A Conversation With Demis Hassabis' Biographer

Demis has a Nobel Prize. Sam didn't

finish his first degree. Therefore,

Demis doesn't take Sam very seriously.

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

Demis in some ways is too sensible.

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

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. Sebastian

Malibby spent over 30 hours with DeepMind CEO and co-founder Deis Abus in preparation to write his book, The Infinity Machine. I got to sit down with

Infinity Machine. I got to sit down with Sebastian to get all his reflections on this experience. Uh it was fascinating

this experience. Uh it was fascinating to talk about some of the stories that Sebastian reported on in this book, including the fact that Reed Hoffman offered a billion dollars to Demis to spin DeepMind out of Google at some

point. We talked about Demis'

point. We talked about Demis' relationship with Elon and how that's evolved over the years, as well as what he thinks of Sam, Daario, 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

are. Just a fascinating conversation with someone who's spent unprecedented time with someone who's really uh at the cutting edge of AI today. I think folks will really enjoy Sebastian's candidates

here. Without further ado, here he is.

here. Without further ado, here he is.

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

it.

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

I feel like The Infinity Machine uh and your kind of story of Deep Mind and Demesis Aabis is uh has captivated a bunch of folks in the AI world. I know

that I inhald this book. I think I finished it in like 24 hours. It's just

a a gripping story and you got this just incredible access to Demis. I think you know uh the sitting in this pub for for multi-ours at a time uh talking about all sorts of things uh just just a

fascinating you know uh opportunity and window into definitely one of the most interesting people and and uh companies in this AI race today and so uh 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 was inevitable or could have gone differently uh if different players or decisions had had been done.

Um I guess I'm curious after doing this work, do you think there was another path or or was this kind of inevitable?

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 um they were going to have a go at it and they are actually doing pretty well. And

then clearly in the US uh and then in a few other countries you've got Mistral in France Coher 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 Xanti that you know when Demis was starting Deep Mind he really hoped that he could avoid the race dynamic. It

seemed naive in retrospect, but that's what he hoped.

One really compelling anecdote you had was I believe Demis in his interviews would say uh 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 uh fly to a bunker and and and figure this all out as kind of

like one team. Would you be willing to, you know, get on that flight and and and do that? You know, I think certainly

do that? You know, I think certainly part of the 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 a key part of starting all these companies. Do you

think these folks I mean particularly Demis like do they still believe this or or how has their kind of thinking evolved uh having seen the way things have played out these past years?

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 deep minded himself. um uh

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

world safer. So he really has shifted to seeing this as a collective action problem that only a government can solve. Because one thing I thought was

solve. Because one thing I thought was really compelling from your book is obviously there was this early AI safety summit that Dennis and and the team put together and they're sharing you know the progress they've made and and why they're worried about it and you know some people in the room like Reed

Hoffman, Elon Musk take that information and then they're like oh this technology really is starting to work. We should

ourselves uh potentially do something around that. You know do you think uh

around that. You know do you think uh obviously to to get safety to work going forward you need these different companies to to agree to share things and and collaborate. Uh I imagine that left a bad taste in in in Demis' mouth.

How do you think he and and other kind of key players in this space think about, you know, potentially sharing what they're doing uh with with given that, you know, past experience?

Yeah, I mean, you're totally right. That

was in 2015. Um, summer of 2015, they have this summit at SpaceX. Elon Musk is hosting it. And the idea is he's going

hosting it. And the idea is he's going to be brought into the tent by Deep Mind. He's going to be part of their

Mind. He's going to be part of their efforts. He's going to be, you know,

efforts. He's going to be, you know, chairing this sort of safety oversight board. And therefore, he wouldn't set up

board. And therefore, he wouldn't set up a competitor. And of course, at the end

a competitor. And of course, at the end of 2015, he did set up a competitor.

open AI 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's point of view or from any

of the lab leaders frankly um you can't trust the other guys uh and therefore the only way you get trust is if you have a government enforcer that comes along and say 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 slow down you know pre-esting of models before you release them all that stuff and you all have to do it. Um and then

the reaction of course will be yeah but what about the guys in China and and that's why ultimately I think this has to be a USChina collaboration you know remote though that prospect may seem to

many listeners right now.

