Ep 88: Unpacking DeepMind's Quest for SuperIntelligence with Demis Hassabis' Biographer

Sebastian Mallaby spent three years and 30+ hours interviewing Demis Hassabis in the back of a British pub to write The Infinity Machine, and the conversation uses that reporting to surface the most underexplored figure in AI. Demis founded the original AI lab in 2010, won a Nobel Prize, runs models that consistently top the leaderboards, and yet remains so unrecognized that Sebastian's own publisher worried no one would buy a book with his face on the cover. The throughline is a paradox: Demis tried to prevent the AI race we're now all living through, and now finds himself one of its central protagonists. He used to believe a single lab could carry the safety burden to AGI; he now sees safety as a collective action problem only governments can solve. He hedged DeepMind's research bets across every promising direction, and as a result missed the two most consumer-defining moments in modern AI — ChatGPT and Claude Code. He nearly spun DeepMind out of Google with a secret $1B Reid Hoffman pledge backing him, but never used the leverage and stayed — and won a Nobel Prize the next year. The episode also zooms out to the structural forces shaping the race — why hyperscalers can't out-recruit concentrated-bet labs, why Sebastian gives OpenAI roughly 50/50 odds of being absorbed by next summer, why he thinks Anthropic should IPO right now, and what the personal histories between Demis, Elon, and Sam reveal about who actually trusts whom.

• 0:00Speaker 00

  Demis has a Nobel Prize, Sam didn't finish his first degree. Therefore, Demis doesn't take Sam very seriously.

• 0:05Speaker 01

  Do you think he feels like or will have to become more of a public figure than he is today?

• 0:09Speaker 00

  Demis in some ways is too sensible.

• 0:12Speaker 01

  What was the moment, I guess, your view of him kind of shifted the most?

• 0:15Speaker 00

  He would sometimes erupt in these conversations I was having and he would start banging the table and saying, maybe if we approach science the right way, we understand more about nature, we will be getting closer to something that we could perhaps call God.

• 0:28Speaker 01

  Sebastian Mallaby spent over 30 hours with DeepMind CEO and co-founder Demis Hassabis in preparation to write his book, The Infinity Machine. I got to sit down with Sebastian to get all his reflections on this experience. It was fascinating to talk about some of the stories that Sebastian reported on in this book, including the fact that Reid Hoffman offered a billion dollars to Demis to spin DeepMind out of Google at some point. We talked about Demis's relationship with Elon and how that's evolved over the years, as well as what he thinks of Sam, Dario, and the general relationship between all the AI leaders in the space today. And we hit on Sebastian's reflections on Demis, what motivates him, what his blind spots are. Just a fascinating conversation with someone who's spent unprecedented time with someone who's really at the cutting edge of AI today. I think folks will really enjoy Sebastian's candid takes here. Without further ado, here he is.

• 1:17Speaker 01

  Well, Sebastian, thanks so much for coming on the podcast. Really appreciate it.

• 1:20Speaker 00

  Thank you. It's great to be with you.

• 1:21Speaker 01

  I feel like The Infinity Machine and your kind of story of DeepMind and Demis Hassabis has captivated a bunch of folks in the AI world. I know that I inhaled this book. I think I finished it in like 24 hours. It's just a gripping story and you got this just incredible access to Demis. I think, you know, the sitting in this pub for multi-hours at a time, talking about all sorts of things. Just a fascinating, you know, opportunity and window into definitely one of the most interesting people and companies in this AI race today. And so I appreciate you writing the book and appreciate you coming on the podcast to talk about it. So our goal today is to, you know, use your reporting to understand the people, decisions and dynamics that shape the AI race that we're living through. And so maybe to start, I feel like one of the interesting themes in your book is whether this race that we have today between the labs

• 2:06Speaker 01

  was inevitable or could have gone differently if different players or decisions had been done. I guess I'm curious, after doing this work, do you think there was another path or was this kind of inevitable?

• 2:16Speaker 00

  You know, I think it was inevitable. I think when you have this sort of supremely strong technology, there's going to be multiple labs in multiple countries that are just desperate to try and build it. And we know the China stack, you know, is pretty strong. And so despite the lack of semiconductors, they were going to have a go at it and they are actually doing pretty well. And then clearly in the US and then in a few other countries, you've got Mistral in France, Coherent in Canada. You know, there was bound to be many players, just as it's, the technology is too sweet for it to be only interesting to one team. What's strange about this whole debate, though, is that's not how people saw it ex ante. That, you know, when Demis was starting DeepMind, he really hoped that he could avoid the race dynamic. It seems naive in retrospect, but that's what he hoped.

• 2:58Speaker 01

  One really compelling anecdote you had was, I believe, Demis in his interviews would say, you know, he was interviewing candidate and he'd say, look, at some point, we may be really close to AGI and we'll go fly to a bunker and figure this all out as kind of like one team. Would you be willing to, you know, get on that flight and do that? You know, I think certainly as part of the founding origin of Anthropic is also this belief like, hey, we need to be the team that like gets, you know, as we get to the precipice of AGI, we're the ones figuring this out. You know, clearly a key part of starting all these companies. Do you think these folks, I mean, particularly Demis, like, do they still believe this? Or how has their kind of thinking evolved, having seen the way things have played out these past years?

• 3:33Speaker 00

  No, I think Demis has swung from one extreme to the other. You know, he began by thinking there could be a singleton scenario, just one lab. And by that, he really meant DeepMind and himself.

• 3:44Speaker 00

  To the opposite extreme, where he now sees that there's a very crowded field and therefore that it's almost pointless for one lab to pursue safety by itself. Because if one lab is safe and then the other ones aren't, it doesn't make the world safer. So he really has shifted to seeing this as a collective action problem that only a government can solve.

• 4:03Speaker 01

  Because one thing I thought was really compelling from your book is obviously there was this early AI safety summit that Demis and the team put together and they're sharing, you know, the progress they've made and why they're worried about it. And, you know, some people in the room like Reid Hoffman and Elon Musk take that information. And then they're like, oh, this technology really is starting to work. We should ourselves potentially do something around that. You know, do you think, obviously, to get safety to work going forward, you need these different companies to agree to share things and collaborate? I imagine that left a bad taste in Demis's mouth. How do you think he and other kind of key players in this space think about, you know, potentially sharing what they're doing, given that, you know, past experience?

