This Is How OpenAI Goes Broke — ft. Sebastian Mallaby

Ed Elson sits down with Sebastian Mallaby to discuss why he believes there's a real chance OpenAI runs out of money within the next 18 months, and what that would mean for the broader AI industry. He also explains why he doesn't necessarily see Meta's decision to sell excess compute capacity as a bear signal, why he was encouraged by the Trump administration's new AI policy, and how he expects the AI race to unfold over the coming years. Sebastian Mallaby is a prominent journalist, author, Pulitzer Prize finalist, and senior fellow at the Council on Foreign Relations. His latest book is The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence. Subscribe to the Prof G Markets Youtube Channel Check out our latest Prof G Markets newsletter Follow Prof G Markets on Instagram Follow Ed on Instagram, X and Substack Follow Scott on Instagram Send us your questions or comments by emailing [email protected] Learn more about your ad choices. Visit podcastchoices.com/adchoices

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

• 0:01Speaker 00

  This episode is brought to you by Michelob Ultra. Good times mean competitions between you and your friends, like seeing who can throw some pigskin the farthest or rush the field the fastest. Winner gets an ultra because it's not just a superior light beer. It's also giving fans superior access to their favorite sporting events. Superior is worth playing for. Visit michelobultra.com/superioraccess to enter. Enjoy responsibly. Copyright 2026 Anheuser Busch InBev Michelob Ultra Beer, St. Louis, Missouri.

• 0:28Speaker 01

  The new LinkedIn Hiring Pro can't undo your last hire. The no show. Who set you back because they didn't show up on day one or day two or ever again? And now your product launch is delayed because you're talking to alien abduction podcasters to track them down. But LinkedIn Hiring Pro can help make sure your next hire sticks. In fact, businesses who use LinkedIn are 24% less likely to reopen a role in the next 12 months. Hire right the first time with LinkedIn Hiring Pro. Post a free job today at LinkedIn.com/quality.

• 0:59Speaker 02

  Energy baja. Falta de concentración? Nebla mental? You might be dehydrated. Gatorade Zero Powders han sido diseñados científicamente para ayudarte a mejorar el balance de hidratación en el cuerpo. Hechos con la mezcla número uno de electrolitos, sin colores ni sabores artificiales. Shop now online or at retailers nationwide. Gatorade hydrates better than water. Is it in you? Signos de dehidratación leve y moderada. Consulta a tu médico si los síntomas persisten.

• 1:29Ed Elson?

  Welcome to Prof G Markets. Cracks are forming in the OpenAI story. Last week, the company reportedly proposed giving the US government a 5% stake worth roughly $43 billion as a way to share the upside of AI with the public. Critics, however, argue it would amount to a government bailout and see it as a troubling signal for both OpenAI and the broader AI boom. That news came after reports that OpenAI had pushed back its IPO plans until 2027,

• 2:12Ed Elson?

  adding to concerns about the company's financial position. So we wanted to speak with someone who has spent years studying the history of AI and who also believes that OpenAI could run out of money in the near future. Sebastian Mallaby is a prominent journalist, author, Pulitzer Prize finalist, and senior fellow at the Council on Foreign Relations. And today, he's joining us to discuss what is next for OpenAI, what is next for the AI industry, and what investors should be watching. Sebastian, thank you so much for joining me on the show. I'd love to start with an article you wrote back in January that was titled, "This is what convinced me OpenAI will run out of money."

• 2:56Ed Elson?

  And you said back then, quote, "My bet is that over the next 18 months, OpenAI runs out of money." We've been seeing a lot of red flags since then, the delaying of the IPO. Later, we saw this proposal for the US government to take a stake in the company. I guess I'll just start with, what do you make of the recent news and do you hold to your prediction?

• 3:21Speaker 04

  Yeah, I do hold to my prediction. Back in January, the burn rate was just crazy. So that although OpenAI had good products and quite a lot of traction, 900 million consumers, they won't be able to charge money for the product. Like 5% of the retail consumers were actually paying. If you look at a chart of where these users are, you know, the US is the number two market, India is first, and the next three are kind of Brazil, Indonesia, and so forth. So these are not rich consumers. You can't charge them very much money. And so they had a business model that imagined that they could throw money in all directions, you know, a collaboration with Johnny Ive to have a new form factor which would supplant

• 4:05Speaker 04

  the iPhone, serving Sora video generation models and all this stuff, all of which is very expensive. And yet, the revenue side simply wasn't there. So the burn seemed to me to be totally unsustainable. And even though Sam Altman is a magician when it comes to raising money, he wasn't going to be raising $660 billion, which is what the internally projected burn rate was for the next five years, when you look to the documents back in January. Now, since then, what's happened is some good news, right? Because OpenAI, I think, has recognized that it had to get the burn rate down. It's pulled out of a bunch of data center building products, you know, Stargate, all that stuff. It's canceled Sora, the video generation model, which was a total money loser.

• 4:48Speaker 04

  And it's tried to impose some sort of order on the chaotic management. But it's only been kind of half successful. And in the meantime, OpenAI is squeezed between Anthropic, which is much better at the frontier enterprise applications like, you know, coding assistance and cybersecurity stuff and agentic stuff. And then, on the other hand, it's squeezed by the Gemini model from Google DeepMind, which has now reached more retail consumers and is way better at monetizing from that because Google has plugged AI into its search advertising business. And that business is now doing more revenue than ever.

• 5:31Speaker 04

  So I just think that, you know, OpenAI is technically a good lab, but it's very hard to monetize when you have a product where there's a lot of competition and it's kind of a commodity. And they're not terribly well managed and they've relied too much on the fake it till you make it Silicon Valley tactic of kind of weird smoke and mirrors fundraising gambits. If you look at the fundraising they did and announced earlier this year, the headline number they raised was $122 billion, which is an astronomical amount. But when you dig in, and I'm amazed the press didn't point this out more, about two-thirds of that amount was kind of promises in the future, conditional upon having a successful

• 6:14Speaker 04

  IPO or payment in kind, like, you know, access to compute. The actual real money was a small share of the total fundraise, which raises the question, why announce this massive $122 billion headline number when anyone who digs into it can see it's rubbish? Well, the answer is they're trying to head fake investors into putting more money in. They're trying to persuade people they have momentum. They don't. And this news that you pointed out just recently, that they have delayed, it seems, their IPO into next year, is just the latest icing on the cake, the latest evidence that they talk a big game, but they are behind where they say they are.

• 6:53Ed Elson?

  Do you think that the delay of the IPO was in large part because of all of this? Because perhaps Sam Altman and the company know that as soon as Wall Street actually gets like an audited review of their financial statements, then suddenly the tide will turn on this company and suddenly people will say, sure, you might have a great product, but this is not a sustainable business model. Do you think that that was the concern, that people might actually see how the company actually works?