I mean do you think Demis and others I think that's actually realistic. I mean

obviously there there's it it it you know is is such a a pressing problem but you know it doesn't seem like uh folks have the most confidence in in governments and certainly you know intergovernment uh collaboration across the world like it it intellectually

makes sense that that is kind of the the clear way to solve this problem but do you think they think there's actually a probability this this does happen successfully? Well, you know, I think

successfully? Well, you know, I think they ought to believe that it could happen. And we do have the Food and Drug

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 Xanti. And I think in Britain

reviewed Xanti. And I think in Britain actually you have an example of a government AI safety institute that's pretty effective has some highass scientists. I mean the chief scientist

scientists. I mean the chief scientist there is Jeffrey Irving who was at deep mind originally did a lot of the post training on the early models who's a really serious researcher was also at open AI by the way and a close

collaborator of Darios. Um 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 vulnerability vulnerabilities. So it

vulnerability vulnerabilities. So it shows that you can aggregate some technical expertise uh inside these public institutions because a certain number of folk feel motivated and public spirited enough to go work for the

government. Um so I don't think we

government. Um so I don't think we should give up on that.

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

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 has found newfound hope in 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 of of coordination across countries.

Yeah. I mean, what he 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 in my

me. I've been I'm pushing him in in my interviews with him. Are you really optimistic? Because it seems like we've

optimistic? Because it seems like we've got a race dynamic. You know, in of course January 2025, you have Deep Seek come out and that really signals the advent of the Chinese labs into this

space. And you know, I'm writing through

space. And you know, I'm writing through 2025 into the end of 2025 and I'm saying, "Are you still optimistic?" And

he says, "Yes, I'm optimistic still."

And I think the reason for that insistence apart from like what what's he going to say is also that you know when something kind of dramatic happens the world does

coales and take action you know 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 um 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.

Um 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 if you have an acute thing like mythos uh an anthropics model comes out you know and people suddenly get worried you see the US government pretty much do a 180 from a

less fair position to oh 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. Switching gears obviously a key

not. Switching gears obviously a key part of you know in through line through your your whole uh work is just the story of of deep mind and it's ultimate relationship with Google and it's just an incredible uh story and so I guess as I recall you started this or you kind of

got demis to agree to this like right before chatgpt and so obviously uh post alpha fold in some really interesting things but certainly the world uh evolved in a bunch of interesting ways from the start of your project people talk about demis and deep mind all the

time you know before you published this book but you kind of done the research what do you think the popular narrative got like most wrong about demis deep mind the way people would talk about, you know, the the two.

Well, the extraordinary thing to me is how people basically discounted Demis. I

mean, you know, I 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 Deise, they say Hassabis instead of Habis. Uh, they kind of barely know who

Habis. Uh, they kind of barely know who he is. You know, my publisher, Penguin

he is. You know, my publisher, Penguin Press, was figuring out what to do the 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 at this point. I assume Sam Dario, they'd be fine for for a cover of a book.

Yeah. Yeah. Totally.

Yeah.

Elon, for sure. And um and Demis, you know, is the guy who founded the OG lab in 2010, way before anybody else really created the model that then open AAI copied later. I remember going to see

copied later. I remember going to see Dario when I was doing my research and you know he said yes Deis 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 um he he is um they underestimated Google uh as a company

because they thought innovators dilemma they were too slow open AAI went first with a model uh with a with a chatbot Um, and now they've got this massive brand effect, uh, Open AAI does, and so

nobody can catch up. Well, that proved wrong. And, you know, as of late 2025,

wrong. And, you know, as of late 2025, uh, the Google Deep Mind models, Gemini 3.0 were better on the leaderboards than the adversaries. Of course, since then,

the adversaries. Of course, since then, we've seen a a big anthropic surge. But

you know the the point is I think people were too quick to crown um open AI and Sam Alman as the winner and underestimated both Demis as a person

and Google Deep Mind as a company.