• 4:38Speaker 00

  Yeah, I mean, you're totally right. That was in 2015, summer of 2015. They have this summit at SpaceX. Elon Musk is hosting it and the idea is he's going to be brought into the tent by DeepMind. He's going to be part of their efforts. He's going to be, you know, chairing this sort of safety oversight board and therefore he wouldn't set up a competitor. And of course, at the end of 2015, he did set up a competitor, OpenAI. And so that kind of really drove home the reality that we're going to have a race. And I think if you ask now, okay, so what about future collaboration? I think from Demis's point of view, or from any of the lab leaders frankly, you can't trust the other guys. And therefore, the only way you get trust is if you have a government enforcer that comes along and say,

• 5:20Speaker 00

  "Look, here's the rules for everybody. There's going to be a level playing field. You're all going to have to, you know, abide by some sort of safety slowdown, you know, pre-testing of models before you release them, all that stuff." And you all have to do it. And then the reaction, of course, will be, "Yeah, but what about the guys in China?" And that's why ultimately I think this has to be a US-China collaboration, you know, remote though that prospect may seem to many listeners right now.

• 5:46Speaker 01

  I mean, do you think Demis and others, I think that's actually realistic. I mean, obviously there's, it, you know, is such a pressing problem, but you know, it doesn't seem like folks have the most confidence in governments and certainly, you know, intergovernmental collaboration across the world. Like, it intellectually makes sense that that is kind of the clear way to solve this problem. But do you think they think there's actually a probability this this does happen successfully?

• 6:07Speaker 00

  Well, you know, I think they ought to believe that it could happen. And we do have the Food and Drug Administration and people complain that it's slow and all that, but but it does, you know, have expert reviewers who look at clinical trials and say, "Is this drug safe or not and can it be released?" And if you can do that for pharmaceuticals, and you should do it, AI models that can be more destructive on a grander scale than a drug certainly need to be reviewed ex ante. And I think in Britain, Actually, you have an example of a government AI safety institute that's pretty effective, has some high-class scientists. I mean, the chief scientist there is Geoffrey Irving, who was at DeepMind originally, did a lot of the post-training on the early models. He's a really serious researcher, was also at OpenAI, by the way, and a close collaborator

• 6:51Speaker 00

  of Darius. So you've got serious people there, and they have found vulnerabilities in systems in the past and quietly shared them with the private labs that have failed to find the same vulnerabilities. So it shows that you can aggregate some technical expertise inside these public institutions because that number of folk feel motivated and public-spirited enough to go work for the government. So I don't think we should give up on that.

• 7:15Speaker 01

  From the outside, it's hard to tell. You know, it's almost like such a dispiriting story of, you know, Demis kind of being idealistic and then realizing this dynamic isn't going to play out. And so it's hard to tell, you know, I guess, reading between the lines in your book, whether he's kind of just resigned to this dynamic or is newfound hope in like the the actual possibility of, you know, intergovernment collaboration. Because obviously, even the examples you give are just within a single country, right? And certainly not at the scale of coordination across countries.

• 7:38Speaker 00

  Yeah, I mean, what he says, I mean, the very last line of my book is, "I'm optimistic still." And that's a quote from Demis trying to, you know, persuade me. I've been, I'm pushing him in my interviews with him. "Are you really optimistic? Because it seems like we've got a race dynamic, you know, in, of course, January 2025, you have DeepSeek come out, and that really signals the advent of the Chinese labs into this space." And, you know, I'm writing through 2025 into the end of 2025, and I'm saying, "Are you still optimistic, Demis?" And he says, "Yes, I'm optimistic still." And I think the reason for that insistence, apart from like, what's he gonna say, is also that, you know, when something kind of dramatic happens, the world does coalesce and take action. You know,

• 8:23Speaker 00

  if you look at COVID, the reaction to COVID, I mean, governments on a national basis really did stuff that people didn't expect beforehand. You know, so in a crisis, people do react. When you have something like, you know, trade, where protectionism has a pretty slow and subtle effect on economic performance, you don't really get a political reaction to try and fix that. And that's what we've seen in the last few years. But when you, when it, if you have an acute thing like Mythos, an anthropics model comes out, you know, and people suddenly get worried, you see the US government pretty much do a 180 from a laissez-faire position to, oh, well, we better control this kind of position. And I think that shows the shock effect. Is extremely important in determining whether you get government action or not.

• 9:05Speaker 01

  Switching gears, obviously a key part of, you know, and through line through your whole work is just the story of DeepMind and its ultimate relationship with Google. And it's just an incredible story. And so I guess, as I recall, you started this or you've kind of got Demis to agree to this like right before ChatGPT. And so obviously, post South Football and some really interesting things, but certainly the world evolved in a bunch of interesting ways from the start of your project. People talk about Demis and DeepMind all the time. You know, before you published this book, but you'd kind of done the research. What do you think the popular narrative got like most wrong about Demis, DeepMind, the way people would talk about, you know, the two?

• 9:37Speaker 00

  Well, the extraordinary thing to me is how people basically discounted Demis. I mean, you know, I've been interviewed a bunch of times since my book came out a few weeks ago. And, you know, often people can't pronounce his name. They say Demis, they say Hassabis instead of Hassabis. They kind of barely know who he is. You know, my publisher, Penguin Press, was figuring out what to do, how to do the cover design. And they wanted to use this picture, but at the same time, they didn't think people would recognize him. So you couldn't sell books based on some, you know, person, nobody recognized him.

• 10:07Speaker 01

  Right. At this point, I assume Sam, Dario, they'd be fine for a cover of a book.

• 10:11Speaker 00

  Yeah, yeah, totally.

• 10:12Speaker 01

  Elon, yeah.

• 10:13Speaker 00

  Elon, for sure. And, and Demis, you know, is the guy who founded the OG Lab in 2010, way before anybody else, really created the model that then OpenAI copied later. I remember going to see Dario when I was doing my research and, you know, he said, yes, Demis was the original figure and he's got the whole AI for science space to himself, which was true at the time. I think it's less true now. But so I think people just underestimated how important he is. They underestimated Google as a company because they thought innovators dilemma, they were too slow. OpenAI went first with a model with a chatbot.

• 10:54Speaker 00

  And now they've got this massive brand effect, OpenAI does. And so, nobody can catch up. Well, that proved wrong. And, you know, as of late 2025, the Google DeepMind models, Gemini 3.0, were better on the leaderboards than the adversaries. Of course, since then, we've seen a big anthropic surge. But, you know, the point is, I think people were too quick to crown OpenAI and Sam Altman as the winner.

• 11:22Speaker 00

  And underestimated both Demis as a person and Google DeepMind as a company.

