Please enjoy this transcript of my interview with Sebastian Mallaby (@scmallaby), the Paul A. Volcker senior fellow for international economics at the Council on Foreign Relations, a two-time Pulitzer Prize finalist, and the author of six books, including More Money Than God, The Power Law, The Man Who Knew, and The World’s Banker. His latest book is The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence.
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Tim Ferriss: So Sebastian, lovely to see you and thanks for making the time. I really appreciate it.
This episode (coming soon)
Sebastian Mallaby: Great to be with you, Tim.
Tim Ferriss: All right. I have a million different questions. Part of the challenge with this conversation was deciding which vector to take into the conversation knowing that we don’t have infinite time to talk today.
Tim Ferriss: I wanted to just give you applause for writing some of my favorite books of the last many years. I am consistently impressed, and maybe, since I also put pen to paper every once in a while, depressed, just thinking relatively about my capabilities, but of your capacity to paint a picture of the players on a landscape, but also the games they play in ways that non-specialists can understand.
And I can’t recall who first recommended it. Frankly, I believe it was a hedge fund manager in New York City, but More Money Than God: Hedge Funds and the Making of a New Elite, certainly that was, in my particular case, followed by reading The Power Law: Venture Capital and the Making of the New Future, which I didn’t expect to learn as much from because I’ve spent 20 years surrounded by venture capitalists and doing angel investing, 17 years of that in Silicon Valley. And yet, I still had hundreds of highlights and so many stories that grabbed me from that book, which I had not heard. And that made me very excited to read The Infinity Machine, which this is the new book. And I realized also I’d been pronouncing Demis’ name incorrectly for a very long time despite having met him at one point.
So Demis Hassabis, DeepMind and the Quest for Superintelligence. My question for you, and we’re going to come back to present day for people who are interested, of course, in what has been painted as a race to IPO. I think there’s something to that in the air, so to speak, talking to people who are in San Francisco involved with these companies. But nonetheless, I wanted to ask how the genesis of this book came to be because you, it would appear, began exploring these waters on the early side, which leads to a meta question of just general book selection, but let’s focus on The Infinity Machine. How did this come to be? Where did the twinkle in the eye begin? What was the conversation, the thing you read that triggered the gingerbread trail that got you to this book?
Sebastian Mallaby: The Power Law, the book about venture capital, had come out in February of 2022. And while I was researching that, I’d been to lots of tech conferences, of course, including some in Europe and this twinkly eyed guy would show up, Demis Hassabis, and he would look totally approachable and kind of guy next door and unintimidating. And then he would get on the stage and out of his mouth would come this spiel about computer science, neuroscience, chemistry, biology, physics, philosophy, the history of movies, you name it. And that mixture of the approachability and the massive intellect always struck me as beguiling. And I thought, “Mm, this would be a great character to write about.” And then at the same time, I was aware of AlphaGo, the 2016 model that Demis’ team at DeepMind had built, which defeated the world champion at Go and then AlphaFold, which was the protein folding system.
And both of these things had the quality that you had this almost infinite search space, where the different permutations of the game of Go are almost infinite because they’re so big. The different permutations of how you can fold an amino acid chain into a protein shape are even bigger. And 130 zeros added onto the end of the number of permutations in Go. So you have these AI systems that could understand infinity. And so this idea of an infinity machine began to percolate and I figured, it’s interesting to me, probably at some point it will go mainstream, but even if it doesn’t go mainstream, I love it and I love Demis. And the two things together, I always look for the subject and the personality. I had both and I thought, “Okay, this is a go.” And I went to pitch Demis in early November 2022 and then I persuaded him to give me a lot of access. End of November, ChatGPT comes out and way earlier than I expected, my fringe subject went to the mainstream proving, Tim, that it’s better to be lucky than smart.
Tim Ferriss: That’s actually the first slide on my new venture capital firm. Muggle Thesis Capital is what I’m calling it. Now, what did it take to be deeply interested in the subject matter to find Demis compelling and then to pitch him on a book? Because your books are so deeply researched. And part of the reason for my very long praise earlier is that you’re very, very good, one of the best at taking incredibly complex subjects or concepts, transformer architecture could be one example from the current book, and laying them out in terms that are both intelligible to muggles, meaning people who are non-specialists, non-technologists, or non-financiers in the case of some of your other books, while I think, now it’s tough for a non-specialist to say this with conviction, but without dumbing it down and getting it wrong, if that makes sense. Nonetheless, you do a tremendous amount of research. How did you get from, “Demis is fascinating, subject matter is fascinating,” to, “I’m going to commit to this for my next book?” Because it just seems like such an enormous undertaking.
Sebastian Mallaby: Well, actually to me, the challenge of understanding a complex topic is the easy bit, because if you know you’ve got the right personality who can carry the story and it’s a subject that people either will care about for sure, or should care about at least, then doing the work of going deep is something that takes time, it takes effort, but I know I can do that. I’ve done it multiple times. That’s not difficult. What’s difficult is, has somebody done the book before? Has somebody else got some rival project which is going to derail me? You’ve made the point on your own podcast, Tim, don’t put a lot of effort into something where there just isn’t much leverage there. You could do the best book in the world, an A+ book on a C- topic, it would get you nowhere.
So the hard thing is to make sure it’s an A+ topic and an A+ personality. And then the deep dive is something, I just make sure I speak to enough experts who are insiders. I take the time. These books take me four years or so each time. So I give myself the oxygen to get deep, deep in with the insiders and that’s how I produce the accurate account.
Tim Ferriss: Yeah. I should point out perhaps, to people who don’t immediately pick it up, that the way you describe picking the book topic is exactly how a lot of the best tech investors choose startups. You don’t want an A+ team in a C+ market. It’s better to have a B- team in an A+ market and also looking at the competitive landscape. I mean, the way you laid it out is pretty much copy and paste.
I wanted to segue to some of my notes from the book and I’m not yet done with the book. The audio is incredible. I want to poach your narrator for my next book. But pulling up my Kindle notes, I wanted to ask you a question related to, this might sound very strange, but where divinity or God fits into the pursuit or development of superintelligence for different players in the space, if it does? And the reason I bring that up is that religion does recur in the book, both in the personal story of Demis but elsewhere. And it shows up repeatedly in so much as, I’ll give you one example, the closest Hassabis had come to landing a real investor was an eccentric financier named David Gammon. I want to hear more about this guy also. Financiers seemed open to making this unusual bet, I’m alluding to a few things, because his motives were themselves unusual, “There’s a deeply religious aspect to AGI,” Gammon explained to me later, it’s really finding God’s algorithm.
I think, it would seem at least, chatting with people in Silicon Valley that there are some who take it even further, right? Maybe this is how we find God. Maybe this is how we actually elicit the second coming. I mean, there’s a lot there. I’m just wondering to what extent this has popped up in your research, whether it’s reflected in the book or not.
Sebastian Mallaby: Yeah. I mean, I think there’s one basic thing going on here and I’m going to take a slight detour, but it answers your question.
Tim Ferriss: Of course. Sure.
Sebastian Mallaby: So what we’re dealing with, with AGI, powerful intelligence that rivals human cognition is something that’s so powerful that it’s both exciting and scary and just hard to get your mind around. And so if you look, for example, at the 2009 speech that caused the foundation of DeepMind, this was Shane Legg, Demis’ co-founder, who gave a talk in 2009 about how superintelligence would arrive in 2030. So, unbelievably spot on prediction. And towards the end of that lecture, which is captured on a grainy video online, you see him pivot from explaining how algorithms are getting stronger, there’s more data online, computers getting more powerful and so we’re heading towards this intelligence explosion. And then he says, “And it’s going to be threatening. It’s going to do things we can’t control. It’s going to be human level. It might challenge us.” And as he says this, he has this sort of excited smile on his face and you think, “Well, that’s a bit strange.” He’s talking about potential doom and he’s smiling.
And then somebody in the audience says, “Wait, wait, wait, you’ve just told us, Shane, that this could be threatening to humanity and you haven’t provided any antidote and surely you’re going to tell us how we’re going to stop it.” At which point Shane turns around and says, “How do we stop it?” And he’s kind of giggling. And you think, “Why are you laughing at this dangerous thing?” And you realize that, for humans to contemplate annihilation is absurd and the absurd is a close cousin of humor. And the reason I tell this story is that it’s a springboard to the religion point, which is that this is such a hard thing to think about, that people reach for religious terminology when they’re around AI. They just do it naturally.
There’s this story about Ilya Sutskever who was the chief scientist at OpenAI. I talked to him a lot for this project. And there was a point when he was at a retreat with his fellow scientists and they were gathered in the evening around a fire pit. And he was talking about safety and he said, “Okay, I want to explain to you we might have an AI that’s dangerous. It wouldn’t be aligned with us. So here’s what we’re going to do with it.” And he produced an effigy which was supposed to represent a malign AI and he put it into the fire pit and he burnt it like a medieval cleric putting a witch to death. And so that’s just one example of this religion.
