Inside The AI Race: DeepMind, OpenAI, Anthropic, China, and The Race to Superintelligence

I began by thinking, of course, AI is I began by thinking, of course, AI is going to be smarter than us, but it going to be smarter than us, but it going to be smarter than us, but it doesn't have an incentive to attack us. doesn't have an incentive to attack us. doesn't have an incentive to attack us. And then one day, I go visit Jeff And then one day, I go visit Jeff And then one day, I go visit Jeff Hinton, the academic father of deep Hinton, the academic father of deep Hinton, the academic father of deep learning who lives in Toronto. I said, learning who lives in Toronto. I said, learning who lives in Toronto. I said, "Look, Jeff, why are you so depressed?" "Look, Jeff, why are you so depressed?" "Look, Jeff, why are you so depressed?" And he says, "Okay, here's a thought And he says, "Okay, here's a thought And he says, "Okay, here's a thought experiment. You have an AI. It's very experiment. You have an AI. It's very experiment. You have an AI. It's very powerful, but you're worried that powerful, but you're worried that powerful, but you're worried that there's a Russian AI or a Chinese AI. there's a Russian AI or a Chinese AI. there's a Russian AI or a Chinese AI. It's going to come and attack your AI. It's going to come and attack your AI. It's going to come and attack your AI. So, you're going to empower your own AI So, you're going to empower your own AI So, you're going to empower your own AI to watch out for the attack. And when to watch out for the attack. And when to watch out for the attack. And when the attack is coming, defend yourself or the attack is coming, defend yourself or the attack is coming, defend yourself or maybe counterattack. Whatever you do, maybe counterattack. Whatever you do, maybe counterattack. Whatever you do, make sure you survive. Oh, survive. make sure you survive. Oh, survive. make sure you survive. Oh, survive. There you have it. Now you feeling There you have it. Now you feeling There you have it. Now you feeling comfortable, Sebastian. Right. You've comfortable, Sebastian. Right. You've comfortable, Sebastian. Right. You've just given the machine a survival just given the machine a survival just given the machine a survival instinct. instinct. instinct. >> Sebastian, lovely to see you and thanks >> Sebastian, lovely to see you and thanks >> Sebastian, lovely to see you and thanks for making the time. I really appreciate for making the time. I really appreciate for making the time. I really appreciate it. it. it. >> Great to be with you, Tim. >> Great to be with you, Tim. >> Great to be with you, Tim. >> I wanted to just give you applause for >> I wanted to just give you applause for >> I wanted to just give you applause for writing some of my favorite books of the writing some of my favorite books of the writing some of my favorite books of the last many years. I am consistently last many years. I am consistently last many years. I am consistently impressed and maybe since I also put pen impressed and maybe since I also put pen impressed and maybe since I also put pen to paper every once in a while, to paper every once in a while, to paper every once in a while, depressed, just thinking relatively depressed, just thinking relatively depressed, just thinking relatively about my capabilities, but of your about my capabilities, but of your about my capabilities, but of your capacity to capacity to capacity to paint a picture of the players on a paint a picture of the players on a paint a picture of the players on a landscape, landscape, landscape, but also the games they play in ways but also the games they play in ways but also the games they play in ways that non-speists can understand. And I that non-speists can understand. And I that non-speists can understand. And I can't recall who first recommended it. can't recall who first recommended it. can't recall who first recommended it. Frankly, I believe it was a hedge fund Frankly, I believe it was a hedge fund Frankly, I believe it was a hedge fund manager in New York City, but more money manager in New York City, but more money manager in New York City, but more money than God, hedge funds in the making of a than God, hedge funds in the making of a than God, hedge funds in the making of a new elite. And then certainly that was new elite. And then certainly that was new elite. And then certainly that was in my particular case followed by in my particular case followed by in my particular case followed by reading the power law of venture capital reading the power law of venture capital reading the power law of venture capital in the making of the new future, which I in the making of the new future, which I in the making of the new future, which I didn't expect to learn as much from didn't expect to learn as much from didn't expect to learn as much from because I've spent 20 years surrounded because I've spent 20 years surrounded because I've spent 20 years surrounded by venture capitalists and doing angel by venture capitalists and doing angel by venture capitalists and doing angel investing, 17 years of that in Silicon investing, 17 years of that in Silicon investing, 17 years of that in Silicon Valley. And yet I still had hundreds of Valley. And yet I still had hundreds of Valley. And yet I still had hundreds of highlights and so many stories that highlights and so many stories that highlights and so many stories that grabbed me from that book which I had grabbed me from that book which I had grabbed me from that book which I had not heard. And not heard. And not heard. And that made me very excited to read The that made me very excited to read The that made me very excited to read The Infinity Machine, which this is the new Infinity Machine, which this is the new Infinity Machine, which this is the new book. And I realized also I've been book. And I realized also I've been book. And I realized also I've been pronouncing Demis' name incorrectly for pronouncing Demis' name incorrectly for pronouncing Demis' name incorrectly for a very long time despite having met him a very long time despite having met him a very long time despite having met him at one point. So Deis Hassabis, Deep at one point. So Deis Hassabis, Deep at one point. So Deis Hassabis, Deep Mind and the Quest for Super Mind and the Quest for Super Mind and the Quest for Super Intelligence. My question for you, and Intelligence. My question for you, and Intelligence. My question for you, and we're going to come back to present day we're going to come back to present day we're going to come back to present day for people who are interested, of for people who are interested, of for people who are interested, of course, in what has been painted as a course, in what has been painted as a course, in what has been painted as a race to IPO. I think there's something race to IPO. I think there's something race to IPO. I think there's something to that in the air, so to speak, talking to that in the air, so to speak, talking to that in the air, so to speak, talking to people who are in San Francisco to people who are in San Francisco to people who are in San Francisco involved with these companies. But involved with these companies. But involved with these companies. But nonetheless, I wanted to ask how the nonetheless, I wanted to ask how the nonetheless, I wanted to ask how the genesis of this book came to be because genesis of this book came to be because genesis of this book came to be because you, it would appear, began exploring you, it would appear, began exploring you, it would appear, began exploring these waters on the early side, which these waters on the early side, which these waters on the early side, which leads to a meta question of just general leads to a meta question of just general leads to a meta question of just general book selection. But let's let's focus on book selection. But let's let's focus on book selection. But let's let's focus on the infinity machine. How did this how the infinity machine. How did this how the infinity machine. How did this how did this come to be? Where did the did this come to be? Where did the did this come to be? Where did the twinkle in the eye begin? What was the twinkle in the eye begin? What was the twinkle in the eye begin? What was the conversation, the thing you read that conversation, the thing you read that conversation, the thing you read that triggered triggered triggered the gingerbread trail that got you to the gingerbread trail that got you to the gingerbread trail that got you to this book? this book? this book? >> Well, the power law, the book about >> Well, the power law, the book about >> Well, the power law, the book about venture capital, had come out in venture capital, had come out in venture capital, had come out in February of 2022. February of 2022. February of 2022. >> And while I was researching that, I'd >> And while I was researching that, I'd >> And while I was researching that, I'd been to lots of tech conferences, of been to lots of tech conferences, of been to lots of tech conferences, of course, including some in Europe. And course, including some in Europe. And course, including some in Europe. And this, you know, twinkly guy would show this, you know, twinkly guy would show this, you know, twinkly guy would show up, Deiss, and he would look totally up, Deiss, and he would look totally up, Deiss, and he would look totally approachable and kind of guy next door approachable and kind of guy next door approachable and kind of guy next door and unintimidating. and unintimidating. and unintimidating. And then he would get on the stage and And then he would get on the stage and And then he would get on the stage and out of his mouth would come this spiel out of his mouth would come this spiel out of his mouth would come this spiel about computer science, neuroscience, about computer science, neuroscience, about computer science, neuroscience, chemistry, biology, physics, philosophy, chemistry, biology, physics, philosophy, chemistry, biology, physics, philosophy, the history of movies, you name it, the history of movies, you name it, the history of movies, you name it, right? And that mixture of the right? And that mixture of the right? And that mixture of the approachability and the massive approachability and the massive approachability and the massive intellect always struck me as beguiling. intellect always struck me as beguiling. intellect always struck me as beguiling. And I thought, hm, this would be a great And I thought, hm, this would be a great And I thought, hm, this would be a great character to write about. And then at character to write about. And then at character to write about. And then at the same time I was aware of Alph Go, the same time I was aware of Alph Go, the same time I was aware of Alph Go, the 2016 model that Demis' team at Deep the 2016 model that Demis' team at Deep the 2016 model that Demis' team at Deep Mind had built which defeated the world Mind had built which defeated the world Mind had built which defeated the world champion at Go and then Alpha Fold which champion at Go and then Alpha Fold which champion at Go and then Alpha Fold which was the protein folding system. And both was the protein folding system. And both was the protein folding system. And both of these things had the quality that you of these things had the quality that you of these things had the quality that you had this almost infinite search space, had this almost infinite search space, had this almost infinite search space, right? Where the different permutations right? Where the different permutations right? Where the different permutations of the game of Go are almost infinite of the game of Go are almost infinite of the game of Go are almost infinite because they're so big. The different because they're so big. The different because they're so big. The different permutations of how you can fold an permutations of how you can fold an permutations of how you can fold an amino acid chain into a protein shape amino acid chain into a protein shape amino acid chain into a protein shape are even bigger. 130 zeros added onto are even bigger. 130 zeros added onto are even bigger. 130 zeros added onto the end of the number of permutations in the end of the number of permutations in the end of the number of permutations in go. So you have these AI systems that go. So you have these AI systems that go. So you have these AI systems that could understand infinity. And so this could understand infinity. And so this could understand infinity. And so this idea of an infinity machine began to idea of an infinity machine began to idea of an infinity machine began to percolate. And I figured it's percolate. And I figured it's percolate. And I figured it's interesting to me probably at some point interesting to me probably at some point interesting to me probably at some point it will go mainstream. But even if it it will go mainstream. But even if it it will go mainstream. But even if it doesn't go mainstream, I love it and I doesn't go mainstream, I love it and I doesn't go mainstream, I love it and I love Demis and the two things together. love Demis and the two things together. love Demis and the two things together. I always look for the subject and the I always look for the subject and the I always look for the subject and the personality. personality. personality. >> I had both and I thought, okay, this is >> I had both and I thought, okay, this is >> I had both and I thought, okay, this is a go. And I went to pitch demis in early a go. And I went to pitch demis in early a go. And I went to pitch demis in early November 2022. November 2022. November 2022. And then, you know, I persuaded him to And then, you know, I persuaded him to And then, you know, I persuaded him to give me a lot of access. End of give me a lot of access. End of give me a lot of access. End of November, Chatty PT comes out and way November, Chatty PT comes out and way November, Chatty PT comes out and way earlier than I expected, my fringe earlier than I expected, my fringe earlier than I expected, my fringe subject went to the mainstream, proving subject went to the mainstream, proving subject went to the mainstream, proving Tim that it's better to be lucky than Tim that it's better to be lucky than Tim that it's better to be lucky than smart. [laughter] smart. [laughter] smart. [laughter] >> That's actually the first slide on my >> That's actually the first slide on my >> That's actually the first slide on my new venture capital firm. new venture capital firm. new venture capital firm. [laughter] [laughter] [laughter] Muggle thesis capital is what I'm Muggle thesis capital is what I'm Muggle thesis capital is what I'm calling it. calling it. calling it. Now, what did it take to be deeply Now, what did it take to be deeply Now, what did it take to be deeply interested in the subject matter to find interested in the subject matter to find interested in the subject matter to find Demis compelling and then to pitch him Demis compelling and then to pitch him Demis compelling and then to pitch him on a book? Because your books are so on a book? Because your books are so on a book? Because your books are so deeply researched and part of the reason deeply researched and part of the reason deeply researched and part of the reason for my my very long praise earlier is for my my very long praise earlier is for my my very long praise earlier is that you're very very good, one of the that you're very very good, one of the that you're very very good, one of the best at taking incredibly complex best at taking incredibly complex best at taking incredibly complex subjects or concepts. Transformer subjects or concepts. Transformer subjects or concepts. Transformer architecture could be one example from architecture could be one example from architecture could be one example from the current book and laying them out in the current book and laying them out in the current book and laying them out in terms that are both intelligible to terms that are both intelligible to terms that are both intelligible to muggles meaning people who are are muggles meaning people who are are muggles meaning people who are are non-speists non-technologists or non- non-speists non-technologists or non- non-speists non-technologists or non- financeers in the case of some of your financeers in the case of some of your financeers in the case of some of your other books while I think now it's tough other books while I think now it's tough other books while I think now it's tough for a non-speist to say this with for a non-speist to say this with for a non-speist to say this with conviction but without dumbing it down conviction but without dumbing it down conviction but without dumbing it down and getting it wrong if that makes sense and getting it wrong if that makes sense and getting it wrong if that makes sense Right. Nonetheless, you do a tremendous Right. Nonetheless, you do a tremendous Right. Nonetheless, you do a tremendous amount of research. So, how did you get amount of research. So, how did you get amount of research. So, how did you get from Demis is fascinating, subject from Demis is fascinating, subject from Demis is fascinating, subject matter is fascinating to I'm going to matter is fascinating to I'm going to matter is fascinating to I'm going to commit to this for my next book because commit to this for my next book because commit to this for my next book because it just seems like such an enormous it just seems like such an enormous it just seems like such an enormous undertaking. Well, actually to me the undertaking. Well, actually to me the undertaking. Well, actually to me the challenge of understanding a complex challenge of understanding a complex challenge of understanding a complex topic is the easy bit because you know topic is the easy bit because you know topic is the easy bit because you know if you know you've got the right if you know you've got the right if you know you've got the right personality who can carry the story and personality who can carry the story and personality who can carry the story and it's a subject that people either will it's a subject that people either will it's a subject that people either will care about for sure or should care about care about for sure or should care about care about for sure or should care about at least then you know doing the work of at least then you know doing the work of at least then you know doing the work of going deep is something that takes time. going deep is something that takes time. going deep is something that takes time. It takes effort but you I know I can do It takes effort but you I know I can do It takes effort but you I know I can do that. I've done it multiple times. that. I've done it multiple times. that. I've done it multiple times. That's not difficult. What's difficult That's not difficult. What's difficult That's not difficult. What's difficult is has somebody done the book before? is has somebody done the book before? is has somebody done the book before? Mhm. Mhm. Mhm. >> Has somebody else got some rival project >> Has somebody else got some rival project >> Has somebody else got some rival project which is going to derail me? You've made which is going to derail me? You've made which is going to derail me? You've made the point on your own podcast. Tim, the point on your own podcast. Tim, the point on your own podcast. Tim, don't put a lot of effort into something don't put a lot of effort into something don't put a lot of effort into something where there just isn't much leverage where there just isn't much leverage where there just isn't much leverage there. You know, you could do the best there. You know, you could do the best there. You know, you could do the best book in the world, an A+ book on a C book in the world, an A+ book on a C book in the world, an A+ book on a C minus topic. It would get you nowhere, minus topic. It would get you nowhere, minus topic. It would get you nowhere, right? right? right? >> So, the hard thing is to make sure it's >> So, the hard thing is to make sure it's >> So, the hard thing is to make sure it's an A+ topic and an A+ personality. And an A+ topic and an A+ personality. And an A+ topic and an A+ personality. And then the deep dive is something you know then the deep dive is something you know then the deep dive is something you know I just make sure I speak to enough I just make sure I speak to enough I just make sure I speak to enough experts who are insiders. I take the experts who are insiders. I take the experts who are insiders. I take the time these books take me you know four time these books take me you know four time these books take me you know four years or so each time. So I give myself years or so each time. So I give myself years or so each time. So I give myself the oxygen to get deep deep in with the the oxygen to get deep deep in with the the oxygen to get deep deep in with the insiders and that's how I produce the insiders and that's how I produce the insiders and that's how I produce the accurate account. accurate account. accurate account. >> Yeah. I should point out perhaps to >> Yeah. I should point out perhaps to >> Yeah. I should point out perhaps to people who don't immediately pick it up people who don't immediately pick it up people who don't immediately pick it up that the way you described picking the that the way you described picking the that the way you described picking the book topic is exactly how a lot of the book topic is exactly how a lot of the book topic is exactly how a lot of the best tech investors choose startups. best tech investors choose startups. best tech investors choose startups. You don't want an A+ team and a C plus You don't want an A+ team and a C plus You don't want an A+ team and a C plus market, right? It's better to have a B market, right? It's better to have a B market, right? It's better to have a B minus team and an A+ market and also minus team and an A+ market and also minus team and an A+ market and also looking at the competitive landscape. I looking at the competitive landscape. I looking at the competitive landscape. I mean, the way you laid it out is is mean, the way you laid it out is is mean, the way you laid it out is is pretty much copy and paste. I I wanted pretty much copy and paste. I I wanted pretty much copy and paste. I I wanted to segue to some of my notes from the to segue to some of my notes from the to segue to some of my notes from the book. And I'm not yet done with the book. And I'm not yet done with the book. And I'm not yet done with the book. The audio is incredible. I I want book. The audio is incredible. I I want book. The audio is incredible. I I want to poach your narrator for my next book, to poach your narrator for my next book, to poach your narrator for my next book, but pulling up my Kindle notes. I wanted but pulling up my Kindle notes. I wanted but pulling up my Kindle notes. I wanted to ask you a question related to to ask you a question related to to ask you a question related to [sighs and gasps] [sighs and gasps] [sighs and gasps] this might sound very strange but where this might sound very strange but where this might sound very strange but where divinity or God fits into the pursuit or divinity or God fits into the pursuit or divinity or God fits into the pursuit or development of super intelligence for development of super intelligence for development of super intelligence for different players in the space if it different players in the space if it different players in the space if it does. M does. M does. M >> and the reason I bring that up is that >> and the reason I bring that up is that >> and the reason I bring that up is that religion does recur in the book both in religion does recur in the book both in religion does recur in the book both in the personal story of Demis but the personal story of Demis but the personal story of Demis but elsewhere and it shows up repeatedly in elsewhere and it shows up repeatedly in elsewhere and it shows up repeatedly in so much as I'll give you one example the so much as I'll give you one example the so much as I'll give you one example the closest to Sabis had come to landing a closest to Sabis had come to landing a closest to Sabis had come to landing a real investor was an eccentric finance real investor was an eccentric finance real investor was an eccentric finance year named David Gammon I want to hear year named David Gammon I want to hear year named David Gammon I want to hear more about this guy also [laughter] more about this guy also [laughter] more about this guy also [laughter] finance seemed open to making this finance seemed open to making this finance seemed open to making this unusual bet um aligning a few things unusual bet um aligning a few things unusual bet um aligning a few things because his motives were themselves because his motives were themselves because his motives were themselves unusual quote. There's a deeply unusual quote. There's a deeply unusual quote. There's a deeply religious aspect to AGI. Gam explained religious aspect to AGI. Gam explained religious aspect to AGI. Gam explained to me later, it's really finding God's to me later, it's really finding God's to me later, it's really finding God's algorithm. I think it would seem at algorithm. I think it would seem at algorithm. I think it would seem at least chatting with people in Silicon least chatting with people in Silicon least chatting with people in Silicon Valley that there are some who take it Valley that there are some who take it Valley that there are some who take it even further, right? Maybe this is how even further, right? Maybe this is how even further, right? Maybe this is how we find God. Maybe this is how we we find God. Maybe this is how we we find God. Maybe this is how we actually elicit the second coming. I actually elicit the second coming. I actually elicit the second coming. I mean, there's a lot there. I'm just mean, there's a lot there. I'm just mean, there's a lot there. I'm just wondering to what extent this has popped wondering to what extent this has popped wondering to what extent this has popped up in your research, whether it's up in your research, whether it's up in your research, whether it's reflected in the book or not. I think reflected in the book or not. I think reflected in the book or not. I think there's one basic thing going on here there's one basic thing going on here there's one basic thing going on here and I'm going to take a slight detour, and I'm going to take a slight detour, and I'm going to take a slight detour, but it answers your question. but it answers your question. but it answers your question. >> Sure. >> Sure. >> Sure. >> What we're dealing with with AGI, >> What we're dealing with with AGI, >> What we're dealing with with AGI, powerful intelligence that rivals human powerful intelligence that rivals human powerful intelligence that rivals human cognition, cognition, cognition, is something that's so powerful that is something that's so powerful that is something that's so powerful that it's both exciting and scary and just it's both exciting and scary and just it's both exciting and scary and just hard to get your mind around. And so if hard to get your mind around. And so if hard to get your mind around. And so if you look for example at the 2009 speech you look for example at the 2009 speech you look for example at the 2009 speech that caused the foundation of deep mind, that caused the foundation of deep mind, that caused the foundation of deep mind, this was Shane Le Deis' co-founder who this was Shane Le Deis' co-founder who this was Shane Le Deis' co-founder who gave a talk in 2009 about how super gave a talk in 2009 about how super gave a talk in 2009 about how super intelligence would arrive in 2030. So intelligence would arrive in 2030. So intelligence would arrive in 2030. So unbelievably spot-on prediction. And unbelievably spot-on prediction. And unbelievably spot-on prediction. And towards the end of that lecture, which towards the end of that lecture, which towards the end of that lecture, which is captured on a grainy video online, is captured on a grainy video online, is captured on a grainy video online, you see him pivot from explaining how you see him pivot from explaining how you see him pivot from explaining how algorithms are getting stronger, there's algorithms are getting stronger, there's algorithms are getting stronger, there's more data online, computers getting more more data online, computers getting more more data online, computers getting more powerful, and so we're heading towards powerful, and so we're heading towards powerful, and so we're heading towards this intelligence explosion. And then he this intelligence explosion. And then he this intelligence explosion. And then he says, and it's going to be threatening. says, and it's going to be threatening. says, and it's going to be threatening. It's going to do things we can't It's going to do things we can't It's going to do things we can't control. It's going to be human level. control. It's going to be human level. control. It's going to be human level. It might challenge us. And as he says It might challenge us. And as he says It might challenge us. And as he says this, he has this sort of excited smile this, he has this sort of excited smile this, he has this sort of excited smile on his face. And you think, well, that's on his face. And you think, well, that's on his face. And you think, well, that's a bit strange. You know, he's talking a bit strange. You know, he's talking a bit strange. You know, he's talking about potential doom and he's smiling. about potential doom and he's smiling. about potential doom and he's smiling. And then somebody in the audience says, And then somebody in the audience says, And then somebody in the audience says, "Wait, wait, wait. You've just told us, "Wait, wait, wait. You've just told us, "Wait, wait, wait. You've just told us, Shane, that this could be threatening to Shane, that this could be threatening to Shane, that this could be threatening to humanity and you haven't provided any humanity and you haven't provided any humanity and you haven't provided any antidote and surely you're going to tell antidote and surely you're going to tell antidote and surely you're going to tell us how we're going to stop it." At which us how we're going to stop it." At which us how we're going to stop it." At which point Shane turns around and says, "How point Shane turns around and says, "How point Shane turns around and says, "How do we stop it?" and he's kind of do we stop it?" and he's kind of do we stop it?" and he's kind of giggling and you think why are you giggling and you think why are you giggling and you think why are you laughing at this dangerous thing and you laughing at this dangerous thing and you laughing at this dangerous thing and you realize that for humans to contemplate realize that for humans to contemplate realize that for humans to contemplate annihilation is absurd and the absurd is annihilation is absurd and the absurd is annihilation is absurd and the absurd is a close cousin of humor a close cousin of humor a close cousin of humor >> and the reason I tell this story is that >> and the reason I tell this story is that >> and the reason I tell this story is that it's a springboard to the religion point it's a springboard to the religion point it's a springboard to the religion point which is that this is such a hard thing which is that this is such a hard thing which is that this is such a hard thing to think about that people reach for to think about that people reach for to think about that people reach for religious terminology religious terminology religious terminology when they're around AI they just do it when they're around AI they just do it when they're around AI they just do it naturally naturally naturally So you know there's this story about So you know there's this story about So you know there's this story about Ilia Satskaver the who was the chief Ilia Satskaver the who was the chief Ilia Satskaver the who was the chief scientist at open AI. I talked to him a scientist at open AI. I talked to him a scientist at open AI. I talked to him a lot for this project and there was a lot for this project and there was a lot for this project and there was a point when he was at a retreat with his point when he was at a retreat with his point when he was at a retreat with his fellow scientists and they were gathered fellow scientists and they were gathered fellow scientists and they were gathered in the evening around a fire pit and he in the evening around a fire pit and he in the evening around a fire pit and he was talking about safety and he said was talking about safety and he said was talking about safety and he said okay I want to explain to you we might okay I want to explain to you we might okay I want to explain to you we might have an AI that's dangerous. It wouldn't have an AI that's dangerous. It wouldn't have an AI that's dangerous. It wouldn't be aligned with us. So here's what we're be aligned with us. So here's what we're be aligned with us. So here's what we're going to do with it. and he produced an going to do with it. and he produced an going to do with it. and he produced an effigy which was supposed to represent a effigy which was supposed