It is interesting obviously like since your 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 open AI you know

with codeex 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's it struggles in like the zeitgeist and like actual usage on the product side if these two monumental moments

were you know chat GPT and consumer products and then cloud code and and and the coding agents you Google doesn't really have 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 people use Gemini more than we realize right you know it's sort bundled into Google search at this point, the AI mode

thing. But I I I take the broader point

thing. But I I I take the broader point which is that both of these sort of seminal moments in um proving out you know the the consumer experience which

were chatpt as you say on the consumer side and then more recently with coding.

Um neither of those came from Google deep mind. And I think what that perhaps

deep mind. And I think what that perhaps shows us is that you know partly because of Demis' 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

artificial intelligence. There's this there's this kind of let's try everything approach to AI 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 just never had a Sora equivalent. Um and you know at the

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 was willing to put out a chatbot that

hallucinated a lot at the beginning.

that honestly has a lot of echoes of of the transformer itself, right? And and

basically, you know, I think OpenAI making the bet of going all in on uh on scaling transformers which were obviously invented at Google and Google pursuing a bunch of different paths and you know, then to your point uh OpenAI starting to do a bunch of things and

Anthropic deciding, hey, we're just focusing on coding. Have you seen kind of a shift in in uh Demis and Deep Mind's focus I guess given given what's played out and given this kind of you know uh it's such a tension right as a

as a as a polymath as a as someone who's so interested in in many facets of AI to focus on a ton of different things versus you know the the thing that's happening at the moment obviously open AAI 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 uh and and catch up to anthropic on coding models you know how do you think demis and and the deep mind folks think about this at this moment

I think you they do have this tendency to try and do everything. Um, as I was saying, I don't think that's been cured.

I mean, you know, I was chatting actually just last week with uh one of the young scientists from the from that shop and um, you know, that was very much the tenor of what he was telling me is like, you know, we always make

multiple bets. We never really go hard

multiple bets. We never really go hard down one avenue. And and you know, from a corporate point of view, maybe this makes sense. I mean if we if we think

makes sense. I mean if we if we think about um 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 chatbt but then they caught up by 2025. 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 you 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

thing. We'll just charge people to put it on our iPhone. That's an extremely handsoff position towards AI. Google is

much more in it. Um but it doesn't mind being a couple of years behind because it figures it can catch up. And I wonder at some point wonder whether that that does shift. Obviously you you've you

does shift. Obviously you you've you know allowed $2 trillion plus we'll see what they're end up being worth.

Companies to to or at least in the in the prediction market seem to be worth that uh you know come come up uh and and gain a lot of territory and you know I think it's it's it'll be really interesting to see going forward um you

know whether that focused or or that like diversity of different things they're pursuing um 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. Um, and I think actually

work together. Um, 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

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?

Yeah. So there was this secret plan called project Mario to spin out and um Demis was not so happy when I kind of discovered about it from other sources.

I had to speak to the general council of Google Deep Mind at one point who tried to persuade me I couldn't write about it. Other people leaked me the documents

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. Um, and the point of this story is is actually

partly it goes back to your safety thing that that um, Demis really really wanted to get safety oversight over the Google Deep Mind models or just the deep mind

as it was then. Um, 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 Reed

spinning out. So he went to Reed Hoffman. Reed Hoffman pledged a billion

Hoffman. Reed Hoffman pledged a billion dollars to finance a spin out and Demis used that to kind of pressure Google. um

uh although I don't think he ever mentioned that Reed 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 advisers who were in the room um when Deep Mind was figuring out you know whether to go with a Reed 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 flexibility the agility that go with that. You can, you know, give all your

that. You can, you know, give all your team, you know, very high powered financial incentives, um, linked to your performance and it'll be awesome and you

should spin out. And then Demis' view in the end was, you know, it's it's legally it's going to be there may be a court battle over it, you know, whether we

have the right to do that. Um, 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 Nobel Prize, you know, in in 2020. So, a year after he gave up

that fight with his parent company, he ships Alphafold uh the protein folding prediction model and that gets him the Nobel Prize.

The kind of way you just framed it was more of like, hey, this was part of negotiating leverage to to, you know, get some of the safety stuff. I mean,

how seriously do you think the Deep Mind team was considering this at the time?