• 7:22Speaker 04

  100%. I mean, everybody remembers the WeWork story, when WeWork was this, you know, rocket ship back in like 2019, and it went out with a prospectus to do the IPO. And people looked at it and said, this is a joke. And nobody wanted to buy the shares and the IPO never happened. So, you know, you can fail in going for the IPO. And OpenAI is in this very tough position where, on the one hand, it needs the IPO because it can't hope to raise enough money if it stays private. On the other hand, if it tries to do the IPO, it may not succeed and then it's really cooked.

• 8:02Ed Elson?

  Just looking at how much they are spending at the moment, we just saw the financials that were released by Ed Zitron, who's this independent journalist, he's, he got his hands on the numbers. They generated $13 billion last year in revenue. They spent $34 billion, which means that their operating loss was $21 billion. I mean, we could talk about the net loss, which was even higher than that, than that number, but that seems to be like a good roughly estimate of how this business is actually doing. And you mentioned that they're stuck between, on the one hand, Gemini and then also Anthropic.

• 8:46Ed Elson?

  Anthropic is an interesting one because we also don't know much about that business. And we know that they're unprofitable and we know that they're in a similar business to OpenAI. And as you say, this technology is becoming increasingly commoditized. They said there were reports that maybe they were coming up on a, on a quarter of operating profitability, but I think we probably have to take that with a grain of salt because we don't know how they're doing their accounting. My question. How does Anthropic compare to OpenAI from a business model perspective?

• 9:23Speaker 04

  I think you're making a good point, and I agree with you that we don't know as much as we would do if Anthropic were a public company or if, you know, the prospectus was public.

• 9:34Speaker 04

  What we do know is that Anthropic has always targeted enterprise customers, which means the type of customer that actually pay for the product. And we know that it's been ahead on stuff like coding assistance and cybersecurity AI. Not that OpenAI is bad, by the way, I mean, it's not far behind. But I think Anthropic is the cutting edge on those particular applications that enterprises are really willing to pay for.

• 10:05Speaker 04

  And meanwhile, Anthropic has not been sort of distracted into announcing a whole suite of retail-oriented business initiatives which came to nothing. I mean, OpenAI announced it was going to do shopping at one point, and that doesn't seem to have happened. It said it was going to do ads, I'm not sure they've got terribly far with the ads. It did generate the, you know, Sora video model, which just was a huge money loser, whereas Anthropic never went down that path. So I think Anthropic has been way more laser-focused on the part of the market that makes sense, which is the enterprise part,

• 10:45Speaker 04

  and just better managed. The other point I'd make is that Anthropic amongst all the frontier labs is known as the one where the churn in terms of the scientists is the lowest. People go there, they believe in the mission, they believe in Dario Amade as the leader, and they tend to stay there, they don't hop around. Whereas all the other labs are subject to job hopping, and that's obviously disruptive.

• 11:08Ed Elson?

  I think one of the questions that is in investors' minds, especially if you're worried about the potential of an AI bubble and the potential that an AI bubble might pop, is is it an OpenAI problem or is it an AI problem? Is it that OpenAI is just bad at managing their finances and they pursue all these side projects and they don't really know how to get their spending under control? Or is AI as a business model just too expensive relative to the amount of revenue that could be generated by charging customers for using ChatGPT? Or charging enterprises for these larger enterprise-wide AI contracts? What is your view on that debate? And just for context for our listeners, you wrote The Power Law,

• 11:53Ed Elson?

  which is one of the most famous books ever on venture capital. And it's kind of about how venture works as a business model, where you do lose money for a number of years and then you figure it out eventually. Is AI going to be that story or is this different?

• 12:10Speaker 04

  My view is that we have an OpenAI bubble, but not a general AI bubble. So I think OpenAI, for the reasons we discussed, is a 50-50. Look, it might work, I'm not saying I'm, I don't know that they're going to fail. I'm just saying there's a 50% chance that by next summer we'll find they couldn't really go public in the private markets, they can't raise enough money, and they have to sort of sell themselves at some sort of discount to another company. It could be, you know, Amazon or Microsoft or some other big company that wants an AI team. Because technically, OpenAI is a good team, right? Now, on the more general issue, yeah, there's debate at the moment about whether enterprise customers are having a oh-my-god moment where they think, oh, these tokens are just so expensive now.

• 12:56Speaker 04

  I've spent the last 18 months telling my teams that they should just go out and go wild with AI and experiment and do whatever they feel like and token max. And the more tokens you use, the better of an employee you are because you're showing that you're AI-forward. And now, wow, this is expensive and I've got, I haven't seen a productivity gain yet. And so what am I doing here? I have to rationalize this. And there are lots of stories out there about how companies are imposing a sort of middle layer between the user in the enterprise and the models. And the middle layer is there to switch a query so that, you know, if it's a simple query that I'm asking, it gets routed to a cheap, low-token consumption model.

• 13:39Speaker 04

  And then only if it's a seriously difficult one will it go to a Fable or something expensive. So I think there's some sort of sensible rationalization about how the AI customers are spending money on this technology. But fundamentally, fundamentally, if you look back at what's happened since the release of ChatGPT, the clear story is this is unbelievably exciting, fast progress in the tech. I mean, when ChatGPT came out, the thing hallucinated non-stop. Then when GPT-4 was plugged in six months later, it basically stopped like 80% of the hallucination. Then you've got very long context windows, so you could put a whole Tolstoy novel into the model and then query it.

• 14:22Speaker 04

  Then you've got, you know, these reasoning systems that could do math and logic, which had been impossible before. Then you get agentic system, then you get coding assistance, then you get cybersecurity systems. Now you've got like bespoke AI autonomous scientists emerging. This is unbelievably fast progress. So I fundamentally think that, you know, AI as a sector and therefore the demand for the semiconductors, the data center businesses, all these things that people worry about, I don't think that's a bubble. I think that's for real. And it's going to take a little bit of time for companies to figure out how to ration their people's use of tokens so it's sort of sensible. But basically, they're going to consume a lot of tokens.

• 15:06Ed Elson?

  Another data point that the bears might present that we saw last week is Meta launching their cloud business. And this would be the argument against what you're saying, which, by the way, I agree with, but I want to play devil's advocate.

• 15:24Ed Elson?