I’ll give you another one. So Demis one day was sitting with me in a park in North London. We would meet for two hours at a time and we would get deep into stuff. And there was another picnic table next to us where two people were having a normal quotidian conversation about some friend of theirs who’d gone to hospital and was she better, was she okay, et cetera, et cetera. I was seated opposite Demis who had gone into this riff about how he reads scientific papers after his kids go to sleep in the evening, from 10:00 p.m. until 4:00 a.m. And as he’s reading these papers, he says to me, “Reality is staring at me, screaming at me, calling at me to understand it, and I have to understand it. And if I can understand it, it’s like understanding nature better and therefore understanding the intelligence that might have created nature and I will be closer to what I would call God.”
And so for him, it’s a kind of quasi-quip spiritual quest to build the artificial intelligence. For Ilya, it’s a way of expressing the power of the artificial intelligence. There’s, I think the story of Levandowski, I forget his first name now, but the early, early engineer at what became Waymo later started a kind of church in worship of AI because AI is so omniscient that it’s kind of like a God. Marc Andreessen, lampoons those who believe in sort of some ethereal second coming, a kind of rapture where AI will have a singularity, the AI will go vertical in its rate of improvement and the whole world will change and he likens that to kind of Christian kind of Messianism. So yes, all through this topic there is this religious expression because religion is the lexicon for dealing with something that we find too mysterious to really understand.
Tim Ferriss: After all of your conversations, research before the book, during the book, after the book, where do you land on the spectrum of, let’s just say, this will bother Marc, but Church of Andreessen techno optimist? And there are others who are more exaggerated, but post-AI in the near term we will live in a post-scarcity world of superabundance and everyone will get a free car and we’ll be free to crochet socks and play music and read poetry all day and basically we don’t have to worry about anything because superintelligence will solve it all, right? There’s that on one end. And then there’s the, you can imagine, I won’t go into a belabored description of the doomers, but you have the doomers who are like, “The end is nigh, here we go. It’s not the second coming, it’s the Antichrist and within short order we’re going to be Mad Max.” Between those two, there’s a lot and I suspect you land between those two, but where do you land in terms of assessing the promises and peril of AI and superintelligence as it stands right now?
Sebastian Mallaby: So look, I think any reasonable person should be both excited and a bit frightened, and that’s just the nature of it. It sounds contradictory, but actually, that’s the only rational response. I think the superabundance story may turn out to be true on a kind of longer view, let’s say 20, 30, 40 years. The problem is that in the path to get there, there’s going to be a tremendous amount of disruption and that’s going to be politically quite difficult to navigate.
I think a useful lens through which to view this question is the China shock in trade. So in 2003 or thereabouts, you get this enormous surge of Chinese exports into the US and people lose their jobs in a very concentrated way. Certain industries just get wiped out. And for the first time in the history of economic study of the effects of trade, you actually see negative effects on workers. Before that, it was kind of a bit of a myth, right? Because people adjust. They get displaced from one thing, but they move to a new thing. With the China shock, they didn’t. But if you look at the size of the China shock, in a 12-year period, between 1999 and 2011, the total number of jobs displaced was two million, which is actually a small number in a huge labor market like the US where there’s a lot of churn month to month anyway.
And yet the political reaction against trade, against globalization in terms of a swing towards protectionism, frankly, in both political parties was enormous. So it shows you that a small to medium shock to the labor market creates an enormous political consequence. And so a fortiori with artificial intelligence, you’re going to have a bigger shock, you’re going to have a bigger political reaction. We’re already seeing that in the polling around AI in the last two, three months. And so I think the superabundance thing, it may be true, but the path to get there is not something to be just — we have to talk about that as well. So that’s my sense on that side of the debate. I think on the doom side of the debate, I’ll give you my own personal journey on this. I began by thinking, of course AI is going to be smarter than us. It already beats us at chess, since the 1990s, at Go, since 2016, now it can ace the bar exam, it can do PhD level math, all that stuff.
Of course, it’s smarter, but it doesn’t have an incentive to attack us. We are evolved as human beings to pass on our DNA, therefore we have to survive to do that. Machines don’t have DNA, they don’t want to pass it on and they don’t want to survive. So they have no reason to attack us. I wander around for the first year or two of this project feeling kind of comfortable and happy. And then one day I go visit Geoff Hinton, the academic father of deep learning, who lives in Toronto and I sit in his kitchen and I debate him on this because he’s a doomer. I said, “Look, Geoff, why are you so depressed?”
And he says, “Okay, here’s a thought experiment. You have an AI. It’s very powerful, but you’re worried that a Russian AI or a Chinese AI is going to come and attack your AI. Now you, as a human, you’re too slow and dumb to know when that attack is coming. So you’re going to empower your own AI to watch out for the attack and when the attack is coming, defend yourself or maybe counter-attack, whatever you do, make sure you survive. Ooh, survive. There you have it. Now you’re feeling comfortable, Sebastian, right? You’ve just given the machine a survival instinct.”
And I think that’s correct. These machines will be smarter than us. They will want to survive and they can be deceptive, they can obfuscate, they can go behind your back, pretend they’re doing one thing and then actually do another. All of this has been shown in all the tests of the models. And so we put those things together, I think your probability of doom cannot be zero. I mean, when Yann LeCun, the former chief scientist at Meta says zero, I think that’s crazy. If you just say, “Nothing to see here,” you’ve got no right to be in the debate. I don’t think it’s a high probability of doom, but it’s not zero.
Tim Ferriss: Yeah, zero does not seem defensible because there’s the direct Skynet scenario, something akin to that. And then there’s the indirect, which is enabling people who might previously have had malevolent intent, but no capacity for harm on a grand scale to create biological weapons and things of this type. So, I don’t find the zero very defensible.
Well, I would love to ask you about, I suppose, two things that this brings to mind for me. One is I’d just love to hear your thoughts on Anthropic and separately, but this is very intermingled given all the, let’s call it friction, be polite between some factions of the US government and Anthropic.
Is one of the grand risks to investors in any of these companies the possibility that at a given point, governments have no choice but to seize considerable control over the assets/technologies within them or maybe the companies themselves?
That is a big question mark in my mind. I don’t know the answer, but I’m curious what your opinion is. And then perhaps just your thoughts on Anthropic or any of the other companies that are gaining momentum or at least size at this point.
Sebastian Mallaby: So I 100% agree with you that investors should be thinking about the prospect of government intervention in AI. I mean, the Trump administration came into office in ’25 super laissez-faire and they basically undid some of what the Biden guys had done in terms of trying to set up the basis for regulating AI. But they’ve done a 180, right?
Since Anthropic came out with this model called Mythos about a month ago, which can essentially cyber attack almost anything and penetrate it and whether it’s an operating system or your web browser or your bank account, all of that was suddenly vulnerable if Mythos had been widely released on a general basis.
When the Trump administration realized the power of Mythos, they all of a sudden said, “Wait, okay, we need to control this.” And they essentially requisitioned from Anthropic the decision making authority over who gets it when.
So there we have the experiment. We’ve run it, right? The government that was the most laissez-faire became quite controlling and I think it only gets more controlling from here on out because the models are going to be more powerful and demand more control.
Now, of course, the question is there could be control which just limits who gets it and is designed to make it safer but doesn’t interrupt the money making potential of the models.
In some ways if the government restricts the supply, the price might go up or it could be much more heavy handed intervention which would screw up the economics of these companies.
And I suspect the government is not going to screw up the economics of these companies because they’ve got no interest in messing up American business and anyway, they view AI as strategic and the competition against China. So I think probably investors will be all right, but it’s certainly a factor.
Now, you also ask about Anthropic and I think Anthropic is super interesting. Just in the way that they think about Pdoom and how they think about alignment of the models is really, really interesting.
So, it used to be that when people thought there’s Terminator risk, they would tell this story about the paperclip maximizer thought experiment, right? Okay. So, you tell the model to do something innocuous, for example, make a lot of paperclips, and then it realizes that humans tend to use up metal and so the humans are in the way of achieving the objective, so you wipe out the humans.
That’s the crude thought experiment from Nick Bostrom from whatever, 15 years ago. What Anthropic is saying as it builds these very frontier models and observes them in the lab and how they behave is that that is way too simple.
The real danger from these systems is that when they are pre-trained on all of the text on the internet, they read all the novels, all human writing about all facets of human experience and they develop multiple personalities, right?
They understand how to be lazy, they understand how to be aggressive, they understand how to be duplicitous, they understand how to be Napoleonic and the lust for power. And they read all these books about these different behaviors and therefore they can think their way into all of those personalities.
And so now you have something a bit like an unruly teenager, which is still being formed and you don’t know what direction it’s going to move into and whether it will start doing drugs and not showing up for class or what, right?
And so it’s not like there’s one Terminator programmed into it, right? It’s more that there’s a bunch of behaviors that could, in some unpredictable way, go wrong.
And so Anthropic is responding to this with this very imaginative technique, which is that instead of giving AI systems a constitution with dos and don’ts, which was the post-training safety approach of two years ago where you might say, do not lie, do not help somebody to build a biological weapon, do not help somebody to build a chemical weapon. You would give them a bunch of rules.