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 to kind of hint uh 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 you don't expect kind of thing but it you know ultimately they never really waved that threat explicitly and they never

exercise a threat even though Google didn't give them the safety oversight that they wanted. So in the end I mean it's in some sense it's a story of the naive day of the of the founders both

Demis himself and Mustafa Sulleman his co-founder who's now at Microsoft I mean they went into this um you know not quite sure what their endgame was and in

the end they never really used that spin out OP I mean they 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 I'm wondering

in 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 and kind of realizing yeah that that was not ideal. Are there any other things

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 you know what does he regret? I'd say

that he would certainly not regret um spending all that energy on AI for science. I love that anecdote in your

science. I love that anecdote in your book, by the way, that like the day they won AlphaGo, AlphaGo won the the the, you know, beat the best player in the world, he was already on to bio and someone picked it up on a mic. Uh, which

which I just thought was uh talking about an ambitious and always moving uh leader.

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 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 at large. If AI doesn't deliver you know clear benefits for

humans. Uh and it's just like lots of

humans. Uh and it's just like lots of job disruption which may be good for productivity but it's kind of painful for people on the receiving end. I'm not

sure that politically AI will really be rolled out with some massive back without some huge backlash. So, so I think you know both from it's good to advance science. You know, I like

advance science. 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

stuff. I think he doesn't regret that for a second. Um, you know, does he regret not being faster onto chat bots?

Yeah, of course. And it would have been better to understand, you know, like Ilioska did at OpenAI. You know, the moment the transformer dropped, Ilio 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

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 for Deep Mind if they had had that same perception that quickly and it took them instead, you know, 2, three years to get

there. And Deis, you know, frankly,

there. And Deis, you know, frankly, still has a bit of a blind spot about this. You know, when I say to him, well,

this. You know, 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 say, "Yeah, yeah, but you didn't release those till the end of 2020." And

Ilia by then had been working on it for three and a half years. So you were late. Uh but he he really resists that

late. Uh but he he really resists that and I think that is because he he does regret that.

Well, I thought it was interesting too, you know, the way you kind of put put it in your book is that, you know, part of uh the resistance maybe the transformer architecture was this, you know, he obviously comes from such a deep neuroscience background and this belief

that like the path to artificial general intelligence has to flow through something that looks much more, you know, like the way humans learn and the human brain, which obviously was the RL world, uh and less of just, you know, how how can you really understand what

it is to be a human by consuming the internet? And I think your point was

internet? And I think your point was actually it turns out uh you can understand it way more than one might have anticipated from scaling these uh these models.

Yeah. And by the way I think there may be some version of that same dilemma with robotics. In order to train

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

those simulations. So potentially if DeepMind gets the right bet on the right pathway to much better robotics, it has

the it has what it takes. Um but we'll see uh whether you know there's some version of what what went wrong with

language uh and the transformer model is the gem just underestimated the importance of language to super intelligence and then maybe you know to get robotics right you know is it for

example something that you're going to do by building physical robots and having them crash and break their arms you know in some kind of physical lab but it's an interesting you know point because actually, you know, I see many

parallels in the robotics space where there's, you know, 10 15 different approaches that people are trying and I imagine if you're Google and like given the way Deep Minds 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, you know, X approach or Y approach and it'll be interesting to see how that plays out because you can certainly empathize today when the recipe is not clear. Uh

it it's it's not particularly irrational for for Deep Mind to keep their options open. uh but also leaves you vulnerable

open. uh but also leaves you vulnerable to someone that that really goes all in on the transformer or on code or whatever the next you know equivalent is in the robotic space.

100%. 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 I have a biased opinion on that one.

Yeah. Yeah. Well, that that's fair. But

but I think but but you know I'm less biased but you know I've written about a hyperscaler now but my previous book was about venture. I kind of done both. But

about venture. I kind of done both. But

I think it's kind of a interestingly balanced um thing because you you know in one sense this is a very capital intensive project. Um you need very deep

intensive project. Um you need very deep pockets. If you look at Open AI's

pockets. If you look at Open AI'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 Open AI had a 50% chance of

going bust by next summer.

Is it still 50 by the way?

