Nobel Economist: Where He Sees The Real Money in AI | Daron Acemoglu

Daron Acemoglu is an MIT economist and a 2024 Nobel laureate in Economic Sciences. He believes some of AI’s biggest business opportunities are being overlooked—but where does he see the real money, and what should you learn to stay valuable? In this episode, I ask Daron where founders should look for opportunities beyond the AI hype. We talk about why helping workers become more productive can be better for business than replacing them, why existing models are already powerful enough to build useful applications, and where high-quality data creates an advantage. Daron also shares what he would study now and why builders need to spend more time talking to workers. Links: 📌 Subscribe to my free newsletter where I go deeper on AI tools, career strategies, and building with AI: https://siliconvalleygirl.beehiiv.com/p/7-skills-that-make-you-irreplaceable-adc6?utm_source=spotify&utm_medium=description&utm_campaign=futureproof-sub&utm_content=7-skills&utm_term=daron-acemoglu https://siliconvalleygirl.beehiiv.com/p/7-skills-that-make-you-irreplaceable-adc6?utm_source=spotify&utm_medium=description&utm_campaign=futureproof-sub&utm_content=7-skills&utm_term=daron-acemoglu ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/𝕏 : ⁠⁠⁠⁠⁠⁠https://x.com/siliconvalleymm⁠⁠⁠⁠⁠⁠ https://x.com/siliconvalleymm 🔗 Instagram: ⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/siliconvalleygirl/⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/ 💼 LinkedIn: ⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/marinamogilko⁠⁠⁠⁠⁠⁠ https://www.linkedin.com/in/marinamogilko 📌 My Companies & Products: ⁠⁠⁠⁠⁠⁠https://Marinamogilko.co https://marinamogilko.co/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

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• 0:02Speaker 01

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• 0:07Speaker 02

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• 0:32Speaker 03

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• 1:00Speaker 04

  I could definitely see a future, very scary future, in which a lot of people would not have meaningful work.

• 1:06Marina Mogilko?

  This is Darren Asimoglu, an MIT economist and winner of the 2024 Nobel Prize in Economics. He thinks we're overlooking some of AI's biggest opportunities. So where should we be looking? And what will keep us valuable? I've seen a lot of posts where people say, oh, instead of hiring someone, I deployed an agent.

• 1:24Speaker 04

  Automation is great. The problem is the only thing we do is automation. That will eliminate too many jobs without creating meaningful, high-paying, high-productivity jobs to create new opportunities for people.

• 1:36Marina Mogilko?

  So what should people be doing? What skills should they be acquiring?

• 1:40Speaker 04

  If anybody wants my opinion, I would say go and study.

• 1:45Marina Mogilko?

  So you said something in 2024 that AI would add about 1% to U.S. GDP in the next 10 years with only 5% of tasks being fully automatable. What do you think about this now in 2026?

• 1:57Speaker 04

  Well, first of all, it's my pleasure to be here. And I do not claim that I have any unique insight into AI. AI is like an elephant. Everybody is feeling a different part of it. But I think I try to bring a little bit of basic economic calculus to the discussions of productivity. And this was more than two years ago. There's a huge amount of uncertainty. And there is even more today. But there have also been some very rapid developments, especially this year with Agentic AI. Probably now I would have put that number a little higher than 5%.

• 2:40Speaker 04

  But I still think that a lot of the discussion exaggerates how quickly we're going to see automation. Because automation is hard. Integrating automation into organizations is hard. Diffusion of new technologies, especially when they require lots of complementary investments and adjustments, is hard and slow. That doesn't mean it's not a transformative technology. It doesn't mean that we cannot get benefits from it. But we have to be grounded in thinking about how quickly and how thoroughly the economy is going to change. And we still have the same discussion that we should have about every technology.

• 3:25Speaker 04

  What's the best way of using it? How it could be misused? And who will benefit from it?

• 3:33Marina Mogilko?

  You mentioned Agentic. Is there anything you're, when you see it, you'll think, okay, now I can expand my figure to like two digits when it comes to impact of AI?

• 3:46Speaker 04

  I think the advances in coding have been really, really rapid. What is it about coding that makes it so conducive to have the most rapid advances of any occupation? I think it's the fact that coding is obviously done in offices. You don't have any interaction with the real world. The people who are supervising coding are very, very fluent in AI and in technology in general. But also, and this is a very important part, just like math, just like chess, coding has rules, something either runs or doesn't run.