• 11:27Speaker 01

  It is interesting, obviously, like since you're reporting, you know, I guess in 2026, you've had these rise of these, you know, coding agents and, uh, you know, certainly, you know, Anthropic and Claude Code were the first there and OpenAI, you know, with Codex was, was, was right behind. It does feel like Google continues to do like amazing science and, you know, they do, you know, they always do well on the leaderboards, but for whatever reason, it, it struggles on like the zeitgeist and like actual usage on the product side. If these two monumental moments were, you know, ChatGPT and consumer products and then Claude Code and, and the coding agents, Google doesn't really have a, a, a super competitive product in either of those spaces, just from a usage perspective. Not the products themselves are, are good, the models are good, but they haven't really figured out the, the latter part. And I'm wondering kind of what you make of that.

• 12:06Speaker 00

  People use Gemini more than we realize, right? You know, it's sort of bundled into Google search at this point, the AI mode thing. But I, I, I take the broader point, which is that both of these sort of seminal moments in proving out, you know, the, the consumer experience, which were ChatGPT, as you say, on the consumer side, and then more recently with coding, uh, neither of those came from Google DeepMind. And I think what that perhaps shows us is that, you know, partly because of Demis's personality and his intellectual formation, which was PhD in neuroscience, this very broad study of what intelligence might be, a very broad approach therefore to building artificial intelligence. There's this, there's this kind of let's try everything approach to AI

• 12:49Speaker 00

  research. Whenever there's like two different paths you could go down, they say, well, we'll do both. And if we can find the third path, we'll probably do that, too. They're very hedged, right? Whereas I think Anthropic, um, got to coding because it was willing to take a more concentrated bet. Um, it never went into the whole field of, you know, generative video, uh, just never had a Sora equivalent. Um, and, you know, at the same time, OpenAI, being a startup, didn't have the reputational baggage that it had to protect that Google has. And so it was willing to put out a chatbot that hallucinated a lot at the beginning.

• 13:26Speaker 01

  It honestly has a lot of echoes of the transformer itself, right? And basically, you know, I think OpenAI making the bet of going all in on scaling transformers, which were obviously invented at Google and Google pursuing a bunch of different paths. And, you know, then to your point, OpenAI started to do a bunch of things and Anthropic deciding, hey, we're just focusing on coding. Have you seen kind of a shift in Demis and DeepMind's focus? I guess, given what's played out and given this kind of, you know, it's such a tension, right? As a polymath, as someone who's so interested in many facets of AI, to focus on a ton of different things versus, you know, the thing that's happening at the moment. Obviously, OpenAI, I think famously, you know, has kind of consolidated down and said, we were doing too much. Now we've got to like really hone in and catch up to Anthropic on coding models.

• 14:10Speaker 01

  You know, how do you think Demis and the DeepMind folks think about this at this moment?

• 14:13Speaker 00

  I think, you know, they do have this tendency to try and do everything. As I was saying, I don't think that's been cured. I mean, you know, I was chatting actually just last week with one of the young scientists from that shop. And, you know, that was very much the tenor of what he was telling me. It's like, you know, we always make multiple bets. We never really go hard down one avenue. And, you know, from a corporate point of view, maybe this makes sense. I mean, if we, if we think about what happened with the consumer side, the sort of general chatbot model, yeah, they were late. They were late both in trying to develop it after the transformer thing dropped in 2017. And then they were late again in productizing, you know, behind ChatGPT. But then they caught up by 2025.

• 14:58Speaker 00

  So maybe if you have very deep pockets like Google and a very deep technical bench and tons of human talent and amazing amounts of compute, you know, you can afford. They're not doing Apple, right? Apple is the absolute extreme where they just say, whatever, we're not going to do this AI thing. We'll just charge people to put it on our iPhone. That's an extremely hands-off position towards AI. Google is much more in it, but it doesn't mind being a couple of years behind because it figures it can catch up.

• 15:26Speaker 01

  And I wonder at some point whether that that does shift. Obviously, you you know, allowed $2 trillion plus. We'll see whether end up being worth companies to to or at least in the in the prediction markets seem to be worth that, you know, come come up and and gain a lot of territory. And, you know, I think it's it'll be really interesting to see going forward, you know, whether that that focused or that like diversity of different things that are pursuing, you know, ends up uh ends up continuing. You know, you talk a lot about in the book about, you know, the governance of DeepMind and Google and like the way these two organizations work together. And I think actually, one of the most interesting parts I thought was this DeepMind was considering spinning out of Google. There was like some financing behind that. They ultimately chose not to. Why don't you think they ended up, you know, deciding to to spin out of Google? And I guess in retrospect, was that the right decision?

• 16:11Speaker 00

  Yeah, so there was this secret plan called Project Mario to spin out. And Demis was not so happy when I kind of discovered about it from other sources. I had to speak to the general counsel of Google DeepMind at one point who tried to persuade me I couldn't write about it. Other people leaked me the documents and I had them and, you know, definitely had good information. It was all true and I wrote it, so whatever. And the point of this story is is actually partly it goes back to your safety thing that that Demis really, really wanted to get safety oversight over the Google DeepMind models or just the DeepMind as it was then. And Google corporate in Mountain View wasn't doing that. And so he had to have a credible threat of spinning out. So he went to Reid

• 16:55Speaker 00

  Hoffman. Reid Hoffman pledged a billion dollars to finance a spin out. And Demis used that to kind of pressure Google. Although I don't think he ever mentioned that Reid Hoffman had done that, but he he he pushed he pushed Google, knowing that he had this back pocket option. And I spoke to a lot of the advisors who were in the room when DeepMind was figuring out, you know, whether to go with a Reid Hoffman spin out option. And there were those who said, Look, you should spin out because then you'll be an independent startup and you'll have all the incentives and the kind of, you know, alertness and the and the and flexibility, the agility that go with that. You can, you know, give all your team, you know, very high powered financial incentives linked

• 17:38Speaker 00

  to your performance and it'll be awesome and you should spin out. And then Demis's view in the end was You know, legally, it's going to be, there may be a court battle over it, you know, whether we have the right to do that. And I just want to do science. I want to not be distracted by legal fights. I want access to tons of compute I'm staying in. And so that was the call he made. Was it right? Was it wrong? Well, I mean, it did lead to him getting a Nobel Prize, right? In 2020, you know, it's a small matter of a Nobel Prize. You know, in 2020, so a year after he gave up that fight with his parent company, he ships AlphaFold, the protein folding prediction model,

• 18:23Speaker 00

  and that gets him the Nobel Prize.

• 18:25Speaker 01

  The kind of way you just framed it was more of like, hey, this was part of negotiating leverage to, you know, get some of the safety stuff. I mean, how seriously do you think the DeepMind team was considering this at the time?