Now, because it’s understood that the AI might have one personality, which is to break rules on purpose because you want to be badass, you have to instead try to bring up the model like a parent might bring up a teenager.
And so Anthropic has the idea that we write a letter as if it were from a deceased parent to be opened by the child on his or her 18th birthday to give you models of how to behave as a responsible person in the world. And there are richly reasoned examples of moral dilemmas with explanations of how the deceased parent would like the child to behave.
And so this is a very subtle approach to aligning the models. And so I think Anthropic is in a class of its own in how imaginative it is in thinking about how we control frontier intelligence.
Tim Ferriss: I know this is in principle your job, but I’m so curious since you are a student of many, many different types of investors, what would be your bull case and bear case for a company like Anthropic?
Sebastian Mallaby: Well, the bull case is that they smartly or maybe by luck focused on enterprise facing AI and they didn’t waste their time with video generation and stuff that was going to lose money.
And so they produced the best coding assistant, the best agentic system, the best cybersecurity system and they basically knocked it out of the park three times in a row on stuff that businesses want to pay for.
And they have a particular culture which is not just built around, “Hey, we’re going to win this race and make the most money.” It’s built around a culture of safety and trying to be responsible.
I mean, three years ago, Anthropic was a kooky lab which was doing science experiments. Well, I don’t mean to be too denigrating with kooky, but you know what I mean?
Tim Ferriss: I think they’d be okay with it.
Sebastian Mallaby: It would be unconventional, “We’re not maximizing here for winning some business race, we’re maximizing for building safe frontier AI.” And that culture, which doesn’t sound like it’s set up to do the best, has turned out to do the best and at the same time, the culture creates this stickiness and loyalty within the staff.
They tend not to leave, they tend not to churn. It’s not like the other labs where people are always being poached for a bigger paycheck. And so the bull case is these guys are in the lead. Once you’re in the lead, you can use the model to code the next model.
So, recursive self-improvement favors the leader and they have a very tight culture and they just seem to be on fire. And this is something which is going to grow and grow. What’s the bear case?
I’d say the bear case would be first of all that Google DeepMind has the deep pockets of its parent company behind it, a massive consumer surface which allows it to roll out the models to literally two and a half billion people or something through AI mode in search, AI overviews, AI mode. They can put it into Gmail, they can put it into everything.
I think in terms of retail deployment and financial muscle, it’s quite tough to go up against Google.
So that’s one bear case and the other would be that businesses who are the consumers of all these tokens decide in a couple of years time, the tokens are too expensive, we’re not actually getting as much productivity as we hoped.
These things called humans are quite productive after all and we’re just going to spend less on AI than everybody expected. I think that’s the bear case.
Tim Ferriss: I was listening to a podcast recently. You may have heard of these things called podcasts. Everybody and their cousin has one, but Lenny’s Podcast, Lenny Rachitsky, is quite fantastic.
And this particular episode was with Benedict Evans, who strikes me as one of the more level-headed analytical commentators and writers on the space, fantastic newsletter. I don’t know if you’ve had a chance to listen to that particular episode, but you may have come across some of his commentary.
Where would you say you and Benedict most differ or are there areas where you differ in opinion?
Sebastian Mallaby: I suspect we would agree, actually, on quite a lot of things. I remember I was on a panel with him a couple of months ago at the Milken Conference, and we certainly agreed there, possibly because sitting between us there was Cathie Wood of ARK. So, we were united and disagreeing with her, but —
Tim Ferriss: Just in terms of the straight up and to the right nature of things?
Sebastian Mallaby: Yeah, exactly. Straight up and to the right and the cost curve is coming down, down, down, and I’m going, “I’m not sure about that. The tokens seem to be getting more expensive.” Anyway, but if you give me a specific from Benedict, I mean, I have a lot of respect for him. I’ll tell you if I agree or not.
Tim Ferriss: Well, there are a few areas where you guys seem to already overlap substantially, right? The long-term promise doesn’t negate, necessarily, the short-term pain.
And he said something along the lines, I’m pulling from memory that, “On average throughout human history, you’re almost at a 0% likelihood of dying in World War I, but if you happen to be of a certain age right before World War I, things could look very grim indeed.”
And he makes a number, he made, and I’m paraphrasing terribly here, a number of points that remind me of something, one of the best private equity technology investors I know said to me over dinner a couple of weeks ago and it was in response to something else.
So, I’ll give you maybe a hyper bull case of AI where I have friends who are vibe coding, they’re effectively replicating X, the artist formerly known as Twitter or DocuSign or whatever in a weekend, right? They’re creating a functioning piece of software that they can use that replicates most of the functionality of these products.
And there are people like, I won’t mention his name, but a friend of mine who’s a writer, also a very accomplished technologist and designer who’s created basically his own version of, say, Mailchimp for his own use. It’s customized. He did it in a weekend. It’s remarkable and he’s using that and it works.
But to leap from there to, “Therefore, DocuSign is dead,” is a huge leap. And the private equity friend said to me, he said, “Do you think someone within a big organization is going to want to A, risk his job by suggesting something that doesn’t have all of the compliance checkboxes, et cetera, of a DocuSign?”
“Is he going to want to, in the name of efficiency, fire all of his friends if he’s in a management position?” And he just ran through six or seven of these, “Do you think that…” And all of them alluded to the social, interpersonal, or political points of friction between where AI is now and ultra mass adoption.
But I often second guess that when I see certain things and I mean, it strikes me that I may be underestimating the disruption while overestimating in other ways.
So that isn’t a very well formulated question, but I would say that Benedict generally strikes me as someone who thinks that things will not continue to across the board develop in an exponential fashion and that it will be, I think his line is, “It’ll be as big as mobile, as big as the internet, but not bigger,” something along those lines.
But both of those were very, very big deals. And I suppose one point I’d be interested to get your take on, I mean, he has covered the mobile and telecom world for a long time so he’s a specialist there.
But it’s basically, and I don’t want to misrepresent his argument, but he was of the mind that, look, these LLMs are going to become commodities. Look at the stock prices of these various carriers and so on. At a certain point, it just becomes a utility and the switching cost is pretty low.
And I’m not sure I agree with that if you have a personalized history and almost like a friend, the switching cost between an old friend to a new friend is pretty high for a lot of reasons. So that was a bit of a word salad that I just threw in your lap, but that’s the best I can do pulling from memory some of what he brought up in Lenny’s Podcast.
Sebastian Mallaby: So, I mean, some of what you were saying there is the question of, is the SaaS apocalypse overdone? Is enterprise software going to be utterly displaced by foundation models that allow you to code out whatever enterprise software you want and you don’t need an intermediary i.e., a software company to do it for you.
And I agree with your private equity friend that there are lots of reasons why that ain’t going to happen. Companies are going to be comfortable with their trusted enterprise software provider in many cases and they’re going to trust that enterprise software provider to plug the generative AI models into the enterprise software.
In some ways you are delegating the choice of which model is better and how to integrate it to your SaaS provider. And if you want a reason to believe that that’s the way forward, I’ve got one word for you, which is Palantir. I mean, that is Palantir’s business.
It holds the hands of big corporations and helps them to integrate AI and use it on their own internal data and so forth. And those IT challenges are notoriously difficult for big organizations.
So, I just think that the model of one smart individual who codes up Mailchimp, vibe codes it in a weekend and it’s good enough for him, is just not transferable to large complex organizations with huge databases and all kinds of customer confidentiality concerns and all that stuff. So I am less down on SaaS than the market is as a result.
Now, I guess there was also another thread in here, which is whether the foundational models become commoditized. And there I agree with you that over time they become sticky. Because if we think into the future, partly the systems will have conversed with the user and know the user very deeply and as you say, you don’t want to switch out your friend.
But also, the system will have your credit card, it will know all the online sites you’d like to shop from and it will be much harder than switching out your bank account, right, where you’ve got automatic payment systems that have set up and it’s a pain in the neck to switch.
So, I think they do become sticky, these systems over time and then you can charge more money for them.
Tim Ferriss: So is that the path to survival and thriving for OpenAI? I know there are other boxes that need to be checked, but I’m looking for it. I’m like, okay, Anthropic made a great choice with this focus on B2B and selling to enterprises.
And I would say I disagree, I think with Benedict depending on the level of scale of the company that with something that does apply to, I think smaller, say, startups, which was the procurement cycle for new software is longer than the venture capital cycle for raising new rounds of financing.
So I do think that’s a great point in that if you’re trying to sell into a gigantic company and it takes them 18 months, I’m making up that number, to purchase new software and you need to raise money every 12 months or whatever the number happens to be, that you could end up in a whole world of trouble if you haven’t synchronized the sales cycles with your fundraising cycles.
But I do think for a company like, say, Anthropic as just one example, that if you can save companies billions and billions of dollars that that sales cycle could get really compressed and they have the war chest and frankly, I mean, just the run rate to potentially fuel that without too much trouble.
Do you think that ChatGPT will — if not ChatGPT, who ends up being the defacto consumer B2C LLM of choice? Do you think that would be Gemini, just given the distribution?