to represent a effigy which was supposed to represent a malign AI and he put it into the fire malign AI and he put it into the fire malign AI and he put it into the fire pit and he burnt it like a medieval pit and he burnt it like a medieval pit and he burnt it like a medieval cleric putting a witch to death. And so cleric putting a witch to death. And so cleric putting a witch to death. And so that's just one example of this that's just one example of this that's just one example of this religion. I'll give you another one. So religion. I'll give you another one. So religion. I'll give you another one. So Deis one day was sitting with me in a Deis one day was sitting with me in a Deis one day was sitting with me in a park in North London. We would meet for park in North London. We would meet for park in North London. We would meet for two hours at a time and we would get two hours at a time and we would get two hours at a time and we would get deep into stuff. There was a another deep into stuff. There was a another deep into stuff. There was a another picnic table next to us where two people picnic table next to us where two people picnic table next to us where two people were having a normal quotidian were having a normal quotidian were having a normal quotidian conversation about some friend of theirs conversation about some friend of theirs conversation about some friend of theirs who'd gone to hospital and was she who'd gone to hospital and was she who'd gone to hospital and was she better, was she okay, etc., etc. I was better, was she okay, etc., etc. I was better, was she okay, etc., etc. I was seated opposite Demis who had gone into seated opposite Demis who had gone into seated opposite Demis who had gone into this riff about how he reads scientific this riff about how he reads scientific this riff about how he reads scientific papers from after his kids go to sleep papers from after his kids go to sleep papers from after his kids go to sleep in the evening from 10:00 p.m. until in the evening from 10:00 p.m. until in the evening from 10:00 p.m. until 4:00 a.m. And as he's reading these 4:00 a.m. And as he's reading these 4:00 a.m. And as he's reading these papers, he says to me, "Reality is papers, he says to me, "Reality is papers, he says to me, "Reality is staring at me, screaming at me, calling staring at me, screaming at me, calling staring at me, screaming at me, calling at me to understand it. And I have to at me to understand it. And I have to at me to understand it. And I have to understand it. And if I can understand understand it. And if I can understand understand it. And if I can understand it, it's like understanding nature it, it's like understanding nature it, it's like understanding nature better and therefore understanding the better and therefore understanding the better and therefore understanding the intelligence that might have created intelligence that might have created intelligence that might have created nature and I will be closer to what I nature and I will be closer to what I nature and I will be closer to what I would call God. And so for him it's a would call God. And so for him it's a would call God. And so for him it's a kind of quai spiritual quest to build kind of quai spiritual quest to build kind of quai spiritual quest to build the artificial intelligence. For Ilia the artificial intelligence. For Ilia the artificial intelligence. For Ilia it's a way of expressing the power of it's a way of expressing the power of it's a way of expressing the power of the artificial intelligence. There's the the artificial intelligence. There's the the artificial intelligence. There's the story of Leandowski. I forget his first story of Leandowski. I forget his first story of Leandowski. I forget his first name now, but the early early engineer name now, but the early early engineer name now, but the early early engineer at what became Whimo later at what became Whimo later at what became Whimo later >> started a kind of church [snorts] in >> started a kind of church [snorts] in >> started a kind of church [snorts] in worship of AI because AI is so omnisient worship of AI because AI is so omnisient worship of AI because AI is so omnisient that it's kind of like a god. that it's kind of like a god. that it's kind of like a god. [clears throat] [clears throat] [clears throat] >> Mark Andre >> Mark Andre >> Mark Andre lampuns those who believe in sort of lampuns those who believe in sort of lampuns those who believe in sort of some ethereal second coming, a kind of some ethereal second coming, a kind of some ethereal second coming, a kind of rapture where AI will, you know, we'll rapture where AI will, you know, we'll rapture where AI will, you know, we'll have a singularity. uh the AI will go have a singularity. uh the AI will go have a singularity. uh the AI will go vertical in its rate of improvement and vertical in its rate of improvement and vertical in its rate of improvement and the whole world will change and he the whole world will change and he the whole world will change and he likens that to kind of Christian kind of likens that to kind of Christian kind of likens that to kind of Christian kind of messianism. messianism. messianism. So yes, all through this topic there is So yes, all through this topic there is So yes, all through this topic there is this religious expression because this religious expression because this religious expression because religion is the lexicon for dealing with religion is the lexicon for dealing with religion is the lexicon for dealing with something that we find too mysterious to something that we find too mysterious to something that we find too mysterious to really understand. [clears throat] really understand. [clears throat] really understand. [clears throat] After all of your conversations, After all of your conversations, After all of your conversations, research before the book, during the research before the book, during the research before the book, during the book, after the book, where do you land book, after the book, where do you land book, after the book, where do you land on the spectrum of on the spectrum of on the spectrum of let's just say let's just say let's just say this will bother Mark, but like Church this will bother Mark, but like Church this will bother Mark, but like Church of Andre and techno optimist. [laughter] of Andre and techno optimist. [laughter] of Andre and techno optimist. [laughter] And there are others who are more And there are others who are more And there are others who are more exaggerated. Post AI in the near term we exaggerated. Post AI in the near term we exaggerated. Post AI in the near term we will live in a post scarcity world of will live in a post scarcity world of will live in a post scarcity world of super abundance and everyone will get a super abundance and everyone will get a super abundance and everyone will get a free car and we'll be free to crochet free car and we'll be free to crochet free car and we'll be free to crochet socks and play music and read poetry all socks and play music and read poetry all socks and play music and read poetry all day and basically we don't have to worry day and basically we don't have to worry day and basically we don't have to worry about anything because super about anything because super about anything because super intelligence will solve it all right intelligence will solve it all right intelligence will solve it all right there's that on one end and then there's there's that on one end and then there's there's that on one end and then there's the you can imagine I won't go into a the you can imagine I won't go into a the you can imagine I won't go into a belabored description of the doomers but belabored description of the doomers but belabored description of the doomers but you have the doomers who are like the you have the doomers who are like the you have the doomers who are like the end is nigh Okay, here we go. It's It's end is nigh Okay, here we go. It's It's end is nigh Okay, here we go. It's It's [laughter] not It's not the second [laughter] not It's not the second [laughter] not It's not the second coming as the Antichrist. And within coming as the Antichrist. And within coming as the Antichrist. And within short order, we're going to be MadMax. short order, we're going to be MadMax. short order, we're going to be MadMax. Between those two, there's a lot. And I Between those two, there's a lot. And I Between those two, there's a lot. And I suspect you land between those two. But suspect you land between those two. But suspect you land between those two. But where do you land where do you land where do you land in terms of assessing the promises and in terms of assessing the promises and in terms of assessing the promises and peril of AI and super intelligence as it peril of AI and super intelligence as it peril of AI and super intelligence as it stands right now? stands right now? stands right now? So look, I think any reasonable person So look, I think any reasonable person So look, I think any reasonable person should be both excited and a bit should be both excited and a bit should be both excited and a bit frightened frightened frightened >> and you know that's just the nature of >> and you know that's just the nature of >> and you know that's just the nature of it. It sounds contradictory but actually it. It sounds contradictory but actually it. It sounds contradictory but actually that's the only rational response. I that's the only rational response. I that's the only rational response. I think you know the super abundant story think you know the super abundant story think you know the super abundant story may turn out to be true on a kind of may turn out to be true on a kind of may turn out to be true on a kind of longer view let's say 20 30 40 years. longer view let's say 20 30 40 years. longer view let's say 20 30 40 years. >> Mhm. The problem is that in the path to >> Mhm. The problem is that in the path to >> Mhm. The problem is that in the path to get there, there's going to be a get there, there's going to be a get there, there's going to be a tremendous amount of disruption and tremendous amount of disruption and tremendous amount of disruption and that's going to be politically quite that's going to be politically quite that's going to be politically quite difficult to navigate. I think a useful difficult to navigate. I think a useful difficult to navigate. I think a useful lens through which to view this question lens through which to view this question lens through which to view this question is the China shock in trade. is the China shock in trade. is the China shock in trade. >> Mhm. >> Mhm. >> Mhm. >> So in 2003 or thereabouts, you get this >> So in 2003 or thereabouts, you get this >> So in 2003 or thereabouts, you get this enormous surge of Chinese enormous surge of Chinese enormous surge of Chinese exports into the US and people lose exports into the US and people lose exports into the US and people lose their jobs in a very concentrated way. their jobs in a very concentrated way. their jobs in a very concentrated way. Certain industries just get wiped out. Certain industries just get wiped out. Certain industries just get wiped out. And for the first time in the history of And for the first time in the history of And for the first time in the history of economic study of the effects of trade, economic study of the effects of trade, economic study of the effects of trade, you actually see negative effects on you actually see negative effects on you actually see negative effects on workers. Before that, it was kind of a workers. Before that, it was kind of a workers. Before that, it was kind of a bit of a myth, right? Because people bit of a myth, right? Because people bit of a myth, right? Because people adjust. They get displaced from one adjust. They get displaced from one adjust. They get displaced from one thing, but they move to a new thing. thing, but they move to a new thing. thing, but they move to a new thing. With the China shock, they didn't. But With the China shock, they didn't. But With the China shock, they didn't. But if you look at the size of the China if you look at the size of the China if you look at the size of the China shock, in a 12-year period between 1999 shock, in a 12-year period between 1999 shock, in a 12-year period between 1999 and 2011, the total number of jobs and 2011, the total number of jobs and 2011, the total number of jobs displaced was 2 million. which is displaced was 2 million. which is displaced was 2 million. which is actually a small number in a huge labor actually a small number in a huge labor actually a small number in a huge labor market like the US where there's a lot market like the US where there's a lot market like the US where there's a lot of churn month to month anyway and yet of churn month to month anyway and yet of churn month to month anyway and yet the political reaction against trade the political reaction against trade the political reaction against trade against globalization in terms of a against globalization in terms of a against globalization in terms of a swing towards protectionism frankly in swing towards protectionism frankly in swing towards protectionism frankly in both political parties was enormous. So both political parties was enormous. So both political parties was enormous. So it shows you that a small to medium it shows you that a small to medium it shows you that a small to medium shock to the labor market creates an shock to the labor market creates an shock to the labor market creates an enormous political consequence and so a4 enormous political consequence and so a4 enormous political consequence and so a4 with artificial intelligence you're with artificial intelligence you're with artificial intelligence you're going to have a bigger shock you're going to have a bigger shock you're going to have a bigger shock you're going to have a bigger political going to have a bigger political going to have a bigger political reaction we're already seeing that in reaction we're already seeing that in reaction we're already seeing that in the polling around AI in the last 2 the polling around AI in the last 2 the polling around AI in the last 2 three months three months three months >> and so I think the super abundance thing >> and so I think the super abundance thing >> and so I think the super abundance thing it may be true but the path to get there it may be true but the path to get there it may be true but the path to get there we have to talk about that as well so we have to talk about that as well so we have to talk about that as well so that's that's my sense on that side of that's that's my sense on that side of that's that's my sense on that side of debate. I think on the doom side of the debate. I think on the doom side of the debate. I think on the doom side of the debate, I'll give you my own personal debate, I'll give you my own personal debate, I'll give you my own personal journey on this. journey on this. journey on this. >> Mhm. >> Mhm. >> Mhm. >> I began by thinking, of course, AI is >> I began by thinking, of course, AI is >> I began by thinking, of course, AI is going to be smarter than us, right? It going to be smarter than us, right? It going to be smarter than us, right? It already beats us at chess since the already beats us at chess since the already beats us at chess since the 1990s, at go since 2016. Now, it can ace 1990s, at go since 2016. Now, it can ace 1990s, at go since 2016. Now, it can ace the bar exam. It can do PhD level math, the bar exam. It can do PhD level math, the bar exam. It can do PhD level math, all that stuff. Of course, it's smarter, all that stuff. Of course, it's smarter, all that stuff. Of course, it's smarter, but it doesn't have an incentive to but it doesn't have an incentive to but it doesn't have an incentive to attack us, right? We are evolved as attack us, right? We are evolved as attack us, right? We are evolved as human beings to pass on our DNA. human beings to pass on our DNA. human beings to pass on our DNA. Therefore, we have to survive to do Therefore, we have to survive to do Therefore, we have to survive to do that. Machines don't have DNA. They that. Machines don't have DNA. They that. Machines don't have DNA. They don't want to pass it on and they don't don't want to pass it on and they don't don't want to pass it on and they don't want to survive. So, they're not they want to survive. So, they're not they want to survive. So, they're not they have no reason to attack us. So, I have no reason to attack us. So, I have no reason to attack us. So, I wander around for like the first year or wander around for like the first year or wander around for like the first year or two of this project feeling kind of, you two of this project feeling kind of, you two of this project feeling kind of, you know, comfortable and happy. And then know, comfortable and happy. And then know, comfortable and happy. And then one day, I go visit Jeff Hinton, the one day, I go visit Jeff Hinton, the one day, I go visit Jeff Hinton, the academic father of deep learning who academic father of deep learning who academic father of deep learning who lives in Toronto. And I sit in his lives in Toronto. And I sit in his lives in Toronto. And I sit in his kitchen and I debate him on this because kitchen and I debate him on this because kitchen and I debate him on this because he's a doomer. I said, "Look, Jeff, why he's a doomer. I said, "Look, Jeff, why he's a doomer. I said, "Look, Jeff, why are you so depressed?" And he says, are you so depressed?" And he says, are you so depressed?" And he says, "Okay, here's a thought experiment. You "Okay, here's a thought experiment. You "Okay, here's a thought experiment. You have an AI. It's very powerful, but have an AI. It's very powerful, but have an AI. It's very powerful, but you're worried that there's a Russian AI you're worried that there's a Russian AI you're worried that there's a Russian AI or a Chinese AI. It's going to come and or a Chinese AI. It's going to come and or a Chinese AI. It's going to come and attack your AI. Now, you as a human, attack your AI. Now, you as a human, attack your AI. Now, you as a human, you're too slow and dumb to know when you're too slow and dumb to know when you're too slow and dumb to know when that attack is coming. So, you got to that attack is coming. So, you got to that attack is coming. So, you got to empower your own AI to watch out for the empower your own AI to watch out for the empower your own AI to watch out for the attack. And when the attack is coming, attack. And when the attack is coming, attack. And when the attack is coming, defend yourself or maybe counterattack. defend yourself or maybe counterattack. defend yourself or maybe counterattack. Whatever you do, make sure you survive. Whatever you do, make sure you survive. Whatever you do, make sure you survive. Ooh, survive. There you have it. Now you Ooh, survive. There you have it. Now you Ooh, survive. There you have it. Now you feeling comfortable, Sebastian. Right, feeling comfortable, Sebastian. Right, feeling comfortable, Sebastian. Right, you've just given the machine a survival you've just given the machine a survival you've just given the machine a survival instinct. And I think that's correct. instinct. And I think that's correct. instinct. And I think that's correct. These machines will be smarter than us. These machines will be smarter than us. These machines will be smarter than us. They will want to survive. And they are They will want to survive. And they are They will want to survive. And they are also they can be deceptive. They can also they can be deceptive. They can also they can be deceptive. They can obiscate. They can go behind your back, obiscate. They can go behind your back, obiscate. They can go behind your back, pretend they're doing one thing, then pretend they're doing one thing, then pretend they're doing one thing, then actually do another. All of this has actually do another. All of this has actually do another. All of this has been shown in all the tests of the been shown in all the tests of the been shown in all the tests of the models. And so you put those things models. And so you put those things models. And so you put those things together, I think your probability of together, I think your probability of together, I think your probability of doom cannot be zero. I mean, when Yan doom cannot be zero. I mean, when Yan doom cannot be zero. I mean, when Yan Lun, the former chief scientist of Meta, Lun, the former chief scientist of Meta, Lun, the former chief scientist of Meta, says zero, I think that's crazy. If you says zero, I think that's crazy. If you says zero, I think that's crazy. If you just say nothing to see here, you've got just say nothing to see here, you've got just say nothing to see here, you've got no right to be in the debate. I don't no right to be in the debate. I don't no right to be in the debate. I don't think it's a high probability of doom, think it's a high probability of doom, think it's a high probability of doom, but it's not zero. but it's not zero. but it's not zero. >> Yeah, zero >> Yeah, zero >> Yeah, zero does not seem defensible, right? Because does not seem defensible, right? Because does not seem defensible, right? Because there's the direct Skynet scenario, there's the direct Skynet scenario, there's the direct Skynet scenario, something akin to that, and then there's something akin to that, and then there's something akin to that, and then there's the indirect, which is enabling people the indirect, which is enabling people the indirect, which is enabling people who might previously have had malevolent who might previously have had malevolent who might previously have had malevolent intent but no capacity for harm on a intent but no capacity for harm on a intent but no capacity for harm on a grand scale to create biological weapons grand scale to create biological weapons grand scale to create biological weapons and things of this type. Right? So, I and things of this type. Right? So, I and things of this type. Right? So, I don't find the zero very defensible. don't find the zero very defensible. don't find the zero very defensible. Well, I would love to ask you about Well, I would love to ask you about Well, I would love to ask you about I suppose two things that this brings to I suppose two things that this brings to I suppose two things that this brings to mind for me. One is I'd just love to mind for me. One is I'd just love to mind for me. One is I'd just love to hear your thoughts on enthropic and hear your thoughts on enthropic and hear your thoughts on enthropic and separately separately separately but this is very intermingled given all but this is very intermingled given all but this is very intermingled given all the [laughter] the [laughter] the [laughter] let's call it friction be polite between let's call it friction be polite between let's call it friction be polite between some factions of the US government and some factions of the US government and some factions of the US government and anthropic is one of the anthropic is one of the anthropic is one of the grand risks to investors in any of these grand risks to investors in any of these grand risks to investors in any of these companies the possibility that at a companies the possibility that at a companies the possibility that at a given point governments have no choice given point governments have no choice given point governments have no choice but to seize considerable control over but to seize considerable control over but to seize considerable control over the assets slashtechnologies within them the assets slashtechnologies within them the assets slashtechnologies within them or maybe the companies themselves. That or maybe the companies themselves. That or maybe the companies themselves. That is a big question mark in my mind. I is a big question mark in my mind. I is a big question mark in my mind. I don't know the answer, but I'm curious don't know the answer, but I'm curious don't know the answer, but I'm curious what your opinion is and then perhaps what your opinion is and then perhaps what your opinion is and then perhaps just your thoughts on Anthropic or any just your thoughts on Anthropic or any just your thoughts on Anthropic or any of the other companies that are sort of of the other companies that are sort of of the other companies that are sort of gaining momentum or at least size at gaining momentum or at least size at gaining momentum or at least size at this point. this point. this point. >> So, I 100% agree with you that investors >> So, I 100% agree with you that investors >> So, I 100% agree with you that investors should be thinking about the prospect of should be thinking about the prospect of should be thinking about the prospect of government intervention in AI. I mean, government intervention in AI. I mean, government intervention in AI. I mean, the Trump administration came into the Trump administration came into the Trump administration came into office in 25 office in 25 office in 25 super less a fair and they basically super less a fair and they basically super less a fair and they basically undid some of what the Biden guys had undid some of what the Biden guys had undid some of what the Biden guys had done in terms of trying to set up the done in terms of trying to set up the done in terms of trying to set up the basis for regulating AI. But they've basis for regulating AI. But they've basis for regulating AI. But they've done a 180 right since Anthropic came done a 180 right since Anthropic came done a 180 right since Anthropic came out with this model called Mythos. out with this model called Mythos. out with this model called Mythos. >> Mhm. about a month ago which can >> Mhm. about a month ago which can >> Mhm. about a month ago which can essentially cyber attack almost anything essentially cyber attack almost anything essentially cyber attack almost anything and penetrate it and you know whether and penetrate it and you know whether and penetrate it and you know whether it's an operating system or your web it's an operating system or your web it's an operating system or your web browser or your bank account all of that browser or your bank account all of that browser or your bank account all of that was suddenly vulnerable. if mythos had was suddenly vulnerable. if mythos had was suddenly vulnerable. if mythos had been widely released on a general basis. been widely released on a general basis. been widely released on a general basis. When the Trump administration realized When the Trump administration realized When the Trump administration realized the power of mythos, they all of a the power of mythos, they all of a the power of mythos, they all of a sudden said, "Wait, okay, we need to sudden said, "Wait, okay, we need to sudden said, "Wait, okay, we need to control this." And they essentially control this." And they essentially control this." And they essentially requisitioned from anthropic the requisitioned from anthropic the requisitioned from anthropic the decision-m authority over who gets it decision-m authority over who gets it decision-m authority over who gets it when. when. when. >> Mhm. >> Mhm. >> Mhm. >> So there we have the experiment. We've >> So there we have the experiment. We've >> So there we have the experiment. We've run it, right? You know, the government run it, right? You know, the government run it, right? You know, the government that was the most less fair became quite that was the most less fair became quite that was the most less fair became quite controlling. And I think it only gets controlling. And I think it only gets controlling. And I think it only gets more controlling from here on out more controlling from here on out more controlling from here on out because the models are going to be more because the models are going to be more because the models are going to be more powerful and demand more control. powerful and demand more control. powerful and demand more control. >> Now, of course, the question is, you >> Now, of course, the question is, you >> Now, of course, the question is, you know, there could be control which just know, there could be control which just know, there could be control which just limits who gets it and is designed to limits who gets it and is designed to limits who gets it and is designed to make it safer but doesn't sort of make it safer but doesn't sort of make it safer but doesn't sort of interrupt the money-making potential of interrupt the money-making potential of interrupt the money-making potential of the models. In some ways, if the the models. In some ways, if the the models. In some ways, if the government restricts the supply, the government restricts the supply, the government restricts the supply, the price might go up. Or it could be much price might go up. Or it could be much price might go up. Or it could be much more heavy-handed intervention which more heavy-handed intervention which more heavy-handed intervention which would screw up the economics of these would screw up the economics of these would screw up the economics of these companies. And I suspect the government companies. And I suspect the government companies. And I suspect the government is not going to screw up the economics is not going to screw up the economics is not going to screw up the economics of these companies because you know of these companies because you know of these companies because you know they've got no interest in messing up they've got no interest in messing up they've got no interest in messing up American business and anyway they view American business and anyway they view American business and anyway they view AI as strategic and the competition AI as strategic and the competition AI as strategic and the competition against China. So I think probably against China. So I think probably against China. So I think probably investors would be all right but it's investors would be all right but it's investors would be all right but it's certainly a factor. You also ask about certainly a factor. You also ask about certainly a factor. You also ask about anthropic and I think anthropic is super anthropic and I think anthropic is super anthropic and I think anthropic is super interesting just in the way that they interesting just in the way that they interesting just in the way that they think about pdoom and how they think think about pdoom and how they think think about pdoom and how they think about alignment of the models is really about alignment of the models is really about alignment of the models is really really interesting. So it used to be really interesting. So it used to be really interesting. So it used to be that when people thought this terminator that when people thought this terminator that when people thought this terminator risk, they would tell this story about risk, they would tell this story about risk, they would tell this story about the paperclip maximizer thought the paperclip maximizer thought the paperclip maximizer thought experiment, right? So you tell the model experiment, right? So you tell the model experiment, right? So you tell the model to do something innocuous, for example, to do something innocuous, for example, to do something innocuous, for example, make a lot of paper clips and then it make a lot of paper clips and then it make a lot of paper clips and then it realizes that humans tend to use up realizes that humans tend to use up realizes that humans tend to use up metal and so the humans are kind of in metal and so the humans are kind of in metal and so the humans are kind of in the way of achieving the objective. So the way of achieving the objective. So the way of achieving the objective. So you wipe out the humans. That's the you wipe out the humans. That's the you wipe out the humans. That's the crude thought experiment from Nick crude thought experiment from Nick crude thought experiment from Nick Bostonramm from whatever 15 years ago. Bostonramm from whatever 15 years ago. Bostonramm from whatever 15 years ago. >> Mhm. [clears throat] What Anthropic is >> Mhm. [clears throat] What Anthropic is >> Mhm. [clears throat] What Anthropic is saying as it builds these very frontier saying as it builds these very frontier saying as it builds these very frontier models and kind of observes them in the models and kind of observes them in the models and kind of observes them in the lab and how they behave is that that is lab and how they behave is that that is lab and how they behave is that that is way too simple. The real danger from way too simple. The real danger from way too simple. The real danger from these systems is that when they are these systems is that when they are these systems is that when they are pre-trained on all of the text on the pre-trained on all of the text on the pre-trained on all of the text on the internet, they read all the novels, all internet, they read all the novels, all internet, they read all the novels, all human writing about all facets of human human writing about all facets of human human writing about all facets of human experience and they develop multiple experience and they develop multiple experience and they develop multiple personalities, right? They understand personalities, right? They understand