• 4:30Speaker 04

  Reinforcement learning is very effective when you have a ground truth that says, no, that's wrong. I think in many occupations, we lack that. Customer service, I think most people would have said customer service is going to be the first occupation to be fully automated by AI. And we're seeing some of it, but it's turning out to be very slow and very difficult. And most of the organizations that I deal with, their AI system is so bad. It's like a nightmare when I call customer service and I get AI. Oh, my God. It's going to be another 10 minutes before somebody can actually help me. And that's because there's a social element. Many of the things that you ask from customer service change over time. They are person-specific.

• 5:15Speaker 04

  Different people use different language for making the same questions. And there are a lot of gray areas, edge cases. So that makes it much messier. And it will require humans to either oversee it or work with it, or ideally a much better collaboration so that AI could be complementary to humans. So all of those are in play. Which ones we choose is going to have a very important impact? How many jobs are going to be displaced? How much productivity effects we're going to get? And who's going to benefit from it?

• 5:50Marina Mogilko?

  With coding, we have deterministic outcome that we can check easily. With customer service, it's much more personalized, as you mentioned. Do you think in the future with stronger models, we'll be able to achieve it? Or it's just the bottleneck of AI in general? That it can't function without a human in there?

• 6:07Speaker 04

  I would definitely not say there's this one thing that AI will not be able to do in the future. I think the last 10 years have given us enough examples of AI, if you put enough resources, enough data, can make progress on many things. But the question is, how fast? And can it do so across all the domains? If I step back and I say, if you ask me, why so little productivity improvements? And what can we do about it? And I would have just one word for my vision of how we can improve the economic effects of AI applications.

• 6:51Speaker 04

  We just need much better and more applications. And in some sense, one can make the argument, and again, I don't know. I'm, this may be incorrect. Look, I'm not in anthropic inside the organization. I'm not in OpenAI. I don't know what they're learning in real time from their experiments and the enormous amount of resources. And they're very, very talented people in all of these organizations. But you can make the case that the United States, Silicon Valley, is putting too much emphasis on foundation models and not enough on applications and not enough in integrating applications into organizations. So when I, when, you know, we're told all the time, US is ahead of China.

• 7:34Speaker 04

  Indeed, in large language models, that seems quite clear. Chinese models are catching up sometimes, but they are using the lead of US models. There's some distilling going on probably. But one thing that I see Chinese companies do that I don't see as much in the United States is they're putting a lot of emphasis in getting robotics, getting AI into the production line. And one thing that they are throwing at that problem is a huge amount of engineering talent. The United States doesn't seem to have that much engineering talent. And whatever we have is all going into foundation models.

• 8:12Marina Mogilko?

  You touched upon such an interesting subject. Because as an immigrant, when I see things, how they work in the US, and ask, why is it so? And the reply is, oh, historically.

• 8:25Speaker 04

  Oh, no, that's the only way to do it. I think here is one thing, one other thing I'll say. I don't want to make, you know, overgeneralizations. But I think as a signpost, if there is one thing that's been not good for US AI industry, in my, in my opinion, it's the inevitability that the view that whatever we're doing is the only way to do it, there's no other way. And if you're raising that question, you're an idiot. So I completely reject that. I think AI is a very versatile technology, as are all general-purpose technologies. GPT is what economists call things like electricity,

• 9:06Speaker 04

  printing press, et cetera, which spawn a lot of other applications. One thing we know from history, one thing we've learned from a lot of economics and looking at previous examples, there are so many different ways of developing these general-purpose technologies. And different ways are not neutral. They bring different types of benefits. They have different types of costs. And just thinking that whatever we're doing is the only way or by far the best way, I think is a mistake.

• 9:35Marina Mogilko?

  And I think another problem that comes with it is measurement, right? So how do you measure the impact of AI on your personal productivity, on your company's productivity? And when I talked to Eric Bjornson, when they were doing this study, they, like, the economic impact of AI was basically zero. We talked to him in May.

• 9:52Speaker 04

  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.

• 10:05Marina Mogilko?

  Do you think there's a measurement problem there as well?

• 10:08Speaker 04

  He's a very, very serious scholar, and he knows this industry pretty much better than any other social scientist. And I think he's more optimistic than I am. So if you ask him about the productivity effects and Twenty thirty five, twenty thirty four, which is what I was writing about at the time, he would have put it somewhat more aggressively. But yeah, I think his conclusion is AI is not having the kinds of productivity effects we're expecting yet, but it will, which is very much where I am as well. We don't see it yet, but it will. But it will be a little slower than, in my opinion, than what Eric would say. But, but I would be very, very happy if he's right. And I think he's got a much better chance of being right

• 10:51Speaker 04

  than many other people in the industry who are just overly optimistic about what's going on.