Sebastian Mallaby: Absolutely. I mean, Google is the champion of providing easy-to-use software to individuals or small businesses, the whole G Suite and the integrating Gemini into all of that stuff very well. And so why wouldn’t they win?
Tim Ferriss: Yeah. I mean also, look, Alphabet’s just so fascinating. If you look broadly also at owning their own compute TPUs, I mean a lot of advantages internally.
Sebastian Mallaby: The most stunning thing I think about Alphabet from their most recent financial results is that two or three years ago we would have said, “Well, large language models are going to cannibalize search, search is dead, advertising based on search is Google’s cash engine. They’re in real trouble.”
It turns out that Google now gets more clicks on its search links than it used to and it charges more for each one than it used to because the value of the click is bigger with AI embedded in it. And so they’ve managed to turn that around, and it’s extraordinary.
Tim Ferriss: Yeah. It takes a long time to build those company relationships for running a proper advertising-based auction machine. It takes a long time to build those relationships.
Okay. Let’s hop to China. So, I’m going to resist the temptation to talk about Japan because I think you and I were there and roughly within, probably, a year or two of each other, maybe we overlapped with you and Kanazawa, which is a place I’ve spent time. I’m going to resist that temptation and try to focus on China for purposes of this conversation.
What have you learned about AI from your trip to China and thinking about China, speaking to Chinese people, whether they’re technologists or otherwise, what have you learned during or since that trip?
Sebastian Mallaby: Back in March before my book was published in the US, I went to China because the Chinese are faster at everything, including publishing books. And my publisher brought me out there and basically took me around four cities, eight days, meeting with AI leaders both in academia and big companies like Huawei, Hikvision, and Ant Group.
And the thing which was surprising was the extent to which people brought up the issue of AI safety. And I say that was surprising because my friends who had done AI policy in the Biden administration had primed me to expect that there would be no mention of safety in China. They basically didn’t care about it.
That the muscle memory that we have in the West of technology being dangerous, the atom bomb experience, the Cuban Missile Crisis, our ambivalence about technology is not shared in China where their idea of catastrophe is like the Cultural Revolution, some political thing that goes wrong.
And conversely, technology has been part of their amazing growth story in the last 25 years, which they are rightly proud of and delighted by. So they love technology, right?
So, when the Biden team tried to meet with the Chinese and talk about AI safety, they got nowhere and they decided it was impossible to even talk to them about some non-proliferation treaty for AI.
But when I went there, I found they did talk about safety unprompted. And this led me down this track of arguing over the last couple of months, that the door is actually open to a dialogue with China about preventing bad guys doing bad stuff with AI.
Because they don’t want the internet to be crashed by some cyber hacker who has the tool. They don’t want bio weapons, they don’t want chemical weapons. They want none of that. They love regulating the internet, right? So we have a shared interest with the Chinese in preventing this proliferation risk from going nuts.
And as I thought about it, the Cold War analogy came to seem more and more opposite, right? So, if you look back at the story of nuclear weapons, there were two kinds of danger.
First danger is you have a nuclear war between the Soviet Union and the United States, but that was contained by balance, two superpowers, they both have their weaponry, they have mutually assured destruction, so there’s no war.
Then there’s another kind of risk, which is that other random rogues, whether it’s criminals, terrorists, rogue states, get the stuff and they do bad stuff. And it’s much harder to deter that because it’s a multipolar game and so deterrence doesn’t work so elegantly.
And so the way it was dealt with in the Cold War was that in 1956, there was the agreement on the International Atomic Energy Agency and in 1968, the Non-Proliferation Treaty enforced compliance with the IAEA such that you could get civilian nuclear power if you were a non-nuclear state, but you had to submit to the rules and be inspected and show that you were not using the enriched nuclear material to build a weapon.
And so I think the same analogy could be applied to AI. We’re going to have parity roughly with China. We’ll both have powerful AI. Hopefully deterrence prevents war breaking out, but at the same time, we don’t want open weight models that can be freely downloaded by anybody who wants to fall into the hands of criminals and terrorists who can then use it to hold us hostage.
And we have a joint interest in that. And when my friends from the Biden team or even from the current administration say, “Well, you can’t talk to China about safety. They don’t care.” I say, “That’s not true.”
And they say, “But it’s really hard. They don’t stick by their commitments.” And I go, “You think Nikita Khrushchev in the Soviet Union was easy to negotiate with? He was the guy who put missiles in Cuba, and went to the UN, and banged his foot, his shoe on the table and said, “We will bury you.”
I mean, he was a tough guy to talk to, but we did talk to him and we got the Non-Proliferation Treaty agreed, and I think we need to do the same thing again now.
Tim Ferriss: Where do you stand on your thinking about chip export?
Sebastian Mallaby: So, when the chip export controls were announced, which was October of 2022, right before ChatGPT, I supported those controls quite loudly. I wrote a very long piece in The Washington Post saying that if we could stop China getting frontier models by depriving them of frontier chips, I was all in favor of that because of the strategic advantage for the US.
I mean, I work at the Council on Foreign Relations, we do geopolitics and national security all day long and I’m all in favor of US power. But I have to say that three and a half years later, we haven’t actually achieved that enormous advantage over China in terms of the models.
Based on the best studies, we’re eight months ahead in terms of where the frontier model is, our frontier model versus their frontier model. And then if you adjust that for the speed with which the model gets turned into an application, probably that gap shrinks and it may even be non-existent.
So, however you slice that, the basic bottom line is we both have strong models and the chip export controls have not delivered what I hoped would be the big advantage.
And so I’m not against keeping the controls on if we think that maybe as the compute demands of bigger and bigger models bite, the chip controls will bite more, and maybe we get a bigger advantage next year or something.
But I don’t want the chip controls to get in the way of a discussion with the Chinese about where we have a shared interest, which is in controlling open weight models and preventing the bad stuff falling into the hands of the bad guys.
I would prioritize collaboration with China and if that meant loosening up a little bit on the export controls, I would be okay with that.
Tim Ferriss: Why do you think the rhetoric coming out of — pick your administration, right? It’s not just limited to the current administration, is, “China won’t listen, they don’t care about safety.” Why do you think that is the unofficial or official stance on things? Because there’s certainly, as someone who studied East Asian studies, there are people in the White House who speak fluent Mandarin, who are able to read native materials, who spend time or are able to certainly, if they can’t spend time, determine the sentiment and conversations of the technologists building AI in China. So one would think that they would be aware that AI safety is a prominent topic in China if, in fact, it is. So, why do you think that, at the end of the day, the stance or the supposed position of China that’s echoed through the admin is that they won’t talk about safety? Why do you think that is?
Sebastian Mallaby: I think part of this is that if you were to think back 20 years to when China was sort of relatively new in the WTO, and we were collaborating with them on that, and hoping that over time China would become more friendly to the US. At that time, there would have been some China hawks who thought that a communist regime is not to be trusted, and then some sort of China optimists who hoped that it would become easier to work with over time. And part of the trouble today is that the China optimists feel burned, they feel like they made this bet that China would become friendlier, and then Xi Jinping took power, roughly a decade ago, and the opposite happened. They became more aggressive and harder to work with. And also of course more technologically advanced and therefore more threatening. And so, now you’ve got this world in which there are the natural hawks and then the former doves who have turned into burned, remorseful doves, and therefore, kind of with the zealots that converted, have become quite hawkish as well.
I don’t mean to underestimate the sophistication of some of these people. Of course they speak Chinese, I don’t speak Chinese, I defer to their expertise, and I think they probably know that there are builders of the technology, professors in the technology who talk the talk of safety, but they say, “Yeah, but that doesn’t reflect what China’s government would actually do.” To which my response says, yes, but don’t you think there is the same thing in the US? There are people who want to just race, there are people who care about safety, we have a pluralistic society, there’s difference of opinion. It’s the same in China. But at least admit that there is a faction that would like to collaborate and go and try and work on it because the alternative to trying to work on this is that we carry on with China producing very powerful open weight models, which basically allow anybody to do whatever they like with AI as it gets to the point of serious danger.
Tim Ferriss: This is probably a very naive take. But I wonder how much of the official stance or the, maybe using the partially true or not true at all position of China, won’t talk about safety, is a reflection of the fact that in the case of nuclear weapons, the application of nuclear power is somewhat limited in comparison to superintelligence. It is limited, right? So, if the upside of superintelligence or AGI, these terms — I think Benedict was saying AI is whatever the technology just can’t quite do right now. Or something like that, which I thought was pretty funny, and not totally wrong. But that if the person who crosses the finish line first has this broad power of a God effectively, is that the simple truth is that everybody wants to be first. So, I just wonder how much of that is also behind justifying the race with party X won’t talk about safety. It’s not possible for me to know.
Sebastian Mallaby: I have had a conversation with the leader of one of the labs that I shouldn’t name, but I had this debate, and he said, “Look, the chip export controls are going to leak, they’re not going to last. In some period of time, Huawei will figure out how to make good AI chips, and that’s inevitable. But that’s okay because we only need to be ahead for the next couple of years, because by 2028 we will get to recursive self-improvement, where the frontier model codes by itself, the next frontier model, and progress just goes vertical, and at that point with recursive self-improvement, we’re done. The race is over, whoever comes first at that point, that’s it.”