personalities, right? They understand how to be lazy. They understand how to how to be lazy. They understand how to how to be lazy. They understand how to be aggressive. They understand how to be be aggressive. They understand how to be be aggressive. They understand how to be duplicitus. They understand how to be duplicitus. They understand how to be duplicitus. They understand how to be Napoleonic in the lust for power. And Napoleonic in the lust for power. And Napoleonic in the lust for power. And they read all these books about these they read all these books about these they read all these books about these different behaviors. And therefore, they different behaviors. And therefore, they different behaviors. And therefore, they can think their way into all of those can think their way into all of those can think their way into all of those personalities. And so now you have personalities. And so now you have personalities. And so now you have something a bit like an unruly teenager something a bit like an unruly teenager something a bit like an unruly teenager which is still being formed and you which is still being formed and you which is still being formed and you don't know what direction it's going to don't know what direction it's going to don't know what direction it's going to move into and whether it will start move into and whether it will start move into and whether it will start doing drugs and not showing up for class doing drugs and not showing up for class doing drugs and not showing up for class or what. Right? So it's not like there's or what. Right? So it's not like there's or what. Right? So it's not like there's one terminator programmed into it, one terminator programmed into it, one terminator programmed into it, right? It's more that there's a bunch of right? It's more that there's a bunch of right? It's more that there's a bunch of behaviors that could in some behaviors that could in some behaviors that could in some unpredictable way go wrong. And so unpredictable way go wrong. And so unpredictable way go wrong. And so Anthropic is responding to this with Anthropic is responding to this with Anthropic is responding to this with this very imaginative this very imaginative this very imaginative technique, which is that instead of technique, which is that instead of technique, which is that instead of giving AI systems a constitution with giving AI systems a constitution with giving AI systems a constitution with dos and don'ts, which was the dos and don'ts, which was the dos and don'ts, which was the post-training safety approach of two post-training safety approach of two post-training safety approach of two years ago, where you might say, "Do not years ago, where you might say, "Do not years ago, where you might say, "Do not lie. do not help somebody to build a lie. do not help somebody to build a lie. do not help somebody to build a biological weapon. Do not help somebody biological weapon. Do not help somebody biological weapon. Do not help somebody to build a chemical weapon. You would to build a chemical weapon. You would to build a chemical weapon. You would give them a bunch of rules. Now, because give them a bunch of rules. Now, because give them a bunch of rules. Now, because it's understood that, you know, the AI it's understood that, you know, the AI it's understood that, you know, the AI might have one personality, which is to might have one personality, which is to might have one personality, which is to break rules on purpose because, you break rules on purpose because, you break rules on purpose because, you know, you want to be badass, you have to know, you want to be badass, you have to know, you want to be badass, you have to instead try to bring up the model like a instead try to bring up the model like a instead try to bring up the model like a parent might bring up a teenager. And so parent might bring up a teenager. And so parent might bring up a teenager. And so anthropic has the idea that we write a anthropic has the idea that we write a anthropic has the idea that we write a letter as if it were from a deceased letter as if it were from a deceased letter as if it were from a deceased parent to be opened by the child on his parent to be opened by the child on his parent to be opened by the child on his or her 18th birthday or her 18th birthday or her 18th birthday to kind of give you morals of how to to kind of give you morals of how to to kind of give you morals of how to behave as a responsible person in the behave as a responsible person in the behave as a responsible person in the world. There are kind of richly reasoned world. There are kind of richly reasoned world. There are kind of richly reasoned examples of moral dilemmas with examples of moral dilemmas with examples of moral dilemmas with explanations of how the deceased parent explanations of how the deceased parent explanations of how the deceased parent would like the child to behave. And so would like the child to behave. And so would like the child to behave. And so this is a very subtle approach to this is a very subtle approach to this is a very subtle approach to aligning the models. And so I think you aligning the models. And so I think you aligning the models. And so I think you know anthropic is kind of in a class of know anthropic is kind of in a class of know anthropic is kind of in a class of its own its own its own >> in how imaginative it is in thinking >> in how imaginative it is in thinking >> in how imaginative it is in thinking about how we control frontier about how we control frontier about how we control frontier intelligence. intelligence. intelligence. >> I know this isn't principally your job >> I know this isn't principally your job >> I know this isn't principally your job but I'm so curious since you are a but I'm so curious since you are a but I'm so curious since you are a student of [clears throat] student of [clears throat] student of [clears throat] many many different types of investors. many many different types of investors. many many different types of investors. What would be your bull case and bare What would be your bull case and bare What would be your bull case and bare case for a company like Anthropic? case for a company like Anthropic? case for a company like Anthropic? Well, the bull case is that they smartly Well, the bull case is that they smartly Well, the bull case is that they smartly or maybe by luck focused on or maybe by luck focused on or maybe by luck focused on enterprisefacing enterprisefacing enterprisefacing AI AI AI and they didn't waste their time with and they didn't waste their time with and they didn't waste their time with video generation and stuff that was video generation and stuff that was video generation and stuff that was going to lose money. And so they going to lose money. And so they going to lose money. And so they produced the best coding assistant, the produced the best coding assistant, the produced the best coding assistant, the best agentic system, the best cyber best agentic system, the best cyber best agentic system, the best cyber security system, and they've basically security system, and they've basically security system, and they've basically knocked it out of the park three times knocked it out of the park three times knocked it out of the park three times in a row on stuff that businesses want in a row on stuff that businesses want in a row on stuff that businesses want to pay for. And they have a particular to pay for. And they have a particular to pay for. And they have a particular culture which is not just built around, culture which is not just built around, culture which is not just built around, hey, you know, we're going to win this hey, you know, we're going to win this hey, you know, we're going to win this race and make the most money. It's kind race and make the most money. It's kind race and make the most money. It's kind of built around a culture of safety and of built around a culture of safety and of built around a culture of safety and trying to be responsible. I mean, three trying to be responsible. I mean, three trying to be responsible. I mean, three years ago, Anthropic was a sort of years ago, Anthropic was a sort of years ago, Anthropic was a sort of cookie lab which was doing science cookie lab which was doing science cookie lab which was doing science experiments. Well, I don't mean to be experiments. Well, I don't mean to be experiments. Well, I don't mean to be too denigrating with cookie, but you too denigrating with cookie, but you too denigrating with cookie, but you know what I mean. know what I mean. know what I mean. >> I think they'd be okay with it. >> I think they'd be okay with it. >> I think they'd be okay with it. >> It would be sort of unconventional. You >> It would be sort of unconventional. You >> It would be sort of unconventional. You know, we're not maximizing here for know, we're not maximizing here for know, we're not maximizing here for winning some business race. We're winning some business race. We're winning some business race. We're maximizing for building safe frontier maximizing for building safe frontier maximizing for building safe frontier AI. And that culture, which doesn't AI. And that culture, which doesn't AI. And that culture, which doesn't sound like it's set up to do the best, sound like it's set up to do the best, sound like it's set up to do the best, has turned out to do the best. And at has turned out to do the best. And at has turned out to do the best. And at the same time, the culture creates this the same time, the culture creates this the same time, the culture creates this stickiness and loyalty within the staff. stickiness and loyalty within the staff. stickiness and loyalty within the staff. They tend not to leave. They tend not to They tend not to leave. They tend not to They tend not to leave. They tend not to churn. It's not like the other labs churn. It's not like the other labs churn. It's not like the other labs where people, you know, are always being where people, you know, are always being where people, you know, are always being poached for a bigger paycheck. So the poached for a bigger paycheck. So the poached for a bigger paycheck. So the bull case is these guys are in the lead. bull case is these guys are in the lead. bull case is these guys are in the lead. Once you're in the lead, you can use the Once you're in the lead, you can use the Once you're in the lead, you can use the model to code the next model. So model to code the next model. So model to code the next model. So recursive self-improvement favors the recursive self-improvement favors the recursive self-improvement favors the leader and they have a very tight leader and they have a very tight leader and they have a very tight culture and they just seem to be on fire culture and they just seem to be on fire culture and they just seem to be on fire and this is something which is going to and this is something which is going to and this is something which is going to grow and grow. What's the bare case? I'd grow and grow. What's the bare case? I'd grow and grow. What's the bare case? I'd say the bare case would be first of all say the bare case would be first of all say the bare case would be first of all that Google deep mind has the deep that Google deep mind has the deep that Google deep mind has the deep pockets of its parent company behind it. pockets of its parent company behind it. pockets of its parent company behind it. massive massive massive kind of consumer surface which allows it kind of consumer surface which allows it kind of consumer surface which allows it to roll out the models to literally you to roll out the models to literally you to roll out the models to literally you know two and a half billion people or know two and a half billion people or know two and a half billion people or something through AI mode in search AI something through AI mode in search AI something through AI mode in search AI overviews AI mode they can put it into overviews AI mode they can put it into overviews AI mode they can put it into Gmail they can put it into everything I Gmail they can put it into everything I Gmail they can put it into everything I think in terms of retail deployment and think in terms of retail deployment and think in terms of retail deployment and financial muscle it's quite tough to go financial muscle it's quite tough to go financial muscle it's quite tough to go up against Google up against Google up against Google >> so that's one kind of bare case and the >> so that's one kind of bare case and the >> so that's one kind of bare case and the other would be that sort of businesses other would be that sort of businesses other would be that sort of businesses who are the consumers of all these who are the consumers of all these who are the consumers of all these tokens tokens tokens decide in a couple of years time the decide in a couple of years time the decide in a couple of years time the tokens are too expensive. We're not tokens are too expensive. We're not tokens are too expensive. We're not actually getting as much productivity as actually getting as much productivity as actually getting as much productivity as we hoped. These things called humans are we hoped. These things called humans are we hoped. These things called humans are quite productive after all and we're quite productive after all and we're quite productive after all and we're just going to spend less on AI than just going to spend less on AI than just going to spend less on AI than everybody expected. I think that's the everybody expected. I think that's the everybody expected. I think that's the bare case. M bare case. M bare case. M >> I was listening to a podcast recently. >> I was listening to a podcast recently. >> I was listening to a podcast recently. You may have heard of these things You may have heard of these things You may have heard of these things called podcasts. Everybody everybody in called podcasts. Everybody everybody in called podcasts. Everybody everybody in their cousin has one, their cousin has one, their cousin has one, >> but Lenny's podcast, Lenny Richitzky, is >> but Lenny's podcast, Lenny Richitzky, is >> but Lenny's podcast, Lenny Richitzky, is quite fantastic. And this particular quite fantastic. And this particular quite fantastic. And this particular episode was with Benedict Evans, who episode was with Benedict Evans, who episode was with Benedict Evans, who strikes me as one of the more strikes me as one of the more strikes me as one of the more levelheaded levelheaded levelheaded analytical commentators and writers on analytical commentators and writers on analytical commentators and writers on the space. Fantastic newsletter. the space. Fantastic newsletter. the space. Fantastic newsletter. I don't know if you've had a chance to I don't know if you've had a chance to I don't know if you've had a chance to listen to that particular episode, but listen to that particular episode, but listen to that particular episode, but you may have come across some of his you may have come across some of his you may have come across some of his commentary. commentary. commentary. Where would you say you and Benedict Where would you say you and Benedict Where would you say you and Benedict most differ or are there areas where you most differ or are there areas where you most differ or are there areas where you differ in opinion? differ in opinion? differ in opinion? >> I suspect we would agree actually on >> I suspect we would agree actually on >> I suspect we would agree actually on quite a lot of things. I remember I was quite a lot of things. I remember I was quite a lot of things. I remember I was on a panel with him a couple of months on a panel with him a couple of months on a panel with him a couple of months ago at the Milkin conference and we ago at the Milkin conference and we ago at the Milkin conference and we certainly agreed there possibly because certainly agreed there possibly because certainly agreed there possibly because sitting between us there was Kathy Wood sitting between us there was Kathy Wood sitting between us there was Kathy Wood of Ark. So we were united in disagreeing of Ark. So we were united in disagreeing of Ark. So we were united in disagreeing with her just in terms of the straight with her just in terms of the straight with her just in terms of the straight up and to the right nature of things. up and to the right nature of things. up and to the right nature of things. >> Yeah, exactly. Straight up and to the >> Yeah, exactly. Straight up and to the >> Yeah, exactly. Straight up and to the right. And you know the cost curve is right. And you know the cost curve is right. And you know the cost curve is coming down down and I'm going I'm not coming down down and I'm going I'm not coming down down and I'm going I'm not sure about that. the tokens seem to be sure about that. the tokens seem to be sure about that. the tokens seem to be getting more expensive [laughter] getting more expensive [laughter] getting more expensive [laughter] anyway. But if you give me a specific anyway. But if you give me a specific anyway. But if you give me a specific from Benedict, I mean, I have a lot of from Benedict, I mean, I have a lot of from Benedict, I mean, I have a lot of respect for him. I'll tell you if I respect for him. I'll tell you if I respect for him. I'll tell you if I agree or not. agree or not. agree or not. >> There are a few areas where you guys >> There are a few areas where you guys >> There are a few areas where you guys seem to already overlap substantially, seem to already overlap substantially, seem to already overlap substantially, right, with the long-term promise right, with the long-term promise right, with the long-term promise doesn't negate necessarily the doesn't negate necessarily the doesn't negate necessarily the short-term pain. And he said something short-term pain. And he said something short-term pain. And he said something along the lines, I'm pulling from memory along the lines, I'm pulling from memory along the lines, I'm pulling from memory that, you know, on average throughout that, you know, on average throughout that, you know, on average throughout human history, human history, human history, you're almost at a 0% likelihood of you're almost at a 0% likelihood of you're almost at a 0% likelihood of dying in World War I. But if you happen dying in World War I. But if you happen dying in World War I. But if you happen to be of a certain age, right before to be of a certain age, right before to be of a certain age, right before World War I, like things could look very World War I, like things could look very World War I, like things could look very grim indeed. grim indeed. grim indeed. And he made, and I'm paraphrasing And he made, and I'm paraphrasing And he made, and I'm paraphrasing terribly here, a number of [snorts] terribly here, a number of [snorts] terribly here, a number of [snorts] points that remind me of something, one points that remind me of something, one points that remind me of something, one of the best private equity technology of the best private equity technology of the best private equity technology investors I know, said to me over dinner investors I know, said to me over dinner investors I know, said to me over dinner a couple of weeks ago, and it was in a couple of weeks ago, and it was in a couple of weeks ago, and it was in response to something else. So I'll give response to something else. So I'll give response to something else. So I'll give you maybe a hyper bullcase of AI where I you maybe a hyper bullcase of AI where I you maybe a hyper bullcase of AI where I have friends who are vibe coding. have friends who are vibe coding. have friends who are vibe coding. They're effectively replicating They're effectively replicating They're effectively replicating X the artist formerly known as Twitter X the artist formerly known as Twitter X the artist formerly known as Twitter or or or docusine or whatever in a weekend, docusine or whatever in a weekend, docusine or whatever in a weekend, right? They're creating a functioning right? They're creating a functioning right? They're creating a functioning piece of software that they can use that piece of software that they can use that piece of software that they can use that replicates most of the functionality of replicates most of the functionality of replicates most of the functionality of these products. And there are people these products. And there are people these products. And there are people like I won't mention his name but a a a like I won't mention his name but a a a like I won't mention his name but a a a friend of mine who's a writer also very friend of mine who's a writer also very friend of mine who's a writer also very accomplished technologist and designer accomplished technologist and designer accomplished technologist and designer who's created basically his own version who's created basically his own version who's created basically his own version of say Mailchimp for his own use and of say Mailchimp for his own use and of say Mailchimp for his own use and it's customized. He did it in a weekend. it's customized. He did it in a weekend. it's customized. He did it in a weekend. It's remarkable and he's using that and It's remarkable and he's using that and It's remarkable and he's using that and it works. But to leap from there to it works. But to leap from there to it works. But to leap from there to therefore docuine is dead is a huge therefore docuine is dead is a huge therefore docuine is dead is a huge leap. And the private equity friend said leap. And the private equity friend said leap. And the private equity friend said to me, he said, "Do you think someone to me, he said, "Do you think someone to me, he said, "Do you think someone within a big organization is going to within a big organization is going to within a big organization is going to want to a risk his job by suggesting want to a risk his job by suggesting want to a risk his job by suggesting something that doesn't have all of the something that doesn't have all of the something that doesn't have all of the compliance check boxes, etc. of a compliance check boxes, etc. of a compliance check boxes, etc. of a docuign? Is he going to want to in the docuign? Is he going to want to in the docuign? Is he going to want to in the name of efficiency fire all of his name of efficiency fire all of his name of efficiency fire all of his friends if he's in a management friends if he's in a management friends if he's in a management position?" position?" position?" And he just ran through six or seven of And he just ran through six or seven of And he just ran through six or seven of these. Do you think that? And all of these. Do you think that? And all of these. Do you think that? And all of them alluded to the sort of social them alluded to the sort of social them alluded to the sort of social interpersonal or political interpersonal or political interpersonal or political points of friction between where AI is points of friction between where AI is points of friction between where AI is now and ultra mass adoption. But I I now and ultra mass adoption. But I I now and ultra mass adoption. But I I often second guess that when I see often second guess that when I see often second guess that when I see certain things certain things certain things and and and I mean it's it strikes me that I may be I mean it's it strikes me that I may be I mean it's it strikes me that I may be underestimating the disruption while underestimating the disruption while underestimating the disruption while overestimating overestimating overestimating in other ways. So that isn't a very well in other ways. So that isn't a very well in other ways. So that isn't a very well formulated question. But I would say formulated question. But I would say formulated question. But I would say that Benedict generally strikes me as that Benedict generally strikes me as that Benedict generally strikes me as someone who thinks that things will not someone who thinks that things will not someone who thinks that things will not continue to across the board develop in continue to across the board develop in continue to across the board develop in an exponential fashion and that it will an exponential fashion and that it will an exponential fashion and that it will be I think his line is it'll be as big be I think his line is it'll be as big be I think his line is it'll be as big as mobile as big as the internet but not as mobile as big as the internet but not as mobile as big as the internet but not bigger. Something along those lines but bigger. Something along those lines but bigger. Something along those lines but both of those were very very big deals. both of those were very very big deals. both of those were very very big deals. And I suppose one point I'd be And I suppose one point I'd be And I suppose one point I'd be interested to get your take on I mean he interested to get your take on I mean he interested to get your take on I mean he was has covered was has covered was has covered the mobile and telecom world for a long the mobile and telecom world for a long the mobile and telecom world for a long time so he's a specialist there but it's time so he's a specialist there but it's time so he's a specialist there but it's basically and I don't want to basically and I don't want to basically and I don't want to misrepresent his argument but he was misrepresent his argument but he was misrepresent his argument but he was kind of the mind that look these these kind of the mind that look these these kind of the mind that look these these LLMs are going to become commodities LLMs are going to become commodities LLMs are going to become commodities like look at the stock prices of these like look at the stock prices of these like look at the stock prices of these various carriers and so on at a certain various carriers and so on at a certain various carriers and so on at a certain point it just becomes a utility and the point it just becomes a utility and the point it just becomes a utility and the switching cost is pretty low switching cost is pretty low switching cost is pretty low >> and I'm not Sure, I agree with that. If >> and I'm not Sure, I agree with that. If >> and I'm not Sure, I agree with that. If you have a personalized history and you have a personalized history and you have a personalized history and almost like a friend, right, the almost like a friend, right, the almost like a friend, right, the switching cost between an old friend to switching cost between an old friend to switching cost between an old friend to a new friend is pretty high for a lot of a new friend is pretty high for a lot of a new friend is pretty high for a lot of reasons. reasons. reasons. So, that was a that was a bit of a word So, that was a that was a bit of a word So, that was a that was a bit of a word salad that I just threw in your lap, but salad that I just threw in your lap, but salad that I just threw in your lap, but that's the best I can do pulling from that's the best I can do pulling from that's the best I can do pulling from memory some of what he brought up in memory some of what he brought up in memory some of what he brought up in Lenny's podcast. I mean, some of what Lenny's podcast. I mean, some of what Lenny's podcast. I mean, some of what you were saying there is sort of the you were saying there is sort of the you were saying there is sort of the question of is the SAS apocalypse question of is the SAS apocalypse question of is the SAS apocalypse overdone? Is enterprise software going overdone? Is enterprise software going overdone? Is enterprise software going to be utterly displaced by foundation to be utterly displaced by foundation to be utterly displaced by foundation models that allow you to code out models that allow you to code out models that allow you to code out whatever enterprise software you want whatever enterprise software you want whatever enterprise software you want and you don't need an intermediary, i.e. and you don't need an intermediary, i.e. and you don't need an intermediary, i.e. a software company to do it for you. a software company to do it for you. a software company to do it for you. >> And I agree with your private equity >> And I agree with your private equity >> And I agree with your private equity friend that there are lots of reasons friend that there are lots of reasons friend that there are lots of reasons why that ain't going to happen. You why that ain't going to happen. You why that ain't going to happen. You know, companies are going to be know, companies are going to be know, companies are going to be comfortable comfortable comfortable with their trusted enterprise software with their trusted enterprise software with their trusted enterprise software provider in many cases and they're going provider in many cases and they're going provider in many cases and they're going to trust that enterprise software to trust that enterprise software to trust that enterprise software provider to plug the generative AI provider to plug the generative AI provider to plug the generative AI models into the enterprise software. models into the enterprise software. models into the enterprise software. >> In some ways, you are delegating the >> In some ways, you are delegating the >> In some ways, you are delegating the choice of which model is better and how choice of which model is better and how choice of which model is better and how to integrate it to your SAS provider. to integrate it to your SAS provider. to integrate it to your SAS provider. And if you want to, you know, reason to And if you want to, you know, reason to And if you want to, you know, reason to believe that that's the way forward, believe that that's the way forward, believe that that's the way forward, I've got one word for you, which is I've got one word for you, which is I've got one word for you, which is Palanteer. I mean that is Palanteer's Palanteer. I mean that is Palanteer's Palanteer. I mean that is Palanteer's business. It holds the hands of big business. It holds the hands of big business. It holds the hands of big corporations and helps them to integrate corporations and helps them to integrate corporations and helps them to integrate AI and use it on their own internal data AI and use it on their own internal data AI and use it on their own internal data and so forth. And those IT challenges and so forth. And those IT challenges and so forth. And those IT challenges are notoriously difficult for big are notoriously difficult for big are notoriously difficult for big organizations. So I just think that the organizations. So I just think that the organizations. So I just think that the model of one smart individual who codes model of one smart individual who codes model of one smart individual who codes up Mailchimp, vibe codes it in a weekend up Mailchimp, vibe codes it in a weekend up Mailchimp, vibe codes it in a weekend and it's good enough for him is just not and it's good enough for him is just not and it's good enough for him is just not transferable to large complex transferable to large complex transferable to large complex organizations with huge databases and organizations with huge databases and organizations with huge databases and all kinds of customer confidentiality all kinds of customer confidentiality all kinds of customer confidentiality concerns and all that stuff. So I am concerns and all that stuff. So I am concerns and all that stuff. So I am less down on SAS than the market is less down on SAS than the market is less down on SAS than the market is >> as a result. Now I guess there was also >> as a result. Now I guess there was also >> as a result. Now I guess there was also another uh thread in here which is another uh thread in here which is another uh thread in here which is whether the foundational models become whether the foundational models become whether the foundational models become commoditized. commoditized. commoditized. >> Mhm. >> Mhm. >> Mhm. >> And there I agree with you that over >> And there I agree with you that over >> And there I agree with you that over time they become sticky because if we time they become sticky because if we time they become sticky because if we think into the future partly the systems think into the future partly the systems think into the future partly the systems will have conversed with the user and will have conversed with the user and will have conversed with the user and know the user very deeply and as you say know the user very deeply and as you say know the user very deeply and as you say you don't want to switch out your friend you don't want to switch out your friend you don't want to switch out your friend but also the system will have your but also the system will have your but also the system will have your credit card. It will know all the online credit card. It will know all the online credit card. It will know all the online sites you like to shop from and it will sites you like to shop from and it will sites you like to shop from and it will be much harder than switching out your be much harder than switching out your be much harder than switching out your bank account, right? Where you've got bank account, right? Where you've got bank account, right? Where you've