• 10:55Marina Mogilko?

  How do you think we should measure impact of AI on organization or even personal productivity?

• 11:01Speaker 04

  Productivity has always been difficult to measure. And there's a quality issue. I think one place where Eric has put a lot of emphasis rightly is that in the digital age more broadly over the last forty years, we don't know how to measure quality. And that might mean that we are understating some of the productivity benefits of not just AI, but computers in general. I agree with that. The problem is, I think, we didn't know how to measure the quality improvements coming from better cars or better highways either. So there, there always been an issue of measuring quality.

• 11:42Marina Mogilko?

  I think it's the happiness measurement. Like, we should eventually just measure happiness and see what happens.

• 11:47Speaker 04

  I mean, that's, that's also great. I think well being is a very important thing. There, I would be more negative about AI and all the associated technologies. I think social media has been horrendous for us. It has damaged community. It has damaged people's ability to deal with adversity because we don't interact with people in real social networks. It has made our worst instincts more salient because algorithms go after rage, emotions, envy. So I think it has been horrible. I hope AI will not repeat social media's mistakes.

• 12:25Marina Mogilko?

  Let's talk about workers sharing gains of this new technology.

• 12:31Speaker 04

  My favorite topic.

• 12:32Marina Mogilko?

  Yeah, you're particularly vocal about it. So when you say, we have these strong models, but we lack applications, is that the education problem? Do you think we still need a stronger model? Or what do we need to see the impact of AI on our day to day? Positive impact?

• 12:46Speaker 04

  I

• 12:46Speaker 01

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• 12:54Speaker 02

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• 13:19Speaker 03

  The action's on the court. And as the official beer sponsor of the NBA, Michelob Ultra is getting fans closer to the game with a chance to win courtside seats, merch, and more. Visit michelobultra.com/courtside to enter. Michelob Ultra Courtside, 26 to 27. No purchase necessary. Open to US residents 21+. Begins on October 1st, 2026 and ends on June 30th, 2027. Multiple entry periods. See official rules at michelobultra.com/courtside for free entry, entry deadlines, prizes, and details.

• 13:48Speaker 04

  Think existing models are already very capable as a foundation upon which to build applications. There are so many things we can do that would be quite feasible with the existing infrastructure. Let me mention a few examples. Much better quality control, very easy with existing models. Tools for nurses. We have a real healthcare crisis in this country. We spend almost one out of every five dollars on healthcare. And look at the outcomes. There are real public health challenges. There are real bottlenecks in

• 14:30Speaker 04

  the healthcare industry. Imagine nurses can be more involved in the care process, in diagnosis, prescription, decisions about patient well being. And one big barrier for that is quality control and information. Those can be very, very easily supplied by existing AI models with the right applications built on top of our current models. Yeah, I think you probably need to train nurses a little differently. So there's a human capital aspect that's going to go complementary to that. But the bottleneck isn't the human capital of the nurses. The bottleneck is the application. The bottleneck is the tech industry not doing it. Teachers, same thing. Electricians. We can give so much better

• 15:15Speaker 04

  tools to electricians. It's just like such an easy, low hanging fruit. Many blue collar tasks can be made much more effective with AI. We're seeing some of them in China. Again, why are we not doing that in the United States? So this is, the bottleneck isn't the foundation model. The bottleneck, I mean, even last year's models would have been good enough for most of the things that we could do.

• 15:35Marina Mogilko?

  A lot of it sounds like a great business opportunity.

• 15:37Speaker 04

  It is, it is.

• 15:38Marina Mogilko?

  AI for electricians. Electric, this is such a huge market. Again, discover it after a month here, how much those people are making, how much you pay for a small thing.

• 15:49Speaker 04

  And there's such a shortage of electricians.

• 15:51Marina Mogilko?

  Oh, yeah.

• 15:51Speaker 04

  And you wait three weeks for the most basic task, and then an electrician comes and says, oh, no, this new machinery, I can't deal with it. Why not? Because I've never seen it before.

• 16:02Marina Mogilko?

  This is where AI comes in.

• 16:03Speaker 04

  This is where AI comes in. It's just such an easy problem.

• 16:06Marina Mogilko?

  When it comes to teachers and healthcare, do you think it's a government problem? Or there's still opportunity for companies?