• 18:34Speaker 00

  Well, I think they always, you know, didn't quite, they never quite got to that decision point in their own heads. Like, they wanted the Plan B spin-out option because why not have that? And they weren't sure how to then use it in the negotiation with Google. And I think they determined never to tell Google explicitly that they had that, but a kind of hint that, you know, there might be something in the background. And if you don't do what we want with safety oversight, we may do something you don't expect kind of thing. But, you know, ultimately, they never really waved that threat explicitly. And they never exercised a threat, even though Google didn't give them the safety oversight that they wanted. So in the end, I mean, in some sense, it's a story of the naivety of the founders,

• 19:19Speaker 00

  both Demis himself and Mustafa Suleyman, his co-founder, who's now at Microsoft. I mean, they went into this, you know, not quite sure what their end game was. And in the end, they never really used that spin-out. I mean, they not, in the end, they didn't use the spin-out option. And so in some sense, spending three years going back and forth to Mountain View with this was a waste of time.

• 19:43Speaker 01

  I'm wondering, in the course of the conversations that you, you know, that you had with Demis, I mean, obviously, we talked about that, the AI safety summit already and kind of realizing, yeah, that was not ideal. Are there any other things that, like, he regrets, I guess, about how the whole, you know, the whole past decade has unfolded?

• 19:59Speaker 00

  What's he regret? I'd say that he would certainly not regret spending all that energy on AI for science.

• 20:07Speaker 01

  I love that anecdote in your book, by the way, that like the day they won AlphaGo, AlphaGo won, you know, beat the best player in the world, he was already on to a bio and someone picked it up on a mic, which I just thought was talking about an ambitious and always moving leader.

• 20:22Speaker 00

  Right, right. He rests on his laurels for about 10 seconds and then he says, the next thing we're going to do is we're going to solve protein folding. Yeah, that was amazing. But I think, you know, the serious point here is that he not only got a Nobel Prize out of it, he also views this, I think, correctly as absolutely central to the whole acceptability of artificial intelligence in society writ large. If AI doesn't deliver, you know, clear benefits for humans, and it's just like lots of job disruption, which may be good for productivity, but is kind of painful for people on the receiving end. I'm not sure that politically AI will really be rolled out with some massive, without some huge backlash. So, so I think, you know, both from it's good to advance science,

• 21:05Speaker 00

  you know, I like winning a Nobel Prize, but also AI can't succeed without this kind of science stuff. I think he doesn't regret that for a second. You know, does he regret not being faster onto chatbots? Yeah, of course. I mean, it would have been better to understand, you know, like Ilya Sutskevich did at OpenAI, you know, the moment the transformer dropped, Ilya is like jumping out of his chair, running down the corridor, going to find Alec Radford, saying, hey, we're going to build a language model based on this transformer architecture. I mean, on the day the paper dropped, because he had this prepared mind, you know, ever since his PhD, he had been thinking about how to deal with, you know, sequential data like text. And so when the transformer paper came out, he immediately saw the significance. And of course, it would have been great.

• 21:51Speaker 00

  For DeepMind, if they had had that same perception that quickly and it took them instead, you know, two, three years to get there. And Demis, frankly, still has a bit of a blind spot about this. When I say to him, "Well, you were sort of three years late." He goes, "No, no, no, no, we had Chinchilla, we had these other models." And I'm saying, "Yeah, yeah, but you didn't release those till the end of 2020. And Ilya, by then, had been working on it for three and a half years, so you were late." But he really resists that, and I think that is because he does regret that.

• 22:23Speaker 01

  I thought it was interesting, too, the way you kind of put it in your book is that part of the resistance maybe to the transformer architecture was this, he obviously comes from such a deep neuroscience background and this belief that the path to artificial general intelligence has to flow through something that looks much more like the way humans learn and the human brain, which obviously was the RL world, and less of just, how can you really understand what it is to be a human by consuming the internet? And I think your point was actually, it turns out, you can understand it way more than one might have anticipated from scaling these models.

• 22:52Speaker 00

  Yeah, and by the way, I think there may be some version of that same dilemma with robotics. In order to train robotics and simulation, you need these real-world simulations, and probably video is one important tool for building those simulations. So potentially, if DeepMind gets the right bet on the right pathway to much better robotics, it has what it takes. But we'll see whether there's some version of what went wrong with language and the transformer model is that Demis just underestimated the importance of language to superintelligence.

• 23:34Speaker 00

  And there may be, to get robotics right, is it, for example, something that you're going to do by building physical robots and having them crash and break their arms in some kind of physical lab?

• 23:46Speaker 01

  But it's an interesting point, because actually, there's many parallels in the robotics space where there's 10, 15 different approaches that people are trying. And I imagine if you're Google, and given the way DeepMind's approached other things, you'll kind of put your hand in a bunch of these approaches. And meanwhile, you'll have startups that were like, "We're all in on X approach or Y approach." It'll be interesting to see how that plays out, because you can certainly empathize today, when the recipe's not clear, it's not particularly irrational for DeepMind to keep their options open, but also leaves you vulnerable to someone that really goes all in on the transformer or on code or whatever the next equivalent is in the robotics space.

• 24:19Speaker 00

  A hundred percent. And I, and I, I think here actually there's a larger fascinating question about whether venture backed innovation beats sort of hyperscaler, you know, tech behemoth, um, you know, AI approaches because.

• 24:34Speaker 01

  I have a biased opinion on that one.

• 24:35Speaker 00

  Yeah, yeah, well, that's fair. But, but I think, but, but, you know, I'm less biased, but, but, you know, I've written about a hyperscaler now, but my previous book was about venture. I kind of done both. But I think it's kind of a interestingly balanced thing because you know, in one sense, this is a very capital intensive project. You need very deep pockets. If you look at OpenAI's ability to fight Google over the long haul on generative AI. You know, I wrote in the New York Times in January that I thought OpenAI had a 50% chance of going bust by next summer.

• 25:06Speaker 01

  Is it still 50, by the way?

• 25:07Speaker 00

  Yeah, I mean, you know, I said fail. I mean, I, I, what I mean is it sells itself as a discount, at a discount to, to some hyperscaler. And I think, you know, I've, I've been impressed by some of the, um, you know, expenditure cuts they've done, you know, cancelling Sora was a smart move. They've done some impressive cost cutting. And so I give them credit for that. On the other hand, of course, the sort of rumors around Sam being replaced by Brett Taylor are pretty widespread. And it's just a general sense of, you know, the leadership is damaged goods. Yeah, I think on net, I'm still around 50 50 that they fail next, by kind of next summer.

• 25:46Speaker 00

  And, and that's not because they don't have great tech, because they do. I mean, the tech is great. It's just the business model, it's the problematic. And it's problematic because you're up against Google, which just has unlimited amounts of cash to spend you into the ground. And so in one sense, this looks like a platform shift that benefits the incumbents. On the other hand, as we've been discussing, when you have the luxury of unlimited amounts of talent and compute and money. You tend not to make these strategic concentrated bets. And if you're up against a bunch of different venture backed startups, that each of them makes different, you know, highly concentrated bets, then maybe one of those bets work out.