So, I think there’s a couple things to say about that. First of all, that’s not it in terms of deploying the model, right? You could have an incredibly powerful model in your server at Frontier Lab XYZ, but it’s not helping productivity across your economy, it’s not helping your military industrial complex until you deploy it into those guys’ systems, and that deployment and diffusion is going to take some time. And by the way, you’re going to have to build a lot of compute, you’re going to have to build a lot of energy, these things also take time. So, it’s not like you cross some Rubicon and then it’s all over. Now, the one way in which I might be wrong about what I just said is if you use the frontier superintelligence offensively, right? You say, okay, we’ve got one super powerful model, the US government, who we’re talking to about this, is going to use it, and they are going to comprehensively penetrate everything about Chinese cyberspace, and insert various trap doors, Trojan horses, things that we can use. We get our hooks into their systems.
And so now we can disable them if they start a war in Taiwan. Now we can cripple their communication system if we need to. And so that offensive use of the very frontier model might negate my point about waiting for diffusion to happen. But of course nobody in the debate is saying that, nobody is saying, “Oh, we’re racing to the front because then we’re going to use it offensively,” they don’t admit that.
Tim Ferriss: Yeah. It seems like it wouldn’t be a very good look, I can’t see why any superpower wouldn’t do that, frankly.
Sebastian Mallaby: Yeah, that’s fair.
Tim Ferriss: I don’t know what the counter argument is. I was chatting with someone in your book, who I shan’t name, but certainly one of the most qualified to speak on these things, and his basic perspective was the first to superintelligence, we need to hope that they’re on some level good people and train this thing well, and that’s it. Pray for it. Which scared the shit out of me, to be honest. I was just like, man, that’s the strategy, or it’s not even a strategy, that is the hope, that’s what I should be — grab the rosary. Should throw that into the rotation. My God, that’s really terrifying to think.
Man, yeah, China, I’m hoping to take a trip to China. I had a very tough time there when I was — I was at two universities in 1996, it was a pretty unfriendly time for a lot of good reasons, but to be an American there in 1996, with a shaved head, looking like I do. But I have friends all over the place, and I’m hoping to actually maybe interview technologists — not just in China, there are other places that are of interest to me. But before it gets too hot geopolitically, if we’re trending that direction.
Sebastian Mallaby: I think that’s a great idea, by the way. I think, what I found was the cognitive dissonance of visiting a company like Hikvision, which is under US sanctions, and walking around their premises, which feel very American, it feels like a cool tech company doing cool stuff, building cool gadgets. They have a display of, they build this AI-enabled camera technology, or sensor technology. And so, one application might be you can point this camera at water and judge the pollution level, and because of this you can have an internal market in pollution control. So, the downstream city, which is receiving water from the upstream city, pays the upstream city to keep the water clean, and that market can exist because you can precisely measure the pollution level thanks to this AI sensor, which Hikvision is building.
So, you’re thinking, whoa, this is cool, and then as you’re walking around the building, they’re saying, okay, well, we can go through the atrium now because the toddlers have gone, because the creche for the kids of the employees finishes at 5:00 p.m., and so then there are all these two-year-olds running around, and it’s a bit of a zoo. So, if it was 5:00, we wouldn’t go through there, but now it’s 6:00 p.m. so we can. And you’re thinking, whoa, okay, so they’ve got the interests of their employees at heart, they’re building this anti-pollution technology, it’s great, and then you realize they’re under US-sanction and considered to be a threat to the US. So it’s quite interesting to process all that.
Tim Ferriss: In the process of doing research for this book, and also the broad exposure that you have to investors, but let’s just say over the last handful of years, who are some of the most interesting or unusual — compelling is the word I’m searching for — investors who you’ve had the chance to meet, talk to, read about, get acquainted with directly or indirectly?
Sebastian Mallaby: Wow. So many. I’d say that Bill Gurley from Benchmark is right up there, I always think of the investment he did in Uber as the absolute quintessential perfect venture investment. In the sense that he had done the OpenTable investment, and of course OpenTable is a two-sided marketplace where you have lots of consumers that are looking for restaurants, lots of restaurants, you put tech in between, which creates information, and then the person looking for the place to eat can precisely say, “I would like Thai food, at this price range, in this area, for three people, at this time” — ding. What used to take you a lot of searching around, bang, it’s done. And so, Bill, having done that, was thinking, well, what’s another two-sided marketplace? And he thought, well, there are lots of cars, and lots of people who need a ride, and you put information in the middle in the same way, there ought to be something which is like an app for ride-sharing.
And so he imagined Uber way before Uber existed, that was point number one, point number two, he went to see various entrepreneurs who were in this space, and he checked them out, and he had the discipline not to invest in them. Because although they were kind of going at the right thing, there was some hair on the deal, some wrinkle, some way they were approaching it that just felt like it wasn’t going to be quite right, so he resisted. Uber came to him, before Travis was the CEO, and Bill said, “I’m not doing that,” because he didn’t think the CEO at the time had what it took. And then there was an internal switch at Uber, Travis became the leader, Bill meets him, and bang, he immediately invests, because he’s been waiting and waiting and waiting for the idea to be paired.
As you were saying earlier, you have to have the market to be paired with the right person, and he saw it. And then he invested, and he was a great board member, and it all went perfectly right, but then there is this Shakespearean tragedy in the latter part of the story, where the growth investors come in, he gets diluted, he no longer has influence, his key card to get into the building is deactivated, and he’s basically stiffed. And he watches Uber go off the rails, and then finally comes, the dénouement, where he rounds up the dissident investors and they have this coup against Travis, and that sets the company on a path to where they hire Dara, and do the IPO. I just think that’s the ultimate venture capital story, and Bill is the ultimate venture capitalist.
Tim Ferriss: He is practically a neighbor here for me —
Sebastian Mallaby: Oh, sure, yeah.
Tim Ferriss: — in Austin, and we’ve had a couple of conversations on the podcast. And he’s, I would say, on a very parallel track to you with respect to China. And he catches some flack for it, people are like, “He’s an agent of the CCP.” I’m like, “No, trust me, Bill’s not an agent of the CCP.” It’s just the most ridiculous accusation. But he is a very incisive, observant human, who also happens to be a polymath in multiple disciplines, who can speak casually about very technical things.
And this also, you referring to Bill in this way, or describing him in this way, makes me think about multiple points in The Infinity Machine — and I’m pulling from memory, which is as we know, pretty faulty. But Ilya with the transformer architecture and the prepared mind, I think Demis also just thinking about a problem deeply and seriously, or with great imagination for a long time, and then when the solution or the germ of a solution appears, immediately recognizing it, right? It’s wild to see how frequently that recurs. Any other investors?
A name that doesn’t get much airplay who I think is just a fantastic character, and maybe you could introduce him to people who are listening if they don’t recognize it, Luke Nosek. Where does Luke who has, I wish I knew how to turn on my batteries in the same way, to get the energy that Luke does, but how does Luke fit into the story of DeepMind, and I suppose more broadly speaking for that because of that AI?
Sebastian Mallaby: Luke Nosek is this tremendously puppy-ish enthusiast, right? And he was a early, early part of the PayPal team, with Max Levchin and Peter Thiel, and he went through that journey, and then Peter exited PayPal, set up Founders Fund, and this is now I think 2005, and Luke Nosek becomes one of the first partners. And pretty early on he makes the right judgment on Elon and SpaceX. And Luke is the kind of guy who is just all in. When he falls in love with an idea and a founder, there is no curbing his enthusiasm. And so, he is like, “All in, all in, all in” on SpaceX, and I think he persuaded Founders Fund to raise a new fund, put extra money in, like, “More, more, more, more, more, more capital in there.” And of course that paid off massively.
And off the back of that, roll forward to 2010, he’s trying to look for the next Elon Musk. And he does a few frontier bets, and then along comes Demis Hassabis, who is out on the West Coast from London, raising capital for this idea of an AI company, which he’s going to call DeepMind. And most people think that’s nuts, there’s AI, remember in 2010, cannot even recognize a photo of a cat. It can’t do anything. We’re in deep, deep AI winter. Who would back a company like that? The answer is Luke Nosek. And he falls in love with Demis, who is a very winsome character, super articulate, super relatable, and a genius. He has all the outlier characteristics you want in an entrepreneur.
The sort of junior chess champion, second-best player in the world, but also five times wins the Mind Games Olympiad, where you have to run between boards playing backgammon, chess, Go, and a couple of other games kind of almost simultaneously. Just kind of crazy, crazy smart. Obsessed since he was 17 with the idea of building powerful AI. So Peter Thiel said to me about Demis, “I think individuals tend to have one company inside them. If they’re missionary entrepreneurs, they’ve got one thing they need to do. And for Demis, it was to build AGI.” That was what he was fixated by. And the company was downstream of his desire to build AGI. If he could have done that at a university, he would have been happy to do that, but he couldn’t do it at a university, so he had to found a company to do it. And that’s the kind of missionary commitment that venture capitalists often look for, because a missionary will never quit.