• 16:12Speaker 04

  Well, I think healthcare and education is a two way street or three way street because most healthcare dollars have involvement from the government one way or another, teachers too. So, so, yeah, I think the government could play an important role. But for electricians, blue collar workers, for customer service representatives, for scientists, that's the private sector, mostly the private sector. So I said three way because even when you take the government out, there are two parties, the tech companies and the management of the business that we're talking about. So both need to be on board. In my own conversations with CEOs of leading companies or high level managers of leading companies, when I mention

• 16:53Speaker 04

  what I call pro worker AI, AI that will make workers more productive, they say, this is exactly what we need. We're on board with it. Do they mean it? I don't know. I can't test because the tools are not being supplied by tech companies. When I mentioned the same thing to people from tech companies, they either nod and turn their head, or they say, you live in a different world. We're gonna create AGI. What are you talking about?

• 17:17Marina Mogilko?

  They're worried about something that's way ahead versus something they can fix now. When you mentioned something for blue collar workers and you mentioned China, what are the good examples?

• 17:26Speaker 04

  Several manufacturing companies I've spoken to in China are integrating both robotics and AI. Foxconn, BYD, their other EV producers, they're all very teched up. And one thing I see from their experience is that it's a slow process and they require more engineers than they always think. Fortunately for them, China has an enormous supply of engineers.

• 17:56Marina Mogilko?

  What would be your advice to entrepreneurs who are watching, who are choosing what to work on? How do they choose the right niche? Because I think from all of the podcasts I've been doing recently, everyone keeps saying riches are in the niches, especially with AI, because opportunity lies in this boring business, which hasn't been automated in the past 20 years. It may be still pen and paper. But if you come in and you tackle that problem, this is where the opportunity looks.

• 18:20Speaker 04

  Absolutely. And the only thing I would not have, I would not have used the word boring. I think it's very exciting. Imagine.

• 18:26Marina Mogilko?

  Boring from outside. AGI sounds a lot more.

• 18:29Speaker 04

  I know, I know. But that's, that's, that's, that's so wrong. First of all, it is pie in the sky. Nobody can even agree on the definition of AGI. It's always in the future. And it is, frankly speaking, not a future that we can fully imagine either. I mean, if, if you really mean by AGI and then the ASI, artificial superintelligence, machines could do everything, everything that humans can do better. That seems quite far to me. But it seems also very dystopian. I mean, there won't be even any jobs for the best physicists, the best mathematicians, the best social communicators. AI will do everything better. So what I don't understand, there's so many like inconsistencies in the public

• 19:12Speaker 04

  debate on AI. One of them is people who they say, oh, AGI is great. It's gonna come soon. But we're gonna also have lots of things for humans to do. That can't be. If you mean anything serious by AGI or ASI, there can't be that many things left for humans.

• 19:29Marina Mogilko?

  I wanted to dig a little deeper into the practical use of AI today. So when you mentioned that business opportunity of researchers, how do you use AI in your daily research? And what can be done?

• 19:40Speaker 04

  I must say, I am behind the frontier. I use AI for background research and I have increasingly over this year.

• 19:50Speaker 04

  Started consulting AI for criticism of things I write and ideas I have, which is very interesting because when I first experimented with this with chat, chat G P T three point five and then chat G P T four, I thought it was very disappointing. And now it's so much better. I also encourage all of my students and research assistants to set up an AI infrastructure for their research and and sort of as support for what they do, never to replace them. I discourage people, even people who have English as a second language, from using AI to write for them. Because I think people should be in charge of their own writing.

• 20:35Speaker 04

  But I encourage every student who's a second, not a native speaker of English, to use AI as a correction, like run it through AI and see where your sentences don't make sense or are too long or are non grammatical. So I think there's a lot of mundane uses of AI already as very, very good. And there are lots of things, but I have, I very much worry about a different kind of loss of control, which is that we collectively, but especially as students and learners, delegate too much to AI so that we don't flex our mental muscles and we don't engage in learning.

• 21:16Speaker 04

  That is, I think, a first order issue. I, I see that a lot with students from studies that I think the use of AI has been pretty disastrous in K through twelve education. Because we've done it blindly, without guardrails. I said that actually in twenty twenty four also, or twenty twenty three, that it was a dangerous, huge social experiment to unleash generative AI on schools without any guardrails all at the same time, because we didn't know what it was gonna do. And I don't think it has, my worst fears have come to pass, but it's been pretty close to that.

• 21:56Marina Mogilko?

  When do you need to say to yourself, okay, I'm gonna stop here and I need to think by myself when it comes to this issue and just use AI maybe to?