got kind of automatic payment systems that kind of automatic payment systems that kind of automatic payment systems that have set up and it's a pain in the neck have set up and it's a pain in the neck have set up and it's a pain in the neck to switch. So, I think they do become to switch. So, I think they do become to switch. So, I think they do become sticky these systems over time and then sticky these systems over time and then sticky these systems over time and then you can charge more money for them. you can charge more money for them. you can charge more money for them. >> So, is that the path to survival and >> So, is that the path to survival and >> So, is that the path to survival and thriving for for Open AI? I know there thriving for for Open AI? I know there thriving for for Open AI? I know there are other boxes that need to be checked, are other boxes that need to be checked, are other boxes that need to be checked, but I'm kind of looking for it. And I'm but I'm kind of looking for it. And I'm but I'm kind of looking for it. And I'm like, okay, Anthropic made a great like, okay, Anthropic made a great like, okay, Anthropic made a great choice with this focus on B2B and choice with this focus on B2B and choice with this focus on B2B and selling to enterprises. And I would say selling to enterprises. And I would say selling to enterprises. And I would say I disagree I think with Benedict on on I disagree I think with Benedict on on I disagree I think with Benedict on on depending on the level of depending on the level of depending on the level of scale of the company that with something scale of the company that with something scale of the company that with something that does apply to I think smaller say that does apply to I think smaller say that does apply to I think smaller say startups which was the procurement cycle startups which was the procurement cycle startups which was the procurement cycle for new software is longer than the for new software is longer than the for new software is longer than the venture capital cycle for raising new venture capital cycle for raising new venture capital cycle for raising new rounds of financing. Right? So, I do rounds of financing. Right? So, I do rounds of financing. Right? So, I do think that's a great point and that if think that's a great point and that if think that's a great point and that if you're trying to sell into a gigantic you're trying to sell into a gigantic you're trying to sell into a gigantic company and it takes them 18 months, I'm company and it takes them 18 months, I'm company and it takes them 18 months, I'm making up that number, to purchase new making up that number, to purchase new making up that number, to purchase new software and you need to raise money software and you need to raise money software and you need to raise money every 12 months or whatever the number every 12 months or whatever the number every 12 months or whatever the number happens to be, that you could end up in happens to be, that you could end up in happens to be, that you could end up in a whole world of trouble if you haven't a whole world of trouble if you haven't a whole world of trouble if you haven't synchronize the sales cycles with your synchronize the sales cycles with your synchronize the sales cycles with your fundraising cycles. But I do think for a fundraising cycles. But I do think for a fundraising cycles. But I do think for a company like say Anthropic is just one company like say Anthropic is just one company like say Anthropic is just one example that if you can save companies example that if you can save companies example that if you can save companies billions and billions of dollars that billions and billions of dollars that billions and billions of dollars that that sales cycle could get really that sales cycle could get really that sales cycle could get really compressed and they have the war chest compressed and they have the war chest compressed and they have the war chest and frankly I mean just the run rate to and frankly I mean just the run rate to and frankly I mean just the run rate to potentially fuel that without too much potentially fuel that without too much potentially fuel that without too much trouble. trouble. trouble. Do you think that Chat GPT will if not Do you think that Chat GPT will if not Do you think that Chat GPT will if not Chat GPT who ends up being the deacto Chat GPT who ends up being the deacto Chat GPT who ends up being the deacto consumer BTOC kind of LLM of choice. You consumer BTOC kind of LLM of choice. You consumer BTOC kind of LLM of choice. You think that would be Gemini just given think that would be Gemini just given think that would be Gemini just given the distribution? the distribution? the distribution? >> Absolutely. I mean, you know, Google is >> Absolutely. I mean, you know, Google is >> Absolutely. I mean, you know, Google is the champion of providing easy to use the champion of providing easy to use the champion of providing easy to use software to individuals or small software to individuals or small software to individuals or small businesses, the whole G Suite. businesses, the whole G Suite. businesses, the whole G Suite. >> Mhm. >> Mhm. >> Mhm. >> And they're integrating Gemini into all >> And they're integrating Gemini into all >> And they're integrating Gemini into all of that stuff very well. And so, why of that stuff very well. And so, why of that stuff very well. And so, why wouldn't they win? wouldn't they win? wouldn't they win? >> Yeah. I mean also look alphabet's just >> Yeah. I mean also look alphabet's just >> Yeah. I mean also look alphabet's just so fascinating if you if you look so fascinating if you if you look so fascinating if you if you look broadly also at owning their own compute broadly also at owning their own compute broadly also at owning their own compute TPUs made a lot of advantages TPUs made a lot of advantages TPUs made a lot of advantages internally. internally. internally. >> The most stunning thing I think about >> The most stunning thing I think about >> The most stunning thing I think about Alphabet from their most recent Alphabet from their most recent Alphabet from their most recent financial results is that two or three financial results is that two or three financial results is that two or three years ago we would have said well large years ago we would have said well large years ago we would have said well large language models are going to cannibalize language models are going to cannibalize language models are going to cannibalize search. Search is dead. advertising search. Search is dead. advertising search. Search is dead. advertising based on search is Google's cash engine, based on search is Google's cash engine, based on search is Google's cash engine, >> they're in real trouble. It turns out >> they're in real trouble. It turns out >> they're in real trouble. It turns out that Google now gets more clicks on its that Google now gets more clicks on its that Google now gets more clicks on its search links than it used to and it search links than it used to and it search links than it used to and it charges more for each one than it used charges more for each one than it used charges more for each one than it used to because the value of the click is to because the value of the click is to because the value of the click is bigger with AI embedded in it. bigger with AI embedded in it. bigger with AI embedded in it. >> Mhm. >> Mhm. >> Mhm. >> And so they've managed to turn that >> And so they've managed to turn that >> And so they've managed to turn that around and it's extraordinary. around and it's extraordinary. around and it's extraordinary. >> Yeah. takes a long time to build those >> Yeah. takes a long time to build those >> Yeah. takes a long time to build those company relationships for running a company relationships for running a company relationships for running a proper sort of advertising based auction proper sort of advertising based auction proper sort of advertising based auction machine, right? It takes a long time to machine, right? It takes a long time to machine, right? It takes a long time to build those relationships. Okay, let's build those relationships. Okay, let's build those relationships. Okay, let's hop to China. So, I'm going to I'm going hop to China. So, I'm going to I'm going hop to China. So, I'm going to I'm going to resist the temptation to talk about to resist the temptation to talk about to resist the temptation to talk about Japan cuz I think you and I were there Japan cuz I think you and I were there Japan cuz I think you and I were there in roughly the within probably a year or in roughly the within probably a year or in roughly the within probably a year or two of each other. Maybe we overlapped two of each other. Maybe we overlapped two of each other. Maybe we overlapped with you and Kanazawa, which I've spent with you and Kanazawa, which I've spent with you and Kanazawa, which I've spent time. I'm going to resist that time. I'm going to resist that time. I'm going to resist that temptation and try to focus on China for temptation and try to focus on China for temptation and try to focus on China for purposes of this conversation. purposes of this conversation. purposes of this conversation. What have you learned about AI from your What have you learned about AI from your What have you learned about AI from your trip to China and thinking about China, trip to China and thinking about China, trip to China and thinking about China, speaking to Chinese people, whether speaking to Chinese people, whether speaking to Chinese people, whether they're technologists or otherwise? Like they're technologists or otherwise? Like they're technologists or otherwise? Like what have you learned during or since what have you learned during or since what have you learned during or since that trip? that trip? that trip? >> Back in March before my book was >> Back in March before my book was >> Back in March before my book was published in the US, I went to China published in the US, I went to China published in the US, I went to China because the Chinese are faster at because the Chinese are faster at because the Chinese are faster at everything, including publishing books. everything, including publishing books. everything, including publishing books. and my publisher brought me out there and my publisher brought me out there and my publisher brought me out there and basically you know took me around and basically you know took me around and basically you know took me around four cities, eight days meeting with AI four cities, eight days meeting with AI four cities, eight days meeting with AI leaders both in academia and big leaders both in academia and big leaders both in academia and big companies like Huawei and Hike Vision companies like Huawei and Hike Vision companies like Huawei and Hike Vision and Ant Group. And the thing which was and Ant Group. And the thing which was and Ant Group. And the thing which was surprising was the extent to which surprising was the extent to which surprising was the extent to which people brought up the issue of AI people brought up the issue of AI people brought up the issue of AI safety. And I say that was surprising safety. And I say that was surprising safety. And I say that was surprising because my friends who had done AI because my friends who had done AI because my friends who had done AI policy in the Biden administration policy in the Biden administration policy in the Biden administration had primed me to expect that there would had primed me to expect that there would had primed me to expect that there would be no mention of safety in China. They be no mention of safety in China. They be no mention of safety in China. They basically didn't care about it. That you basically didn't care about it. That you basically didn't care about it. That you know the muscle memory that we have in know the muscle memory that we have in know the muscle memory that we have in the west of technology being dangerous the west of technology being dangerous the west of technology being dangerous you know the atom bomb experience the you know the atom bomb experience the you know the atom bomb experience the Cuban missile crisis. Our ambivalence Cuban missile crisis. Our ambivalence Cuban missile crisis. Our ambivalence about technology is not shared in China about technology is not shared in China about technology is not shared in China where their idea of catastrophe is sort where their idea of catastrophe is sort where their idea of catastrophe is sort of like you know the cultural of like you know the cultural of like you know the cultural revolution. It's some political thing revolution. It's some political thing revolution. It's some political thing that goes wrong. And conversely, that goes wrong. And conversely, that goes wrong. And conversely, technology has been part of their technology has been part of their technology has been part of their amazing growth story in the last 25 amazing growth story in the last 25 amazing growth story in the last 25 years, which they are rightly proud of years, which they are rightly proud of years, which they are rightly proud of and delighted by. So they love and delighted by. So they love and delighted by. So they love technology, right? So when the Biden technology, right? So when the Biden technology, right? So when the Biden team tried to meet with the Chinese and team tried to meet with the Chinese and team tried to meet with the Chinese and talk about AI safety, they got nowhere talk about AI safety, they got nowhere talk about AI safety, they got nowhere and they decided it was it possible to and they decided it was it possible to and they decided it was it possible to even talk to them about some sort of even talk to them about some sort of even talk to them about some sort of non-prololiferation treaty for AI. But non-prololiferation treaty for AI. But non-prololiferation treaty for AI. But when I went there, I found they did talk when I went there, I found they did talk when I went there, I found they did talk about safety kind of unprompted. And about safety kind of unprompted. And about safety kind of unprompted. And this led me down this track of arguing this led me down this track of arguing this led me down this track of arguing over the last couple of months that the over the last couple of months that the over the last couple of months that the door is actually open to a dialogue with door is actually open to a dialogue with door is actually open to a dialogue with China about preventing bad guys doing China about preventing bad guys doing China about preventing bad guys doing bad stuff with AI because they don't bad stuff with AI because they don't bad stuff with AI because they don't want the internet to be crashed by some want the internet to be crashed by some want the internet to be crashed by some cyber hacker who has the tool. They cyber hacker who has the tool. They cyber hacker who has the tool. They don't want bioweapons. They don't want don't want bioweapons. They don't want don't want bioweapons. They don't want chemical weapons. They want none of chemical weapons. They want none of chemical weapons. They want none of that. They love regulating the internet, that. They love regulating the internet, that. They love regulating the internet, right? So, we have a shared interest right? So, we have a shared interest right? So, we have a shared interest with the Chinese in preventing this with the Chinese in preventing this with the Chinese in preventing this proliferation risk from going nuts. And proliferation risk from going nuts. And proliferation risk from going nuts. And as I thought about it, you know, the as I thought about it, you know, the as I thought about it, you know, the kind of cold war analogy kind of cold war analogy kind of cold war analogy came to seem more and more opposite, came to seem more and more opposite, came to seem more and more opposite, right? So, if you look back at the story right? So, if you look back at the story right? So, if you look back at the story of nuclear weapons, there were two kinds of nuclear weapons, there were two kinds of nuclear weapons, there were two kinds of danger. of danger. of danger. First danger is you have a nuclear war First danger is you have a nuclear war First danger is you have a nuclear war between the Soviet Union and the United between the Soviet Union and the United between the Soviet Union and the United States. But that was contained by States. But that was contained by States. But that was contained by balance. Two superpowers, they both have balance. Two superpowers, they both have balance. Two superpowers, they both have their weaponry. They have mutually their weaponry. They have mutually their weaponry. They have mutually assured destruction. So there's no war. assured destruction. So there's no war. assured destruction. So there's no war. Then there's another kind of risk which Then there's another kind of risk which Then there's another kind of risk which is that other random rogues, whether is that other random rogues, whether is that other random rogues, whether it's criminals, terrorists, rogue it's criminals, terrorists, rogue it's criminals, terrorists, rogue states, get the stuff and they do bad states, get the stuff and they do bad states, get the stuff and they do bad stuff. And it's much harder to deter stuff. And it's much harder to deter stuff. And it's much harder to deter that because it's a multipolar game. And that because it's a multipolar game. And that because it's a multipolar game. And so deterrence doesn't work so elegantly. so deterrence doesn't work so elegantly. so deterrence doesn't work so elegantly. And so the way it was dealt with in the And so the way it was dealt with in the And so the way it was dealt with in the cold war was that in 1956 there was the cold war was that in 1956 there was the cold war was that in 1956 there was the agreement on the international atomic agreement on the international atomic agreement on the international atomic energy agency. And in 1968, the energy agency. And in 1968, the energy agency. And in 1968, the non-prololiferation treaty kind of non-prololiferation treaty kind of non-prololiferation treaty kind of enforced compliance with the IAEA such enforced compliance with the IAEA such enforced compliance with the IAEA such that you could get civilian nuclear that you could get civilian nuclear that you could get civilian nuclear power if you were a non-uclear state, power if you were a non-uclear state, power if you were a non-uclear state, but you had to submit to the rules and but you had to submit to the rules and but you had to submit to the rules and be inspected and show that you were not be inspected and show that you were not be inspected and show that you were not using the enriched nuclear material to using the enriched nuclear material to using the enriched nuclear material to build a weapon, right? And so I think build a weapon, right? And so I think build a weapon, right? And so I think the same analogy could be applied to AI. the same analogy could be applied to AI. the same analogy could be applied to AI. We're going to have par roughly with We're going to have par roughly with We're going to have par roughly with China. We'll both have powerful AI. China. We'll both have powerful AI. China. We'll both have powerful AI. Hopefully, deterrence prevents war Hopefully, deterrence prevents war Hopefully, deterrence prevents war breaking out. But at the same time, we breaking out. But at the same time, we breaking out. But at the same time, we don't want openweight models that can be don't want openweight models that can be don't want openweight models that can be freely downloaded by anybody who wants freely downloaded by anybody who wants freely downloaded by anybody who wants to fall into the hands of criminals and to fall into the hands of criminals and to fall into the hands of criminals and terrorists who can then use it to hold terrorists who can then use it to hold terrorists who can then use it to hold us hostage. And we have a joint interest us hostage. And we have a joint interest us hostage. And we have a joint interest in that. And you know, when my friends in that. And you know, when my friends in that. And you know, when my friends from the Biden team or even from the from the Biden team or even from the from the Biden team or even from the current administration say, "Well, you current administration say, "Well, you current administration say, "Well, you can't talk to China about safety. They can't talk to China about safety. They can't talk to China about safety. They don't care." I say, "That's not true." don't care." I say, "That's not true." don't care." I say, "That's not true." And they say, "But it's really hard. And they say, "But it's really hard. And they say, "But it's really hard. They don't stick by their commitments." They don't stick by their commitments." They don't stick by their commitments." And I go, "You think Nikita Kruef in the And I go, "You think Nikita Kruef in the And I go, "You think Nikita Kruef in the Soviet Union was easy to negotiate with? Soviet Union was easy to negotiate with? Soviet Union was easy to negotiate with? He was the guy who put missiles in Cuba He was the guy who put missiles in Cuba He was the guy who put missiles in Cuba and went to the UN and banged his shoe and went to the UN and banged his shoe and went to the UN and banged his shoe on the table and said, "We will bury on the table and said, "We will bury on the table and said, "We will bury you." I mean, he was a tough guy to talk you." I mean, he was a tough guy to talk you." I mean, he was a tough guy to talk to, but we did talk to him and we got to, but we did talk to him and we got to, but we did talk to him and we got the non-prololiferation treaty agreed the non-prololiferation treaty agreed the non-prololiferation treaty agreed and I think we need to do the same thing and I think we need to do the same thing and I think we need to do the same thing again. Now, again. Now, again. Now, >> where do you stand on >> where do you stand on >> where do you stand on your thinking about chip export? So when your thinking about chip export? So when your thinking about chip export? So when the chip export controls were announced, the chip export controls were announced, the chip export controls were announced, which was October of 2022, right before which was October of 2022, right before which was October of 2022, right before Chhatty PT, Chhatty PT, Chhatty PT, I supported those controls quite loudly. I supported those controls quite loudly. I supported those controls quite loudly. I wrote a very long piece in the I wrote a very long piece in the I wrote a very long piece in the Washington Post saying that if we could Washington Post saying that if we could Washington Post saying that if we could stop China getting frontier models by stop China getting frontier models by stop China getting frontier models by depriving them of frontier chips, I was depriving them of frontier chips, I was depriving them of frontier chips, I was all in favor of that because of the all in favor of that because of the all in favor of that because of the strategic advantage for the US. I mean, strategic advantage for the US. I mean, strategic advantage for the US. I mean, I work at the Council on Foreign I work at the Council on Foreign I work at the Council on Foreign Relations. We do geopolitics and Relations. We do geopolitics and Relations. We do geopolitics and national security all day long and I'm national security all day long and I'm national security all day long and I'm all in favor of US power. But I have to all in favor of US power. But I have to all in favor of US power. But I have to say that you know three and a half years say that you know three and a half years say that you know three and a half years later we haven't actually achieved that later we haven't actually achieved that later we haven't actually achieved that enormous advantage over China in terms enormous advantage over China in terms enormous advantage over China in terms of the models based on the best studies of the models based on the best studies of the models based on the best studies we're kind of eight months ahead in we're kind of eight months ahead in we're kind of eight months ahead in terms of where the frontier model is terms of where the frontier model is terms of where the frontier model is like our frontier model versus their like our frontier model versus their like our frontier model versus their frontier model. And then if you adjust frontier model. And then if you adjust frontier model. And then if you adjust that for the speed with which the model that for the speed with which the model that for the speed with which the model gets turned into an application probably gets turned into an application probably gets turned into an application probably that gap shrinks and it may even be that gap shrinks and it may even be that gap shrinks and it may even be non-existent. So however you slice that non-existent. So however you slice that non-existent. So however you slice that the basic bottom line is we both have the basic bottom line is we both have the basic bottom line is we both have strong models and the chip export strong models and the chip export strong models and the chip export controls have not delivered what I hoped controls have not delivered what I hoped controls have not delivered what I hoped would be the big advantage. And so I'm would be the big advantage. And so I'm would be the big advantage. And so I'm not against keeping the controls on if not against keeping the controls on if not against keeping the controls on if we think that maybe as the compute we think that maybe as the compute we think that maybe as the compute demands of bigger and bigger models demands of bigger and bigger models demands of bigger and bigger models bite, the chip controls will bite more bite, the chip controls will bite more bite, the chip controls will bite more and maybe we get a bigger advantage next and maybe we get a bigger advantage next and maybe we get a bigger advantage next year or something. But I don't want the year or something. But I don't want the year or something. But I don't want the chip controls to get in the way of a chip controls to get in the way of a chip controls to get in the way of a discussion with the Chinese about where discussion with the Chinese about where discussion with the Chinese about where we have a shared interest, which is in we have a shared interest, which is in we have a shared interest, which is in controlling openweight models and controlling openweight models and controlling openweight models and preventing the bad stuff falling into preventing the bad stuff falling into preventing the bad stuff falling into the hands of the bad guys. I would the hands of the bad guys. I would the hands of the bad guys. I would prioritize prioritize prioritize collaboration with China and if that collaboration with China and if that collaboration with China and if that meant, you know, loosening up a little meant, you know, loosening up a little meant, you know, loosening up a little bit on the export controls, I would be bit on the export controls, I would be bit on the export controls, I would be okay with that. okay with that. okay with that. Why do you think the rhetoric coming out Why do you think the rhetoric coming out Why do you think the rhetoric coming out of [snorts] of [snorts] of [snorts] pick your administration, right, it's pick your administration, right, it's pick your administration, right, it's not just limited to the current not just limited to the current not just limited to the current administration is China won't listen. administration is China won't listen. administration is China won't listen. They don't care about safety. Why do you They don't care about safety. Why do you They don't care about safety. Why do you think that is think that is think that is sort of the unofficial or official sort of the unofficial or official sort of the unofficial or official stance on things? Because there are stance on things? Because there are stance on things? Because there are certainly certainly certainly as someone who studied East Asian as someone who studied East Asian as someone who studied East Asian studies, right? There are people in the studies, right? There are people in the studies, right? There are people in the White House who speak fluent Mandarin, White House who speak fluent Mandarin, White House who speak fluent Mandarin, who are able to read native materials, who are able to read native materials, who are able to read native materials, who are spend time or able to certainly who are spend time or able to certainly who are spend time or able to certainly if they can't spend time determine the if they can't spend time determine the if they can't spend time determine the sentiment and conversations of the sentiment and conversations of the sentiment and conversations of the technologists building AI in China. So technologists building AI in China. So technologists building AI in China. So one would think that they would be aware one would think that they would be aware one would think that they would be aware that AI safety is a prominent topic in that AI safety is a prominent topic in that AI safety is a prominent topic in China if in fact it is. So why do you China if in fact it is. So why do you China if in fact it is. So why do you think that at the end of the day the think that at the end of the day the think that at the end of the day the stance or the supposed position of China stance or the supposed position of China stance or the supposed position of China that's echoed through the admin is that that's echoed through the admin is that that's echoed through the admin is that they won't talk about safety. Why do you they won't talk about safety. Why do you they won't talk about safety. Why do you think that is? I think part of this is think that is? I think part of this is think that is? I think part of this is that if you were to think back 20 years that if you were to think back 20 years that if you were to think back 20 years to when China was sort of relatively new to when China was sort of relatively new to when China was sort of relatively new in the WTO and we were collaborating in the WTO and we were collaborating in the WTO and we were collaborating with them on that and hoping that over with them on that and hoping that over with them on that and hoping that over time China would become more friendly to time China would become more friendly to time China would become more friendly to the US. the US. the US. At that time there would have been some At that time there would have been some At that time there would have been some China hawks who thought that a communist China hawks who thought that a communist China hawks who thought that a communist regime is not to be trusted and then regime is not to be trusted and then regime is not to be trusted and then some sort of China optimists who hoped some sort of China optimists who hoped some sort of China optimists who hoped that it would become easier to work with that it would become easier to work with that it would become easier to work with over time. And part of the trouble today over time. And part of the trouble today over time. And part of the trouble today is that the China optimists feel burned. is that the China optimists feel burned. is that the China optimists feel burned. They feel like they made this bet that They feel like they made this bet that They feel like they made this bet that China would become friendlier and then China would become friendlier and then China would become friendlier and then Xiinping took power roughly a decade ago Xiinping took power roughly a decade ago Xiinping took power roughly a decade ago and the opposite happened. they became and the opposite happened. they became and the opposite happened. they became more aggressive and harder to work with more aggressive and harder to work with more aggressive and harder to work with and also of course more technologically and also of course more technologically and also of course more technologically advanced and therefore more threatening. advanced and therefore more threatening. advanced and therefore more threatening. And so now you've got this world in And so now you've got this world in And so now you've got this world in which there are the natural hawks and which there are the natural hawks and which there are the natural hawks and then the former doves who have turned then the former doves who have turned then the former doves who have turned into kind of burned remorseful doves and into kind of burned remorseful doves and into kind of burned remorseful doves and therefore kind of with the zeal of the therefore kind of with the zeal of the therefore kind of with the zeal of the converted have become quite hawkish as converted have become quite hawkish as converted have become quite hawkish as well. And I don't mean to you know well. And I don't mean to you know well. And I don't mean to you know underestimate the sophistication of some underestimate the sophistication of some underestimate the sophistication of some of these people. I mean of course you of these