... they would, they would find a way to win, and that's what humans are good at doing.

    14. MM

      How do you see, um, resource distribution when it's not companies hiring humans? W- what is it?

    15. EB

      I'm super worried about this. You know, you heard me earlier say that I'm optimistic about growth, and I think we're gonna have higher productivity growth, a lot more wealth creation. I'm concerned that that's gonna be very concentrated, more concentrated than it is right now. It's not an inevitability. We have choices going forward. Um, 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.

    16. MM

      Mm-hmm.

    17. EB

      And we need to, m- you know, 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, um, 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, we need to

  15. 33:58 – 44:37

    What to do if you don't have a podcast or a startup

    1. EB

      be mindful of that.

    2. MM

      What can someone like me do? Or, or someone who doesn't have a podcast. Like, how can they make sure they participate? Um, buy, buy stocks of-

    3. EB

      Well, literally one of the reasons I created-

    4. MM

      ... companies?

    5. EB

      No, no. I think... Well, m- stocks is a bit, but I, I think what you really need to do is create the value.

    6. MM

      Mm-hmm.

    7. EB

      And that's why I created the master class. That's what I teach in my, my Stanford class, is how can you use AI to create new goods and services?

    8. MM

      Mm-hmm.

    9. EB

      Not to be a rule follower who, you know, just does stuff because you're gonna be replaced by a machine if you do that, but how can you be one of those people who ask 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. Um, but if everybody's just following instructions, then we're gonna have that concentration of wealth. So that's the number one thing. Another thing, look, I, I think we do have to look at, you know, different kinds of redistribution. It's not my first choice, but we need to have it as, as a, 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, you know, progressive income taxes, wealth taxes. I know a lot of my Silicon Valley friends are gonna yell at me for that. But I think that you, you, you don't wanna have all the wealth and power too concentrated.

    10. MM

      Mm.

    11. EB

      It's not in anyone's interest, including the billionaires. Um, people will come after them with pitchforks. [laughs] And, uh, and so we wanna have a world where everyone can participate. And in the end, people create more value. You know, I've visited some of these, um, you know, developing countries or parts of Latin America where wealthy people live in gated communities with these walls, and they have, like-

    12. MM

      Yeah, we don't want that

    13. EB

      ... m- machine guns.

    14. MM

      Mm-hmm.

    15. EB

      And, you know, they have their own schools, their own doctors, and private police forces. No, it's not fun for anybody. I, I had a, a friend, she, uh, she lived in Brazil, and she said she and all of her rich friends were in prison. I said, "What do you mean? You're not in prison." She said, "No, we're a prison of our own creation. I sit, 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."

    16. MM

      Totally.

    17. EB

      And, you know, I don't think anybody wants to live in a world like that.

    18. MM

      Yeah, it's 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.

    19. EB

      Let me push back on that a little bit. Uh, uh, 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 ran their own thing. And then we created these societies with big corporations where people became kind of like cogs and, you know, created a lot of wealth. But I think we make potentially, and I'm not for sure, but I think we could try to go back to a world where a lot of us, our initiative, our agency became more important. And, you know, I really think using these tools, like we show in the master class, is, is exactly what you want to do, is, is figure out how to do it. 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 could just take a Saturday afternoon and just brainstorm with a sheet of paper or with one of the LLMs helping you all the types of things you might be able to create a- and try some of them out. And it, 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, and for most of it, it's kind of fun. Honestly, I think it's more fun c- creating new things than it is just following instructions. So I, I would encourage Probably every one of your listeners to at least give it a try.

    20. MM

      Yeah, that makes total sense. That's what the, the purpose of this channel [laughs] is-

    21. EB

      Good. No, I'm so glad-

    22. MM

      ... honestly, to inspire people to-

    23. EB

      You... Yeah, you are doing it. And, and, and we need more people like you. We need more people listening to, to this show to, to give it a shot and, and have it work. And, and it, it'll not only be good for them, it'll be good for all the people.

    24. MM

      If you want more conversations like this with the people who can see where the economy's 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 all studied market economies. Do you think we're gonna 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 gonna be?

    25. EB

      It could be. It could be different. You know, the... We need an economist who can think through what the e- economics of the future is. I'm trying to help play that role. You know, Adam Smith helped define, uh, the market economy, and, uh, John Maynard Keynes helped, uh, update it in, in the early 20th century. I think for the 21st century, we're gonna need some new economic rules to understand it. Um, AI and AI agents, we're gonna have billions or trillions of them. We're gonna have a lot of routine work done automatically. Um, the kinds of things that worked in the old mar- uh, old market economy won't necessarily work going forward. I mean, one way I think about it, as I learned in my, 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 a, 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 gonna have a million-fold more powerful information processing AI. It would be a miracle if those two institutions just stayed the way they are.

    26. MM

      Mm.

    27. EB

      I, I'm pretty sure they're gonna change. Exactly how, I'm not sure. Um, you asked about monies particularly. I, I think it's very likely that we will have a world where our basic needs, you know, the base of Maslow's hierarchy, um, will be taken care of, and we'll be able to... Just like you gave me some water here for free. You didn't charge it for me. Thank you. Um, you know, it'll be like that for most goods and services.

    28. MM

      Mm-hmm.