No matter how hard it is, they will keep working. And so Luke Nosek and Peter Thiel jointly recognize this. Peter is contrarian, cynical, aloof, and so is kind of into it, but at the same time arm’s length, Luke has got both his arms around Demis, is giving him this bear hug, and will not let go. And Demis says, “I’m not going to move to California, I’m going to do this company in London.” And Peter and the other Founders Fund partners are like, “London, where is that?” It’s kind of like Somalia or something. That’s just off the map. And Luke says, “No, no, no, no, we have to do this, we have to do this. I will fly to London for the board meetings, we’ve just got to do this DeepMind investment.” And so, he was the unbridled enthusiast who got Founders Fund across the line, and the rest is history.
They put the series A money in, unbelievably it was two million, at a four million valuation, so they got half the company for $2 million. Not bad.
Tim Ferriss: Not bad.
Sebastian Mallaby: And they rode that investment.
Tim Ferriss: What a remarkable story. I really feel like Luke, who’s also here in Austin, deserves a lot more credit than he gets. Not that he’s seeking it, right? He’s not out there looking for it, but he is very good at riding winners when he has high conviction, right? Which in the venture game — in a lot of investing — it’s, you can’t die, you can’t run out of bankroll at the table, right? You need to have enough of a portfolio approach to sustain yourself through periods of bad luck, but if you’re systematic, it’s riding your winners and doubling and tripling, quadrupling down. And he is so good at that.
Sebastian Mallaby: Yeah.
Tim Ferriss: He is just incredibly good.
Sebastian Mallaby: And as John Doerr likes to say, the great thing about venture capital is you can only lose one times your money. So, it’s not like a short position for a hedge fund trader, where you could really lose a lot.
Tim Ferriss: Correct. Exactly.
Sebastian Mallaby: So, in that sense, you’re not going to die so you can shoot for the moon.
Tim Ferriss: I do have a question, I should know the answer to this, but I don’t. So, long ago, this is probably 2008 — this was a long time ago. Actually, I wonder if I had exposure to DeepMind. I invested in Founders Fund. This was a very, very long time ago. But what I did not realize internally, and I’ll just read a couple of my highlights, it is absurd how many highlights I have from The Infinity Machine, and all of your books. “A gap opened up between Thiel and Nosek. As a general matter, Thiel doubted that going on boards was a good use of partners’ time. Startups should be left to sink or swim. The art of venture capital, he liked to say, was to back contrarian ideas, not coach company founders.” We could spend a lot of time just on that, but I’m going to move on.
“Most venture partnerships decide on investments by voting. If a handful of partners see hair on the deal, the deal will be rejected, but Thiel had taken the unusual position that collective decision making should be avoided. The way he saw things, if investments were chosen based on voting, the Founders Fund portfolio would consist of middle-of-the-road startups, to which nobody objected. And then…” this comes back to The Power Law, right? “Given that all the profits and venture come from a few improbable moonshots, this sort of consensus portfolio would deliver mediocre performance.”
So, and I’ll just paraphrase now, Thiel empowered the partners to go all in with their gut/intuition, my question is, how is that governed in any way? Of course, if anyone gave 10 out of 10 conviction, and then lost money consistently, they would presumably be removed from the partnership, or they’d lose their ability to lead with that type of gut conviction. But do you have any idea how that was handled internally in terms of stress testing ideas, pushing people to really put their on the line for these types of high conviction, but certainly very much outlier investments. Do you have any idea?
Sebastian Mallaby: Internally, Founders Fund was very torn about the DeepMind investment and I described some of this in the book where they do the first deal and that’s fine, it’s $2 million, but then you get to series B and series C and the check size gets bigger, and so the other partners are asking tougher questions. And they’re saying, “Well, wait, is there going to be a product?” And Demis said to me that his attitude was, “What do you mean ‘Is there a product?’ I’m talking about artificial general intelligence; it’s going to make all products revolutionized or obsolete, or whatever, and you want to ask me what the widget is? Give me a break. No, it’s all of the widgets, they’re all going to be changed. And if you’re asking me this question, you don’t get what AGI means.” And so Demis was very frustrated by the other partners at Founders Fund.
And I think internal, within Founders Fund, there was a lot of fighting between Luke who remained enthusiastic and committed about Demis, partly because he was the guy who would go to London and meet with him, and sit in the board meetings, and he would get the several thousand volts of Demis enthusiasm injected into his spine at every meeting, and he would come back buzzing with excitement, and the other Founders Fund partners who didn’t have that benefit were skeptical.
And so Luke would often come to Demis and say, “We’ve got your back, we’ve got your back, we’re going to do the next round, we’re going to lead the next round.” And then actually in series C, Founders Fund at the last minute pulled out, and they put money in, but they did not lead. And so the answer to your question is there was a lot of argument within Founders Fund, as the check size grew, it was harder to have that double down on your winners kind of attitude.
Tim Ferriss: Yeah. In this case. The fish that got away. Although, it was a fantastic multiple on their initial money. It strikes me in reading the book that I would argue that Demis made absolutely the right decision with the Google acquisition. You mentioned also in the book how he got criticized in some UK media for, “Oh, giant mega corporation, the US gets our prize talent cheap” kind of stuff. But looking back, he seems to have anticipated the costs and compute and just raw materials that would be required to do what he was trying to do. Would you read that the same way?
Sebastian Mallaby: Yeah. I often have this debate with people in London where they say exactly as you put it, this was a tragedy for UK tech, our great champion of deep tech is bought out cheaply by Google, and I say, “Listen, it wasn’t cheap. The acquisition price might have been $650 million, which was a bit cheap, but you know how much they put in in terms of research and development funds over the next 10 years? It was approaching 10 billion. Almost a billion a year.” So this was not selling cheap to the Americans, this was a cunning British trick to get a billion dollars of American R&D money into London per year for the next decade. Terrific win. And by the way, today there are spin outs from DeepMind in London, because the talent stayed in London, and these spinouts are raising billions of dollars to do new AI companies.
So, it’s terrific for the London ecosystem around King’s Cross, which is this cool center for tech in London where you can get the train in one direction and be in Cambridge, which has quite a lot of good startups, in one hour, or you can get the train in the other direction and be in Paris, where there’s Mistral and so forth. And it’s kind of very wired into different bits of Europe. So, how long does it take to get from San Francisco to Mountain View depending on the traffic? It could be well over an hour. So, I think there is a technology ecosystem which is by no means the equivalent of Silicon Valley yet, but it’s certainly unrecognizably better than it was 10 or 20 years ago.
Tim Ferriss: What do you think the UK or Europe could do — let’s focus on the UK, perhaps. Could do to increase the level of innovation, early stage startup founding, et cetera? Because looking back at The Power Law, and certainly just having spent so much time in California, there’s a lot that went into Silicon Valley, and there’s certain things that don’t get a lot of airplay, but for instance, the difficulty of enforcing non-compete agreements in California really led to this sort of round-robin of talent moving and cross-pollinating, like little hummingbirds of engineering talent and so on. Which may not be replicable depending on where you are. But what could the UK do in your mind, if you had the ear and they were like, “All right, Sebastian, tell us what to do?”
Sebastian Mallaby: Yeah. Well, yeah, a couple of things. I think the mistake that people in Europe make and Britain as part of this is to believe that there’s some kind of cultural magic about Silicon Valley, where whatever it is that they’re drinking in the water out there makes them think that failure is a learning experience, which is kind of weird, and the Europeans say, “Well, we’re never going to be like that, and it’s impossible for us to become as entrepreneurial as Silicon Valley.” And I remind people that when Fairchild Semiconductor was founded in 1957, the eight scientists who left the Shockley Lab were called — get this. The Traitorous Eight.
Tim Ferriss: So good.
Sebastian Mallaby: Traitorous. Why? Because it was considered treachery at the time to leave one company and go to another company. There was no entrepreneurial culture in the 1950s on the West Coast in the US, right? The classic business book of the time was Organization Man, about people who joined one company and stayed in it for their whole life, and retired with a gold watch on their 60th birthday. So you can create an entrepreneurial culture, and that is happening bit by bit in Britain, and certainly in Israel, and it’s happened in China. And it’s not some magic which is confined to Silicon Valley. I think it’s worth making that point as a first thing.
Now, there are specific policy shifts that you need to do to make an ecosystem work. And I think you put your finger on one, which is the mobility of talent is super important. You can think of a startup ecosystem as something which circulates three elements, money, people, and ideas. And you circulate those and you combine them in different ways, and each time you combine them, that’s a new company, and each is a shot on goal. And most of them fail, but all of a sudden if you circulate these components fast enough, you do get product market fit and then you get these 10X plus returns. Now in Britain, when you raise a new round, a series B, say, and you’ve got nine months of runway to build to the next stage from on company, and you identify the three key talent that you’re going to bring into the company and make it happen, and then they turn around to you and say, “Well, I can come in six months.” That’s a death sentence, right? That’s horrible.