Yeah. I mean, you know, it said fail. I

mean, I I I what I mean is it it sells itself as a dis at a discount to to some hack to scal it. And I think you know I' I've been impressed by some of the um

you know expenditure cuts they've done.

you know, canceling Sora was a smart move. They've done some impressive cost

move. They've done some impressive cost cutting. Um, so I give them credit for

cutting. Um, so I give them credit for that. On the other hand, of course, um,

that. On the other hand, of course, um, the sort of rumors around Sam being replaced by Brett Taylor are pretty widespread and there's just a general sense of, you know, the leadership is

damaged goods. Yeah, I think on net I'm

damaged goods. Yeah, I think on net I'm still around 50/50 that they fail next by kind of next summer. Um, 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.

is great. It's just the business model.

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 um 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 um you know highly concentrated bets then maybe one of those bets work out 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 or 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 and they keep raising the amount of capital that's required to stay in this game. The coding models hit and they hit in a in a huge way and and

now you know Anthropic is is the the hottest company in the in the bay. But

like it's a really interesting counterfactual if that stuff had hit 6 months later or 12 months later. It

certainly wasn't inevitable uh that that it was really going to start working then. And um yeah, a lot of this stuff I

then. And um yeah, a lot of this stuff I mean it seems you can write a very clean narrative in in retrospect but it's not uh an inevitability uh that things play out that way. You know, I want to go back to something you said on on Demis,

which was, you know, him uh, you know, obviously focusing on science and this kind of belief that, hey, we have to show that, you know, AI can have beneficial uh, you know, outcomes for society or or else the world will turn

against it. And one thing I'm struck by

against it. And one thing I'm struck by in particular, you you kind of alluded to it right at the beginning that you couldn't put Deis on the cover because people wouldn't recognize, you know, who he was. And in many ways like the people

he was. And in many ways like the people that the world associates with AI today are Sam and Daario and they're kind of you know uh taking a lot of the oxygen in the public conversation about uh you

know how people think about AI and I'm wondering one how Demus feels about that and two you know over time do you think he feels like or or will have to like become more of a public figure than he is today to you know shape that

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

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 that's what

cool picture. So, that's that's what they did. But yeah, I think to your

they did. But yeah, I think to your question, you know, Deis does understand that um he needs to be out there more.

He's good at kind of selfnarrativizing in one way which is that you know he's the kind of 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

communicating. He spends more than 30 hours talking to me and of course there is a reason for that. Uh but but also you know there was the documentary the thinking game which a lot of people have

seen and before that there was a documentary about Alph Go which didn't get made by mistake you know that they actually brought a documentary team with them to soul when they played that game

against the soul gold go champion. So so

there is a 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 um kill the attention on a new um Deep Mind release simply by putting out a couple

of tweets.

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

happen. And yeah, and when we say go one day before, we mean, you know, just trail something on on on X, just put out a couple of posts and and and you know, not even

release something. But that was but

release something. But that was but because Sam's following on X is like five six times Demises uh the echo chamber is so much more 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 Deep Mind but also like talent recruitment um you

know controlling the narrative does matter and I think they kind of get that uh over at Google deep mind um whether they quite have the ability to fight it.

I We'll see. I mean, what, you know, what they don't do is is do what, you know, Darius does, which is pick a, you know, public fight with the Pentagon.

Um, you know, unreleased mythos, uh, all these things which just, you know, immediately turn you into a massive celebrity. And Demis in some ways is too

celebrity. And Demis in some ways is too sensible to pick a public fight with the government. And so maybe that makes him

government. And so maybe that makes him less notorious. and uh you know maybe

less notorious. and uh you know maybe it's it's a kind of byproduct of strategic caution that he's a bit less uh front of mind in the media and 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 uh you know uh like Jack Ray who you talk about

in the in the book who left then came back then left again um you know how do you think about or or in in talking with folks the types of people that are attracted to deep mind, the extent to which they, you know, do or don't need

to change their hiring brand to kind of stay uh on par with the others.

Yeah, it's a great question and I think it sort of um overlaps with what we were saying about the rivalry between the you know deep pocketed hyperscaler and the

and the venture model. Um because I think you know for Jack Ray when he left um Deep Mind 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 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

about it. you know, there's a kind of passion piece that's that's missing.