• 26:29Speaker 01

  But it is a game of very thin margins, right? I mean, even if you take Anthropic, I think there was always a question about whether they would be able to raise enough capital to stay in this race or basically, you know, have to sell or be deeply embedded with a hyperscaler, which they are to some extent. And it felt like it might be headed on that track with with either Google or Amazon. And then, you know, right at the moment where you might have wondered, hey, can they keep raising the amount of capital that's required to stay in this game? The coding models it and they hit in a huge way. And now, you know, Anthropic is the hottest company in the in the Bay. But like, it's a really interesting counterfactual. If that stuff had hit six months later, 12 months later, it certainly wasn't inevitable that it was really going to start working then. And yeah, a lot of this stuff, I mean, it seems you can write a very clean narrative in retrospect, but it's not an inevitability that things play out that way.

• 27:12Speaker 01

  You know, I want to go back to something you said on on Demis, which was, you know, him, you know, obviously focusing on science and this kind of belief that, hey, we have to show that, you know, AI can have beneficial, you know, outcomes for society or or else the world will turn against it. And one thing I'm struck by in particular, you kind of alluded to it right at the beginning, that you couldn't put Demis on the cover because people wouldn't recognize, you know, who he was. And in many ways, like the people that the world associates with AI today are Sam and Dario, and they're kind of, you know, taking a lot of the oxygen in the public conversation about, you know, how people think about AI. And I'm wondering, one, how Demis feels about that? And two, you know, over time, do you think he feels like or will have to like become more of a public figure than he is today to, you know, shape that conversation?

• 27:57Speaker 01

  And, you know, obviously, he's deeply passionate about this stuff in a way that's more, you know, analogous with how he feels.

• 28:04Speaker 00

  Yeah, I mean, first of all, to be clear, I mean, the compromise that my publisher struck was to have him on the cover, but to kind of fuzz it up so that he looks like a kind of enigmatic geek. And if you don't recognize him, it's still a cool picture. So that's what they did. But yeah, I think to your question, you know, Demis does understand that he needs to be out there more. He's good at kind of self narrativizing in one way, which is that, you know, he's The retrospective story of his journey up until where he is now is something that he's been good at communicating. He spends more than 30 hours talking to me and of course, of course, there is a reason for that. But also, you know, there was the documentary,

• 28:45Speaker 00

  The Thinking Game, which a lot of people have seen. And before that, there was a documentary about AlphaGo, which didn't get made by mistake, you know, that they actually brought a documentary team with them to Seoul when they played that game against the Seoul Gold Champion. So there is this retrospective storytelling, which he's good at. What he doesn't do is prospective. He doesn't, like Sam, you know, has, or at least in his heyday, had the ability to kill the attention on a new DeepMind release, simply by putting out a couple of tweets.

• 29:19Speaker 01

  They would always go one day before, like they maybe know something was going to happen.

• 29:23Speaker 00

  Yeah. And when we say go one day before, we mean, you know, just trail something on X, just put out a couple of posts and, you know, not even release something. But that was, but because Sam's following on X is like five, six times Demis', the echo chamber is so much stronger that he could really dominate the narrative, even when the other guys were releasing the product. And that's a problem, both in terms of product adoption for DeepMind, but also like talent recruitment. You know, controlling the narrative does matter. And I think they kind of get that over at Google DeepMind. Whether they quite have the ability to fight it, we'll see. I mean, what, you know, what they don't

• 30:07Speaker 00

  do is do what Dariel does, which is pick a, you know, public fight with a Pentagon, you know, unreleased Mythos, all these things, which just, you know, immediately turn you into a massive celebrity. And Demis, in some ways, is too sensible.

• 30:25Speaker 00

  To pick a public fight with the government. And so maybe that makes him less notorious. And, you know, maybe it's a kind of byproduct of strategic caution that he's a bit less front of mind in the media.

• 30:39Speaker 01

  I'm curious like what you ended up learning about the extent to which that does or doesn't attract researchers, right? Obviously, a huge part of the battle here is the battle for talent. You've had, I mean, Google has incredible researchers. You've had folks like, you know, like Jack Ray, who you talk about in the, in the book, who left, then came back, then left again. You know, how do you think about, or in talking with folks, the types of people that are attracted to DeepMind, the extent to which they, you know, do or don't need to change their hiring brand to kind of stay on par with the others?

• 31:08Speaker 00

  Yeah, it's a great question. And I think it sort of overlaps with what we were saying about the rivalry between the, you know, deep pocketed hyperscaler and the, and the venture model. Because I think, you know, for Jack Ray, when he left DeepMind and went to OpenAI, he explained to me that, you know, this was because there was a concentrated bet on language models at OpenAI, and that's all they cared about at the time. And that's what Jack was doing. And so he wanted to be there. And I think, you know, that would be true today in robotics. There'll be certain researchers who are really great, and they want to go somewhere where there's one concentrated bet which they believe in themselves. And then they will just, you know, work all out to, to be part of that team and, and

• 31:53Speaker 00

  to, to realize what they think is the right path. Whereas if you put a robotics researcher in this big, careful, diversified, you know, lab and say, you know, we really believe in you, but we also believe in these other five bets we're making at the same time, you don't feel so good about it. You know, there's a kind of passion piece that's, that's missing.

• 32:12Speaker 01

  Well, what do you make of the decision? You know, obviously, the AlphaFold work eventually got folded out or spun out into, you know, Isomorphic, right? And a different company that kind of, I mean, Alphabet still owns a bunch, but is a separate effort. Like, does that make sense? Or is that a path forward, do you think, will be repeated?

• 32:27Speaker 00

  That's a great question. I mean, I think Isomorphic.

• 32:31Speaker 00

  Does have that effect. I think the spin out was also about trying to be able to do partnerships with pharma, big pharma partners. And maybe that was a bit easier in a spun out format.

• 32:43Speaker 01

  Just because they wouldn't want to work with Google directly or something?

• 32:46Speaker 00

  Maybe, yeah, and maybe, maybe also just the sense was that this by itself as a freestanding thing could become so big. And there'd been this history of Google and DeepMind separately doing AI for health. And health has got its own politics, its own kind of, you know, very long lead times. You know, they felt it just needed to be in a different home. So they did that. But I think you're raising a good point that there might have been an extra reason to do it. Which is that you you give people the option of working somewhere where they are the bet. Their thing is the thing. And I think that's a super important recruiting tool.