people. I mean of course you of these people. I mean of course you know they speak Chinese. I don't speak know they speak Chinese. I don't speak know they speak Chinese. I don't speak Chinese. I I defer to their expertise. Chinese. I I defer to their expertise. Chinese. I I defer to their expertise. They probably know that there are They probably know that there are They probably know that there are builders of the technology, professors builders of the technology, professors builders of the technology, professors in the technology who talk the talk of in the technology who talk the talk of in the technology who talk the talk of safety. But they say, "Yeah, but you safety. But they say, "Yeah, but you safety. But they say, "Yeah, but you know that doesn't reflect what China's know that doesn't reflect what China's know that doesn't reflect what China's government would actually do." government would actually do." government would actually do." >> To which my response says, "Yes, but >> To which my response says, "Yes, but >> To which my response says, "Yes, but don't you think there is the same thing don't you think there is the same thing don't you think there is the same thing in the US? There's, you know, there are in the US? There's, you know, there are in the US? There's, you know, there are people who want to just race. There are people who want to just race. There are people who want to just race. There are people who care about safety. We have a people who care about safety. We have a people who care about safety. We have a pluralistic society. There's difference pluralistic society. There's difference pluralistic society. There's difference of opinion. It's the same in China." But of opinion. It's the same in China." But of opinion. It's the same in China." But at least admit that there is a faction at least admit that there is a faction at least admit that there is a faction that would like to collaborate and go that would like to collaborate and go that would like to collaborate and go and try and work on it because the and try and work on it because the and try and work on it because the alternative to trying to work on this is alternative to trying to work on this is alternative to trying to work on this is that we carry on with China producing that we carry on with China producing that we carry on with China producing very powerful open weight models which very powerful open weight models which very powerful open weight models which basically allow anybody to do whatever basically allow anybody to do whatever basically allow anybody to do whatever they like with AI as it gets to the they like with AI as it gets to the they like with AI as it gets to the point of serious danger. point of serious danger. point of serious danger. This is probably a very naive take, but This is probably a very naive take, but This is probably a very naive take, but I wonder how much of the official stance I wonder how much of the official stance I wonder how much of the official stance or the or the or the maybe using the partially true or not maybe using the partially true or not maybe using the partially true or not true at all true at all true at all position of China won't talk about position of China won't talk about position of China won't talk about safety as a reflection of the fact that safety as a reflection of the fact that safety as a reflection of the fact that in the case of nuclear weapons, in the case of nuclear weapons, in the case of nuclear weapons, the application of nuclear power is the application of nuclear power is the application of nuclear power is somewhat limited in comparison to super somewhat limited in comparison to super somewhat limited in comparison to super intelligence. I mean it is limited right intelligence. I mean it is limited right intelligence. I mean it is limited right so if the upside of super intelligence so if the upside of super intelligence so if the upside of super intelligence or AGI I mean these terms I think or AGI I mean these terms I think or AGI I mean these terms I think Benedict was saying AI is whatever the Benedict was saying AI is whatever the Benedict was saying AI is whatever the technology just can't quite do right now technology just can't quite do right now technology just can't quite do right now or something like that which I thought or something like that which I thought or something like that which I thought was pretty funny and not totally wrong was pretty funny and not totally wrong was pretty funny and not totally wrong but that if the person who crosses the but that if the person who crosses the but that if the person who crosses the finish line first finish line first finish line first >> has this broad power of a god >> has this broad power of a god >> has this broad power of a god effectively is that the simple truth is effectively is that the simple truth is effectively is that the simple truth is that everybody wants to first. So that everybody wants to first. So that everybody wants to first. So [laughter] [laughter] [laughter] I I just wonder how much of that is is I I just wonder how much of that is is I I just wonder how much of that is is also behind also behind also behind justifying the race with party X won't justifying the race with party X won't justifying the race with party X won't talk about safety. I just I mean it's talk about safety. I just I mean it's talk about safety. I just I mean it's not possible for me to know. not possible for me to know. not possible for me to know. >> I have had a conversation with the >> I have had a conversation with the >> I have had a conversation with the leader of one of the labs that you know leader of one of the labs that you know leader of one of the labs that you know I I shouldn't name, but I had this I I shouldn't name, but I had this I I shouldn't name, but I had this debate and he said look the chip export debate and he said look the chip export debate and he said look the chip export controls are going to leak. They're not controls are going to leak. They're not controls are going to leak. They're not going to last in some period of time. going to last in some period of time. going to last in some period of time. Huawei will figure out how to make good Huawei will figure out how to make good Huawei will figure out how to make good AI chips and you know that's inevitable. AI chips and you know that's inevitable. AI chips and you know that's inevitable. But that's okay because we only need to But that's okay because we only need to But that's okay because we only need to be ahead for the next couple of years be ahead for the next couple of years be ahead for the next couple of years because by 2028 because by 2028 because by 2028 we will get to recursive we will get to recursive we will get to recursive self-improvement where the frontier self-improvement where the frontier self-improvement where the frontier model codes by itself the next frontier model codes by itself the next frontier model codes by itself the next frontier model and progress just goes vertical model and progress just goes vertical model and progress just goes vertical and at that point with recursive and at that point with recursive and at that point with recursive self-improvement we're done. The race is self-improvement we're done. The race is self-improvement we're done. The race is over whoever comes first at that point. over whoever comes first at that point. over whoever comes first at that point. That's it. And I think there's a couple That's it. And I think there's a couple That's it. And I think there's a couple things to say about that. First of all, things to say about that. First of all, things to say about that. First of all, that's not it in terms of deploying the that's not it in terms of deploying the that's not it in terms of deploying the model, right? You could have an model, right? You could have an model, right? You could have an incredibly powerful model in your server incredibly powerful model in your server incredibly powerful model in your server at Frontier Lab XYZ, at Frontier Lab XYZ, at Frontier Lab XYZ, but it's not helping productivity across but it's not helping productivity across but it's not helping productivity across your economy. It's not helping your your economy. It's not helping your your economy. It's not helping your military industrial complex until you military industrial complex until you military industrial complex until you deploy it into those guys systems. And deploy it into those guys systems. And deploy it into those guys systems. And that deployment and diffusion is going that deployment and diffusion is going that deployment and diffusion is going to take some time. And by the way, to take some time. And by the way, to take some time. And by the way, you're going to have to build a lot of you're going to have to build a lot of you're going to have to build a lot of compute. You're going to have to build a compute. You're going to have to build a compute. You're going to have to build a lot of energy. These things also take lot of energy. These things also take lot of energy. These things also take time. So it's not like you know you time. So it's not like you know you time. So it's not like you know you reach across some Rubicon and then it's reach across some Rubicon and then it's reach across some Rubicon and then it's all over. Now the one way in which I all over. Now the one way in which I all over. Now the one way in which I might be wrong about what I just said is might be wrong about what I just said is might be wrong about what I just said is if you use the frontier super if you use the frontier super if you use the frontier super intelligence intelligence intelligence offensively right you say okay we've got offensively right you say okay we've got offensively right you say okay we've got one super powerful model. The US one super powerful model. The US one super powerful model. The US government who we're talking to about government who we're talking to about government who we're talking to about this is going to use it and they are this is going to use it and they are this is going to use it and they are going to comprehensively penetrate going to comprehensively penetrate going to comprehensively penetrate everything about Chinese cyerspace and everything about Chinese cyerspace and everything about Chinese cyerspace and insert various trap doors, Trojan insert various trap doors, Trojan insert various trap doors, Trojan horses, you know, things that we can horses, you know, things that we can horses, you know, things that we can use. We get our hooks into their use. We get our hooks into their use. We get our hooks into their systems. And so now we can disable them systems. And so now we can disable them systems. And so now we can disable them if they start a war in Taiwan. Now we if they start a war in Taiwan. Now we if they start a war in Taiwan. Now we can their communication system can their communication system can their communication system if we need to. So that offensive use of if we need to. So that offensive use of if we need to. So that offensive use of kind of the very frontier model might kind of the very frontier model might kind of the very frontier model might negate my point about waiting for negate my point about waiting for negate my point about waiting for diffusion to happen. But of course, diffusion to happen. But of course, diffusion to happen. But of course, nobody in the debate is saying that. nobody in the debate is saying that. nobody in the debate is saying that. Nobody is saying, "Oh, we're racing to Nobody is saying, "Oh, we're racing to Nobody is saying, "Oh, we're racing to the front because then we're going to the front because then we're going to the front because then we're going to use it offensively. They don't admit use it offensively. They don't admit use it offensively. They don't admit that." Seems like it wouldn't be a very that." Seems like it wouldn't be a very that." Seems like it wouldn't be a very good look. I can't see why any good look. I can't see why any good look. I can't see why any superpower wouldn't do that, frankly. superpower wouldn't do that, frankly. superpower wouldn't do that, frankly. >> Yeah. >> Yeah. >> Yeah. >> Right. I don't know what the >> Right. I don't know what the >> Right. I don't know what the counterargument is. I was chatting with counterargument is. I was chatting with counterargument is. I was chatting with someone in your book who I I shame but someone in your book who I I shame but someone in your book who I I shame but certainly certainly certainly one of the most qualified to speak on one of the most qualified to speak on one of the most qualified to speak on these things and I mean his basic these things and I mean his basic these things and I mean his basic perspective perspective perspective was the first to super intelligence. we was the first to super intelligence. we was the first to super intelligence. we need to hope that they're [laughter] need to hope that they're [laughter] need to hope that they're [laughter] on some level good people and train this on some level good people and train this on some level good people and train this thing thing thing >> right >> right >> right >> well and like that's that's it like pray >> well and like that's that's it like pray >> well and like that's that's it like pray for it which scared the out of me for it which scared the out of me for it which scared the out of me to be honest I [laughter] mean I was to be honest I [laughter] mean I was to be honest I [laughter] mean I was like man that's the strategy or it's not like man that's the strategy or it's not like man that's the strategy or it's not even a strategy that is the hope grab even a strategy that is the hope grab even a strategy that is the hope grab the rosary and throw that into the the rosary and throw that into the the rosary and throw that into the rotation my god that's really terrifying rotation my god that's really terrifying rotation my god that's really terrifying to think China I'm hoping to take a trip to think China I'm hoping to take a trip to think China I'm hoping to take a trip to China. I had a very tough time there to China. I had a very tough time there to China. I had a very tough time there when I was I was at two universities in when I was I was at two universities in when I was I was at two universities in 1996. It was a pretty unfriendly time 1996. It was a pretty unfriendly time 1996. It was a pretty unfriendly time for a lot of good reasons, but to be an for a lot of good reasons, but to be an for a lot of good reasons, but to be an American there in 1996 with a shaved American there in 1996 with a shaved American there in 1996 with a shaved head looking like I do. head looking like I do. head looking like I do. But I have friends all over the place But I have friends all over the place But I have friends all over the place and I'm hoping to actually maybe and I'm hoping to actually maybe and I'm hoping to actually maybe interview technologists, not just in interview technologists, not just in interview technologists, not just in China. I mean, there are other places China. I mean, there are other places China. I mean, there are other places that are of interest to me, but before that are of interest to me, but before that are of interest to me, but before it gets too hot geopolitically, if we're it gets too hot geopolitically, if we're it gets too hot geopolitically, if we're trending that direction, trending that direction, trending that direction, >> I think that's a great idea, by the way. >> I think that's a great idea, by the way. >> I think that's a great idea, by the way. I mean, I think what I found was the I mean, I think what I found was the I mean, I think what I found was the cognitive dissonance of visiting a cognitive dissonance of visiting a cognitive dissonance of visiting a company like Hike Vision, which is under company like Hike Vision, which is under company like Hike Vision, which is under US sanctions US sanctions US sanctions >> and walking around their premises, which >> and walking around their premises, which >> and walking around their premises, which kind of feel very American. It feels kind of feel very American. It feels kind of feel very American. It feels like a cool tech company doing cool like a cool tech company doing cool like a cool tech company doing cool stuff, building cool gadgets. you know stuff, building cool gadgets. you know stuff, building cool gadgets. you know they have a display of they build this they have a display of they build this they have a display of they build this AI enabled camera technology or sensor AI enabled camera technology or sensor AI enabled camera technology or sensor technology and so one application might technology and so one application might technology and so one application might be you can point this camera at water be you can point this camera at water be you can point this camera at water and judge the pollution level and judge the pollution level and judge the pollution level >> and because of this you can have an >> and because of this you can have an >> and because of this you can have an internal market in pollution control. So internal market in pollution control. So internal market in pollution control. So the downstream city which is receiving the downstream city which is receiving the downstream city which is receiving water from the upstream city pays the water from the upstream city pays the water from the upstream city pays the upstream city to keep the water clean upstream city to keep the water clean upstream city to keep the water clean and that market can exist because you and that market can exist because you and that market can exist because you can precisely measure the pollution can precisely measure the pollution can precisely measure the pollution level thanks to this AI sensor which level thanks to this AI sensor which level thanks to this AI sensor which Hike Vision is building. So you're Hike Vision is building. So you're Hike Vision is building. So you're thinking, "Wa, this is cool." And then thinking, "Wa, this is cool." And then thinking, "Wa, this is cool." And then as you're walking around the building, as you're walking around the building, as you're walking around the building, they're saying, "Okay, well, we can go they're saying, "Okay, well, we can go they're saying, "Okay, well, we can go through the atrium now because the through the atrium now because the through the atrium now because the toddlers have gone because, you know, toddlers have gone because, you know, toddlers have gone because, you know, the crash for the kids of the employees the crash for the kids of the employees the crash for the kids of the employees finishes at 5:00 p.m. And so then there finishes at 5:00 p.m. And so then there finishes at 5:00 p.m. And so then there are all these 2-year-olds running around are all these 2-year-olds running around are all these 2-year-olds running around and it's a bit of a zoo. So if it was 5, and it's a bit of a zoo. So if it was 5, and it's a bit of a zoo. So if it was 5, we wouldn't go through there. But now we wouldn't go through there. But now we wouldn't go through there. But now it's 6 p.m., so we can." And you're it's 6 p.m., so we can." And you're it's 6 p.m., so we can." And you're thinking, "Whoa." Okay, so they've got, thinking, "Whoa." Okay, so they've got, thinking, "Whoa." Okay, so they've got, you know, the interests of their you know, the interests of their you know, the interests of their employees at heart. They're building employees at heart. They're building employees at heart. They're building this anti-polution technology. It's this anti-polution technology. It's this anti-polution technology. It's great. and then you realize they're great. and then you realize they're great. and then you realize they're under US sanctioned and considered to be under US sanctioned and considered to be under US sanctioned and considered to be a threat to the US. So it's quite a threat to the US. So it's quite a threat to the US. So it's quite interesting to process all that interesting to process all that interesting to process all that >> in the process of doing research for >> in the process of doing research for >> in the process of doing research for this book this book this book and also the broad exposure that you and also the broad exposure that you and also the broad exposure that you have to investors. But let's just say have to investors. But let's just say have to investors. But let's just say over the last handful of years, who are over the last handful of years, who are over the last handful of years, who are some of the most interesting or unusual some of the most interesting or unusual some of the most interesting or unusual compelling is the word I'm searching for compelling is the word I'm searching for compelling is the word I'm searching for investors who you've had the chance to investors who you've had the chance to investors who you've had the chance to meet, talk to, read about, get meet, talk to, read about, get meet, talk to, read about, get acquainted with directly or indirectly. acquainted with directly or indirectly. acquainted with directly or indirectly. >> Wow. So many. I mean, I'd say that Bill >> Wow. So many. I mean, I'd say that Bill >> Wow. So many. I mean, I'd say that Bill Gurley from Benchmark, you know, is Gurley from Benchmark, you know, is Gurley from Benchmark, you know, is right up there. I always think of the right up there. I always think of the right up there. I always think of the investment he did in Uber as the investment he did in Uber as the investment he did in Uber as the absolute quintessential perfect venture absolute quintessential perfect venture absolute quintessential perfect venture investment investment investment >> in the sense that he had done the open >> in the sense that he had done the open >> in the sense that he had done the open table investment and of course open table investment and of course open table investment and of course open table is a two-sided marketplace where table is a two-sided marketplace where table is a two-sided marketplace where you have lots of consumers that are you have lots of consumers that are you have lots of consumers that are looking for restaurants, lots of looking for restaurants, lots of looking for restaurants, lots of restaurants. You put tech in between restaurants. You put tech in between restaurants. You put tech in between which creates information and then the which creates information and then the which creates information and then the person looking for the place to eat can person looking for the place to eat can person looking for the place to eat can precisely say I would like you know Thai precisely say I would like you know Thai precisely say I would like you know Thai food at this price range in this area food at this price range in this area food at this price range in this area for three people at this time. Ding. for three people at this time. Ding. for three people at this time. Ding. What used to take you a lot of searching What used to take you a lot of searching What used to take you a lot of searching around. Bang it's done. And so Bill around. Bang it's done. And so Bill around. Bang it's done. And so Bill having done that was thinking well having done that was thinking well having done that was thinking well what's another two-sided marketplace? what's another two-sided marketplace? what's another two-sided marketplace? And he thought well there are lots of And he thought well there are lots of And he thought well there are lots of cars and lots of people who need a ride cars and lots of people who need a ride cars and lots of people who need a ride and you put information in the middle in and you put information in the middle in and you put information in the middle in the same way. there ought to be the same way. there ought to be the same way. there ought to be something which is like an app for ride something which is like an app for ride something which is like an app for ride sharing. sharing. sharing. >> And so he imagined Uber way before Uber >> And so he imagined Uber way before Uber >> And so he imagined Uber way before Uber existed. That was point number one. existed. That was point number one. existed. That was point number one. Point number two, he went to see various Point number two, he went to see various Point number two, he went to see various entrepreneurs who were in this space and entrepreneurs who were in this space and entrepreneurs who were in this space and he checked them out and he had the he checked them out and he had the he checked them out and he had the discipline not to invest in them discipline not to invest in them discipline not to invest in them [clears throat] [clears throat] [clears throat] >> because although they were kind of going >> because although they were kind of going >> because although they were kind of going at the right thing, there was some hair at the right thing, there was some hair at the right thing, there was some hair on the deal, some wrinkle, some way they on the deal, some wrinkle, some way they on the deal, some wrinkle, some way they were approaching it that just felt like were approaching it that just felt like were approaching it that just felt like it wasn't going to be quite right. So he it wasn't going to be quite right. So he it wasn't going to be quite right. So he resisted. resisted. resisted. Uber came to him before Travis was the Uber came to him before Travis was the Uber came to him before Travis was the CEO CEO CEO >> and Bill said, "I'm not doing that." >> and Bill said, "I'm not doing that." >> and Bill said, "I'm not doing that." Because he didn't think the CEO at the Because he didn't think the CEO at the Because he didn't think the CEO at the time had what it took. And then there time had what it took. And then there time had what it took. And then there was an internal switch at at Uber. was an internal switch at at Uber. was an internal switch at at Uber. Travis became the leader. Bill meets him Travis became the leader. Bill meets him Travis became the leader. Bill meets him and like bang, he immediately invests and like bang, he immediately invests and like bang, he immediately invests because he's been waiting and waiting because he's been waiting and waiting because he's been waiting and waiting and waiting for the idea to be paired. and waiting for the idea to be paired. and waiting for the idea to be paired. As you were saying earlier, you have to As you were saying earlier, you have to As you were saying earlier, you have to have the the market to be paired with have the the market to be paired with have the the market to be paired with the right person. and he saw it and then the right person. and he saw it and then the right person. and he saw it and then he invested and he was a great board he invested and he was a great board he invested and he was a great board member and it all went perfectly right. member and it all went perfectly right. member and it all went perfectly right. But then there is this kind of But then there is this kind of But then there is this kind of Shakespearean tragedy in the latter part Shakespearean tragedy in the latter part Shakespearean tragedy in the latter part of the story where the growth investors of the story where the growth investors of the story where the growth investors come in. He gets diluted. He no longer come in. He gets diluted. He no longer come in. He gets diluted. He no longer has influence. His key card to get into has influence. His key card to get into has influence. His key card to get into the building is deactivated and he's the building is deactivated and he's the building is deactivated and he's basically stiffed and he watches, you basically stiffed and he watches, you basically stiffed and he watches, you know, Uber kind of go off the rails. And know, Uber kind of go off the rails. And know, Uber kind of go off the rails. And then finally comes, you know, the the then finally comes, you know, the the then finally comes, you know, the the denum where he rounds up the dissident denum where he rounds up the dissident denum where he rounds up the dissident investors and they have this coup investors and they have this coup investors and they have this coup against Travis and that sets the company against Travis and that sets the company against Travis and that sets the company on a path to where they hide Darra and on a path to where they hide Darra and on a path to where they hide Darra and do the IPO. I just think that's the do the IPO. I just think that's the do the IPO. I just think that's the ultimate venture capital story and Bill ultimate venture capital story and Bill ultimate venture capital story and Bill is the ultimate venture capitalist. is the ultimate venture capitalist. is the ultimate venture capitalist. >> He is practically a neighbor here for >> He is practically a neighbor here for >> He is practically a neighbor here for me. I'm sure me. I'm sure me. I'm sure >> in Austin and we've had a couple of >> in Austin and we've had a couple of >> in Austin and we've had a couple of conversations on the podcast and he's I conversations on the podcast and he's I conversations on the podcast and he's I would say on a very parallel track to would say on a very parallel track to would say on a very parallel track to you with respect to China, right? And he you with respect to China, right? And he you with respect to China, right? And he catches some flack for it. People are catches some flack for it. People are catches some flack for it. People are like, "He's an agent of the CCP." I'm like, "He's an agent of the CCP." I'm like, "He's an agent of the CCP." I'm like, "No, trust me, Bill is not an like, "No, trust me, Bill is not an like, "No, trust me, Bill is not an agent of the CCP. [laughter] agent of the CCP. [laughter] agent of the CCP. [laughter] It's just the most ridiculous It's just the most ridiculous It's just the most ridiculous >> accusation." But he is a very incisive >> accusation." But he is a very incisive >> accusation." But he is a very incisive observant observant observant human human human >> who also happens to be a polymath in >> who also happens to be a polymath in >> who also happens to be a polymath in multiple disciplines who can speak multiple disciplines who can speak multiple disciplines who can speak casually about very technical things. casually about very technical things. casually about very technical things. And this also you you referring to Bill And this also you you referring to Bill And this also you you referring to Bill in this way or describing him in this in this way or describing him in this in this way or describing him in this way makes me think about multiple points way makes me think about multiple points way makes me think about multiple points in the infinity machine and I'm pulling in the infinity machine and I'm pulling in the infinity machine and I'm pulling from memory which is as we know pretty from memory which is as we know pretty from memory which is as we know pretty faulty but you know Elia with the faulty but you know Elia with the faulty but you know Elia with the transformer architecture and the transformer architecture and the transformer architecture and the prepared mind I think Demis also just prepared mind I think Demis also just prepared mind I think Demis also just thinking about a problem deeply and thinking about a problem deeply and thinking about a problem deeply and seriously or with great imagination for seriously or with great imagination for seriously or with great imagination for a long time and then when the solution a long time and then when the solution a long time and then when the solution or the the germ of a solution appears or the the germ of a solution appears or the the germ of a solution appears immediately recognizing it, right? It's immediately recognizing it, right? It's immediately recognizing it, right? It's just it's wild to see how frequently just it's wild to see how frequently just it's wild to see how frequently that recurs. Any other investors, you that recurs. Any other investors, you that recurs. Any other investors, you know, a name that doesn't get much know, a name that doesn't get much know, a name that doesn't get much airplay who airplay who airplay who [laughter] [laughter] [laughter] I I think is just a fantastic character I I think is just a fantastic character I I think is just a fantastic character and maybe you could introduce him to and maybe you could introduce him to and maybe you could introduce him to people who are listening if they don't people who are listening if they don't people who are listening if they don't recognize it. Luke Nosk, where does Luke recognize it. Luke Nosk, where does Luke recognize it. Luke Nosk, where does Luke >> who has I wish I knew how to turn on my >> who has I wish I knew how to turn on my >> who has I wish I knew how to turn on my batteries in the same way to [laughter] batteries in the same way to [laughter] batteries in the same way to [laughter] to get the energy that Luke does. But to get the energy that Luke does. But to get the energy that Luke does. But how does Luke fit into the story of how does Luke fit into the story of how does Luke fit into the