Well, what do you make of the decision? You

know, obviously the Alpha Fold work eventually got folded out or spun out into, you know, isomeorphic, right, in 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 a path forward you think will be repeated?

That's a great question. I mean, I think um isomorphic does have that effect. I I think the spin out was also about trying to be able to do partnerships with farmer big

farmer part you know partners and maybe that was a bit easier in a in a spun out format just cuz they wouldn't want to work with Google directly or something or

maybe yeah and maybe maybe also just um the sense was that this by itself as a freestanding thing could become so big uh and there'd been this history of um

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, it they

they felt it just needed to be in a different home. Um, so they did that.

different home. Um, so they did that.

But I 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. I also

you would note I mean it's probably you know fairly well known in the valley I guess but you know the anthropic churn is very low relative to everybody else and I think that shows you that if you

if you're kind of identity as a leader is like Dario where you're kind of out there and unfiltered about your extreme concern for safety uh and your extreme concern for responsibility and social

impact. You write these long essays. you

impact. You write these long essays. you

know, it 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.

I mean, another kind of key character in in in your work that we haven't talked about is, you know, David Silbert, who obviously played a key role in in a bunch of the reinforcement learning work and was one of the early uh collaborators with them on a bunch of

things. you know, he he obviously I

things. you know, he he obviously I think you know, since publishing your book, he recently left uh Deep Mind, right, to start another company. I'm

curious what you made of that.

I think it kind of fits your narrative here actually. I mean, in the sense that

here actually. I mean, in the sense that he was this he he is this intensely determined believer in reinforcement learning to a point where I think most

of his colleagues at Deep Mind ended up thinking it was just too much. um you

know he was the hero when uh first of all the Atari you know game playing system uh that Deep Mind rolled out 2012 2013 you know made these incredible

breakthroughs and you know that was almost like ImageNet but for early agents and David Silva was a key person in that and then there was Alph Go and then there was Alpha Zero which I was you know pretty much all learning from

reinforcement learning and no uh you know what there was deep learning but it was but it was it heavily skewed towards the reinforcement

learning side. And and then came this

learning side. And and then came this paper from David Silva and Rich Sutton, his PhD supervisor. Um called what was

it called? Um um experience is enough. I

it called? Um um 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 super intelligent And so you need to learn, the machine needs to learn from its own experience,

not rely on the kind of crystallized knowledge of humans passed on through text or other kinds of data. Um, 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 super intelligence. Um

intelligence. Um because ultimately yes you know if you if the machine learns from its own data that is purer 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 count reinforcement learning from human it didn't work if if their base models weren't you know weren't strong enough right except for these these specific domains and And you know, I think 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

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 um you know these these models. And so I

you know it almost would have made more sense a few years ago when when uh when reinforcement learning was more out of favor. Uh I thought it was interesting

favor. Uh I thought it was interesting timing that that it happened now.

Yeah. I mean, I think I think you're right, but it 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 um 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. Uh

and then even within reinforcement learning, you know, his vision of how it should be done differed from some other people's. I think he is kind of a

people's. I think he is kind of a classic uh startup sort of person who who wants to be in a small organization where his vision is the vision um

because you know he's he's extremely visionary on on reinforcement learning and so it makes sense for him to go do his own thing.

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. uh Elon,

Sam, all coming out in this uh in this current court case. You know, I think Demis' relationship with each of them is probably less well understood by uh you know, by the general public. Um and I'm wondering if you could just talk a little bit about that uh 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.

So, um you know, Deis's relationship with Elon is very interesting. Um you

know, tried to buy him, right?

Yeah. Uh but 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 Deis is the deep mind 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.