• 33:29Speaker 00

  I also would note, I mean, it's probably, you know, fairly well known in the valley, I guess. But, you know, Anthropic's churn is very low relative to everybody else. And I think that shows you that if you, if your kind of identity as a leader is like Dario, where you're kind of out there and unfiltered about your extreme concern for safety and your extreme concern for responsibility and social impact, you write these long essays. You know, it probably means that some people would never join you because they think you're a bit wacky. But the ones who do join absolutely love you and believe in you. And there's this kind of amazing loyalty, which is kind of special.

• 34:08Speaker 01

  I mean, another kind of key character in your work that we haven't talked about is, you know, David Silver, who obviously played a key role in a bunch of the reinforcement learning work and was one of the early collaborators with them on a bunch of things. You know, he obviously, I think, you know, since publishing your book, he recently left DeepMind, right? To start another company. I'm curious what you made of that.

• 34:27Speaker 00

  I think it kind of fits your narrative here, actually. I mean, in the sense that he was this, he is this intensely determined believer in reinforcement learning, to a point where I think most of his colleagues at DeepMind ended up thinking it was just too much. You know, he was the hero when, first of all, the Atari, you know, game playing system that DeepMind rolled out 2012, 2013, you know, made these incredible breakthroughs. And, you know, that was almost like ImageNet, but for early agents. And David Silver was a key person in that. And then there was AlphaGo, and then there was AlphaZero, which was, you know, pretty much all learning from reinforcement learning and no, you know, there was deep learning,

• 35:12Speaker 00

  but it was, but it was, it was heavily skewed towards the reinforcement learning side. And, and then came this paper from David Silver and Rich Sutton, his PhD supervisor,

• 35:26Speaker 00

  called, what was it called?

• 35:30Speaker 00

  "Experience is enough," I think it was something like that. And, and basically, "Learn from experience, learn from reinforcement learning, don't learn from data," was, was the, was the message of that paper. And David is, is just very, very hard over on that vision, that, you know, learning from data is inferior because the data includes mistakes, right? The kind of, if you train, if you take the Go analogy, if you train on a human Go players' past games, even if it's expert games, those players don't have perfect understanding of Go. And you need to get beyond that to have superintelligence. And so you need to learn, the machine needs to learn from its own experience, not rely

• 36:14Speaker 00

  on the kind of crystallized knowledge of humans passed on through text or other kinds of data.

• 36:22Speaker 00

  And he is such a believer in that, that it's all agents for him, only agents, and they have to learn from themselves. And I think, you know, Demis told me once, or actually more than once, that, you know, that kind of approach may ultimately win in some future when you already have AGI and now you're just sort of like perfecting it into even greater superintelligence.

• 36:46Speaker 00

  Because ultimately, yes, you know, if you if the machine learns from its own data, that is pure and ultimately will get you further. But to try and get you to that AGI, you need to bootstrap yourself with existing data. And that's what the whole language model revolution showed us, that for a long time, there wasn't much, you know, there was almost no reinforcement learning unless you can't reinforcement learning from human feedback.

• 37:09Speaker 01

  It didn't work if their base models weren't, you know, weren't strong enough, right, except for these these specific domains. And then, you know, what's fascinating, obviously, about this moment in time is the combination of, you know, large pre-training LMs and then reinforcement learning on these on these verifiable domains. But it's just interesting that he that he left at this time, because obviously, it feels like generally reinforcement learning is back in vogue as like the way to improve, you know, these these models. And so, you know, it almost would have made more sense a few years ago when when when reinforcement learning was more out of favor. I thought it was interesting timing that that had happened now.

• 37:39Speaker 00

  Yeah, I mean, I think I think you're right, but it sort of reflects the human lag between feeling something and acting on it, right? I think he'd been feeling for a while, frankly, that he was swamped in a big organization, which didn't fundamentally want to put more than a sort of small amount of its chips on the reinforcement learning table. And then even within reinforcement learning, you know, his vision of how it should be done differed from some other people's. And I think he is kind of a classic startup sort of person who who wants to be in a small organization where his vision is the vision. Because, you know, he's he's extremely visionary on reinforcement learning. And so it makes sense for him to go do his own thing.

• 38:21Speaker 01

  One thing that we've kind of talked about a bunch here is just like, in the end of the day, there's a few people here that that run these labs and have intensely like personal histories and relationships. And I think the obviously, the Sam Dario relationship has been incredibly well documented. Elon, Sam, all coming out in this in this current court case. You know, I think Demis's relationship with each of them is probably less well understood by, you know, by the general public. And I'm wondering if you could just talk a little bit about that and his, you know, the extent to which those relationships exist, his kind of feelings on the the two main, you know, other protagonists of the of the space right now.

• 38:52Speaker 00

  So, you know, Demis's relationship with Elon is very interesting.

• 38:57Speaker 01

  Elon tried to buy him, right?

• 38:59Speaker 00

  Yeah, but just going back, I mean, you know, the the origin of this whole thing is that they were both funded by the same VC shop, Founders Fund, right? Where, you know, Elon was the SpaceX guy and Demis is the DeepMind guy. And they both in 2012 or thereabouts get invited to an LP offsite and they both present and then they get talking to each other. And, you know, Elon's always very competitive.

• 39:24Speaker 01

  That would have been a pretty valuable offsite to pay attention at. Two pretty.

• 39:27Speaker 00

  Yeah. So Elon is like, you know, well, I've got the most important technology in the world because, you know, even if the world is screwed up by your AI, we can all move to Mars, be a multiplanetary species. I've got the most important thing, at which point Demis says, yeah, but if you think you're going to be safe on Mars, remember that my AI will be able to conquer spaceflight and it will just follow you to Mars. So then you won't be safe after all. And then there's a silence and then Elon goes, hmm. And then the next thing he says, well, I'd like to invest in your Series B.

• 39:59Speaker 00

  And he writes a $5 million check into the Series B. And then, as you say, he wants to buy DeepMind at the start of 2014 to prevent it from being sold to Google. And and Demis just like waves him off. And there's this crazy story where Luke Nozak, the Founders Fund partner, who is sitting on SpaceX's board, you know, sees Elon at a party in LA and they agree that they've just got to stop DeepMind from being sold to Larry Page. He's a transhumanist, you can't trust him. So they go up to some closet upstairs in this party and they Skype Demis in the middle of the night in London and say, you got to sell to us, not to them. And, you know, sell it to SpaceX, sell it to Tesla, do something, but just don't sell to Google because they're evil.