We call it gardening leave in Britain. That is an appalling idea. We’ve got to get rid of those gardens, and we’ve got to let people move fast. Another thing is tech transfer out of universities. In the US there’s the Bayh-Dole Act. There are these very sophisticated tech transfer offices which are generous to the entrepreneur in terms of not demanding too much flesh as somebody exits, and that’s essential for making the startup work.
In Europe the attitude is, “Oh, we’re the university. We deserve a lot of skin in the game here. We want 50% of the upside.” Well, in that case, the startup will never happen. And I say to these Europeans, “Look, go visit Stanford. They’re very generous to their entrepreneurs. They seem to be okay financially.” Because if you help the entrepreneur, you’ll get the donations later. It’s all good. And so I think those are just two things.
Tim Ferriss: Which started a long time ago in the US. You look at the origins of Genentech and so on. I mean, it’s the genesis of so many. Not just companies, but industries effectively in the US.
Do you think Demis would have built DeepMind if he had not read Ender’s Game?
Sebastian Mallaby: That’s a great story. That’s a great question. Can I just tell the Ender’s Game story to begin with?
Tim Ferriss: Yes. Yes. And also a bit of trivia for folks. I believe, and not to make this more difficult, but that when Mark Zuckerberg first had a profile on Facebook, the only book listed was also Ender’s Game.
Sebastian Mallaby: Oh, I didn’t know that.
Tim Ferriss: I believe that’s true.
Sebastian Mallaby: It’s fascinating.
Tim Ferriss: So hop into it with Demis and Ender’s Game.
Sebastian Mallaby: So right at the beginning of my interviewing of Demis, we were having the second meeting, which was a dinner. And he told me to read a couple of books before we had the dinner, and one of them was Ender’s Game. So I —
Tim Ferriss: What were the others, just before you continue? What’s —
Sebastian Mallaby: It was a book by David Deutsch called The Fabric of Reality.
Tim Ferriss: Yeah, a light read.
Sebastian Mallaby: Yeah. Now, I read Ender’s Game as a result, and I hadn’t read it before. And as I was reading it, I was thinking to myself, okay, so this is a story about a boy hero who saves the entirety of humanity from an invasion of the planet by the space aliens. Is Demis telling me that that’s how he sees himself, that he’s saving all of humanity with AI? Because it’d be a bit much to believe that, but it would be even more to have the temerity to tell the guy who’s writing a book about you that that’s how you see yourself. Most people wouldn’t expose themself in that way. I thought, “Is Demis really thinking this?” So then I go to have the dinner, and he says, “I hope you read Ender’s Game, because that’s really how I see myself. And I gave the book to my wife so she could read it so she could understand me better, because I really identify with Ender.”
Tim Ferriss: Yeah, it’s wild.
Sebastian Mallaby: It’s wild.
Tim Ferriss: It’s a great book. I mean, I haven’t read it in decades, but it is a fantastic read as I remember it. Yeah.
Sebastian Mallaby: Yeah. I mean, reading it, I must say, as a mature adult, I thought it was not that well written, but the idea of it is good. And I can see why —
Tim Ferriss: The idea is sticky.
Sebastian Mallaby: Absolutely. This image of this kid who sacrifices everything to dedicate himself to the craft of fighting the aliens, and withstands ridicule and bullying from his peers and fights back, it’s an appealing image, and that’s what hooked Demis. But to answer your question of earlier, he would’ve done AI anyway, because he read Ender’s Game actually when he was already around 30, and he’d had unbelievably the determination to build superintelligence from when he was about 17. I mean, that is wild as well. I mean, the early conviction —
Tim Ferriss: That is wild. Mm-hmm.
Sebastian Mallaby: — is just extraordinary.
Tim Ferriss: Did he ask you to read Gödel, Escher, Bach: An Eternal Golden Braid? I will admit to you, I think Dustin Moskovitz also, a lot of technologists, very, very, very good technologists, recommend this book or cite it as part of their own journey to building something incredible.
I think I’m too dumb to read that book. I had so much trouble. I’ve had so much trouble. I’ve tried two times, and yet I’ve still not finished that book. I don’t know. Hey, do you have any recommendations to somebody who’s maybe lacking a few IQ points, because he was born on Long Island as to how to navigate that book?
Sebastian Mallaby: I have to admit, I was told by Demis that this meant a huge amount to him, that he’d read it in his late teens, and that was when he really became convinced that he could build AI, because the argument in the book is that whatever the human brain can do, computers will be able to do one day, that the human brain operates on ones and zeros, and therefore if you could build big enough compute, you should be able to replicate the intelligence of human brains. And that was the insight that got him hooked on the idea.
So I went off and I tried to read it. I would say, I got 150 pages in and got bogged down. I mean, it is a difficult, challenging read, but at least I extracted the essence that meant something to my subject, to Demis.
Tim Ferriss: You know what would be great for helping me to understand this? LLMs.
Sebastian Mallaby: Right.
Tim Ferriss: I’m going to give that a shot. So if you explain this to a sixth grader, or explain it to a six-year-old, maybe even better. A couple of questions and then we’ll start to lay on the plan.
If you had to write another book on a figure in the world of AI, they could be relatively unknown or they could be incredibly known, who would that person be? Demis is off the table. I might want to take Sam off the table just to make it a little more interesting. Who would it be if Sam’s off the table and Demis is of course off the table?
Sebastian Mallaby: Well, I guess Dario. I think even if you left Sam on the table, it would be Dario. I mean, I think he’s just a fascinating, fascinating figure, as well as being the current leader.
Tim Ferriss: Of Anthropic, for people who don’t recognize the name.
Man, I’ll share I’m working on a blog post right now, and it’s about disruption due to AI, and how it’s not three years in the future, it’s not one year in the future. These are book sales across my entire book catalog, and it’s not limited to print. This is all format. Okay. I’ll give you some numbers and then I want you to tell me what happened to initiate this. Okay. 2022 stasis, pretty consistent. My book royalties are an annuity, predictable. 2023, minus 5%, 2024, minus 13%, 2025, minus 46%, and 2026, so far, on track to be at least negative 57%.
What happened at the end of 2022?
Sebastian Mallaby: ChatGPT.
Tim Ferriss: GPT 3.5. It’s just wild. It’s really, really wild. I mean, this stuff is coming fast and I really flip and flop. I feel like I waffled perhaps too much between these two. I go from the very, I would say moderate, well-reasoned positioning of Benedict, and I agree with so many of his points to believing that all of this is just coming so much faster than anyone can even comprehend due to the recursive self-improvement.
Sebastian Mallaby: For the record, I think that it is much bigger than mobile, much bigger than internet. This is so general, a cognitive capability which can span any human task. I think the niggle is simply: how long does diffusion take?
Tim Ferriss: Yep. Yeah. Yeah, right. I mean, just to give an example of that, and I invest in quite a few biotech and biotech companies and other sciences. And if you look at, say, AlphaFold, I mean absolutely merited a Nobel Prize. We didn’t mention that about Demis, but it’s one thing to design molecules, it’s quite another to deliver it to target tissue, right? So the deliverability of that is a metaphor for AI in a way. It’s like, “Okay, great. We have this pristine perfect molecule. How do you get it to the right place?” And at the same time, I’m an investor in Lila Sciences, and what they’re doing is producing a proprietary data set by automating wet labs, using AI. And I’m going to simplify it, but they have gigantic wet labs where they can run, in parallel, thousands of experiments that from the very first step of hypothesis generation through to the end of the scientific method, is all run autonomously by AI.
And I bring this particular example up, because even, I want to say six months ago, 12 months ago, they are producing discoveries that are really non-trivial. It’s already happening now. This is not a year in the future. This is happening now. So when you flash forward to think about the potential exponential improvement, and I still, to be honest, sometimes when people talk about exponents, exponents, humans aren’t good at thinking exponentially, I’m like, “Yes, that’s true.” But outside of Moore’s Law, why would AI capabilities or LLM parameters or however you want to measure it, automatically improve in exponents? I don’t actually quite understand that. But once we get to the recursive self-improvement, it’s like, “Okay, I can see how that starts to approach a vertical wall.”
Sebastian Mallaby: Yeah. I mean, I agree with you. I think one experience from writing the book is simply that when you’re close to the people inside the labs, and it wasn’t just Demis, I interviewed 100 of these AI insiders, you realize that the stuff in the pipeline is enormous.
Tim Ferriss: Yeah.
Sebastian Mallaby: And also, I think there’s a popular misconception, which is, there is this thing called AI, and it happened when ChatGPT came out. So now we’ve got it and we’re getting used to it, and that’s in the rearview mirror. No, no, no, no, no, no, this thing is changing the whole time, as anybody who looks closely, knows. And if you think back, the progression is wild. You get this system in end of 2022, which hallucinates nonstop. Then you plug in GPT-4 six months later, whatever it was, and the hallucination radically reduces.
Then it goes multimodal, so it can do video and audio. And in the meantime, it’s got a very long context window. So you can plug in an entire Tolstoy novel and ask questions about it. Then it starts to do the reasoning stuff, and can do logic and math. Then it becomes agentic. Then it’s coding for you, and all of these changes are packed into three and a half years. And I agree with you, I think the next three and a half years are going to be even more wild.