• 22:06Speaker 04

  Yeah, I mean, because for me, it's like different. First of all, I'm from a different generation, unfortunately. And second, I just love thinking. I love taking pen and paper and sketching things out. So in fact, I have to prompt myself, run this through AI. I have never used, for example, AI to do math. I should. I mean, AI is much better than me in math, but I like doing the math myself. When I write down models or when I do calculations, I do them by hand, because that's what I'm used to. And I can do it fast and I can learn something. I've learned something from my mistakes. But I'm sure if I step back, I could do more.

• 22:45Marina Mogilko?

  When you do math by yourself, do you ever think about this? It was the calculator problem because teachers were protesting it's the calculator. Now we still use it everywhere and it sped up the complexity of things that we can solve. Do you think AI is gonna enable us to do that? Or because it's taking away in general, a critical thinking, we won't be able to progress further?

• 23:02Speaker 04

  I worry about the latter. I think the analogy to the calculator is useful, but you have to do it carefully. The calculator could only take over some very, very, very small part of it. And I know of nobody who used the calculator to sort of do thinking for them. And I know of no school ever that said to students, now you have a calculator, you don't learn how to multiply. I mean, there were calculators when I was a student. We all learned how to multiply, how to divide. And I can't imagine not knowing that. That is such an important building block for many other things. Same thing for differentiation. Like, if you don't know calculus, how could you even take the next step?

• 23:44Marina Mogilko?

  Yeah, it's how far the skill goes. With calculators, basically just this function. With AI, it's the whole critical thing.

• 23:51Speaker 04

  It's the whole critical thinking and, and, and the boundaries are very gray. Like, where do you stop in using AI? How do you still engage in the key steps of human innovation and human creativity together with AI?

• 24:11Marina Mogilko?

  One thing before we keep going. If you want to stay updated on all the things these founders say here, we take the use cases and prompts from every conversation and put them into my newsletter called Future Proof. And of course, we also put my own beautiful use cases there. For example, we'll put together the seven skills that came up again and again, the ones people will need in the next few years. These are the skills that my team and I are also using daily. Subscribe through the link in the description. You'll get that one and you can also read our previous newsletters and see what you missed. Going back to businesses. So when you look at all those posts on X where someone's saying, you know, I haven't seen a lot of like, I fired my whole team and hired agents, but I've seen a lot of posts where people say, oh,

• 24:56Marina Mogilko?

  instead of hiring someone, I deployed an agent. What do these companies and people miss in the long term when they do that? Because I know you're against marketing agents as a replacement for a person.

• 25:08Speaker 04

  Let me actually clarify my stance on this. And this is based, you know, on work I've done on robots, work I've done on early twentieth century technology, work I've done on the Industrial Revolution and theoretical work I've done. I am not against automation, nor do I think it's possible to stop automation. We want automation and anybody who wants to reverse automation, I'm against that. We don't want to go back to the days where we carry heavy things on our backs. Automation is great. The problem is if we, the only thing we do is automation. A, we're not gonna get the full productivity because it's not that easy to do everything with automation.

• 25:54Speaker 04

  And B, we're not gonna create shared prosperity because that will eliminate too many jobs without creating meaningful, high paying, high productivity jobs to create new opportunities for people. So my point has always been use new technology, both for automation and other things, have a balanced approach. And digital technologies, I have argued, have provided a lot of opportunities for generating new things for humans to do, and it's doubly true with AI. So I want AI to be used for automation, so long as we also use AI for creating new things, like for nurses, for electricians, for many occupations we can't even dream about right now.

• 26:33Marina Mogilko?

  How do you explain it to a small business owner whose only metric is increasing profits? What is the economic downside?

• 26:41Speaker 04

  Productivity, I think, again, and, and my experience, again, it may be cheap talk. People may be nice to me when I talk to them, or I might have fooled them, but most businesses actually get that. Not everybody, but most of them get that. Your workers are your greatest asset. Imagine making them more productive. That's fantastic for the business. And in fact, it's a better calculus. Like, imagine you have one hundred employees and I tell you, you can automate five percent of those jobs and reduce the cost by half in those five percent. Or you can make your workers five percent more productive. The latter is gonna get you much more.

• 27:16Marina Mogilko?

  Oh, and your competitors are gonna be automating their workforce. And if you're getting rid of people, then your productivity is gonna eventually.

• 27:23Speaker 04

  Right. I mean, it's, it's, it's just, your workers have a lot of experience, have a lot of, some of them, if you've been a good boss, they have commitment to the worker, to the, to the, to the business. If you can make them more innovative, better at problem solving, better at doing new things, that's the kind of business that's gonna succeed. Let me ask you a question. What are the companies, which ones are the companies from the past that you remember? And you'll give me familiar answers, like Ford Motor Company, General Motors, General Electric,

• 27:53Marina Mogilko?

  the first that comes to mind.