  You know, the very thing that Meta said they wouldn't do, they're now doing. They said that they would only launch a cloud business if they had, quote, "overbuilt." These were Mark Zuckerberg's words just a few months ago. The plan was, let's build out all of these data centers, build out all of this compute capacity, because we within the Meta organization need it so desperately because we're going to build all of these internal AI products and we're going to, you know, AI turbocharge our business. And then they turn around and say, actually, we don't have the demand internally that we thought we did, and so we're going to sell it to someone else and we're going to let someone else figure out how to sell an AI product

• 16:09Ed Elson?

  and how to make that a profitable business, which seems quite bearish from a bubble perspective. Because it basically said, I mean, who else but Meta would be the one to build out their own suite of AI products? If Meta can't crack it, if OpenAI is struggling to crack it, TBD on Anthropic, then who's going to crack this? Who's going to make this not just an interesting technology, but an interesting technology that makes money?

• 16:37Speaker 04

  And we've seen the same with SpaceX, of course, that they also decided to sell their compute capacity to Anthropic and others because XAI, their own model, hasn't really got much of an uptake. And so they don't need all the compute they've built for their own model, therefore they're selling it to others. So you could view this as a bear signal, as you've just described, or you could view it as a bull signal because it means that you've got some consolidation going on in the frontier model space. And less competition means better margins for the remaining participants. It means that maybe there will be more pricing power for the ones that are left standing. So I find, I don't agree that that's, I think that's the proper reading.

• 17:22Speaker 04

  The proper reading is we have a rationalization of the market. If you looked at the whole sort of US ecosystem, you know, three, four months ago, you had XAI trying to compete, Meta trying to compete. And then on top of that, you had the big three, Google, DeepMind, OpenAI and Anthropic. So that's five, and that's before you count Mistrada in France, Cohere in Canada and all the Chinese models, right? That's a lot of competition. And I don't think that this thing is going to consolidate down to a winner-takes-all sort of, you know, 2010's social media platform or something. But I think some consolidation is in order such that it looks like cloud computing, where there's kind of three or four big providers.

• 18:06Speaker 04

  So now we've got, you know, three leaders who are still standing within the US, plus the foreign ones. That feels good to me in terms of the future business stability of the sector.

• 18:17Ed Elson?

  If OpenAI runs out of money, as you say, per your prediction,

• 18:22Ed Elson?

  what do you think the outcome would be? I mean, one of the things that you wrote is that maybe it would be absorbed by another company. I mean, how does that play out if indeed what you're saying might happen does happen?

• 18:36Speaker 04

  So, look, I think, you know, we've seen lots of examples of either acquisitions or more recently acquihires, where, you know, you have a smallish AI company like Inflection, which Mustafa Suleyman was running, and then it got sort of sucked into Microsoft, or like Character AI, which got sucked back into Google. So there's a playbook here. Now, OpenAI is a lot bigger than either of those two. So it would be a more complex playbook. But basically, it seems to me that,

• 19:08Speaker 04

  you know, the demand for AI talent and for AI products, and therefore the compute infrastructure that serves that demand, I don't think that's going away because fundamentally, I think this is useful stuff that people are going to figure out how to use productively. And so I don't know whether the whole of OpenAI gets bought by Amazon or Microsoft or some other acquirer, or alternatively, there's some kind of fancy acquihire deal where part of OpenAI is sucked into a big company. Or alternatively, that like, you know, there's a bit of a splintering and the staff, the technical staff at OpenAI, get individually hired into other labs. Who knows, right?

• 19:51Speaker 04

  What I'm saying is that there's a fundamental problem with the way they're going about their business model. I think they understand that, which is why, you know, they pulled out of data center building and various other things in the last six months. But they've got some way to go to fix things and patch it up. And, you know, one of the lessons about how you do startups, you know, coming out of my previous book, The Power Law, is that when you have a very high valuation, a down round is super painful, right? You know, they're valued in the last round at $852 billion post-money.

• 20:30Speaker 04

  And in the secondary market, they're trading for a lot less than that. And if they were to sort of just say, okay, we accept, we're really worth 600 billion.

• 20:40Speaker 04

  You know, the hit to everybody's equity options inside OpenAI would be horrible and they would lose people. And the hit to investors who had believed in OpenAI would be bad and they would get pissed off. And the whole momentum machine that Sam Altman has built would really go through a convulsion. Now, it might be what you have to do to make this thing sustainable because, but my point is, once you ratchet all the way up to this very high valuation, it's difficult to climb down. And that is why I think he says, why doesn't the government have 5%? Because a strategy to get out of this box that he's in is for Altman to give 5% to the government.

• 21:21Speaker 04

  And then the government will say, right, you know, OpenAI is too important to fail now because we own 5% or 10% or something. And they'll do what they did with Intel, which they took a 10% stake in last year. And next thing you know, the Commerce Secretary Lutnick is like calling other tech companies in the valley, saying, you're going to do a deal with Intel, you're going to bring Intel in as a partner on your next project, blah, blah, blah. And so, you know, you've got the US government, a Trumpy US government, strong-arming other companies into giving business once they're in your corner. So that, I think that is what Sam Altman's strategy is here to kind of recruit the, you know, the investment banker to whom you can't say no, the US government.

• 22:03Ed Elson?

  Which seems like he's basically just trying to take some sort of workaround shortcut around capitalism. And it seems like we are increasingly seeing that, like, if you can't figure it out in the free market, then, oh, let's just go over to Washington, walk in the White House, kiss the president's feet, and then hopefully he'll save us. And we are increasingly seeing that that is what is actually happening. We're seeing the government taking up stakes in multiple companies. We're seeing the odds that the government will take stakes in even more companies. Those are going up. They may indeed take a stake in OpenAI. Last I checked on the prediction markets, the odds of that happening were more than a third.

• 22:44Ed Elson?

  It's possible that they would do the same with Anthropic, with Palantir, with Andrew. It makes me very upset because I think of it as cheating. I think that you're kind of cheating the game of capitalism. I'd be curious to get your views there. And then following up on that, if that actually happens, say, OpenAI is running out of money and then Trump just bails them out in whatever way. We use taxpayer dollars to just continue to subsidize the business. What comes after that? Does that mean that OpenAI is fine? Does that mean that the rest of the AI industry is on shaky ground? I'm not even, I'm not quite sure how to even model out that potential scenario.

• 23:29Speaker 04

  First of all, I think your formulation that they're cheating capitalism and, you know, they're going to the government and doing an N-round around capitalism. I mean, I think that's a good perceptive and quite amusing insight. So, so thank you for that. I also, though, would say that, you know, this is like just the way the world is going. I mean, or at least the US is going. So if you look at the number of American companies in which the US government has announced either done a deal or has announced the deal and it's yet to be consummated. You know, a colleague of mine called Jonathan Hillman at the Council on Foreign Relations did a formal count, which just went up on the Council on Foreign Relations website. And the answer is there are 30 of them,

• 24:14Speaker 04

  30 such companies since the Trump team came into power in January 2025, where there's an equity stake from the US government in a private company. So this is where the world is going. And I think this trend has been very much encouraged by the deceptive example of Intel, right? So in the case of Intel, if you look at what their performance has been since the government took a stake last August, it's been fantastic. I mean, it's been way better than the Philadelphia Semiconductor Index, which is the normal index you would look at as a kind of comparable for how Intel has done. Intel, I think, is up like almost 400%. The SOX or the Semiconductor Index in Philadelphia, that's up like 150%.