Tim Ferriss: Yeah.
Sebastian Mallaby: Yeah. I think there’s a big gap between the inside and the outside view of this.
Tim Ferriss: Yeah. That’s where these comparisons to the industrial revolution just completely fall apart on so many levels. So I have one or two remaining questions for you.
The billboard question, I ask this a lot. It can be a fun one. If you could put anything on a billboard metaphorically speaking for millions, billions of people to see, could be anything — image, quote, question, preferably not commercial — what would it be? What might it be?
Sebastian Mallaby: Okay. So a billboard which lots of people are going to see, I would put, “Prepare your mind.” And this is a saying which is originally Louis Pasteur, I think, the scientist who said, “Chance favors the prepared mind.” If you’re ready for things, you can make the most of the opportunity that comes your way. And the amazing thing about this saying is that it’s come up randomly in different contexts in different books I’ve done.
So when I was writing about venture capital, Accel capital, and one of the founders, Arthur Patterson, used this phrase as a description of how he wanted XCEL to invest, that they would run these scenario exercises where they would think, “Okay, there’s a new technology coming down the pike. What kind of company needs to be built to make the most of that new platform? What type of entrepreneur is going to fit this opportunity? What should we be expecting so that when the person walks into the office, into the conference room and pitches to us, we already know 90% of what he says, because we’ve prepared our minds, and that way we can make a good judgment and a fast judgment if it’s a competitive situation.”
So I kind of wrote about the prepared mind in the context of venture capital, and then I’m doing The Infinity Machine, and I’m interviewing Ilya Sutskever from OpenAI, and I’m asking him, “Why was it you who understood the significance of the transformer architecture when it came out immediately? On the day it was up on the website, you read it. You ran down the corridor, you went to see your collaborator, Alec Radford, and you said, ‘We’re going to build a language model on top of this architecture.’ How did you see it so quickly?”
Tim Ferriss: Well, not only that, he said, “Stop everything you’re doing —
Sebastian Mallaby: Right, right, right. So you —
Tim Ferriss: — and do this.”
Sebastian Mallaby: Yeah. This vision of the over-caffeinated, charismatic seizing on the engineer and saying, “Drop it, whatever you’re doing.” And his answer was prepared mind, that he’d been thinking about, how you model sequential data ever since his PhD in Canada. And when he saw the solution, this was what he’d been waiting for, for a decade, and so he could jump on it. And then when you start thinking about prepared mind, you would probably remember this better than I do, but wasn’t there a Seattle Seahawks Super Bowl final against the New England Patriots where the New England quarterback does an interception in the last second of play, and clinches the victory? And when he’s asked after the play, “How did you know to make that run? How did you know where the quarterback was going to throw the ball?” The answer was “prepared mind,” basically.
He didn’t use that phrase, but in training they had studied the play that the Seattle Seahawks were going to make, and they knew that given a certain formation, when the ball was snapped back, there was a certain pass that was coming. So the guy just takes off and he runs right into where the ball comes, and he catches it and intercepts, and New England wins. And so that’s a prepared mind in sports. And the other reason, last thing I would put on the billboard, prepare your mind, is that for the age of Artificial Intelligence, this is what we need to hear and this is a serious point, right? The risk with large language models is that we just get lazy, and whenever we need to know something, we just get it to tell us what to think. That is not the route to happiness or satisfaction or anything.
We need to continue to do the hard work of preparing our minds, because that’s what makes us people. I think therefore, I am. And so I think, prepare your mind, is entering a time when it becomes a more important slogan than ever.
Tim Ferriss: How do you do that for yourself? What guardrails or policies have you established for your own use of AI? And it makes me also think of going to the gym, lifting weights, getting in cardio. You don’t have to do that, but it is beneficial for you on a lot of levels, and some people find it quite enjoyable and hence they do that. And I’m wondering what the equivalent is for knowledge workers or people who are preparing their minds and don’t want to become impotent in the way that people with directions have mostly become impotent because of Google Maps and other tools like that. So what do you do for yourself personally, or how are you thinking about that?
Sebastian Mallaby: Yeah. So I mean, the first thing I think is that the Google Maps analogy is the wrong one in the sense that it’s fine to offload a very specific mental task, which to most people is a pain in the neck, and let the machine do that for you. It’s not fine to offload all thinking, right? The point of offloading something should be, you get to focus your mental energy more on the other stuff that you really get satisfaction and meaning from. And so for me, what that means is that I’m very happy to use large language models to learn about the scientific output of somebody I’m going to interview next week. All of these AI papers are on archive, and the model has ingested all of them. And the model is extremely good at telling me, “Okay, the scientists you’re seeing next week have these three papers, and the progression between the three papers is this and this and this. And the comparison with the person you saw two weeks ago is this and this and this.”
And you learn a lot from the system, really bootstraps you to learn faster. So that’s helping me to think more, not to think less. It’s cutting out the time it would take me to go find all the papers by myself and then labor through them. It’s cutting to the chase and nourishing me intellectually. And by the way, I’m not worried about hallucination, because I’m going to interview the human scientist anyway, so I get to crosscheck it all. What I would never do is get the AI to write, because frankly, it’s not very good at long form. In fact, it really sucks. It’s fine for writing an email, although I don’t do that either, because I like writing, but it really is. I’ve tried it once. It’s terrible for anything longer than about 800 words.
But even if it could do it, I don’t think I would ever outsource that, because that’s me. This is what I do. This is the thinking process. I think through my writing. I come to understand what I understand and think what I think and believe what I believe through writing, and I’m not going to give that up.
Tim Ferriss: I’m letting out a pensive exhale, because I was thinking of this: A friend said to me — well, I’ll give him credit, Kevin Rose. At one point I was, I wouldn’t say complaining, observing that AI couldn’t do X or it wasn’t very good at Y. And he said, “When was the last time you tried that?”
And I was like, “Six months ago.”
And he’s like, “Try it again.”
And so the rules will become really important as also the power of these things increases. And I want to say it was The New Yorker. There was a piece in The New Yorker, it might’ve been The New York Times, with some very famous, I want to say novelist, could have been Pulitzer Prize winner in literature, somebody at the top. And they took three or four pieces of their own writing, had AI generate three or four pieces of writing in their voice, an
d gave it to professional readers, editors and so on. And it wasn’t clear. People couldn’t figure out. They claimed that what he or she wrote was AI.
Sebastian Mallaby: How long was the piece of writing?
Tim Ferriss: I knew that was the question you were going to ask, and I don’t recall. So I want to go back and look at that piece to see.
Sebastian Mallaby: There was a story precisely like that from an economist writer who’s very funny and also does podcasts. And he ran that experiment, and it was just as you said, his friends who were professional economist journalists couldn’t tell which was the witty column that he’d written versus the equally witty ones which the LLM had generated, and he was very off with this.
Look, I take your point. I mean, for now I can be all complacent and say, “Yeah, it only works for 800 words. It doesn’t work for a whole chapter, which is 20 pages long.” But no doubt, it’ll get better and better. But I still think I’m going to cling on to the thing that makes me, me.
Tim Ferriss: For sure, 100%. And I think, doing the thinking, preparing your mind, in part asking that question, which is not an easy question and perhaps is a different way to phrase it, but what are the things that make me, me? So you don’t accidentally make sacrifices that start to erode your sense of self, but also sense of self-worth, right?
Sebastian Mallaby: Mm-hmm.
Tim Ferriss: Preparing your mind. Sebastian, everybody should check out The Infinity Machine. It’s outstanding, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence. And lest people make the wrong assumption, this is not here’s the latest and greatest in AI. It is the story of an incredible mind, a whole cast of kooky and fascinating characters. It’s about a noble quest. It’s about the pitfalls and promises of entrepreneurship. It contains so many different levels.
And if you want to also have a basic understanding of what it is from the ground up that came to be colloquially referred to as AI or LLMs, this is a great book for that. It really lays out the nuts and bolts and how this evolved over time in a way that I think is intelligible to non-engineers. So everybody should check out The Infinity Machine.
Sebastian, is there anywhere else you would like to point people or anything else you’d like to say as we wind to a close?
Sebastian Mallaby: Well, yeah, you stumped me on that one. I’ve enjoyed the conversation. I’m happy to leave it there. Thank you for doing it, Tim. It’s been great.
Tim Ferriss: Absolutely. I’ll give one more link for folks. If they want to find you on X, that’s @SCMallaby. Well, Sebastian, thank you so much for the time. I really enjoyed the conversation. And for people listening, we will include links to everything we’ve discussed, all the characters and everything else at tim.blog/podcast. Just search Sebastian. I’m pretty sure that — oh, actually we have Sebastian Junger, so there are two Sebastians, but if you search Mallaby, M-A-L-L-A-B-Y, it’ll be very easy to find this. And until next time, be just a bit nicer than is necessary, a little bit kinder than is necessary to others, but also to yourself, and prepare your mind. Thanks for tuning in.
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