• 40:41Speaker 00

  And Demis is like, you know, no, no, Google's got the computer, I'm selling to them, goodbye, good night. And he puts the phone down and then Elon goes nuts, right? And starts calling Demis an evil genius, which is a reference to a game that Demis worked on when he was a video game designer. And, you know, vilifies Demis and is obsessed with this. And I think this has come out again a bit more in the trial that we've just been having recently, you know, the Elon versus Sam trial. And the kind of obsession that Elon had with Demis as the evil genius that had to be counteracted. So that's the history there. Demis is kind of very keen to say that these days they get on fine. And I haven't had that directly from Elon, but I kind of

• 41:26Speaker 00

  feel that probably water under the bridge, Elon has moved on to fighting with Sam. It probably is true that he's okay with with Demis now. But, you know, that's the history there. And then with Sam, it's just such a complete difference in personality and background if you compare the two of them, right? So Demis has a Nobel Prize, Sam didn't finish his first degree. Therefore, Demis doesn't take Sam very seriously, right? He doesn't have a college degree, let alone a PhD or Nobel Prize. And and he just, you know, to to Demis, Sam, and I think there's some truth in this, frankly, that, you know, Sam is the sort of skillful, ultimate embodiment of the Silicon Valley network, who knows

• 42:08Speaker 00

  how to fake it until you make it, to tell the premature truth, who is just a master at raising money, at leveraging his connections in the valley. And, you know, that's all great, but, you know, it's not the same as being a serious scientist, and you can't trust somebody like that. And I think, you know, I think people have come around to Demis's view to quite some, you know, some extent with Sam. But but Demis always felt that. And so I don't think he ever liked Sam.

• 42:38Speaker 01

  I think it was the economist that in talking about your book, you know, said it's really like a test of this, like, great man theory of history. But I'm curious, we've talked a lot about like these, these four protagonists. And I'm wondering, after having done all this work, like, to, you know, was all of this kind of inevitable? Or like, to what extent did it matter that it was like, you know, Demis and maybe some of these other folks at the at the helm of the companies? Yeah, I'd be curious your thoughts on that.

• 43:02Speaker 00

  I once wrote a book about Alan Greenspan, a super powerful player in global economics, but ultimately unable to stop the financial bubble of 2008. And I called the book The Man Who Knew because he understood that bubbles were dangerous. That's in fact what he wrote his PhD about. He was obsessed with bubbles blowing up, but he couldn't stop this thing from happening. And I feel that one could have almost used the same title for the Demis book, right? I called it The Infinity Machine, but it could have been called The Man Who Knew because Demis has known from the beginning that this thing is dangerous. But as the leader of one lab, even a very powerful, rich lab, even he with his stature as a Nobel Prize winner, he understands that it's dangerous, but what can he do? Because

• 43:45Speaker 00

  if he makes his own lab safe, it doesn't stop the other guys from being unsafe. And so I think there is a sort of this inevitability to the race dynamic. And it does matter who is leading these labs. I mean, clearly, you know, Dario changed the narrative on AI safety, first by fighting with the Pentagon in public, and secondly, and more importantly, by the way that he did the Mythos release. So that's a clear example. On the other hand, you know, Sam decided to release ChatGPT even though it was hallucinating. That was a choice. Didn't have to do that. And that completely colored the way the AI race played

• 44:27Speaker 00

  out. And Demis, in a more understated way, told Rishi Sunak, the UK Prime Minister at the time, hey, we should have a global AI safety summit. And, you know, that's what happened. And there was one in Bletchley Park, and the Chinese came. And that was kind of the beginnings of international conversation on AI safety. So I think Demis is more behind the scenes in what he does. But yeah, I think it matters. The personalities of these leaders, but it perhaps, you know, it's not the only thing that matters. There are underlying forces as well.

• 45:00Speaker 01

  Well, before we wrap up, I definitely, you know, given that you spent so much time with Dennis, I think there's a few just questions about that process. I was, I was really curious to dig into, you know, you spent, I guess, three years meeting him at the same pub. And I'm wondering across those series of conversations, like, what was the moment, I guess, your view of him kind of shifted the most?

• 45:18Speaker 00

  There were lots of surprises, but one of them is the depth of his conviction about discovering the deep mysteries of science. And this is actually a kind of spiritual conviction, which I had no inkling of before. But, you know, he would sometimes erupt in these conversations I was having. They went on for two hours each time. So we already could, you know, get deep on stuff. And he would start banging the table and saying, look, this table, it's a mystery. It's a mystery. We don't understand it. Like these atoms jumping around and there are gaps between them. And yet the table is solid. And why is your laptop able to think when it's just a bunch of sand and copper? And, and, you know, what's going on here? Why is the world set up like this? That it functions is a mystery. We have to understand.

• 46:01Speaker 00

  There must be some sort of intelligence behind it. This can't just be coincidence that it's like this. Maybe, maybe it's like God. Maybe if we approach science the right way, we understand more about nature, we will be getting closer to something that we could perhaps call God. Now, I had no idea that he would feel that way, but I think he does. And, and it explains why he nonetheless pushes forward to, you know, develop this super strong AI, which he knows to be dangerous is because it's, it's, it's this kind of quasi spiritual quest for him.

• 46:35Speaker 01

  And it feels like it's for, for a lot of people in the space, there's like a religious or spiritual element to, to like getting to AGI. And it's really interesting to see play out. And certainly I thought that that came through really clearly in your work. I guess, you know, it feels like you, you had free, you know, such a wide ranging and open book. Were there things that Dennis like, wasn't really willing to talk about or areas that you would have liked to put in the book, but, but, but, you know, didn't end up coming through?

• 47:00Speaker 00

  Yeah, he was very clear at the start that he would talk a lot about himself, but he wouldn't talk about his family. And so I left that out. And there are passing references. He's, you know, he married his girlfriend from Cambridge. They're still married. They have two children. It's all very normal.

• 47:18Speaker 00

  But I basically don't talk, I mean, most, when I wrote about Greenspan, going to speak to his many, many, many ex-girlfriends was one way of understanding the guy.

• 47:30Speaker 00

  And actually brought out the human side of him in a good way. And I, you know, I didn't really go there with Demis.

• 47:38Speaker 00

  Another thing is, you know, he didn't want to talk about fights between himself and Sundar Pichai and the leadership of Google and Mountain View. I did write about that because other people told me about that. And so I get into that quite a lot. So, you know, his preference that this should be left out was not honored.

• 48:01Speaker 00

  You know, he didn't really want me to talk about the way that he fired his co-founder Mustafa Soleimani. And he would sort of say, I didn't really fire him. You know, there was a process, some sort of inquiry into bullying done by my general counsel and so forth. And, you know, that was kind of the trigger, but it wasn't the deep reason why Mustafa was pushed out. Nothing happened at DeepMind unless Demis wanted it to happen. And I think he just decided it was time for Mustafa to go because they disagreed on too many issues. So, yeah, there were some things that he didn't want included. And sometimes he won that preference and other times he didn't.