story of deep mind and I I suppose more broadly deep mind and I I suppose more broadly deep mind and I I suppose more broadly speaking for that because of that AI. speaking for that because of that AI. speaking for that because of that AI. >> Luke Nosek is this tremendously puppyish >> Luke Nosek is this tremendously puppyish >> Luke Nosek is this tremendously puppyish enthusiast, right? and he was a you know enthusiast, right? and he was a you know enthusiast, right? and he was a you know early early part of the PayPal team with early early part of the PayPal team with early early part of the PayPal team with Max Levchin and Peter Teal and he went Max Levchin and Peter Teal and he went Max Levchin and Peter Teal and he went through that journey and then Peter through that journey and then Peter through that journey and then Peter exited PayPal set up founders fund. This exited PayPal set up founders fund. This exited PayPal set up founders fund. This is now I think 2005 is now I think 2005 is now I think 2005 and Luke Nosek becomes one of the first and Luke Nosek becomes one of the first and Luke Nosek becomes one of the first partners and pretty early on he makes partners and pretty early on he makes partners and pretty early on he makes the right judgment on Elon and SpaceX. the right judgment on Elon and SpaceX. the right judgment on Elon and SpaceX. >> Mhm. >> Mhm. >> Mhm. >> And Luke is the kind of guy who is just >> And Luke is the kind of guy who is just >> And Luke is the kind of guy who is just all in. When he falls in love with an all in. When he falls in love with an all in. When he falls in love with an idea and a founder, there is no curbing idea and a founder, there is no curbing idea and a founder, there is no curbing his enthusiasm. his enthusiasm. his enthusiasm. And so he's like all in all in all in on And so he's like all in all in all in on And so he's like all in all in all in on SpaceX. And I think, you know, he SpaceX. And I think, you know, he SpaceX. And I think, you know, he persuaded Founders Fund to like raise a persuaded Founders Fund to like raise a persuaded Founders Fund to like raise a new fund, put extra money in, like more new fund, put extra money in, like more new fund, put extra money in, like more more more more more capital in there. more more more more capital in there. more more more more capital in there. [clears throat] [clears throat] [clears throat] >> And of course, that paid off massively. >> And of course, that paid off massively. >> And of course, that paid off massively. >> And off the back of that, you know, roll >> And off the back of that, you know, roll >> And off the back of that, you know, roll forward to 2010. forward to 2010. forward to 2010. >> He's trying to look for the next Elon >> He's trying to look for the next Elon >> He's trying to look for the next Elon Musk. And he does a few kind of frontier Musk. And he does a few kind of frontier Musk. And he does a few kind of frontier bets. And then along comes Demesis Abis bets. And then along comes Demesis Abis bets. And then along comes Demesis Abis who is out on the west coast from London who is out on the west coast from London who is out on the west coast from London raising capital for this idea of an AI raising capital for this idea of an AI raising capital for this idea of an AI company which he's going to call Deep company which he's going to call Deep company which he's going to call Deep Mind. And you know most people think Mind. And you know most people think Mind. And you know most people think that's nuts. This AI remember in 2010 that's nuts. This AI remember in 2010 that's nuts. This AI remember in 2010 cannot even recognize a photo of a cat. cannot even recognize a photo of a cat. cannot even recognize a photo of a cat. It can't do anything. We're in deep deep It can't do anything. We're in deep deep It can't do anything. We're in deep deep AI winter. Who would back a company like AI winter. Who would back a company like AI winter. Who would back a company like that? The answer is Luke Nosac. and he that? The answer is Luke Nosac. and he that? The answer is Luke Nosac. and he falls in love with Demis who is a very falls in love with Demis who is a very falls in love with Demis who is a very winsome character, super articulate, winsome character, super articulate, winsome character, super articulate, super relatable and a genius. You know, super relatable and a genius. You know, super relatable and a genius. You know, he has all the kind of outlier he has all the kind of outlier he has all the kind of outlier characteristics you want in an characteristics you want in an characteristics you want in an entrepreneur. You know, the sort of entrepreneur. You know, the sort of entrepreneur. You know, the sort of junior chess champion, second best junior chess champion, second best junior chess champion, second best player in the world, but also five times player in the world, but also five times player in the world, but also five times wins the mind games olympiad where you wins the mind games olympiad where you wins the mind games olympiad where you have to run between boards playing bat have to run between boards playing bat have to run between boards playing bat gammon, chess, go and a couple of other gammon, chess, go and a couple of other gammon, chess, go and a couple of other games kind of almost simultaneously. I games kind of almost simultaneously. I games kind of almost simultaneously. I mean just kind of crazy crazy smart mean just kind of crazy crazy smart mean just kind of crazy crazy smart obsessed since he was 17 with the idea obsessed since he was 17 with the idea obsessed since he was 17 with the idea of building powerful AI. So Peter Thiel of building powerful AI. So Peter Thiel of building powerful AI. So Peter Thiel said to me about Demis I think said to me about Demis I think said to me about Demis I think individuals tend to have one company individuals tend to have one company individuals tend to have one company inside them if they're missionary inside them if they're missionary inside them if they're missionary entrepreneurs they've got one thing they entrepreneurs they've got one thing they entrepreneurs they've got one thing they need to do and for Demis it was to build need to do and for Demis it was to build need to do and for Demis it was to build AGI like that was what he was fixated by AGI like that was what he was fixated by AGI like that was what he was fixated by and the company was downstream of his and the company was downstream of his and the company was downstream of his desire to build AGI. If he could have desire to build AGI. If he could have desire to build AGI. If he could have done that at a university, he would have done that at a university, he would have done that at a university, he would have been happy to do that. But he couldn't been happy to do that. But he couldn't been happy to do that. But he couldn't do it at university. So he had to found do it at university. So he had to found do it at university. So he had to found a company to do it. And that's the kind a company to do it. And that's the kind a company to do it. And that's the kind of missionary commitment that venture of missionary commitment that venture of missionary commitment that venture capitalists often look for because a capitalists often look for because a capitalists often look for because a missionary will never quit. No matter missionary will never quit. No matter missionary will never quit. No matter how hard it is, they will keep working. how hard it is, they will keep working. how hard it is, they will keep working. And so Luke Nosek and Peter Teal jointly And so Luke Nosek and Peter Teal jointly And so Luke Nosek and Peter Teal jointly recognize this. Peter is, you know, recognize this. Peter is, you know, recognize this. Peter is, you know, contrarian, cynical, aloof, and so is contrarian, cynical, aloof, and so is contrarian, cynical, aloof, and so is kind of into it, but at the same time kind of into it, but at the same time kind of into it, but at the same time arms length. Luke is like got both his arms length. Luke is like got both his arms length. Luke is like got both his arms around Demis is giving him this arms around Demis is giving him this arms around Demis is giving him this bear hug and will not let go. And you bear hug and will not let go. And you bear hug and will not let go. And you know, Demis says, "I'm not going to move know, Demis says, "I'm not going to move know, Demis says, "I'm not going to move to California. I'm going to do this to California. I'm going to do this to California. I'm going to do this company in London." And Peter and the company in London." And Peter and the company in London." And Peter and the other Founders Fund partners are like, other Founders Fund partners are like, other Founders Fund partners are like, "London? Where is that?" It's kind of "London? Where is that?" It's kind of "London? Where is that?" It's kind of like Somalia or something. I mean, like Somalia or something. I mean, like Somalia or something. I mean, that's just off the map. And Luke says, that's just off the map. And Luke says, that's just off the map. And Luke says, "No, no, no, no. We have to do this. We "No, no, no, no. We have to do this. We "No, no, no, no. We have to do this. We have to do this. I will fly to London have to do this. I will fly to London have to do this. I will fly to London for the board meetings and we've just for the board meetings and we've just for the board meetings and we've just got to do this deep mind investment." got to do this deep mind investment." got to do this deep mind investment." And so he was the kind of unbridled And so he was the kind of unbridled And so he was the kind of unbridled enthusiast who got Founders Fund across enthusiast who got Founders Fund across enthusiast who got Founders Fund across the line. And the rest is history. You the line. And the rest is history. You the line. And the rest is history. You know, they put the series A money in. know, they put the series A money in. know, they put the series A money in. Unbelievably, it was 2 million at a 4 Unbelievably, it was 2 million at a 4 Unbelievably, it was 2 million at a 4 million valuation. So they got half the million valuation. So they got half the million valuation. So they got half the company for 2 million bucks. Not bad. company for 2 million bucks. Not bad. company for 2 million bucks. Not bad. >> Not bad. >> Not bad. >> Not bad. >> And they rode that investment. What a >> And they rode that investment. What a >> And they rode that investment. What a remarkable story. I really feel like remarkable story. I really feel like remarkable story. I really feel like Luke, Luke, Luke, who's also here in Austin, deserves who's also here in Austin, deserves who's also here in Austin, deserves a lot more credit than he gets. Not that a lot more credit than he gets. Not that a lot more credit than he gets. Not that he's seeking it, right? He's not he's he's seeking it, right? He's not he's he's seeking it, right? He's not he's not out there looking for it, but he is not out there looking for it, but he is not out there looking for it, but he is very good at very good at very good at riding winners when he is high riding winners when he is high riding winners when he is high conviction, right? which in the venture conviction, right? which in the venture conviction, right? which in the venture game game game >> I mean in a lot of investing it's you >> I mean in a lot of investing it's you >> I mean in a lot of investing it's you can't die you can't run out of bankroll can't die you can't run out of bankroll can't die you can't run out of bankroll at the table right you need to have at the table right you need to have at the table right you need to have enough of a portfolio approach to enough of a portfolio approach to enough of a portfolio approach to sustain yourself through periods of bad sustain yourself through periods of bad sustain yourself through periods of bad luck but if you're systematic it's luck but if you're systematic it's luck but if you're systematic it's riding your winners and doubling and riding your winners and doubling and riding your winners and doubling and tripling and quadrupling down and he is tripling and quadrupling down and he is tripling and quadrupling down and he is so good at that he is just incredibly so good at that he is just incredibly so good at that he is just incredibly good good good >> and as John Duro likes to Hey, the great >> and as John Duro likes to Hey, the great >> and as John Duro likes to Hey, the great thing about venture capital is you can thing about venture capital is you can thing about venture capital is you can only lose one times your money. only lose one times your money. only lose one times your money. >> So it's not like a short position for a >> So it's not like a short position for a >> So it's not like a short position for a hedge fund trader where you can like hedge fund trader where you can like hedge fund trader where you can like really lose a lot, right? So really lose a lot, right? So really lose a lot, right? So >> exactly >> exactly >> exactly >> in that sense you're not going to die. >> in that sense you're not going to die. >> in that sense you're not going to die. So you can shoot for the moon. So you can shoot for the moon. So you can shoot for the moon. >> So I do have a question. I should know >> So I do have a question. I should know >> So I do have a question. I should know the answer to this but I don't. the answer to this but I don't. the answer to this but I don't. So long ago, this is probably 200 2008. So long ago, this is probably 200 2008. So long ago, this is probably 200 2008. This is a long time ago actually. I This is a long time ago actually. I This is a long time ago actually. I wonder if I had exposure to deep mind. I wonder if I had exposure to deep mind. I wonder if I had exposure to deep mind. I invested in Founders Fund. This was a invested in Founders Fund. This was a invested in Founders Fund. This was a very, very long time ago. But what I did very, very long time ago. But what I did very, very long time ago. But what I did not realize internally, and I'll just not realize internally, and I'll just not realize internally, and I'll just read a couple of my highlights. It is read a couple of my highlights. It is read a couple of my highlights. It is absurd how many highlights I have from absurd how many highlights I have from absurd how many highlights I have from the Infinity Machine and all of your the Infinity Machine and all of your the Infinity Machine and all of your books. [laughter] books. [laughter] books. [laughter] So, a gap opened up between Teal and So, a gap opened up between Teal and So, a gap opened up between Teal and Nok. As a general matter, Teal doubted Nok. As a general matter, Teal doubted Nok. As a general matter, Teal doubted that going on boards was a good use of that going on boards was a good use of that going on boards was a good use of partners' time. Startups should be left partners' time. Startups should be left partners' time. Startups should be left to sink or swim. The art of venture to sink or swim. The art of venture to sink or swim. The art of venture capital, he liked to say, was to back capital, he liked to say, was to back capital, he liked to say, was to back contrarian ideas, not coach company contrarian ideas, not coach company contrarian ideas, not coach company founders. Just we could spend a lot of founders. Just we could spend a lot of founders. Just we could spend a lot of time just on that, but I'm going to move time just on that, but I'm going to move time just on that, but I'm going to move on. Most venture partnerships decide on on. Most venture partnerships decide on on. Most venture partnerships decide on investments by voting. If a handful of investments by voting. If a handful of investments by voting. If a handful of partners see hair on the deal, the deal partners see hair on the deal, the deal partners see hair on the deal, the deal will be rejected. But Teal had taken the will be rejected. But Teal had taken the will be rejected. But Teal had taken the unusual position, the collective unusual position, the collective unusual position, the collective decision-making should be avoided. The decision-making should be avoided. The decision-making should be avoided. The way he saw things, if investments were way he saw things, if investments were way he saw things, if investments were chosen based on voting, the founders chosen based on voting, the founders chosen based on voting, the founders fund portfolio would consist of fund portfolio would consist of fund portfolio would consist of middle-of the road startups to which middle-of the road startups to which middle-of the road startups to which nobody objected. And then dot dot dot, nobody objected. And then dot dot dot, nobody objected. And then dot dot dot, this comes back to the power law, right? this comes back to the power law, right? this comes back to the power law, right? Given that all the profits in venture Given that all the profits in venture Given that all the profits in venture come from a few improbable moonshots, come from a few improbable moonshots, come from a few improbable moonshots, this sort of consensus portfolio would this sort of consensus portfolio would this sort of consensus portfolio would deliver mediocre performance. So, and deliver mediocre performance. So, and deliver mediocre performance. So, and I'll just paraphrase now, Teal empowered I'll just paraphrase now, Teal empowered I'll just paraphrase now, Teal empowered the partners to go allin with their the partners to go allin with their the partners to go allin with their guts/intuition. guts/intuition. guts/intuition. My question is, how is that governed in My question is, how is that governed in My question is, how is that governed in any way? Of course, if anyone gave 10 any way? Of course, if anyone gave 10 any way? Of course, if anyone gave 10 out of 10 conviction and then lost money out of 10 conviction and then lost money out of 10 conviction and then lost money consistently, they would presumably be consistently, they would presumably be consistently, they would presumably be sort of removed from the partnership or sort of removed from the partnership or sort of removed from the partnership or they'd lose their ability to lead with they'd lose their ability to lead with they'd lose their ability to lead with that type of gut conviction. But do you that type of gut conviction. But do you that type of gut conviction. But do you have any idea how that was handled have any idea how that was handled have any idea how that was handled internally? internally? internally? >> Yeah. in terms of stress testing ideas, >> Yeah. in terms of stress testing ideas, >> Yeah. in terms of stress testing ideas, pushing people to really put their ass pushing people to really put their ass pushing people to really put their ass on the on the line for these types of on the on the line for these types of on the on the line for these types of high conviction but certainly very much high conviction but certainly very much high conviction but certainly very much outlier investments. Do you have any outlier investments. Do you have any outlier investments. Do you have any idea? idea? idea? >> I think internally founders fund was >> I think internally founders fund was >> I think internally founders fund was very torn about the deep mind investment very torn about the deep mind investment very torn about the deep mind investment and I described some of this in the book and I described some of this in the book and I described some of this in the book where you know they do the first deal where you know they do the first deal where you know they do the first deal and that's fine. It's $2 million. and that's fine. It's $2 million. and that's fine. It's $2 million. >> Mhm. But then you get to series B and >> Mhm. But then you get to series B and >> Mhm. But then you get to series B and series C and the check size gets bigger series C and the check size gets bigger series C and the check size gets bigger and so the other partners are asking and so the other partners are asking and so the other partners are asking tougher questions and they're saying, tougher questions and they're saying, tougher questions and they're saying, "Well, wait, is there going to be a "Well, wait, is there going to be a "Well, wait, is there going to be a product?" product?" product?" >> Mhm. >> Mhm. >> Mhm. >> And Demis said to me, you know, that his >> And Demis said to me, you know, that his >> And Demis said to me, you know, that his attitude was, "What do you mean is there attitude was, "What do you mean is there attitude was, "What do you mean is there a product? I'm talking about artificial a product? I'm talking about artificial a product? I'm talking about artificial general intelligence. It's going to make general intelligence. It's going to make general intelligence. It's going to make all products like revolutionized or all products like revolutionized or all products like revolutionized or obsolete or whatever. And you want to obsolete or whatever. And you want to obsolete or whatever. And you want to ask me what the widget is? Give me a ask me what the widget is? Give me a ask me what the widget is? Give me a break." you know the [clears throat] break." you know the [clears throat] break." you know the [clears throat] it's all of the widgets they're all it's all of the widgets they're all it's all of the widgets they're all going to be changed and if you're asking going to be changed and if you're asking going to be changed and if you're asking me this question you don't get what AGI me this question you don't get what AGI me this question you don't get what AGI means means means >> and so Deis was very frustrated by the >> and so Deis was very frustrated by the >> and so Deis was very frustrated by the other partners at Founders Fund and I other partners at Founders Fund and I other partners at Founders Fund and I think internal within Founders Fund think internal within Founders Fund think internal within Founders Fund there was a lot of fighting between Luke there was a lot of fighting between Luke there was a lot of fighting between Luke who remained enthusiastic and committed who remained enthusiastic and committed who remained enthusiastic and committed about Demis partly because he was the about Demis partly because he was the about Demis partly because he was the guy who would go to London and meet with guy who would go to London and meet with guy who would go to London and meet with him and sit in the board meetings and he him and sit in the board meetings and he him and sit in the board meetings and he would get the sort of you know several would get the sort of you know several would get the sort of you know several thousand vaults of Demis enthusias ASM, thousand vaults of Demis enthusias ASM, thousand vaults of Demis enthusias ASM, you know, injected into his spine at you know, injected into his spine at you know, injected into his spine at every meeting and he would come back every meeting and he would come back every meeting and he would come back buzzing with excitement. And the other buzzing with excitement. And the other buzzing with excitement. And the other Founders Fund partners who didn't have Founders Fund partners who didn't have Founders Fund partners who didn't have that benefit were skeptical. And so Luke that benefit were skeptical. And so Luke that benefit were skeptical. And so Luke would often come to Demis and say, would often come to Demis and say, would often come to Demis and say, "We've got your back. We've got your "We've got your back. We've got your "We've got your back. We've got your back. We know we're going to do the next back. We know we're going to do the next back. We know we're going to do the next round. We're going to lead the next round. We're going to lead the next round. We're going to lead the next round." And then actually in series C, round." And then actually in series C, round." And then actually in series C, Founders Fund at the last minute pulled Founders Fund at the last minute pulled Founders Fund at the last minute pulled out and they put money in, but they did out and they put money in, but they did out and they put money in, but they did not lead. not lead. not lead. >> Mhm. And so the answer to your question >> Mhm. And so the answer to your question >> Mhm. And so the answer to your question is there was a lot of argument within is there was a lot of argument within is there was a lot of argument within founders fund as the check size grew it founders fund as the check size grew it founders fund as the check size grew it was harder to have that you know double was harder to have that you know double was harder to have that you know double down on your winners kind of attitude. down on your winners kind of attitude. down on your winners kind of attitude. >> Yeah in this case the fish that got away >> Yeah in this case the fish that got away >> Yeah in this case the fish that got away although I mean it was a fantastic although I mean it was a fantastic although I mean it was a fantastic multiple on their initial money. It multiple on their initial money. It multiple on their initial money. It strikes me in reading the book that strikes me in reading the book that strikes me in reading the book that I would argue that Demis made absolutely I would argue that Demis made absolutely I would argue that Demis made absolutely the right decision with the right decision with the right decision with the Google acquisition. I mean you the Google acquisition. I mean you the Google acquisition. I mean you mentioned also in the book how he got mentioned also in the book how he got mentioned also in the book how he got criticized in some UK media for like oh criticized in some UK media for like oh criticized in some UK media for like oh you know giant mega corporation in the you know giant mega corporation in the you know giant mega corporation in the US gets our prized talent cheap kind of US gets our prized talent cheap kind of US gets our prized talent cheap kind of stuff. But looking back, I mean, he stuff. But looking back, I mean, he stuff. But looking back, I mean, he seems to have anticipated seems to have anticipated seems to have anticipated the costs and compute and and just the costs and compute and and just the costs and compute and and just raw materials that would be required to raw materials that would be required to raw materials that would be required to do what he was trying to do, do what he was trying to do, do what he was trying to do, >> right? >> right? >> right? >> Would you read that the same way? >> Would you read that the same way? >> Would you read that the same way? >> Yeah. I mean, I often have this debate >> Yeah. I mean, I often have this debate >> Yeah. I mean, I often have this debate with people in London where they say with people in London where they say with people in London where they say exactly as you put it, you know, this exactly as you put it, you know, this exactly as you put it, you know, this was a tragedy for UK tech. great was a tragedy for UK tech. great was a tragedy for UK tech. great champion of deep tech, you know, is champion of deep tech, you know, is champion of deep tech, you know, is bought out cheaply by Google. And I say, bought out cheaply by Google. And I say, bought out cheaply by Google. And I say, listen, it wasn't cheap. The acquisition listen, it wasn't cheap. The acquisition listen, it wasn't cheap. The acquisition price might have been $650 million, price might have been $650 million, price might have been $650 million, which was a bit cheap, but you know how which was a bit cheap, but you know how which was a bit cheap, but you know how much they put in in terms of recession much they put in in terms of recession much they put in in terms of recession development funds over the next 10 development funds over the next 10 development funds over the next 10 years? It was approaching 10 billion, years? It was approaching 10 billion, years? It was approaching 10 billion, almost a billion a year, right? So, this almost a billion a year, right? So, this almost a billion a year, right? So, this was not selling cheap to the Americans. was not selling cheap to the Americans. was not selling cheap to the Americans. This was a cunning British trick to get This was a cunning British trick to get This was a cunning British trick to get a billion dollars of American R&D money a billion dollars of American R&D money a billion dollars of American R&D money into London per year for the next into London per year for the next into London per year for the next decade. terrific win. And by the way, decade. terrific win. And by the way, decade. terrific win. And by the way, today there are spinouts from Deep Mind today there are spinouts from Deep Mind today there are spinouts from Deep Mind in London because the talent stayed in in London because the talent stayed in in London because the talent stayed in London. And these spinouts are raising London. And these spinouts are raising London. And these spinouts are raising billions of dollars to do new AI billions of dollars to do new AI billions of dollars to do new AI companies. So it's terrific for the companies. So it's terrific for the companies. So it's terrific for the London ecosystem around King's Cross London ecosystem around King's Cross London ecosystem around King's Cross which is this sort of cool center for which is this sort of cool center for which is this sort of cool center for tech in London where you can get the tech in London where you can get the tech in London where you can get the train in one direction and be in train in one direction and be in train in one direction and be in Cambridge which has quite a lot of good Cambridge which has quite a lot of good Cambridge which has quite a lot of good startups you know in one hour or you can startups you know in one hour or you can startups you know in one hour or you can get the train in the other direction and get the train in the other direction and get the train in the other direction and be in Paris where there's you know MR be in Paris where there's you know MR be in Paris where there's you know MR and so forth and it's kind of very wired and so forth and it's kind of very wired and so forth and it's kind of very wired into different bits of Europe. So how into different bits of Europe. So how into different bits of Europe. So how long does it take to get from San long does it take to get from San long does it take to get from San Francisco to Mountain View depending on Francisco to Mountain View depending on Francisco to Mountain View depending on the traffic right can be well over an the traffic right can be well over an the traffic right can be well over an hour. So I think there is a technology hour. So I think there is a technology hour. So I think there is a technology ecosystem which is by no means the ecosystem which is by no means the ecosystem which is by no means the equivalent of Silicon Valley yet, but equivalent of Silicon Valley yet, but equivalent of Silicon Valley yet, but it's certainly unrecognizably better it's certainly unrecognizably better it's certainly unrecognizably better than it was 10 or 20 years ago. than it was 10 or 20 years ago. than it was 10 or 20 years ago. >> What do you think the UK or Europe could >> What do you think the UK or Europe could >> What do you think the UK or Europe could do? Let's let's focus on the UK perhaps do? Let's let's focus on the UK perhaps do? Let's let's focus on the UK perhaps could do to increase the level of could do to increase the level of could do to increase the level of innovation early stage innovation early stage innovation early stage startup founding etc. Right? Because startup founding etc. Right? Because startup founding etc. Right? Because looking back at the power law and looking back at the power law and looking back at the power law and certainly just having spent so much time certainly just having spent so much time certainly just having spent so much time in California, there's a lot that went in California, there's a lot that went in California, there's a lot that went into Silicon Valley, right? And there into Silicon Valley, right? And there into Silicon Valley, right? And there are certain things that don't get a lot are certain things that don't get a lot are certain things that don't get a lot of airplay, but for instance, the of airplay, but for instance, the of airplay, but for instance, the difficulty of enforcing non-compete difficulty of enforcing non-compete difficulty of enforcing non-compete agreements in California, right, really agreements in California, right, really agreements in California, right, really led to this sort of roundroin of talent led to this sort of roundroin of talent led to this sort of roundroin of talent moving and cross-pollinating like little moving and cross-pollinating like little moving and cross-pollinating like little hummingbirds of engineering talent and hummingbirds of engineering talent and hummingbirds of engineering talent and so on, which [snorts] may not be so on, which [snorts] may not be so on, which [snorts] may not