That would have been a pretty valuable offsite to pay attention at pretty two too pretty. Yeah. But so Elon is like,

too pretty. Yeah. But 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, um, we can all move to Mars, be a multi multiplanetary

species. So, I've got the most important

species. So, I've got the most important thing. At which point, Demis says,

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 space flight, 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, "Hm." And then the next thing he

goes, "Hm." And then the next thing he he says, "Well, I'd like to invest in your series B and he writes a $5 million check into the series B." And then, as you say, he wants to buy Deep Mind um 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 Nosek, 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. She's a

transhumanist. You can't trust him. So

they go up um to some closet upstairs in this party and they Skype Deis 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. 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 uh

nuts, right? and starts calling Demis uh an evil genius which is a reference to a game that Demis worked on um when he was a video game designer uh 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 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 um 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 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 um but um 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,

right? He doesn't have a college degree, let alone a PhD or a 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 how to

fake it till you make it to tell the premature truth who is just a master at raising money uh at leveraging his connections in the valley and you know

that's all great but you know it's not the same as being um a serious scientist and you can't trust somebody like that and I think you know look I think people have come around to Demis' view to quite

some you know some extent with Sam um but but Deis always felt that and so I don't think he ever liked Sam I think it was the economist that in talking about your book you know said

it's really like a a test of this like great man theory of history but I'm curious we talked a lot about like these these four protagonists um 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 you know, Demis and maybe some of these other folks at the at the helm of the companies. Um, yeah, I'd be curious your

companies. Um, yeah, I'd be curious your thoughts on that.

I once wrote a book about Alan Greenspan, uh, super powerful player in global economics, um, 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 read his PhD about. He was obsessed with bubbles

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 if he makes his own lab safe, it doesn't stop the other guys from being unsafe. Um and so I think there is a sort of this inevitability to the race dynamic. Um

and it does matter who is leading these labs. I mean clearly you know Dario um

labs. I mean clearly you know Dario um 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. Um

so that's a clear example. On the other hand, you know, Sam um decided to release Chatty PT even though it was hallucinating. That was a choice. It

hallucinating. That was a choice. It

didn't have to do that. And that

completely colored the way the AI race uh played out. And Demis, in a more understated way, um told Rishi Sunnak, the UK prime minister at the time, hey, we should have a global AI safety

summit. And you know, that's what

summit. And you know, that's what happened. Then there was one in

happened. Then there was one in Bletchley Park and the Chinese came and that was kind of the beginnings of an international conversation on AI safety.

Um 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 there are underlying forces as well. Well,

before we wrap up, I definitely, you know, uh, given that you spent so much time with Dennis, uh, 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. Um,

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?

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

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

was having. They went on for two hours each time. So we already could you know

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

here? Why is the world set up like this so that it functions? It's a mystery we have to understand. 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

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. It's

because it's it's it's a kind of quai spiritual quest for him.

It feels like it's for for a lot of people in the space, there's like a religious or spiritual element to uh to like getting to AGI. Um, and it's really interesting to see play out. Um and

certainly I thought that that came through really clearly in in in your work. Um I guess you know it feels like

work. Um I guess you know it feels like you you had free you know such a wide ranging and open book. Were there things that Deus like wasn't really willing to talk about or or areas that you would have liked to put in the book but uh but but you know didn't end up coming through.

Yeah. He was very clear at the start that um he would talk a lot about himself but he wouldn't talk about his family and so I left that out. Um and um there are passing references he's you

know he married his girlfriend from Cambridge. They're still married. They

Cambridge. They're still married. They

have two children. It's all very normal.

Um um but I basically don't talk I mean most by when I wrote about Greenspan uh going to speak to his many many many ex-girlfriends was one way of

understanding the guy. Um and actually brought out the human side of him in a good way. Um and I you know I didn't

good way. Um and I you know I didn't really go there with Deis. Um the

another thing is you know he didn't want to talk about fights between himself and Sunda Pichai and the leadership of Google in Mountain View. I did write about that because other people told me

about that and so I get into that quite a lot. Um so you know his preference

a lot. Um so you know his preference that this should be left out was not um honored.

um uh you know he didn't really want me to talk about the way that he fired his co-founder Mustafa Sleman um 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 Deep Mind unless Demist 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

issues. So yeah, there were some things that he didn't want included and sometimes um he won that preference and other times he didn't.