• 24:59Speaker 04

  So these, Intel has done incredibly well since the government came in. And I think people just lose sight of the fact that, you know, yeah, it did well because you've got, you know, the Commerce Department calling up other companies and ordering them to do business with Intel. So Intel gets a whole bunch of contracts and it's like turning its game around.

• 25:20Speaker 04

  Because you've got the government behind, you know, picking a winner. Now, it's one thing to say the government might have a justification picking a winner when we have a problem with, you know, all of the cutting-edge semiconductors being made in Taiwan. We don't want to be reliant on an island that could be invaded by China. And so we want domestic US semiconductor manufacturing. I get that argument, right? I don't believe in extending the same argument to OpenAI, which is just one of multiple American foundation model builders. We don't need OpenAI for any strategic reason, right? So there would be no justification for picking a winner around OpenAI.

• 26:02Speaker 04

  So I think it's, I think that the, you know, capitalism is sometimes justifiably twisted because you have a national security reason to do so. Backing OpenAI would not be a justifiable instance.

• 26:15Ed Elson?

  Well, I could imagine that the justification that would be floated is OpenAI isn't systemic to the real economy, but they'd maybe try to say that, but it's systemic to the stock market because, you know, Microsoft's future revenues depend so heavily on OpenAI. So do, I mean, I mean, Google, Amazon, X, I mean, all of, basically all the hyperskills, Oracle. A lot of these companies are very, very important to portfolios. They are what make wealthy people wealthy in a lot of cases. And maybe the argument for Trump would be, oh, well, we, we need to keep this thing afloat. Otherwise, people's stocks are going to go down.

• 27:00Ed Elson?

  What would you make of that argument?

• 27:02Speaker 04

  I'd say, welcome to China. I mean, that's what the kind of thing the Chinese government would do is prop up the stock market with government intervention of that sort. I mean, look, in the United States, when the Federal Reserve, you know, operates a policy that looks like it might be about stabilizing the stock market, people freak out and say, well, that's a Fed put. And, you know, that creates bubbles, more bubbles in the future. And, you know, capitalism doesn't work unless there's real risk involved. And that's the Fed. If you have like a bunch of political types in Washington, you know, the Commerce Department and so forth, picking winners and distorting outcomes in the market, you don't have a market anymore. It's not a free market. Your point about this is an N-run around, you know, capitalism, or to say the same point differently,

• 27:46Speaker 04

  you know, this is an N-run against the notion of a fair level playing field on which different companies compete fairly and then the most efficient ones win. That's what we're supposed to believe in as the wellspring of efficiency in American capitalism. Well, if you start de-leveling the playing field by picking OpenAI as a winner, you've just trashed that.

• 28:10Ed Elson?

  We'll be right back after the break. And if you're enjoying the show so far, send it to a friend. And please follow us on YouTube, Spotify, or wherever you get your podcasts.

• 28:26Speaker 00

  This episode is brought to you by Michelob Ultra. Good times mean competitions between you and your friends, like seeing who can throw some pigskin the farthest or rush the field the fastest. Winner gets an ultra because it's not just a superior like beer, it's also giving fans superior access to their favorite sporting events. Superior is worth playing for. Visit michelobultra.com/superioraccess to enter. Enjoy responsibly. Copyright 2026 Anheuser-Busch InBev Michelob Ultra Beer, St. Louis, Missouri.

• 28:55Speaker 01

  The new LinkedIn Hiring Pro can't undo your last hire. The human postponer. They were the master of one phrase. I'll circle back on that. But three months later, you were the one doing all their work and wondering how big that circle is. But LinkedIn Hiring Pro can take the hiring load off your plate by automating the hiring busy work from the initial job post to scheduling interviews. Hire right the first time with LinkedIn Hiring Pro. Post a free job today at LinkedIn.com/quality.

• 29:25Speaker 02

  Energy baja, falta de concentración, nebla mental? You might be dehydrated. Gatorade Zero Powders han sido diseñados científicamente para ayudarte a mejorar el balance de hidratación en el cuerpo. Hechos con la mezcla número uno de electrolitos, sin colores ni sabores artificiales. Shop now online or at retailers nationwide. Gatorade hydrates better than water. Is it in you? Signos de dehidratación leve y moderada. Consulta tu médico si los síntomas persisten.

• 29:55Ed Elson?

  We're back with Prof G Markets. This is a good segue into China, which is a topic that you also wrote about recently. The title of your piece was, quote, "I went to China to see its progress on AI. We can't beat it." That was in the New York Times recently, your op ed. What did you learn about Chinese progress on AI and why do you think that we can't beat it?

• 30:25Speaker 04

  We haven't mentioned this yet, but I'm going to mention it now because you've given the excuse. So I published a book this year called The Infinity Machine about Demi Sissabis and...

• 30:35Ed Elson?

  I was going to get to it.

• 30:36Speaker 04

  There you are.

• 30:37Ed Elson?

  I'm glad you mentioned it.

• 30:38Speaker 04

  Go read The Infinity Machine, folks. But no, seriously, the thing about China is it does everything faster. And so they published, although they got my manuscript last, and then they had to translate it into Chinese, and then they wanted photographs and other embellishments, they produced in a way a more complex product, but they actually published it before Penguin Press in the United States or any of the other deals I had in other countries. So I go to China right at the beginning of my book tour. I spent eight days, you know, going to four different cities, Hangzhou, Shenzhen, you know, Shanghai, Beijing, talking to both computer scientists at the private labs like, you know, Huawei and Ant Group and so forth, and then also talking to academic computer

• 31:22Speaker 04

  scientists from universities. And what struck me about these guys is that, first of all, they talk about safety. They just bring it up. The notion, which I've heard from friends in Washington, that the Chinese don't give a damn about AI safety is just not true. They do talk about safety. Now, I'm not claiming that the government's policy is to pursue safety or that the majority view in China is that they want safety. China is like the US. China has some accelerationists and some people who want to go slower because they're worried about the safety issue. That's the same as the US. So neither side is going to de-escalate and start going slower unless the other one does as well.