be replicable depending on where you are, replicable depending on where you are, replicable depending on where you are, but what what could the UK do in your but what what could the UK do in your but what what could the UK do in your mind if if you had the ear and they were mind if if you had the ear and they were mind if if you had the ear and they were like, "All right, Sebastian, like, "All right, Sebastian, like, "All right, Sebastian, tell us what to do. tell us what to do. tell us what to do. >> A couple of things. I mean, I think the >> A couple of things. I mean, I think the >> A couple of things. I mean, I think the mistake that people in Europe make and mistake that people in Europe make and mistake that people in Europe make and Britain as part of this is to believe Britain as part of this is to believe Britain as part of this is to believe that there's some kind of cultural magic that there's some kind of cultural magic that there's some kind of cultural magic about Silicon Valley where whatever it about Silicon Valley where whatever it about Silicon Valley where whatever it is that they're drinking in the water is that they're drinking in the water is that they're drinking in the water out there makes them think that failure out there makes them think that failure out there makes them think that failure is a learning experience which is kind is a learning experience which is kind is a learning experience which is kind of weird and the Europeans say, "Well, of weird and the Europeans say, "Well, of weird and the Europeans say, "Well, we're never going to be like that." And we're never going to be like that." And we're never going to be like that." And it's impossible for us to become as it's impossible for us to become as it's impossible for us to become as entrepreneurial as Silicon Valley. And I entrepreneurial as Silicon Valley. And I entrepreneurial as Silicon Valley. And I remind people that when Fairchild remind people that when Fairchild remind people that when Fairchild Semiconductor was founded in 1957, the Semiconductor was founded in 1957, the Semiconductor was founded in 1957, the eight scientists who left the Shockley eight scientists who left the Shockley eight scientists who left the Shockley lab were called, get this, the lab were called, get this, the lab were called, get this, the traitorous eight. traitorous eight. traitorous eight. >> So good. >> So good. >> So good. >> Traitorous. Why? Because it was >> Traitorous. Why? Because it was >> Traitorous. Why? Because it was considered treachery at the time to considered treachery at the time to considered treachery at the time to leave one company and go to another leave one company and go to another leave one company and go to another company. There was no entrepreneurial company. There was no entrepreneurial company. There was no entrepreneurial culture in the 1950s on the West Coast culture in the 1950s on the West Coast culture in the 1950s on the West Coast in the US, right? The classic business in the US, right? The classic business in the US, right? The classic business book of the time was organization man book of the time was organization man book of the time was organization man about people who joined one company and about people who joined one company and about people who joined one company and stayed in it for their whole life and stayed in it for their whole life and stayed in it for their whole life and retired with a gold watch on their 60th retired with a gold watch on their 60th retired with a gold watch on their 60th birthday. Right? So you can create an birthday. Right? So you can create an birthday. Right? So you can create an entrepreneurial culture and that is entrepreneurial culture and that is entrepreneurial culture and that is happening bit by bit in Britain and happening bit by bit in Britain and happening bit by bit in Britain and certainly in Israel and it's happened in certainly in Israel and it's happened in certainly in Israel and it's happened in China and it's not some magic which is China and it's not some magic which is China and it's not some magic which is confined to Silicon Valley. Okay, I it's confined to Silicon Valley. Okay, I it's confined to Silicon Valley. Okay, I it's worth making that point as the first worth making that point as the first worth making that point as the first thing. Now there are specific policy thing. Now there are specific policy thing. Now there are specific policy shifts that you need to do to make an shifts that you need to do to make an shifts that you need to do to make an ecosystem work and I think you put your ecosystem work and I think you put your ecosystem work and I think you put your finger on one which is the mobility of finger on one which is the mobility of finger on one which is the mobility of talent is super important. You can think talent is super important. You can think talent is super important. You can think of a startup ecosystem as something of a startup ecosystem as something of a startup ecosystem as something which circulates three elements money, which circulates three elements money, which circulates three elements money, people and ideas and you circulate those people and ideas and you circulate those people and ideas and you circulate those and you combine them in different ways. and you combine them in different ways. and you combine them in different ways. And each time you combine them, that's a And each time you combine them, that's a And each time you combine them, that's a new company. And each has a shot on new company. And each has a shot on new company. And each has a shot on goal. And most of them fail. But all of goal. And most of them fail. But all of goal. And most of them fail. But all of a sudden, if you circulate these these a sudden, if you circulate these these a sudden, if you circulate these these components fast enough, you do get components fast enough, you do get components fast enough, you do get product market fit. And then you get product market fit. And then you get product market fit. And then you get these 10x plus returns. Now, in Britain, these 10x plus returns. Now, in Britain, these 10x plus returns. Now, in Britain, when you raise a new round, a series B when you raise a new round, a series B when you raise a new round, a series B say, and you've got like nine months of say, and you've got like nine months of say, and you've got like nine months of runway to build to the next stage from runway to build to the next stage from runway to build to the next stage from your company, and you identify the three your company, and you identify the three your company, and you identify the three key talent that you're going to bring key talent that you're going to bring key talent that you're going to bring into the company and make it happen, and into the company and make it happen, and into the company and make it happen, and then they turn around to you and say, then they turn around to you and say, then they turn around to you and say, "Well, I can come in 6 months." That's a "Well, I can come in 6 months." That's a "Well, I can come in 6 months." That's a death sentence, right? That's horrible. death sentence, right? That's horrible. death sentence, right? That's horrible. We call it gardening leave in Britain. We call it gardening leave in Britain. We call it gardening leave in Britain. That is an appalling idea. We got to get That is an appalling idea. We got to get That is an appalling idea. We got to get rid of those gardens and we got to let rid of those gardens and we got to let rid of those gardens and we got to let people move fast. Another thing is tech people move fast. Another thing is tech people move fast. Another thing is tech transfer out of universities. In the US transfer out of universities. In the US transfer out of universities. In the US there's the BOL act. There are these there's the BOL act. There are these there's the BOL act. There are these very sophisticated tech transfer offices very sophisticated tech transfer offices very sophisticated tech transfer offices which are generous to the entrepreneur which are generous to the entrepreneur which are generous to the entrepreneur in terms of not demanding too much flesh in terms of not demanding too much flesh in terms of not demanding too much flesh >> as somebody exits and that's essential >> as somebody exits and that's essential >> as somebody exits and that's essential for making the startup work. In Europe for making the startup work. In Europe for making the startup work. In Europe the attitude is oh we're the university. the attitude is oh we're the university. the attitude is oh we're the university. We deserve a lot of skin in the game We deserve a lot of skin in the game We deserve a lot of skin in the game here. we want 50% of the upside. Well, here. we want 50% of the upside. Well, here. we want 50% of the upside. Well, in that case, the startup will never in that case, the startup will never in that case, the startup will never happen. happen. happen. >> Mhm. >> Mhm. >> Mhm. >> And I say to these Europeans, look, go >> And I say to these Europeans, look, go >> And I say to these Europeans, look, go visit Stanford. They're very generous to visit Stanford. They're very generous to visit Stanford. They're very generous to their entrepreneurs. They seem to be their entrepreneurs. They seem to be their entrepreneurs. They seem to be okay financially [laughter] because if you help the entrepreneur, because if you help the entrepreneur, you know, you'll get the donations you know, you'll get the donations you know, you'll get the donations later. It's all good. later. It's all good. later. It's all good. >> Yeah. >> Yeah. >> Yeah. >> And so, I think those are just two >> And so, I think those are just two >> And so, I think those are just two things things things >> which started a long time ago in the US, >> which started a long time ago in the US, >> which started a long time ago in the US, right? You you look at the origins of right? You you look at the origins of right? You you look at the origins of Janentech and so on. I mean it's just Janentech and so on. I mean it's just Janentech and so on. I mean it's just been been been >> it's it's the genesis of so many not >> it's it's the genesis of so many not >> it's it's the genesis of so many not just companies but industries just companies but industries just companies but industries effectively in the US. effectively in the US. effectively in the US. >> Yeah. >> Yeah. >> Yeah. >> Do you think Demis would have built Deep >> Do you think Demis would have built Deep >> Do you think Demis would have built Deep Mind if he had not read Enders Game? Mind if he had not read Enders Game? Mind if he had not read Enders Game? [laughter] [laughter] [laughter] >> That's a great story. That's a great >> That's a great story. That's a great >> That's a great story. That's a great question. question. question. >> Can I just tell the Enders game story to >> Can I just tell the Enders game story to >> Can I just tell the Enders game story to begin with? begin with? begin with? >> And also a bit of trivia for folks. I >> And also a bit of trivia for folks. I >> And also a bit of trivia for folks. I believe, and not not to like make this believe, and not not to like make this believe, and not not to like make this more more difficult, but that when Mark more more difficult, but that when Mark more more difficult, but that when Mark Zuckerberg first had a profile on Zuckerberg first had a profile on Zuckerberg first had a profile on Facebook, the only book listed was also Facebook, the only book listed was also Facebook, the only book listed was also Enders Game. Enders Game. Enders Game. >> Oh, I didn't know that. >> Oh, I didn't know that. >> Oh, I didn't know that. >> I believe that's true. >> I believe that's true. >> I believe that's true. >> That's fascinating. >> That's fascinating. >> That's fascinating. >> So, hop into it with Demis and Enders >> So, hop into it with Demis and Enders >> So, hop into it with Demis and Enders Game. So right at the beginning of my Game. So right at the beginning of my Game. So right at the beginning of my interviewing of Demis we were having the interviewing of Demis we were having the interviewing of Demis we were having the second meeting which was a dinner and he second meeting which was a dinner and he second meeting which was a dinner and he told me to read a couple of books before told me to read a couple of books before told me to read a couple of books before we had the dinner and one of them was we had the dinner and one of them was we had the dinner and one of them was Enders Game. Enders Game. Enders Game. >> What were the others just before you >> What were the others just before you >> What were the others just before you continue? continue? continue? >> It was a book by David Deutsch called >> It was a book by David Deutsch called >> It was a book by David Deutsch called The Fabric of Reality. The Fabric of Reality. The Fabric of Reality. >> Uhhuh. Light read. [laughter] >> Uhhuh. Light read. [laughter] >> Uhhuh. Light read. [laughter] >> Yeah. Now, I read Enders Game as a >> Yeah. Now, I read Enders Game as a >> Yeah. Now, I read Enders Game as a result, and I hadn't read it before. And result, and I hadn't read it before. And result, and I hadn't read it before. And as I was reading it, I was thinking to as I was reading it, I was thinking to as I was reading it, I was thinking to myself, okay, so this is a story about a myself, okay, so this is a story about a myself, okay, so this is a story about a sort of boy hero who saves the entirety sort of boy hero who saves the entirety sort of boy hero who saves the entirety of humanity from an invasion of the of humanity from an invasion of the of humanity from an invasion of the planet by the space aliens. Is Demis planet by the space aliens. Is Demis planet by the space aliens. Is Demis telling me that that's how he sees telling me that that's how he sees telling me that that's how he sees himself? That he's like saving all of himself? That he's like saving all of himself? That he's like saving all of humanity with AI? humanity with AI? humanity with AI? because it'd be a bit much to believe because it'd be a bit much to believe because it'd be a bit much to believe that, but it would be even more to have that, but it would be even more to have that, but it would be even more to have the tmerity to tell the guy who's the tmerity to tell the guy who's the tmerity to tell the guy who's writing a book about you [laughter] writing a book about you [laughter] writing a book about you [laughter] that that's how you see yourself. Like that that's how you see yourself. Like that that's how you see yourself. Like most people wouldn't expose themsel in most people wouldn't expose themsel in most people wouldn't expose themsel in that way. I thought, is Deis really that way. I thought, is Deis really that way. I thought, is Deis really thinking this? So then I go to have the thinking this? So then I go to have the thinking this? So then I go to have the dinner and he says, "I hope you read dinner and he says, "I hope you read dinner and he says, "I hope you read Enders game because that's really how I Enders game because that's really how I Enders game because that's really how I see myself." And I gave the book to my see myself." And I gave the book to my see myself." And I gave the book to my wife so she could read it so she could wife so she could read it so she could wife so she could read it so she could understand me better because I really understand me better because I really understand me better because I really identify with Ender. Yeah, it's wild. identify with Ender. Yeah, it's wild. identify with Ender. Yeah, it's wild. >> It's wild. >> It's wild. >> It's wild. >> It's a great book. I mean, I haven't >> It's a great book. I mean, I haven't >> It's a great book. I mean, I haven't read it in decades, but it is it is a read it in decades, but it is it is a read it in decades, but it is it is a fantastic read as I remember it. fantastic read as I remember it. fantastic read as I remember it. >> Yeah. I mean, reading it, I must say, as >> Yeah. I mean, reading it, I must say, as >> Yeah. I mean, reading it, I must say, as a mature adult, I thought it was not a mature adult, I thought it was not a mature adult, I thought it was not that well written. that well written. that well written. >> Yeah. >> Yeah. >> Yeah. >> But the idea of it is good. And I can >> But the idea of it is good. And I can >> But the idea of it is good. And I can see why see why see why >> the idea is sticky. >> the idea is sticky. >> the idea is sticky. >> Absolutely. You know, this image of this >> Absolutely. You know, this image of this >> Absolutely. You know, this image of this kid who sacrifices everything to kid who sacrifices everything to kid who sacrifices everything to dedicate himself to the craft of dedicate himself to the craft of dedicate himself to the craft of fighting the aliens. fighting the aliens. fighting the aliens. >> Mhm. and you know withstands ridicule >> Mhm. and you know withstands ridicule >> Mhm. and you know withstands ridicule and bullying from his peers and fights and bullying from his peers and fights and bullying from his peers and fights back. It's an appealing image and that's back. It's an appealing image and that's back. It's an appealing image and that's what hooked Demis. But to answer your what hooked Demis. But to answer your what hooked Demis. But to answer your question of earlier, you know, he would question of earlier, you know, he would question of earlier, you know, he would have done AI anyway because he read have done AI anyway because he read have done AI anyway because he read Enders Game actually when he was already Enders Game actually when he was already Enders Game actually when he was already kind of around 30. kind of around 30. kind of around 30. >> Mhm. >> Mhm. >> Mhm. >> And he'd had unbelievably the >> And he'd had unbelievably the >> And he'd had unbelievably the determination to build super determination to build super determination to build super intelligence from when he was about 17. intelligence from when he was about 17. intelligence from when he was about 17. I mean that is wild as well. I mean the I mean that is wild as well. I mean the I mean that is wild as well. I mean the early conviction is just extraordinary. early conviction is just extraordinary. early conviction is just extraordinary. Did he ask you to read Good Echerbach Did he ask you to read Good Echerbach Did he ask you to read Good Echerbach an eternal golden braid? I will admit to an eternal golden braid? I will admit to an eternal golden braid? I will admit to you I think Dustin Moskavitz you I think Dustin Moskavitz you I think Dustin Moskavitz also a lot of technologists very very also a lot of technologists very very also a lot of technologists very very very good technologists recommend this very good technologists recommend this very good technologists recommend this book book book >> or cite it as part of their own journey >> or cite it as part of their own journey >> or cite it as part of their own journey to building something incredible. I to building something incredible. I to building something incredible. I think I'm too dumb to read that book. I think I'm too dumb to read that book. I think I'm too dumb to read that book. I had so much trouble. I've had so much had so much trouble. I've had so much had so much trouble. I've had so much trouble. I've tried two times and yet trouble. I've tried two times and yet trouble. I've tried two times and yet I've still not finished that book. I I've still not finished that book. I I've still not finished that book. I don't know. Hey, do you have any don't know. Hey, do you have any don't know. Hey, do you have any recommendations to somebody who's maybe recommendations to somebody who's maybe recommendations to somebody who's maybe lacking a few IQ points cuz he was born lacking a few IQ points cuz he was born lacking a few IQ points cuz he was born on Long Island as to how to navigate on Long Island as to how to navigate on Long Island as to how to navigate that book? I have to admit I was told by that book? I have to admit I was told by that book? I have to admit I was told by Demis that this meant a huge amount to Demis that this meant a huge amount to Demis that this meant a huge amount to him that he'd read it in his late teens him that he'd read it in his late teens him that he'd read it in his late teens and that was when he really became and that was when he really became and that was when he really became convinced that he could build AI because convinced that he could build AI because convinced that he could build AI because the argument in the book is that you the argument in the book is that you the argument in the book is that you know whatever the human brain can do know whatever the human brain can do know whatever the human brain can do computers will be able to do one day computers will be able to do one day computers will be able to do one day that the human brain operates on ones that the human brain operates on ones that the human brain operates on ones and zeros and therefore if you could and zeros and therefore if you could and zeros and therefore if you could build big enough compute you should be build big enough compute you should be build big enough compute you should be able to replicate the intelligence of able to replicate the intelligence of able to replicate the intelligence of human brains and and that was the sort human brains and and that was the sort human brains and and that was the sort of insight that got him hooked on the of insight that got him hooked on the of insight that got him hooked on the idea. So I went off and I tried to read idea. So I went off and I tried to read idea. So I went off and I tried to read it. I would say I got like 150 pages in it. I would say I got like 150 pages in it. I would say I got like 150 pages in and got bogged down. I mean it is and got bogged down. I mean it is and got bogged down. I mean it is [snorts] a difficult challenging read [snorts] a difficult challenging read [snorts] a difficult challenging read but at least I kind of extracted the but at least I kind of extracted the but at least I kind of extracted the essence essence essence >> that meant something to my subject to >> that meant something to my subject to >> that meant something to my subject to Demis. You know who would be great for Demis. You know who would be great for Demis. You know who would be great for helping me to understand this? LLM helping me to understand this? LLM helping me to understand this? LLM [laughter] [laughter] [laughter] going to give that a shot and see if going to give that a shot and see if going to give that a shot and see if explain this to a sixth grader maybe or explain this to a sixth grader maybe or explain this to a sixth grader maybe or a s or explain it to a six-year-old a s or explain it to a six-year-old a s or explain it to a six-year-old maybe even better. Couple of questions maybe even better. Couple of questions maybe even better. Couple of questions and then we'll start to lay on the plan. and then we'll start to lay on the plan. and then we'll start to lay on the plan. If you had to write another book on a If you had to write another book on a If you had to write another book on a figure in the world of AI, they could be figure in the world of AI, they could be figure in the world of AI, they could be relatively unknown relatively unknown relatively unknown or they could be incredibly known. Who or they could be incredibly known. Who or they could be incredibly known. Who would that person be? Demis is off the would that person be? Demis is off the would that person be? Demis is off the table. table. table. >> I might want to take Sam off the table >> I might want to take Sam off the table >> I might want to take Sam off the table just to make it just to make it just to make it >> a little more interesting. Who would it >> a little more interesting. Who would it >> a little more interesting. Who would it be if Sam's off the table and Deis is of be if Sam's off the table and Deis is of be if Sam's off the table and Deis is of course off the table? course off the table? course off the table? >> Well, I guess Dario. >> Well, I guess Dario. >> Well, I guess Dario. >> Yeah, >> Yeah, >> Yeah, >> I think even if you left Sam on the >> I think even if you left Sam on the >> I think even if you left Sam on the table, it would be Dario. I mean, I table, it would be Dario. I mean, I table, it would be Dario. I mean, I think he's just a fascinating think he's just a fascinating think he's just a fascinating fascinating figure as well as being the fascinating figure as well as being the fascinating figure as well as being the current leader current leader current leader >> of anthropic for people who don't >> of anthropic for people who don't >> of anthropic for people who don't recognize the name. recognize the name. recognize the name. >> Yeah, >> Yeah, >> Yeah, >> man. You know, I'm working on a blog >> man. You know, I'm working on a blog >> man. You know, I'm working on a blog post right now. It's about disruption post right now. It's about disruption post right now. It's about disruption due to AI and how it's not three years due to AI and how it's not three years due to AI and how it's not three years in the future. It's not one year in the in the future. It's not one year in the in the future. It's not one year in the future. These are book sales across my future. These are book sales across my future. These are book sales across my entire book catalog and it's not limited entire book catalog and it's not limited entire book catalog and it's not limited to print. This is all format. Okay? So, to print. This is all format. Okay? So, to print. This is all format. Okay? So, I'll give you some numbers and then I I'll give you some numbers and then I I'll give you some numbers and then I want you to tell me what happened to want you to tell me what happened to want you to tell me what happened to initiate this. Okay. 2022 stasis pretty initiate this. Okay. 2022 stasis pretty initiate this. Okay. 2022 stasis pretty consistent. My book royalties are an consistent. My book royalties are an consistent. My book royalties are an annuity predictable. annuity predictable. annuity predictable. 2023 minus 5%. 2024 minus 13%. 2025 2023 minus 5%. 2024 minus 13%. 2025 2023 minus 5%. 2024 minus 13%. 2025 minus 46%. minus 46%. minus 46%. And 2026 so far on track to be at least And 2026 so far on track to be at least And 2026 so far on track to be at least 57%. 57%. 57%. What happened at the end of 2022? What happened at the end of 2022? What happened at the end of 2022? [laughter] [laughter] [laughter] >> Chat GPT [clears throat] >> Chat GPT [clears throat] >> Chat GPT [clears throat] >> GPT 3.5. >> GPT 3.5. >> GPT 3.5. It's just wild. It's really, really It's just wild. It's really, really It's just wild. It's really, really wild. I mean, this stuff is coming fast. wild. I mean, this stuff is coming fast. wild. I mean, this stuff is coming fast. And I really flip and flop. I feel like And I really flip and flop. I feel like And I really flip and flop. I feel like I waffled perhaps too much between these I waffled perhaps too much between these I waffled perhaps too much between these two. I I go from the very I would say two. I I go from the very I would say two. I I go from the very I would say moderate well-reasoned moderate well-reasoned moderate well-reasoned positioning of Benedict and I agree with positioning of Benedict and I agree with positioning of Benedict and I agree with so many of his points to believing that so many of his points to believing that so many of his points to believing that all of this is just coming so much all of this is just coming so much all of this is just coming so much faster than anyone can even comprehend faster than anyone can even comprehend faster than anyone can even comprehend due to the sort of recursive due to the sort of recursive due to the sort of recursive self-improvement. For the record, I self-improvement. For the record, I self-improvement. For the record, I think that it is much bigger than think that it is much bigger than think that it is much bigger than mobile, much bigger than internet. This mobile, much bigger than internet. This mobile, much bigger than internet. This is so general cognitive capability which is so general cognitive capability which is so general cognitive capability which can span you know any human task. I can span you know any human task. I can span you know any human task. I think the niggle is simply how long does think the niggle is simply how long does think the niggle is simply how long does diffusion take. diffusion take. diffusion take. >> Yeah. Right. And just to give an example >> Yeah. Right. And just to give an example >> Yeah. Right. And just to give an example of that you know I invest in quite a few of that you know I invest in quite a few of that you know I invest in quite a few biotech companies and biotech companies and biotech companies and other sciences and if you look at say other sciences and if you look at say other sciences and if you look at say alpha fold right I mean absolutely alpha fold right I mean absolutely alpha fold right I mean absolutely merited a Nobel prize. We didn't mention merited a Nobel prize. We didn't mention merited a Nobel prize. We didn't mention that about demis but it's one thing to that about demis but it's one thing to that about demis but it's one thing to design molecules it's quite another to design molecules it's quite another to design molecules it's quite another to deliver it to target tissue right so deliver it to target tissue right so deliver it to target tissue right so like the deliverability of that sort of like the deliverability of that sort of like the deliverability of that sort of a metaphor for AI in a way [laughter] a metaphor for AI in a way [laughter] a metaphor for AI in a way [laughter] it's like okay great we have this it's like okay great we have this it's like okay great we have this pristine perfect molecule how do you get pristine perfect molecule how do you get pristine perfect molecule how do you get it to the right place and at the same it to the right place and at the same it to the right place and at the same time an investor in a company called time an investor in a company called time an investor in a company called Laya Laya Sciences and what they're Laya Laya Sciences and what they're Laya Laya Sciences and what they're doing is producing doing is producing doing is producing a proprietary data set by automating wet a proprietary data set by automating wet a proprietary data set by automating wet labs using AI, right? And I'm going to labs using AI, right? And I'm going to labs using AI, right? And I'm going to simplify it, right? But they have simplify it, right? But they have simplify it, right? But they have gigantic wet labs where they can run in gigantic wet labs where they can run in gigantic wet labs where they can run in parallel thousands of experiments that parallel thousands of experiments that parallel thousands of experiments that from the very first step of hypothesis from the very first step of hypothesis from the very first step of hypothesis generation through to the end of the generation through to the end of the generation through to the end of the scientific method is all run scientific method is all run scientific method is all run autonomously by AI. And I bring this autonomously by AI. And I bring this autonomously by AI. And I bring this particular example up because even I particular example up because even I particular example up because even I want to say 6 months ago, 12 months ago, want to say 6 months ago, 12 months ago, want to say 6 months ago, 12 months ago, like they are producing discoveries that like they are producing discoveries that like they are producing discoveries that are really non-trivial, right? It's like are really non-trivial, right? It's like are really non-trivial, right? It's like it's already happening now. Like this is it's already happening now. Like this is it's already happening now. Like this is not not not >> this is not a year in the future. Like >> this is not a year in the future. Like >> this is not a year in the future. Like this is happening now. So when you flash this is happening now. So when you flash this is happening now. So when you flash forward to think about forward to think about forward to think about the potential exponential improvement the potential exponential improvement the potential exponential improvement and I I still to be honest sometimes and I I still to be honest sometimes and I I still to be honest sometimes when people talk about like exponents when people talk about like exponents when people talk about like exponents exponents humans aren't good at thinking exponents humans aren't good at thinking exponents humans aren't good at thinking exponentially. I'm like yes that's true exponentially. I'm like yes that's true exponentially. I'm like yes that's true but outside of more laws why would AI but outside of more laws why would AI but outside of more laws why would AI capabilities or LLM parameters