There's been a ton of public discourse about your book. I feel like you've been on on you know lots lots of of folks have been talking about it um and I think have picked up on a lot of the themes we've discussed here today. Are

there parts of the book or or aspects of it that you think are like underdised or you wish people you know would would would talk about more? I think there are sort of interesting um takeaways for

scientific innovation. Um how you do

scientific innovation. Um how you do deep tech companies um you know we talked about some of the shortcomings at Deep Mind, you know, the lack of concentrated bets and and so

forth. But on the upside, I think it's

forth. But on the upside, I think it's very interesting how he developed what he calls scientific taste. Uh, and what I mean here is that, you know, when he had his first um startup, which was this

video game production company called Elixia, 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? Um, 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 a bad outcome. You know, he got some money

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 deep mind 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 Sior 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 that's impossible um we're the best in the world. That's

good enough. And Demi said like, "No, no, that's not the point. We we want to predict proteins so that, you know, research biologists and and medical

researchers can use our predictions to build medicines and other fantastic breakthroughs. There's no point just

breakthroughs. There's no point just being the best better than the other labs. We want to solve this problem."

labs. We want to solve this problem."

And the guy who's running the team, Andrew Sior, says that's impossible. So

Demis sits in the meetings of the team and this is where the scientific taste come in. He he listens to what he calls

come in. He he listens to what he calls um 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 other then that's fluent um and and you should move forward, put more resources in, push

harder. If there was kind of a silence

harder. If there was kind of a silence and nobody had any good ideas, of course you should give up. And so he he does that test. He listens to the team. they

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 Sior does something else. He puts in this other

something else. He puts in this other guy, John Jumper, who then goes on to become the co-winner of the Nobel Prize with him. Um, and I think that evolution

with him. Um, and I think that evolution uh from blowing up his first startup to being right about protein folding holds a lesson for how you do the sort of

frontier science inside a company.

I love that story. I guess you know obviously um you know part of uh of of this work really helps elevate you know and and uh tell the story of Demis more broadly which is maybe an under underk 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 uh deserve uh uh more more light shown on them or or an expose you know some some kind of

biography you know other unsung heroes in this kind of AI world. Well, in my book, I do have this sort of pairing of Ilia Satska and David Silva. One of them

representing the deep learning tradition PhD under um Jeffrey Hinton in Toronto and then on the other hand David Silva

representing what I call the kind of Edmonton Alberta tradition, PhD under Rich Sutton who was the sort of guru of 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.

Certainly um you know I think at various times as as Google has AI fortunes have seemed better or worse you know folks have speculated that maybe uh one day Demis will become CEO of Google. Uh you

think that's possible?

Yeah it's possible. Um there's a question about whether he would want to do that because I think he does love being a little bit uh able to think about the science and he does this

double shift every day meaning you know he he eats dinner uh with his family and then goes back to his desk until 4:00 a.m. and that's when he's doing science.

a.m. and that's when he's doing science.

Um 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. Um 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. But it seems to me that it it all depends kind of like if Sunda were to decide that he's leaving.

Um 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, uh 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. Well, uh, Sebastian, it's been

with. Well, uh, Sebastian, it's been such an interesting conversation. I

always like to leave the last word to the guest. Uh, I I think in this case,

the guest. Uh, I I think in this case, it's pretty clear where you might want to point people, but, uh, I'll leave the last word to you. Anything our listeners should should take away further from this conversation? And also, please

this conversation? And also, please please plug the book.

Well, look, I really enjoyed writing this book. It's my sixth book and um,

this book. It's my sixth book and um, spending, you know, more than 30 hours with Deis Sabis 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. Uh 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. Um it it was just wild.

science fiction. Um it it was just wild.

And I do try to communicate that energy on the page. Um it's the first time I've ever used the the first person as a device in a book. Um because I wanted to

use that dialogue I had with him. And so

having Demis Rifford then me kind of ask him a question or just you know in that in interspersed in what he's saying this kind of what I'm thinking um to help the reader sort of understand it. Uh I was

this was such an extraordinary experience that I was driven to you innovate the craft of writing in terms of my own craft. Um so yeah I enjoyed it. I hope you do too if you read it

it. I hope you do too if you read it everybody out there.

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

Fantastic. Thank you Jacob. It was

great. I'm Jacob Efron 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

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