    29. EB

      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 [laughs] 'cause it's kind of zero sum. It's like a hierarchy. Um, you know, or there'll be a few physical things, like, you know, I wanna go to the far side of Pluto or something, you know, that would be-

    30. MM

      [laughs] Yeah

  16. 44:37 – 45:18

    What Erik would pay to keep AI for one month (his real number)

    1. MM

      What's the number for you? How much would you pay to ne-

    2. EB

      Oh, my God

    3. MM

      ... to not touch AI this month? [laughs]

    4. EB

      Oh, my God, it almost-- I mean, for me it, it's tens of thousands. You know, somebody-- Because I, I, it's my life. Like it's, it's-- I use it every day. I use it every night until too late at night. You know, I'm working with Claude Cowork and-

    5. MM

      Mm-hmm

    6. EB

      ... and, and testing out different research ideas. Uh, 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. Uh, it's just so integrated into my life. It would be like, uh, tearing off my left arm.

    7. MM

      Is, 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?

  17. 45:18 – 50:46

    How to start if you only use AI like a search bar

    1. EB

      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. [laughs] So how-

    2. MM

      But if they haven't used it enough, I don't think there's-

    3. EB

      No

    4. MM

      ... like enough, enough data.

    5. EB

      No, no, no. No, no, no. You have, you have, you, you have the conversation. So what you do is, is you, is you ask ChatGPT or Claude, say, "Hey, tell me how you can be useful to me, and ask me questions." You can literally say-

    6. MM

      Mm-hmm

    7. EB

      ... "Keep asking me questions."

    8. MM

      Interview me. Yeah.

    9. EB

      Interview me. And it'll say, "Okay, you know, what's your job? You know, do you have kids?" You know, whatever. Um, "What are some of the problems you worried on, uh, 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.

    10. MM

      What would you never delegate to AI?

    11. EB

      What would I never delegate to AI? Um, hmm.

    12. MM

      There's nothing like that-

    13. EB

      I know. [laughs]

    14. MM

      ... anymore.

    15. EB

      I know. I keep-

    16. MM

      AI does everything

    17. EB

      ... something pops in my head, I say, "No, I could see doing that."

    18. MM

      [laughs]

    19. EB

      You know, delegate entirely, you know, there are some really like life or death decisions I, I like I, I use it before I go to the doctor, and it gives me some, um, think questions to ask. But at the end of the day, I still wanna have-

    20. MM

      It still does go to the doctor

    21. EB

      ... a real human make the call. And you know, they, they're just not good enough. They, they have the issues. Um, and, um, 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, a partner.

    22. MM

      How much more productive have you become in the past few years?

    23. EB

      I think I've become a lot more productive. I'm not sure it would show up in official GDP statistics-

    24. MM

      Mm-hmm

    25. EB

      ... but I feel like the research-

    26. MM

      The amount of papers maybe you can... Can you track that? Are you-

    27. EB

      A little bit

    28. MM

      ... citations?

    29. EB

      I think it's also like the qual- I'm, I'm working on some more interesting problems that I probably wouldn't have.

    30. MM

      Mm.

  18. 50:46 – 53:31

    The best decade in history, or the worst: his answer

    1. MM

      Yeah. Okay, my last question: If my daughter, she's four, five years old, just turned five-

    2. EB

      That's right

    3. MM

      ... asks me tomorrow-

    4. EB

      Yeah

    5. MM

      ... what is my life gonna look like in 30 years?

    6. EB

      Yeah.

    7. MM

      What would you say?

    8. EB

      Oh, nobody knows 30 years, no. I think it's gonna be hard enough either, even five or, 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 gonna have noticeable improvements in longevity. I, I mentioned I was over at, in, you know, Google, and I was talking to... I'm sorry, at DeepMind. I was talking to Demis Hassabis. He thinks that, uh, they'll start curing a majority of diseases within 10 years. I hope he's right. That sounds ambitious, but your daughter-

    9. MM

      Fingers crossed

    10. EB

      ... 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, like, there's the potential for catastrophic risk. You know, viruses being created in the lab and released, uh, AIs, uh, taking over social media and manipulating people from vast centralization of power. We already see AI-powered drones, like, hunting down people. These are, like, so tragic. The dystopian s- you know, these drones, like, chasing a soldier.

    11. MM

      Mm.

    12. EB

      It's like, oh, my God, and I, you know. Doesn't matter which side of the war I'm on. I kinda sympathize with the human being chased-

    13. MM

      Yeah

    14. EB

      ... by the drone. So all those things are also possible. Um, the thing I would say is that we have a tremendous amount of agency, and so we should s- 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 do we wanna shape? And don't take it for granted that AI's just gonna 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're... I totally think they're possible.

    15. MM

      Yeah.

    16. EB

      The doomers are not wrong, that there's a real risk there. Um, they are wrong if they think those are inevitable, um, because we'll have choices. And 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.

    17. MM

      Fingers crossed we're gonna land on the positive scenario.

    18. EB

      Yeah.

    19. MM

      Uh, I keep telling my daughters that they won't have as many problems as I have.

    20. EB

      [laughs]

    21. MM

      I mean, like, house-

    22. EB

      They'll have different ones. [laughs]

    23. MM

      ... problems. They will have different ones, but the ones that I'm having, they're probably not gonna have them. [laughs]

    24. EB

      That's probably true.

    25. MM

      Thank you so much, Erik. This was so insightful.

    26. EB

      Oh, my God. It was such fun talking to you.

    27. MM

      Thank you.

    28. EB

      Thank you for having me on.

    29. MM

      Amazing. Thank you so much

Episode duration: 53:32

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