• 32:03Speaker 04

  But what I'm saying is, it's to caricature China as like only accelerationist 100%, that's just wrong. And so there is scope to talk to them about safety, and maybe we'll come on to that. But the other thing which I observed is that China is very good and very focused on applications. And so if you go to a company like, you know, Hikvision, which is under US sanctions, and it's kind of a out-of-body sort of double-take experience when you go there, because on the one hand, it feels like an American tech company. I love tech companies. They're kind of all about building cool things and making the world better. I kind of buy that. I drink that Kool-Aid. I kind of believe in it. I like technology, right? So I see these people trying to build cool technology, and they show me stuff like, for

• 32:46Speaker 04

  example, there is an AI kind of scanning camera thing, and you point it at some water and you get a reading on the pollution count in the water. And because they've created that, guess what? There is an internal market in water pollution reduction between different Chinese cities. So if you're the downstream city, you will pay the upstream city to reduce the pollution in the water that's going to come downstream to you. And so you can do pollution reduction when you can measure the pollution, and this is what they're doing at this company, this is what they're building. But they're also under sanctions, these guys, by the US because the US says, and, you know, historically, this was actually true, that they're building other kinds of cameras

• 33:28Speaker 04

  which are good for surveillance of civilians and so forth in Xinjiang and whatever. So they're both bad guys and they're cool guys. It's a difficult thing to figure out. But whatever they think, whether they are bad or cool, they ain't going away. These guys are for real. They are building cool technology. You go to Huawei, they've got application after application. You know, here is our special, you know, AI to service the bullet train between Shanghai and Beijing every evening. We used to have human technicians, mechanics, who would go under the train and make sure it's all fine. Now we just have AI cameras and a couple of robots and they fix the train for you. They are doing this. We're not stopping them. We have imposed chip export controls on China to try to hold them back.

• 34:13Speaker 04

  It hasn't worked. These guys are moving ahead. And the latest thing was, you probably saw, is this model from a group called Jipu in China, which isn't quite as good as Mythos from Anthropic, but it's pretty close. So we are kidding ourselves if we kind of assume away the reality of China being a technology superpower. And we need to, on the contrary, get our heads out of the ostrich position in the sand and start talking to China about what happens when they have a Mythos-level model which could hack every single bank in the global financial system and wreak havoc. We need to persuade them not to release it on an open source, open weight basis, because then any criminal can do it and there won't be an off switch.

• 34:57Ed Elson?

  What is your view then on AI policy with China? Obviously, the big debate is, should we have these export controls? Should we sell chips to China? Are we selling weapons to our enemy? Or do we need to sell dumber chips, basically dumber weapons to the enemy? Or should we not have these export controls at all? I mean, do you think that we should have a policy or is the path forward more of a method of diplomacy?

• 35:23Speaker 04

  I believe in American power, first of all. I work at the Council on Foreign Relations in New York, and, you know, we do geopolitics all day long, and I believe that US power is generally a force for good. So I would rather that the Chinese were behind on AI, okay? And so to the extent that a chip export ban helps us to be ahead, I support it. And indeed, when it was first announced in 2022, I wrote a massive long essay in the Washington Post about why this was a good idea. But the reason I've had my doubts recently is that I look at the results and I'm not seeing that Chinese models are that far behind. And in the meantime, because they're not far behind, I think we have to reckon with

• 36:07Speaker 04

  the reality that they are building models which are going to destabilize the global cyber system. And unless we persuade them not to release them on an open weight basis, which is what they're doing at the moment, we have serious trouble on our hands. Like, everything in cyberspace will be destabilized. And we need a policy to deal with this proliferation risk. And I would be willing, I'm in favor of the chip export ban if we could have it for free and there'd be no downside. But if the effect of having chip export controls is that we can't talk to them about an agreement on not doing open weight Mythos, then I'm willing to trade a bit on the chip export ban.

• 36:48Ed Elson?

  Just looking at some of the, how these models have affected the ecosystem, and something we were saying earlier. In the US, you've got Anthropic, you've got OpenAI, you've got Gemini. Those are kind of the heavyweights in the US right now. But it does seem that as pricing becomes more of an issue, companies are more interested in cheaper models, which usually means Chinese models. And indeed, that is exactly what we're seeing. When we look at OpenRouter, which is basically a tracks developer marketplace for AI models, Chinese models went from less than a third of developer traffic in late 2025 to 60% by mid 2026. There are some companies that, American companies that have started using Chinese

• 37:33Ed Elson?

  models, Cursor, Airbnb, Shopify, Uber. Microsoft is currently testing DeepSeek. What do you make of this transition over to the Chinese models, specifically the cheap Chinese models? And what role does that play in potentially a policy discussion?

• 37:52Speaker 04

  Well, I mean, it shows you that they make good models that serious American companies are thinking of using. And so that's another argument for why you can't just pretend that China can be beaten and that's the end of it. And these guys are for real and we have to work with them, not just against them. Now, I think it's useful to just for a moment, think through the lens of the Cold War, where when there were nuclear weapons in the Cold War, there were two kinds of big risk, right? One was a nuclear conflagration between the Soviet Union and the United States. And the way we prevented that was through mutually assured destruction, basically close to parity in the power of the two arsenals and therefore deterrents.

• 38:36Speaker 04

  On the other hand, there was a different category of risk from nuclear weapons, which was the proliferation of these systems to rogue states or terrorists and so forth. And we dealt with that with a separate mechanism, which was the nonproliferation regime. Now, the point is, we were both competing with Russia, having an arms race with Russia, having a Cuban missile crisis with Russia, being told by the Russians at the United Nations, "We will bury you," as Khrushchev said when he banged his shoe on the table. So there was deadly serious competition between the two superpowers, but at the same time, there was cooperation on a nonproliferation agreement. And the way I see the future with AI is that we'll do the same.

• 39:20Speaker 04

  We will have inevitable competition between China and the US. But we will also, I hope, have collaboration because the proliferation risk is too awful to contemplate unless you have some collaboration.

• 39:34Ed Elson?

  It seems, though, that what they're doing is basically stealing what we have. People are calling it distillation. And you wrote about this, and your definition of distillation, quote, "Every time a US lab produces a cutting-edge model, Chinese rivals quickly reverse-engineer its capabilities and build a copycat version. The follower has the advantage." And when I look at how, I mean, companies are switching to Chinese models because Chinese models are cheaper. And as you say, maybe they'll use the more advanced, cutting-edge models in America that are more expensive for certain tasks, the Chinese models for other tasks, which essentially means that we are kind of, maybe we're collaborating, but also you could say that we're sort of

• 40:15Ed Elson?

  ceding advantage to the enemy, to the Chinese players in the AI ecosystem. And it seems that the reason that those models are good, cheap, is because of distillation, i.e. theft. I don't know if I'm being crude by calling it theft. I don't think I am. And I think the Chinese have shown a pretty strong track record of stealing intellectual property from the US and then going out and monetizing it on their own terms. I mean, what do we know about this process of distillation? And what do we know about why and how the Chinese models have become so cheap and therefore so successful on a global scale?