• 27:54Speaker 04

  You know, IBM, Microsoft. What's common about, all of them did something new. They didn't just cut costs and take, took what others were doing. They imagined new markets, new technologies, new tasks. Henry Ford himself captured that. He said, if I had asked the consumers what they wanted, they would have told me faster horses. So that's what we need from successful companies and cutting your costs by five percent, that's not gonna make you go down the Hindu history books.

• 28:22Marina Mogilko?

  When it comes to automation, do you have a rule on how do you know which task, you mentioned five percent of tasks are automatable? How do you know when to automate something or when it's not worth automating?

• 28:33Speaker 04

  If you cut your cost by five percent, GDP is typically gonna go up by five percent times your share of GDP. That's not hard. The hard part is, A, if you're changing quality, that's a little bit more complicated. And B, actually, even if you're gonna cut costs by five percent. In a number of years, it's gonna take you a lot of work to get there. And, and what I'm seeing from a few businesses that I know is that they take the automation plunge, sometimes because they think it's gonna be easy, sometimes because they think they're under pressure, and then they can't make it work. Because automation is not that easy. You have to integrate it into the company.

• 29:13Speaker 04

  Now, many people have started using the word AI has a jagged frontier. I, I like that image, but it's also a code word for saying, oh, we overestimated AI. It's much harder to do it. And we thought we could lay off workers because of AI. No, we can't. But if you take that really seriously, it means, oh, it's so jagged. I'm gonna keep more or less the same number of workers. And now I'm doing AI and it's just a melange.

• 29:41Marina Mogilko?

  Yeah, it's just a lot more work. From businesses, moving on to education. As someone who's in academia, is university is still gonna be worth a lot of money in 10 years?

• 29:51Speaker 04

  I think so, but let me preface that with a few caveats. First, I have never believed, and I'm even more convinced about this, both for economic and social reasons, that everybody should go to college. People have diverse skills, they have different interests. We should be engaged in lifelong learning, but the traditional liberal arts type education is not for everyone, nor do we want everybody to have exactly the same perspective in society. Second, a lot of what university does, I'm afraid to say, is also a signal. When you go to Harvard, MIT, Stanford, you're also buying the name. And that's good for you, not necessarily that good for society.

• 30:32Speaker 04

  And third, every university is understanding now that they have to change their business model. They have to change how they deliver education. They are in search of what types of skills they should focus on. And my very, very superficial opinion is that universities understand that. But you know what? The same actually applies to high schools and middle schools, and they are not. They're, they're even further behind the curve. One thing I have experienced in the United States is that many students, when they come to college, are unprepared, and that is gonna get worse. And that really will make colleges' business even harder.

• 31:14Speaker 04

  I've seen that because I have, you know, written a textbook for first years in economics, how often weakly prepared students are and how that's actually getting worse over time, even before AI. How many colleges are finding it necessary to do more and more remedial stuff? Because high schools aren't doing well enough. And the evidence we have, I mean, it's, we are in early days. Things can change, but the evidence is that AI has made learning harder in middle schools and high schools. And so that problem's gonna get worse.

• 32:22Speaker 03

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• 32:49Marina Mogilko?

  Totally, if you start using AI to solve all the problems, and then university is only gonna get worse then. So what should people be doing? What skills should they be acquiring? And do you agree with the notion that we'd need even more academic knowledge to acquire those skills? Because I'm thinking about taste and strategic thinking.

• 33:06Speaker 04

  Absolutely. Because I don't believe true AGI is just around the corner. I think we need human creativity more than ever. And human creativity without strong foundations is an oxymoron. If anybody wants my opinion, and I'm not the most qualified to give you this opinion. I'm not a mathematician and I'm not a computer scientist. But if anybody wants my opinion, I would say, go and study math. Don't be put off by Astra's solving difficult math problems. We need human mathematical knowledge. We need human mathematical maturity. That's gonna be so important. Go and study physics, go and study economics, go and study engineering. I think those are gonna be great, but also learn flexibility. I think we're gonna need a lot of flexibility.

• 33:51Speaker 04

  Because even within the same occupation, we will often need to change what we do as the technology changes, as the social context changes. So I think those are really, really important things. But human ingenuity and human creativity, I think, are gonna be critical in the next 10 years.

• 34:11Marina Mogilko?

  Yeah, and normalizing nonlinear careers, something that resonated with me a lot. Someone who changed careers throughout their life, I think this is gonna be our new normal.