• 48:41Speaker 01

  There's been a ton of public discourse about your book. I feel like you've been on, you know, lots, lots of folks have been talking about it. And I think you've picked up on a lot of the themes we've discussed here today. Are there parts of the book or aspects of it that you think are like under discussed? Or you wish people, you know, would, would, would talk about more?

• 48:56Speaker 00

  I think there's sort of interesting takeaways for scientific innovation, how you do deep tech companies. You know, we talked about some of the shortcomings at DeepMind, you know, the lack of concentrated bets and so forth. But on the upside, I think it's very interesting how he developed what he calls scientific taste. And what I mean here is that, you know, when he had his first startup, which was this video game production company called Elixir, he basically blew it up by being too ambitious in terms of the product engineering, like, how fantastic could the graphics be? Could you do kind of early reinforcement learning to make the characters more interesting?

• 49:38Speaker 00

  And he drove his technical team like over the brink in terms of how ambitious they had to be, and they couldn't ship product on time as a result. And so that was a bad outcome. You know, he got some money out of that first company, but it wasn't as much of a success as he hoped. But then you fast forward and he's doing DeepMind, and he gets this moment in 2018 where, you know, he's two years into the AlphaFold research and his AlphaFold team has produced the best protein prediction system in the world. And the boss of that team, Andrew Senior, says, "Okay, boss, you know, we've, okay, Demis, we've done this now, let's declare victory and move on because we're not going to predict proteins precisely, give me a break.

• 50:23Speaker 00

  That's impossible. We're the best in the world, that's good enough." And Demis is like, "No, no, that's not the point. We want to predict proteins so that, you know, research biologists and medical researchers can use our predictions to build medicines and other fantastic breakthroughs. There's no point just being the best, better than the other labs. We want to solve this problem." And the guy who's running the team, Andrew Senior, says, "That's impossible." So Demis sits in the meetings of the team, and this is where the scientific taste comes in. He listens to what he calls, you know, he listens for the fluidity and the fluidity of ideas. If the research team are like bouncing possibilities of stuff they could look into off of each

• 51:06Speaker 00

  other, then that's fluent and you should move forward, put more resources in, push harder. If there was kind of a silence and nobody had any good ideas, of course you should give up. And so he does that test, he listens to the team, they are fluid in their exchange of ideas. And so then he basically switches out the project lead, Andrew Senior does something else, he puts in this other guy, John Jumper, who then goes on to become the co-win of the Nobel Prize with him. And I think that evolution from blowing up his first startup to being right about protein folding holds a lesson for how you do this sort of frontier science inside a company.

• 51:46Speaker 01

  I love that story. I guess, you know, obviously, you know, part of this work really helps elevate, you know, and tell the story of Demis more broadly, which may be an under, under known story. Throughout this work, or, you know, in general, are there other folks in the AI ecosystem that you're, that you feel similarly just, you know, deserve a more, more light shone on them, or an exposé, you know, some, some kind of biography, you know, other undersung heroes in this kind of AI world?

• 52:12Speaker 00

  Well, in my book, I do have this sort of pairing of Ilya Sutskevra and David Silver. One of them representing the deep learning tradition, PhD under Geoffrey Hinton in Toronto. And then, on the other hand, David Silver representing what I call the kind of Edmonton, Alberta tradition, PhD under Rich Sutton, who was the sort of guru of reinforcement learning. And I think both of those individuals, and I have kind of embedded in my story about Demis, a kind of mini biography of the two of them, kind of juxtaposing their two approaches. And there could be a fantastic double biography of the two of them.

• 52:50Speaker 01

  Certainly. You know, I think at various times, as Google has, AI fortunes have seemed better or worse. You know, folks have speculated that maybe one day Demis will become CEO of Google. You think that's possible?

• 53:03Speaker 00

  Yeah, it's possible. There's a question about whether he would want to do that, because I think he does love being a little bit able to think about the science. And he does this double shift every day, meaning, you know, he eats dinner with his family and then goes back to his desk until 4:00 AM, and that's when he's doing science. Now, I think actually, these days, a lot of that late time is spent doing calls to Mountain View and trying to coordinate with all, you know, all of the many people who report to him out of, out of Mountain View. So already that science time is being eaten into, but if he became CEO, obviously, there'd be nothing left of it at all. And I think he's genuinely conflicted about whether he would want that.

• 53:46Speaker 00

  But it seems to me that it all depends kind of like if Sundar were to decide that he's leaving, you know, who would be the likely CEO replacement if it wasn't Demis? And if it was somebody that Demis felt very comfortable with, he'd probably be perfectly happy not to do that. If he was not comfortable with it, then he might kind of push his hat into the ring because he wouldn't, he wouldn't want to work for somebody that he didn't agree with.

• 54:14Speaker 01

  Well, Sebastian, it's been such an interesting conversation. I always like to leave the last word to the guest. I think in this case, it's pretty clear where you might want to point people, but I'll leave the last word to you. Anything our listeners should, should take away further from this conversation and also please, please plug the book.

• 54:29Speaker 00

  Well, look, I really enjoyed writing this book. It's my sixth book and spending, you know, more than 30 hours with Demis Sabres in the top of a British pub was really quite special. He would like me to point out that we were drinking coffee, not pints of beer. But just simply the intellectual range of this guy who can riff about neuroscience, computer science, physics, biology, chemistry, you know, the history of movies, novels, science fiction. It was just wild. And I do try to communicate that energy on the page. It's the first time I've used the first person as a device in a book because I wanted to use that dialogue I had with him. And so having Demis riff and then me kind of ask

• 55:12Speaker 00

  him a question or just, you know, in that, interspersed in what he's saying, this kind of what I'm thinking to help the reader sort of understand it. I was, this was such an extraordinary experience that I was driven to innovate the craft of writing in terms of my own craft. So yeah, I enjoyed it. I hope you do too, if you read it, everybody out there.

• 55:34Speaker 01

  Amazing. Well, thanks so much for coming on the podcast to talk about it.

• 55:36Speaker 00

  Fantastic. Thank you, Jacob. It's great.

• 55:38Speaker 01

  I'm Jacob Effron, and this has been Unsupervised Learning, a podcast where I get to talk to the smartest people in AI and ask them tons of questions about what's happening with models and what it means for businesses in the world. As I hope is clear, I have a ton of fun doing this. It's a nights and weekends project in addition to my day job as an investor at Redpoint. But our ability to get these incredible guests on really comes from folks like you subscribing to the podcast, sharing it with friends. It's really what ultimately makes this whole thing work. And so please consider doing that. And thank you so much for your support and listening. We'll see you next episode.