• 40:59Speaker 04

  So, distillation is a process which involves asking a very strong model, like a new American model comes out. A Chinese copycat would ask a ton of questions to that model and get the answers. And the answers amount to training data such that, you know, you can train the Chinese model, like if the question is like this, the answer should be like that. And when a frontier, like a first mover, an American lab, has to train the model in some specific, very complicated frontier expertise, like, let's say, you know, quantum

• 41:38Speaker 04

  physics, they expensively hire a bunch of quantum physicists and engage them in creating problem sets and, you know, model questions and answers. And generating that training data for the AI is a super expensive, time-consuming, painful process. But if you've, once you've created the AI that can replicate all those quantum physicists, the Chinese can come along and not hire the human quantum physicists, but just query the machine equivalent. And that's what distillation is. Now, when these Chinese companies do this, it is not illegal, but it is against contract.

• 42:21Speaker 04

  In other words, you, when you, when you sign up to use an American model, you sign, you check some boxes and you sign an agreement saying, you know, "I'm not going to like query you gazillion times and then train my own model by copying what you've done." And so they are violating contract, but not sort of federal law. That's my understanding of it. Now, whatever the legal niceties, the question is, can you stop it? I mean, I'm all in favor of stopping that if we can. And it seems to me that Anthropic and, you know, Google and OpenAI have all of the commercial incentives in the world to put in anti-distillation safeguards if they can come up with some.

• 43:01Speaker 04

  So I think this is a, like a self-solving problem insofar as it has a solution. And by the way, I should add, you know, Elon Musk, the other day or a few months ago, casually admitted that his company, XAI, had distilled from one of the US frontier competitors. So it's not just the Chinese who do this. But look, this is, this is the rough and tumble of, of the marketplace. It's not nice, but the practical question is, you know, let's stop it if we can. But insofar as we can't, we have to live with a reality on the ground, which is that the Chinese models are good.

• 43:38Ed Elson?

  Something I can't figure out is, I mean, if these AI labs are as capable as they say they are, can they not figure out some cybersecurity method to stop the distillation from happening? If Mythos is the most powerful cybersecurity technology and software that the world has ever seen, but we can't figure out how to get these Chinese developers to stop querying and replicating the same software, I'm sort of like, surely you guys can, can figure it out? I guess my follow-up would be, say they do figure it out, say we do put an end to Chinese distillation of US AI. Would that not, one, solve America's problems in one fell swoop?

• 44:22Ed Elson?

  And two, kind of put an end to Chinese AI, or at least the progress that they have been making? I mean, is that not kind of a poison pill for, for China?

• 44:35Speaker 04

  I'm not sure, is the answer, whether, if you could stop distillation, and by the way, I think the latest Anthropic models do have some anti-distillation technology built into them. So we'll see how effective that turns out to be. It's going to be obviously a, you know, cat and mouse, both sides trying to get smarter on this one. But, but to answer your question, let's posit that US labs figure out a way to stop distillation. Would the Chinese fall behind like a lot or just a bit? I'm not sure anybody really knows the answer. I kind of suspect that, you know, if they needed to generate their own data, they would.

• 45:19Speaker 04

  And they would pay more money and it would be more expensive and it would take them a bit more time, but they would get there because they've got plenty of extremely smart Chinese scientists that they could engage in generating training data.

• 45:30Ed Elson?

  We'll be right back. And for even more markets content, sign up for our newsletter at profitemarkets.com.

• 45:47Speaker 00

  This episode is brought to you by Michelob Ultra. Good times mean competitions between you and your friends, like seeing who can throw some pigskin the farthest or rush the field the fastest. Winner gets an ultra. Because it's not just a superior light beer, it's also giving fans superior access to their favorite sporting events. Superior is worth playing for. Visit michelobultra.com/superioraccess to enter. Enjoy responsibly. Copyright 2026 Anheuser-Busch InBev Michelob Ultra Beer, St. Louis, Missouri.

• 46:16Speaker 01

  The new LinkedIn Hiring Pro can't undo your last hire. The no show. Who set you back because they didn't show up on day one? Or day two, or ever again? And now your product launch is delayed because you're talking to alien abduction podcasters to track them down. But LinkedIn Hiring Pro can help make sure your next hire sticks. In fact, businesses who use LinkedIn are 24% less likely to reopen a role in the next 12 months. Hire right the first time with LinkedIn Hiring Pro. Post a free job today at LinkedIn.com/quality.

• 46:47Speaker 05

  Low energy, unfocused, foggy? You might be dehydrated. Whether it's hot yoga, over 100-degree weather, or too much sun. Gator Light, with a specialized blend of five electrolytes, was scientifically designed to help move fluids into the body faster for rapid rehydration. Shop now at retailers nationwide. Gator Light hydrates faster than water. Is it in you? Symptoms noted are signs of mild to moderate dehydration. Consult a healthcare professional if symptoms persist.

• 47:20Ed Elson?

  We're back with Prof G Markets. Okay, let's turn to the book for a moment. Your most recent book was The Infinity Machine, Demis Hassabis, DeepMind, and the Quest for Superintelligence. There was one quote from the book that really stood out. You said, quote, "If you couldn't negotiate safety mechanisms inside one company, what chance would there be to negotiate common safeguards among multiple labs in multiple countries?" Which really relates to kind of what we're discussing here in terms of AI safety and AI policy. But also, there's an important implication in that, which is that safety mechanisms were not able to be negotiated within a company.

• 48:03Ed Elson?

  What did you learn about the inner workings of these AI labs? And why can't they figure that stuff out?

• 48:09Speaker 04

  Embedded in the story of Google DeepMind and Demis Hassabis is this sort of morality tale about somebody who really wanted to make AI safe, and that was his sort of driving passion from the time he founded DeepMind in 2010. He bonded with his co-founder Shane Legg at a safety lecture in which they discussed the potential for AI to attack humanity by the year 2030, which turns out to be perhaps a prescient projection. At least the capability is going to be there. Whether the AI attacks is a different question. But I mean, anyway, the point is, Demis Hassabis was thinking about safety since the beginning. And so when he sold his company to Google in 2014,

• 48:54Speaker 04

  a condition of the sale was, "You've got to give me safety and ethics oversight board. I can't let AI be rolled out into the world just on the say-so of the Google corporate board. There has to be these independent people from outside." They mentioned Barack Obama as an example when Barack Obama was leaving the presidency. You know, could we have somebody of that stature who would be on a board saying, "When it's safe to roll it out"? Right? That was Demis's vision. And it was coupled with another hope, which was that all of the major scientists would come together in one single effort to roll AI out into the world. So there would be no competitive pressure to go unsafely and too quickly. Right?