• 34:20Speaker 04

  But then two other principles that I think are very important that we have to, we should impart, starting from middle school. Learn how to work alongside and with AI, absolutely. But also even more emphasis on civic responsibility. We have lost in this country and in most of the rest of the world, a true sense of community and a true sense of caring for the common good and working for our collective good. And this is even more important in the age of AI. And that starts with being informed, being engaged.

• 34:58Marina Mogilko?

  That starts with elementary school, really?

• 35:00Speaker 04

  Yeah, it starts with elementary school. I, I, yeah, I could have extended it to elementary school, absolutely. We may have encouraged too much selfishness. We may have encouraged people to draw into their cocoons via social media and other things. We may have denigrated some aspects of community that are really important. That's also like a major theme of my new book on what happened to liberal democracy, that we really need community to be at the center of a liberal democratic society. You know, here's what, I am not pessimist. I'm not an optimist. I'm not, but I'm definitely not a pessimist either. I try to remain hopeful. And two things I find hope in are that humans are infinitely adaptable.

• 35:45Speaker 04

  And second, even in this polarized, divided world, we have very similar notions of fairness, moral, shared understanding on both sides of the aisle. They just need to come up more clearly in our communication. And we need to see the other side, not as the enemy. Even the deepest disagreements should be situated within a context in which we recognize we're on the same boat.

• 36:14Marina Mogilko?

  We're talking about all of this, but then there comes Dario and says, there's a 25% chance things go really, really badly with AI.

• 36:23Speaker 06

  I'm relatively an optimist, so I think there's a 25% chance that things go really, really badly and a 75% chance that things go really, really well with, with not much, not much space.

• 36:34Marina Mogilko?

  What do you think when you hear this, especially coming from someone who has to IPO soon and prove that their technology is very transformational?

• 36:42Speaker 04

  I think if people in the leading AI labs are worried about some of the implications of AI, of course they should voice that. I welcome and I applaud their courage to speak. But I am myself much more worried about a general sense of loss of control of society over AI than existential risk as articulated by Dario or by Elon Musk or by Clarkson. Loss of control I'm talking about is that the general public, the workers, students,

• 37:23Speaker 04

  parents lose control of their own agency because of AI. Not necessarily because AI has become super intelligent and an entity onto itself, but partly because of the way that other companies or frontier labs are developing AI, are using AI and are rolling out AI. So I am very worried about loss of control. We should have that conversation, but we should not ignore other risks.

• 37:53Marina Mogilko?

  You have this rating for AI going from minus 10 to plus 10, and you said it was minus 6. Did it get worse from then? I think it was 2025 when you gave this rating.

• 38:03Speaker 04

  No, I actually don't think so, because in 2024, 2025.

• 38:09Speaker 04

  I thought there was a lot of damage that AI could do. And I was not clear about what bottlenecks would be facing us in improving it. I think the improvements have been fast, so the dangers have increased. But I think the good things we can do with AI are also becoming clear. But we just need to redirect it. We need to steer AI. And AI cannot just be the decision of a handful of people. Very smart, many of them very well meaning people. But it's too important to be left to just a few people.

• 38:43Marina Mogilko?

  How do you foster that on an individual level?

• 38:46Speaker 04

  Information. I think we have to start with an information and a narrative. And let me tell you where I think, and this is a very risky thing to say, because I'm going to say something negative about the biggest gatekeepers in our world. But here is my opinion of which institutions have failed us worst. The media, absolutely the media. The media has been awful in cultivating a true sense of information debate about AI. For 10 years, it's starting to change slowly, but very slowly and very unevenly. For 10 years, either Elon Musk and others like that could do no wrong.

• 39:30Speaker 04

  And every criticism of them was just elevating them. And everything about AI was just fascinating, bewildering, wonderful. Or we just talked of like AI as like the enemy, as stochastic parrots or killer robots. The middle ground where people could get engaged, could understand the issues and could actually exercise their role of stewards of our future as consumers, as democratic citizens, that was not cultivated. And this is true for pretty much every organization in the news media.

• 40:04Marina Mogilko?

  And let's wrap up with some questions that are super practical because I want people to walk away from this with an action plan, what they can do. First of all, what do you do with your savings when you hear all the news? Or nothing has changed?

• 40:18Speaker 04

  Nothing much has changed. I try to have a balanced portfolio. I think it's very, very difficult to beat the market. I'm not saying it's impossible, but you need to be either extremely skilled, spend a lot of time or have inside information. I don't have that much time. I have zero inside information. I wouldn't want to have inside information. So I try to have a balanced portfolio.

• 40:41Marina Mogilko?

  Let's jump into 2035. If everything goes in a positive way, what is a typical workday of a knowledge worker who spends eight hours by their computer now?