• 49:37Speaker 04

  And it turns out, and this kind of transpires through the story that I tell, that all of Demis's optimistic stories about how he was going to make AI safe, they all crashed and burned. The idea of just one lab building AI turned out to be a pipe dream. It turns out that humanity is too tribal and competitive and disputatious. There will be multiple labs. When you are kind of confronted with the prospect of being able to build a god machine, there'll be plenty of different sects of worshippers trying to do that, right? And the idea of oversight within Google, ultimately, the Google board would not agree to giving some outside grandees a veto over how they use this technology

• 50:22Speaker 04

  that they were spending billions of dollars on developing. They weren't going to do that on a fiduciary basis, obligation to their shareholders. They couldn't, they felt. Right? So the point being that, you know, the experiment that Demis ran at DeepMind, and I discovered all these internal documents, which had the back and forth between the red lines from one team of lawyers to the other team about the exact safety mechanisms that they might use and all this sort of secret strategizing that Demis did to threaten to spin out of Google if he didn't get the safety oversight he wanted. And then the Google DeepMind general counsel threatened me and said, "I wasn't allowed to publish any of this." And I said, "The heck with you, I'm publishing it anyway." So it was all quite dramatic.

• 51:06Speaker 04

  But the bottom line of the story is, you know, it turns out to be impossible to impose safety restraints within one AI lab when that lab is in a competitive posture with respect to others. And we saw the same thing play out, of course, at OpenAI, but more in public, when the safety board temporarily fired Sam Altman for like five days. So, you know, what this shows us is that if you want to stop a race which has multiple players, you need the government to enforce restraint on all of the players at once. And if there are players in China, you need the Chinese government to buy in and also agree to put restraints on their guys when the US puts restraints on labs within the US.

• 51:51Speaker 04

  France, Canada, that's fine. Basically, the US can compel compliance in those places because Coheer and Canada or Mistrada and France depend on American technology and the American market to function. But with China, you can't compel them. So there needs to be two countries, two governments, that do a deal where everybody agrees to put some caution and like checking of models before they're released.

• 52:19Ed Elson?

  It was the policy of the US government that they were going to do none of that. And they said, I mean, they even issued an executive order banning states from trying to regulate AI in their own way. But then it seems like they've kind of turned on this. Last month, Trump signed a new executive order where he basically asks companies to hand over their models to the government, let the government check them and then kind of greenlight them. But, I mean, on the one hand, it's progress in the direction that you think is the right direction. But also, it's not very harsh or strict or strong.

• 53:03Ed Elson?

  It's basically just like, "Hey, could you please send your model over? We'd appreciate that." What do you make of Trump's AI policy at this point in terms of government oversight over these AI models and their safety?

• 53:18Speaker 04

  Given my perspective that government needs to get involved, I've been very much cheered up by what's happened since April when Mythos first came on the scene and galvanized the US government into paying attention and restricting the release. Because although you could argue correctly that on paper, the executive order is kind of a voluntary collaboration system with Frontier Labs, blah, blah, blah. The reality is, it's not voluntary in the least. Right? I mean, Commerce recently called up Sam Altman at OpenAI and ordered him to seek government permission before he gave his latest model to any customer. Right? Government has to sign off on each customer, customer by customer.

• 54:03Speaker 04

  This is extremely heavy-handed. Right? So I think they're in it for real, the government. They have realized that they can't let this stuff disseminate around the world without being controlled by a government. And... And so we're going to get pretty tough controls. I think the gap in the system is that they're not talking about doing this in coordination with China. Because the US policy world has two kinds of China expert. That you've got the kind of people who are always hawkish on China. And then the people who used to be a bit hopeful about collaborating with China, but then Xi Jinping rose to power and seemed to kind of frustrate all those hopes of collaboration.

• 54:47Speaker 04

  And so that group of former doves flipped and became Uber hawks on China. So you've basically got the traditional hawks and the new hawks, but they're both hawkish and nobody wants to say they want to talk to China. This is the problem. This is the huge gap in the posture because the US government has done a 180 on domestic regulation of domestic models, and I welcome that. The next thing that's going to come, just because it's necessary and they're not going to have a choice, is they're going to have to get over their inhibition about talking to China.

• 55:19Ed Elson?

  So is that sort of the solution is get in a room with Xi Jinping and become partners in tackling this together? I mean, it sounds like kind of simplistic, but maybe that actually is the way to do it. The alternative would be, you know, force their hand in some way, create some sort of policy where you say, no, you're not going to get any chips, or you're not going to get this, you're not going to get that. Your view is we just need to talk with them and have more of a relationship?

• 55:48Speaker 04

  It's a bit more complicated than that. I mean, I think you can talk and also put pressure on them at the same time. I mean, going back to that Cold War analogy, there was a vicious competition between the Soviet Union and the United States at the same time as there was collaboration over proliferation. And so I think there will be competition. And by the way, you know, there are ideas around strengthening the chip export controls. And I'm not against that. There is one theory of the case, you know, economists sometimes talk about corner solutions. You can either have a fully pegged currency or a fully floating one, but if you go for some mushy middle ground where it's kind of semi-pegged, then hedge fund speculators

• 56:31Speaker 04

  are going to see that you're not really determined to defend that and they're going to eat you for lunch, breakfast and dinner. So it's the same thing with this AI policy. There are corner solutions. You could either like give up the export controls or be willing to give them up and go talk to them and say, okay, we know you didn't like that. As a show of our sincerity in wanting to work with you, you know, we're going to offer to loosen those controls, but in return, we want you to collaborate on fixing this non-proliferation risk, right? That would be one corner solution. Or the other corner solution is you don't say that. To the contrary, you tighten up the chip export controls. There's this massive loophole right now whereby if you're a Chinese model builder,

• 57:12Speaker 04

  get this, you can train on Nvidia chips, the most advanced versions, all day long because the cloud compute that you access is in Malaysia or some other offshore place, which is fully allowed to import Nvidia chips, the most recent sort. Right? This is a crazy loophole. You're telling the Chinese they can't use Nvidia chips, but then you're letting them just like use a data center kind of across the border. It's nuts that that loophole exists, right? So the other corner solution is get serious about the policy that you've enunciated and cut off the loophole and cut off the destination and put China in a position where it's so weak, it's kind of begging for collaboration. Now, I'm agnostic.

• 57:56Speaker 04

  I'm like, we need to collaborate. I'm flexible on how we get there. I think there's different theories.

• 58:01Ed Elson?