• 40:52Speaker 04

  Great question. I don't know. I can definitely see a future in which people would work a few less hours. I could, and still be very productive. I could definitely see a future, very scary future, in which a lot of people would not have meaningful work. And I could definitely see a future in which people still go to the office or do some combination of office work and work from home, but differently because of true complementary AI, but still human innovation and human talent are central. I think we live, even leaving AI aside, we live at an uniquely turbulent time.

• 41:33Speaker 04

  Climate change, aging population, changing geopolitical balances, a huge middle class emerging from the developing world. All of these require new goods, new services, new tasks, new ways of approaching the market, new ways of organizing companies. And who's going to do that? Not AI, humans. And if we can help those humans be more creative, amazing.

• 42:02Marina Mogilko?

  What would be your advice for someone who sees this amazing wealth being created by AI? At least what we see in companies' valuations. We still have to see the ROI on the actual, the actual economy. But when you say, you know, we want to participate in this goods creation, how can we do that practically in the next?

• 42:22Speaker 04

  Look, I actually don't see how foundation models are going to make a lot of money. Despite all of the investment going into frontier labs.

• 42:31Marina Mogilko?

  So we'll see some failed IPOs?

• 42:32Speaker 04

  Not failed IPOs right away, because I think the enthusiasm is there at the moment. But I really don't see how you're going to make trillions of dollars from foundation models because of potential bottlenecks, either in the development of the models or in their diffusion. But even if those don't happen, the next layer of model, open source models are going to be very good as well. So it's not the monetization opportunities are going to be limited. So I think the money is in high quality data and in applications. That's where I see the edge. If your company, your line of business has a lot of data, cultivate it, use it, even build better data, find ways of capturing that data

• 43:17Speaker 04

  while also compensating your workers who are generating that data. That's fairness, that's also a good business decision. Think of applications. That's where the edge is going to be. Applications are going to be much harder to replicate by open source if they're really creative and fill a niche. So I think those are the places. And as a worker, have that flexibility, have that understanding of AI, but maintain your creativity and maintain your base.

• 43:43Marina Mogilko?

  Yeah, also for a worker, I think it's also very important to realize that they are the source of data. They've been working with customers and product for years.

• 43:51Speaker 04

  And, and by the way, this is another important thing if you're going to develop applications. A lot of the tacit knowledge is with the workers. You can't just sit in your office in front of a computer and put a very powerful AI and come up with an application. You need to go there, get your hands dirty and talk to workers.

• 44:06Marina Mogilko?

  Well, hopefully this conversation sparks some bright ideas in entrepreneurs who are watching, who want to make practical applications of AI. I want to finish with the last question. You said students going to work at AI companies need a strong moral compass. I think in general, like even as an entrepreneur, a member of society, strong moral compass. Can you explain what you mean by that?

• 44:27Speaker 04

  Look, let me give one example from a different field, which is gene editing, another amazing technology that has seen a lot of big advances. But I would say most people who go into medicine or gene editing adjacent occupations recognize that this is a very morally ambiguous technology. And many organizations that have oversight role from NIH to the Surgeon General and the Medical Association have put out general ethical guidelines for gene editing, like germline editing is off right now because it's just too dangerous.

• 45:10Speaker 04

  We don't understand it, too many risks and et cetera. So that's the kind of moral compass that I think we need in AI as well. And we don't have it. People think of, oh, I'm doing it for the good. And I completely believe it. Almost everybody I know who goes into the field of AI is well meaning and they, they, they want something good for not just themselves, but for society. But there is no clear moral compass. And that's what I'm talking about.

• 45:37Marina Mogilko?

  It's another interesting conversation that might be.

• 45:40Speaker 04

  Hopefully to continue at some point again. Thank you so much.

• 45:42Marina Mogilko?

  Thank you so much. It was amazing.

• 45:43Speaker 04

  This was my pleasure.

• 45:44Speaker 00

  Where some see heroes and others see egos. Bloomberg sees the era of billionaire athletes. A fad to some, the future of money to others. We see crypto's trillion dollar swings. The end of jobs or the end of human struggle. We see the endless funds fueling the AI hype. While others follow the noise, we follow the money.

• 46:12Speaker 00

  Learn more at Bloomberg.com. Hey, Bose here. On your next track, listen for the decisions. Distortion on the bass, the vocal right up front. Every choice deliberate. With Bose, you'll hear it all. Your music deserves Bose.

• 46:29Speaker 04

  Thank you.

• 46:29Marina Mogilko?

  Thanks a lot.