  Just going back to Trump's changing of his positioning. It used to be, we're not going to regulate, we're not going to have any oversight because we believe that if we do that, then it stifles innovation and we want, you know, markets to do their thing and AI labs to sort of run free, uninhibited, et cetera. Then Mythos happens, Anthropic's model that was a real concern for cybersecurity. And then Trump changes his tune and issues this executive order, which you believe in this, I think is fairly so, that that actually is like stringent, they do take it seriously. Why do you think that happened? What was it about Mythos?

• 58:46Ed Elson?

  Was it maybe something to do with China? Like, why did they do this thing that ultimately did amount to a 180 on AI policy?

• 58:55Speaker 04

  Simply because Mythos was so powerful, it was very threatening. I mean, the prospect that you could take this model and find code vulnerabilities in every single entity on the internet and then hack it, like, that's curtains for the financial system. So that's why they took it seriously.

• 59:13Ed Elson?

  Yeah, fair enough.

• 59:14Speaker 04

  I mean, I think throughout this topic, throughout this topic, the logic of the technology is going to force governments to do things which six months earlier, they said they would never, ever do. And that's happened with domestic regulation already in the US. I believe it's going to happen with international collaboration.

• 59:32Ed Elson?

  I've already started to see pushback from, I mean, Silicon Valley spent a long time not being friends with Washington. And then in the last couple of years, they became very close friends with people in Washington. And I would assume that this is going to be, I don't know, this is going to cause a rift again because a lot of the technologists said that what we want is government to have no involvement in this, in AI and in artificial intelligence capabilities. And Trump said, sounds good, I'm with you. And now he's not. I'm not really sure what that means for the relationship between Silicon Valley and Washington, but I assume, I don't know if you have any insight into this, I assume

• 1:00:17Ed Elson?

  it's not going to be great.

• 1:00:19Speaker 04

  Well, look, I mean, you've got this sort of Putin and the oligarchs sort of story. You've got, you know, endless examples of authoritarian governments with, you know, big business titans. And, you know, where does the power lie and how stable is that relationship? And the answer is, it tends not to be stable, point one, and point two, the government wins because they have the monopoly on coercion. And so I think Silicon Valley, you know, is figuring that out and they realize that the government is too powerful to ignore. I mean, you know, Dari Amade tried to say to the government, you shouldn't use these tools for certain things like mass domestic surveillance. The government said, get lost, we are going to call you a supply chain risk and we're

• 1:01:04Speaker 04

  not going to be dictated to you. I mean, who won that fight? Clearly the government won.

• 1:01:08Ed Elson?

  It has been fascinating watching Trump use the full power of that coercion, even this week when he decided to step into the proceedings of the World Cup and he got exactly what he wanted and the US

• 1:01:21Speaker 04

  Absolutely.

• 1:01:22Ed Elson?

  Got their player back. Just as we start to wrap up here, you have studied a lot of the characters in AI. You wrote your book about Demis Hassabis, founder of Google DeepMind, kind of the OpenAI before OpenAI. You studied a lot of these characters. From your research, from writing that book, what did you learn about the people in AI? And what has that kind of told you about what might ultimately happen next and who might ultimately win the AI race?

• 1:01:52Speaker 04

  You've got Sam Altman, who is essentially a commercial opportunist who wants to win commercially or at least survive commercially. And, you know, his drive is to be a big shot. And he thought of running for governor of California at one point and being a political big shot, but then he decided that building AI was like a bigger big shot. And he wants to just like put his imprint on it. He's not obviously a PhD scientist, he doesn't have even a first degree because he dropped out of Stanford to do other stuff. It's not to say he isn't anything other than massively smart, but he isn't a deep scientist. Then you've got people like Dari Amade and Demis Hassabis, who are PhD scientists, who come at this from that perspective, who want to use AI to advance deep science, that's

• 1:02:36Speaker 04

  their deepest motivation. And I believe it's very deep with both of them. And I believe that's the reason why they are number one and number two in this race. It's good for recruiting the best scientists. It's also good for holding together and leading a fundamentally scientific enterprise like building artificial general intelligence. And the point where this came home to me is, you know, when I was talking to Demis one day about his motivation for building AI, and he started to say, listen, when I'm reading scientific papers at two o'clock in the morning, Sebastian, I see reality staring at me in the face, calling at me, saying, I'm here to be discovered.

• 1:03:18Speaker 04

  And if I had artificial general intelligence, I could discover the fundamental rules that explain the fabric of reality. It would be like understanding all of nature, which presumably may have been created by some kind of divine intelligence. And so in this sense, my quest for AGI is kind of like my way of getting closer to what I might call God.

• 1:03:41Ed Elson?

  Sebastian Mallaby is the Paul A. Volcker Senior Fellow for International Economics, the Council on Foreign Relations, a two-time Pulitzer Prize finalist. He is the author of six books, including More Money Than God and The Power Law, which have become investment classics. His latest book is The Infinity Machine, Demis Hassabis' DeepMind and the Quest for Superintelligence. He also co-hosts a weekly CFR podcast, The Spillover, which examines the ripple effects of global events across policy, geopolitics, economics, technology and financial markets. Sebastian, thank you so much for your time.

• 1:04:12Speaker 04

  Thank you so much. Nice to talk to you.

• 1:04:14Ed Elson?

  This episode was produced by Claire Miller and Alison Weiss and engineered by Benjamin Spencer. Our video editor is Jorge Carty, our research team is Dan Shulon, Kristin Adonahue and Mia Silverio. Jake McPherson is our social producer, Drew Burrows is our technical director, and Catherine Dillon is our executive producer. Thank you for listening to Prof G Markets from Prof G Media. If you liked what you heard, give us a follow and join us for a fresh take on markets on Monday. [Music]

• 1:04:42Speaker 06

  Close your eyes, exhale, feel your body relax and let go of whatever you're carrying today.

• 1:05:27Speaker 07

  Well, I'm letting go of the worry that I wouldn't get my new contacts in time for this class. I got them delivered free from 1-800-CONTACTS. Oh my gosh, they're so fast. And breathe. Oh, sorry. I almost couldn't breathe when I saw the discount they gave me on my first order. Oh, sorry. Namaste. Visit 1-800-CONTACTS.com today to save on your first order.

• 1:05:47Speaker 06

  1-800-CONTACTS.

• 1:05:49Speaker 08

  Booking.com is the easiest way from a day surrounded by noise to a stay surrounded by nature. That's nice. Go on, book it. It's easy. Booking.com. Booking dot yeah.

• 1:06:20Speaker 09

  They're running a cover two defense, but you're running to get game day deals on Uber Eats. Keep everyone at tonight's watch party fed with deals from your go-to restaurants or stock up with deals on your favorite snacks and groceries. Whatever the occasion, get game day deals on Uber Eats all season long. It's not football without food. Uber Eats, order now. Terms apply. Product availability varies by region. See app for details.