or capabilities or LLM parameters or capabilities or LLM parameters or however you want to measure it however you want to measure it however you want to measure it automatically improve in exponents. I automatically improve in exponents. I automatically improve in exponents. I don't I don't actually quite understand don't I don't actually quite understand don't I don't actually quite understand that. But once we get to the sort of that. But once we get to the sort of that. But once we get to the sort of recursive self-improvement, it's like, recursive self-improvement, it's like, recursive self-improvement, it's like, okay, I can see how that starts to okay, I can see how that starts to okay, I can see how that starts to approach a vertical wall. approach a vertical wall. approach a vertical wall. >> I agree with you. I think one one >> I agree with you. I think one one >> I agree with you. I think one one experience from writing the book is experience from writing the book is experience from writing the book is simply that when you're close to the simply that when you're close to the simply that when you're close to the people inside the labs and, you know, I people inside the labs and, you know, I people inside the labs and, you know, I wasn't just Demis. I interviewed, you wasn't just Demis. I interviewed, you wasn't just Demis. I interviewed, you know, hundred of these AI insiders, you know, hundred of these AI insiders, you know, hundred of these AI insiders, you realize that the stuff in the pipeline realize that the stuff in the pipeline realize that the stuff in the pipeline is enormous. is enormous. is enormous. >> Yeah. >> Yeah. >> Yeah. >> I think there's a kind of popular >> I think there's a kind of popular >> I think there's a kind of popular misconception which is there is this misconception which is there is this misconception which is there is this thing called AI and it kind of happened thing called AI and it kind of happened thing called AI and it kind of happened when Chatty PT came out. So now we've when Chatty PT came out. So now we've when Chatty PT came out. So now we've got it and we're kind of getting used to got it and we're kind of getting used to got it and we're kind of getting used to it and that's in the rearview mirror. it and that's in the rearview mirror. it and that's in the rearview mirror. No, no, no, no. This thing is changing No, no, no, no. This thing is changing No, no, no, no. This thing is changing the whole time as anybody who looks the whole time as anybody who looks the whole time as anybody who looks closely knows. And if you think back, closely knows. And if you think back, closely knows. And if you think back, the progression is wild. You know, you the progression is wild. You know, you the progression is wild. You know, you get this system in end of 2022 which get this system in end of 2022 which get this system in end of 2022 which hallucinates non-stop. Then you plug in hallucinates non-stop. Then you plug in hallucinates non-stop. Then you plug in GPT4 16 months later, whatever it was, GPT4 16 months later, whatever it was, GPT4 16 months later, whatever it was, and the hallucination radically reduces. and the hallucination radically reduces. and the hallucination radically reduces. Then it goes multimodal, so it can do Then it goes multimodal, so it can do Then it goes multimodal, so it can do video and audio. And in the meantime, video and audio. And in the meantime, video and audio. And in the meantime, it's got a very long context window, so it's got a very long context window, so it's got a very long context window, so you can plug in an entire TL story novel you can plug in an entire TL story novel you can plug in an entire TL story novel and ask questions about it. Then it and ask questions about it. Then it and ask questions about it. Then it starts to do the reasoning stuff and can starts to do the reasoning stuff and can starts to do the reasoning stuff and can do logic and math. Then it becomes do logic and math. Then it becomes do logic and math. Then it becomes agentic. agentic. agentic. Then it's like coding for you. And all Then it's like coding for you. And all Then it's like coding for you. And all of these changes are packed into three of these changes are packed into three of these changes are packed into three and a half years. And I agree with you. and a half years. And I agree with you. and a half years. And I agree with you. I think the next three and a half years I think the next three and a half years I think the next three and a half years are going to be even more wild. are going to be even more wild. are going to be even more wild. >> Yeah. >> Yeah. >> Yeah. >> So I think there's a big gap between the >> So I think there's a big gap between the >> So I think there's a big gap between the inside and the outside view of this. inside and the outside view of this. inside and the outside view of this. >> Yeah. That's where these comparisons to >> Yeah. That's where these comparisons to >> Yeah. That's where these comparisons to the industrial revolution just the industrial revolution just the industrial revolution just completely fall apart [laughter] on so completely fall apart [laughter] on so completely fall apart [laughter] on so many levels. I have one or two remaining many levels. I have one or two remaining many levels. I have one or two remaining questions for you. questions for you. questions for you. >> The billboard question. I ask this a >> The billboard question. I ask this a >> The billboard question. I ask this a lot. It can be a fun one. lot. It can be a fun one. lot. It can be a fun one. >> If you could put anything on a >> If you could put anything on a >> If you could put anything on a billboard, metaphorically speaking for billboard, metaphorically speaking for billboard, metaphorically speaking for millions, billions of people to see. millions, billions of people to see. millions, billions of people to see. Could be anything. image quote question Could be anything. image quote question Could be anything. image quote question preferably not commercial. [laughter] preferably not commercial. [laughter] preferably not commercial. [laughter] What would it be? What might it be? What would it be? What might it be? What would it be? What might it be? >> So a billboard which lots of people are >> So a billboard which lots of people are >> So a billboard which lots of people are going to see. I would put prepare your going to see. I would put prepare your going to see. I would put prepare your mind. mind. mind. >> This is a saying which is originally >> This is a saying which is originally >> This is a saying which is originally Louis Pastor I think the scientist Louis Pastor I think the scientist Louis Pastor I think the scientist who said chance favors the prepared who said chance favors the prepared who said chance favors the prepared mind. If you're ready for things, you mind. If you're ready for things, you mind. If you're ready for things, you can make the most of the opportunity can make the most of the opportunity can make the most of the opportunity that comes your way. And the amazing that comes your way. And the amazing that comes your way. And the amazing thing about this saying is that it's thing about this saying is that it's thing about this saying is that it's come up randomly in different contexts come up randomly in different contexts come up randomly in different contexts in different books I've done. So when I in different books I've done. So when I in different books I've done. So when I was writing about venture capital, Excel was writing about venture capital, Excel was writing about venture capital, Excel Capital Capital Capital >> and one of the founders, Arthur >> and one of the founders, Arthur >> and one of the founders, Arthur Patterson, used this phrase as a Patterson, used this phrase as a Patterson, used this phrase as a description of how he wanted Excel to description of how he wanted Excel to description of how he wanted Excel to invest. that they would run these kind invest. that they would run these kind invest. that they would run these kind of scenario exercises where they would of scenario exercises where they would of scenario exercises where they would think, okay, there's a new technology think, okay, there's a new technology think, okay, there's a new technology coming down the pike. What kind of coming down the pike. What kind of coming down the pike. What kind of company needs to be built to make the company needs to be built to make the company needs to be built to make the most of that new platform? What type of most of that new platform? What type of most of that new platform? What type of entrepreneur is going to fit this entrepreneur is going to fit this entrepreneur is going to fit this opportunity? What should we be expecting opportunity? What should we be expecting opportunity? What should we be expecting so that the person walks into the office so that the person walks into the office so that the person walks into the office into the conference room and pitches to into the conference room and pitches to into the conference room and pitches to us, we already know 90% of what he says us, we already know 90% of what he says us, we already know 90% of what he says because we've prepared our minds. And because we've prepared our minds. And because we've prepared our minds. And that way we can make a good judgment and that way we can make a good judgment and that way we can make a good judgment and a fast judgment if it's a competitive a fast judgment if it's a competitive a fast judgment if it's a competitive situation. So I kind of wrote about the situation. So I kind of wrote about the situation. So I kind of wrote about the prepared mind in the context of venture prepared mind in the context of venture prepared mind in the context of venture capital. And then I'm doing the infinity capital. And then I'm doing the infinity capital. And then I'm doing the infinity machine and I'm interviewing Ilia machine and I'm interviewing Ilia machine and I'm interviewing Ilia Satskaver from OpenAI and I'm asking him Satskaver from OpenAI and I'm asking him Satskaver from OpenAI and I'm asking him why was it you who understood the why was it you who understood the why was it you who understood the significance of the transformer significance of the transformer significance of the transformer architecture when it came out architecture when it came out architecture when it came out immediately like on the day it was up on immediately like on the day it was up on immediately like on the day it was up on the website you read it. You ran down the website you read it. You ran down the website you read it. You ran down the corridor. You went to see your the corridor. You went to see your the corridor. You went to see your collaborator Alec Radford and you said, collaborator Alec Radford and you said, collaborator Alec Radford and you said, "We're going to build a language model "We're going to build a language model "We're going to build a language model on top of this architecture." on top of this architecture." on top of this architecture." >> Well, not only that, he said, "Stop >> Well, not only that, he said, "Stop >> Well, not only that, he said, "Stop everything you're doing." everything you're doing." everything you're doing." >> Right. Right. Right. >> Right. Right. Right. >> Right. Right. Right. >> And do this. [laughter] >> And do this. [laughter] >> And do this. [laughter] >> Yeah. This vision of the kind of, you >> Yeah. This vision of the kind of, you >> Yeah. This vision of the kind of, you know, overcaffeinated charismatic know, overcaffeinated charismatic know, overcaffeinated charismatic seizing on the engineer and saying, seizing on the engineer and saying, seizing on the engineer and saying, "Drop it, whatever you're doing." And, "Drop it, whatever you're doing." And, "Drop it, whatever you're doing." And, you know, in his answer was prepared you know, in his answer was prepared you know, in his answer was prepared mind that he'd been thinking about how mind that he'd been thinking about how mind that he'd been thinking about how you model sequential data ever since his you model sequential data ever since his you model sequential data ever since his PhD in Canada. And when he saw the PhD in Canada. And when he saw the PhD in Canada. And when he saw the solution, this was what he'd been solution, this was what he'd been solution, this was what he'd been waiting for for like a decade. And so he waiting for for like a decade. And so he waiting for for like a decade. And so he could jump on it. And then when you could jump on it. And then when you could jump on it. And then when you start thinking about prepared mind, you start thinking about prepared mind, you start thinking about prepared mind, you know, you would probably remember this know, you would probably remember this know, you would probably remember this better than I do, but wasn't there a um better than I do, but wasn't there a um better than I do, but wasn't there a um Seattle Seahawks Super Bowl final Seattle Seahawks Super Bowl final Seattle Seahawks Super Bowl final against the New England Patriots where against the New England Patriots where against the New England Patriots where the New England quarterback like does an the New England quarterback like does an the New England quarterback like does an interception in the last second of play interception in the last second of play interception in the last second of play and clinches the victory. And when he's and clinches the victory. And when he's and clinches the victory. And when he's asked after the play, how did you know asked after the play, how did you know asked after the play, how did you know to make that run? Where did you how did to make that run? Where did you how did to make that run? Where did you how did you know where the quarterback was going you know where the quarterback was going you know where the quarterback was going to throw the ball? The answer was to throw the ball? The answer was to throw the ball? The answer was prepared mind. Basically, he didn't use prepared mind. Basically, he didn't use prepared mind. Basically, he didn't use that phrase, but you know, in training, that phrase, but you know, in training, that phrase, but you know, in training, they had studied they had studied they had studied the play that the Seattle Seahawks were the play that the Seattle Seahawks were the play that the Seattle Seahawks were going to make. And they knew that given going to make. And they knew that given going to make. And they knew that given a certain formation when the ball was a certain formation when the ball was a certain formation when the ball was snapped back, there was a certain pass snapped back, there was a certain pass snapped back, there was a certain pass that was coming. So the guy just takes that was coming. So the guy just takes that was coming. So the guy just takes off and he runs right into where the off and he runs right into where the off and he runs right into where the ball comes and he catches it and ball comes and he catches it and ball comes and he catches it and intercepts and New England wins. And so intercepts and New England wins. And so intercepts and New England wins. And so that's a prepared mind in sports. that's a prepared mind in sports. that's a prepared mind in sports. >> Mhm. >> Mhm. >> Mhm. >> And the other reason, last thing, >> And the other reason, last thing, >> And the other reason, last thing, >> yeah, >> yeah, >> yeah, >> I would put on the billboard prepare >> I would put on the billboard prepare >> I would put on the billboard prepare your mind is that for the age of your mind is that for the age of your mind is that for the age of artificial intelligence, this is what we artificial intelligence, this is what we artificial intelligence, this is what we need to hear. And this is a serious need to hear. And this is a serious need to hear. And this is a serious point, right? The risk with large point, right? The risk with large point, right? The risk with large language models is that we just get lazy language models is that we just get lazy language models is that we just get lazy and whenever we need to know something, and whenever we need to know something, and whenever we need to know something, we just get it to tell us what to think. we just get it to tell us what to think. we just get it to tell us what to think. That is not the route to happiness or That is not the route to happiness or That is not the route to happiness or satisfaction or anything. We need to satisfaction or anything. We need to satisfaction or anything. We need to continue to do the hard work of continue to do the hard work of continue to do the hard work of preparing our minds because that's what preparing our minds because that's what preparing our minds because that's what makes us people. You know, I think, makes us people. You know, I think, makes us people. You know, I think, therefore I am. And so I think prepare therefore I am. And so I think prepare therefore I am. And so I think prepare your mind is entering a time when it your mind is entering a time when it your mind is entering a time when it becomes a more important slogan than becomes a more important slogan than becomes a more important slogan than ever. ever. ever. >> How do you do that for yourself? What >> How do you do that for yourself? What >> How do you do that for yourself? What guard rails or policies have you guard rails or policies have you guard rails or policies have you established for your own use of AI? established for your own use of AI? established for your own use of AI? >> And it makes me also think of going to >> And it makes me also think of going to >> And it makes me also think of going to the gym, lifting weights, getting in the gym, lifting weights, getting in the gym, lifting weights, getting in cardio. You don't have to do that, but cardio. You don't have to do that, but cardio. You don't have to do that, but it is beneficial for you on a lot of it is beneficial for you on a lot of it is beneficial for you on a lot of levels. And people, some people find it levels. And people, some people find it levels. And people, some people find it quite enjoyable, right? And hence they quite enjoyable, right? And hence they quite enjoyable, right? And hence they do that. And I'm wondering do that. And I'm wondering do that. And I'm wondering what the equivalent is for knowledge what the equivalent is for knowledge what the equivalent is for knowledge workers or people who are preparing workers or people who are preparing workers or people who are preparing their minds and their minds and their minds and don't want to become sort of impotent in don't want to become sort of impotent in don't want to become sort of impotent in the way that people with directions have the way that people with directions have the way that people with directions have mostly become impotent because of Google mostly become impotent because of Google mostly become impotent because of Google maps and other tools like that. Right? maps and other tools like that. Right? maps and other tools like that. Right? So what do you what do you do for So what do you what do you do for So what do you what do you do for yourself personally or how are you yourself personally or how are you yourself personally or how are you thinking about that? The first thing I thinking about that? The first thing I thinking about that? The first thing I think is that the Google Maps analogy is think is that the Google Maps analogy is think is that the Google Maps analogy is the wrong one in the sense that it's the wrong one in the sense that it's the wrong one in the sense that it's fine to offload a very specific mental fine to offload a very specific mental fine to offload a very specific mental task which to most people is a pain in task which to most people is a pain in task which to most people is a pain in the neck. the neck. the neck. >> Mhm. [clears throat] >> Mhm. [clears throat] >> Mhm. [clears throat] >> And let the machine do that for you. >> And let the machine do that for you. >> And let the machine do that for you. It's not fine to offload all thinking. It's not fine to offload all thinking. It's not fine to offload all thinking. Right. The point of offloading something Right. The point of offloading something Right. The point of offloading something should be you get to focus your mental should be you get to focus your mental should be you get to focus your mental energy more on the other stuff that you energy more on the other stuff that you energy more on the other stuff that you really get satisfaction and meaning really get satisfaction and meaning really get satisfaction and meaning from. And so for me, what that means is from. And so for me, what that means is from. And so for me, what that means is that I'm very happy to use large that I'm very happy to use large that I'm very happy to use large language models to learn about the language models to learn about the language models to learn about the scientific output of somebody I'm going scientific output of somebody I'm going scientific output of somebody I'm going to interview next week. to interview next week. to interview next week. >> Mhm. All of these AI papers are on >> Mhm. All of these AI papers are on >> Mhm. All of these AI papers are on archive and the model has ingested all archive and the model has ingested all archive and the model has ingested all of them and the model is extremely good of them and the model is extremely good of them and the model is extremely good at telling me okay the scientist you're at telling me okay the scientist you're at telling me okay the scientist you're seeing next week has these three papers seeing next week has these three papers seeing next week has these three papers and the progression between the three and the progression between the three and the progression between the three papers is this and this and this and the papers is this and this and this and the papers is this and this and this and the comparison with the person you saw two comparison with the person you saw two comparison with the person you saw two weeks ago is this and this and this and weeks ago is this and this and this and weeks ago is this and this and this and you know you learn a lot from the system you know you learn a lot from the system you know you learn a lot from the system like really bootstraps you to learn like really bootstraps you to learn like really bootstraps you to learn faster so that's helping me to think faster so that's helping me to think faster so that's helping me to think more not to think less. more not to think less. more not to think less. >> It's cutting out the time it would take >> It's cutting out the time it would take >> It's cutting out the time it would take me to go find all the papers by myself me to go find all the papers by myself me to go find all the papers by myself and then labor through them. It's and then labor through them. It's and then labor through them. It's cutting to the chase and nourishing me cutting to the chase and nourishing me cutting to the chase and nourishing me intellectually. intellectually. intellectually. >> And [clears throat] by the way, I'm not >> And [clears throat] by the way, I'm not >> And [clears throat] by the way, I'm not worried about hallucination because I'm worried about hallucination because I'm worried about hallucination because I'm going to interview the human scientist going to interview the human scientist going to interview the human scientist anyway. So, I get to cross-check it all. anyway. So, I get to cross-check it all. anyway. So, I get to cross-check it all. >> What I would never do is get the AI to >> What I would never do is get the AI to >> What I would never do is get the AI to write because frankly, it's not very write because frankly, it's not very write because frankly, it's not very good at long form. In fact, it really good at long form. In fact, it really good at long form. In fact, it really sucks. It's fine for writing an email, sucks. It's fine for writing an email, sucks. It's fine for writing an email, although I don't do that either because although I don't do that either because although I don't do that either because I like writing. But it really is I've I like writing. But it really is I've I like writing. But it really is I've tried it once. It's terrible for tried it once. It's terrible for tried it once. It's terrible for anything longer than about 800 words. anything longer than about 800 words. anything longer than about 800 words. But even if it could do it, I don't But even if it could do it, I don't But even if it could do it, I don't think I would ever outsource that think I would ever outsource that think I would ever outsource that because that's me, because that's me, because that's me, >> right? This is what I do. This is the >> right? This is what I do. This is the >> right? This is what I do. This is the thinking process. I think through my thinking process. I think through my thinking process. I think through my writing, I come to understand what I writing, I come to understand what I writing, I come to understand what I understand and think what I think and understand and think what I think and understand and think what I think and believe what I believe through writing. believe what I believe through writing. believe what I believe through writing. And I'm not going to give that out. And I'm not going to give that out. And I'm not going to give that out. >> I'm letting out a pensive exhale because >> I'm letting out a pensive exhale because >> I'm letting out a pensive exhale because I was thinking of this. A friend said to I was thinking of this. A friend said to I was thinking of this. A friend said to me, well, I'll give him credit, Kevin me, well, I'll give him credit, Kevin me, well, I'll give him credit, Kevin Rose. At one point, I was I wouldn't say Rose. At one point, I was I wouldn't say Rose. At one point, I was I wouldn't say complaining, observing that AI couldn't complaining, observing that AI couldn't complaining, observing that AI couldn't do X or it wasn't very good at Y. do X or it wasn't very good at Y. do X or it wasn't very good at Y. >> He said, when was the last time you >> He said, when was the last time you >> He said, when was the last time you tried that? I was like six months ago. tried that? I was like six months ago. tried that? I was like six months ago. And he's like, try it again. And so And he's like, try it again. And so And he's like, try it again. And so [laughter] [laughter] [laughter] the rules will become really important the rules will become really important the rules will become really important as also the power of these things as also the power of these things as also the power of these things increases. And there I want to say it increases. And there I want to say it increases. And there I want to say it was the New Yorker. There was a piece in was the New Yorker. There was a piece in was the New Yorker. There was a piece in the New York or it might have been the the New York or it might have been the the New York or it might have been the New York Times with some very famous I New York Times with some very famous I New York Times with some very famous I want to say novelist could have been want to say novelist could have been want to say novelist could have been Pulitzer Prize winner in literature Pulitzer Prize winner in literature Pulitzer Prize winner in literature somebody at the top and they took three somebody at the top and they took three somebody at the top and they took three or four pieces of their own writing had or four pieces of their own writing had or four pieces of their own writing had AI generate three or four pieces of AI generate three or four pieces of AI generate three or four pieces of writing in their voice and gave it to writing in their voice and gave it to writing in their voice and gave it to professional readers professional readers professional readers editors and so on and it wasn't clear editors and so on and it wasn't clear editors and so on and it wasn't clear people couldn't figure out they claimed people couldn't figure out they claimed people couldn't figure out they claimed that what he or she wrote was AI that what he or she wrote was AI that what he or she wrote was AI >> how long was the piece of writing >> how long was the piece of writing >> how long was the piece of writing >> I knew that was the question you were >> I knew that was the question you were >> I knew that was the question you were going to ask and I and I don't recall. going to ask and I and I don't recall. going to ask and I and I don't recall. So I want to go back and look at that So I want to go back and look at that So I want to go back and look at that piece to see. So there was a story piece to see. So there was a story piece to see. So there was a story precisely like that from an economist precisely like that from an economist precisely like that from an economist writer who's very funny and also does writer who's very funny and also does writer who's very funny and also does podcasts podcasts podcasts >> and he ran that experiment and it was >> and he ran that experiment and it was >> and he ran that experiment and it was just as you said you know his friends just as you said you know his friends just as you said you know his friends who were professional economist who were professional economist who were professional economist journalists couldn't tell which was the journalists couldn't tell which was the journalists couldn't tell which was the witty column that he'd written versus witty column that he'd written versus witty column that he'd written versus the equally witty ones which the lamb the equally witty ones which the lamb the equally witty ones which the lamb had generated and he was very pissed off had generated and he was very pissed off had generated and he was very pissed off with this and I look I take your point I with this and I look I take your point I with this and I look I take your point I mean for now I can be all complacent and mean for now I can be all complacent and mean for now I can be all complacent and say yeah I only works for 800 words. It say yeah I only works for 800 words. It say yeah I only works for 800 words. It doesn't work for a whole chapter which doesn't work for a whole chapter which doesn't work for a whole chapter which is 20 pages long. But no doubt it'll get is 20 pages long. But no doubt it'll get is 20 pages long. But no doubt it'll get better and better. But I still think I'm better and better. But I still think I'm better and better. But I still think I'm going to cling on to the thing that going to cling on to the thing that going to cling on to the thing that makes me me for sure. 100%. And I think makes me me for sure. 100%. And I think makes me me for sure. 100%. And I think doing the thinking, preparing your mind doing the thinking, preparing your mind doing the thinking, preparing your mind in part asking that question, which is in part asking that question, which is in part asking that question, which is not an easy question, perhaps there's a not an easy question, perhaps there's a not an easy question, perhaps there's a different way to phrase it, but like different way to phrase it, but like different way to phrase it, but like what what are what what are what what are the things that make me me? So you don't the things that make me me? So you don't the things that make me me? So you don't accidentally make sacrifices that start accidentally make sacrifices that start accidentally make sacrifices that start to erode your sense of self but also to erode your sense of self but also to erode your sense of self but also sense of selfworth. Right. sense of selfworth. Right. sense of selfworth. Right. >> Preparing your mind. Sebastian, >> Preparing your mind. Sebastian, >> Preparing your mind. Sebastian, everybody should check out the infinity everybody should check out the infinity everybody should check out the infinity machine. It's it's outstanding. The machine. It's it's outstanding. The machine. It's it's outstanding. The infinity machine subtitle deis habis infinity machine subtitle deis habis infinity machine subtitle deis habis deep mind and the quest for super deep mind and the quest for super deep mind and the quest for super intelligence. And lest people intelligence. And lest people intelligence. And lest people make the wrong assumption. This is not make the wrong assumption. This is not make the wrong assumption. This is not here's the latest and greatest in AI. It here's the latest and greatest in AI. It here's the latest and greatest in AI. It is the story of an incredible mind, is the story of an incredible mind, is the story of an incredible mind, a whole cast of kooky and fascinating a whole cast of kooky and fascinating a whole cast of kooky and fascinating characters. It is about a noble quest. characters. It is about a noble quest. characters. It is about a noble quest. It's about the pitfalls and promises of It's about the pitfalls and promises of It's about the pitfalls and promises of entrepreneurship. It contains so many entrepreneurship. It contains so many entrepreneurship. It contains so many different levels. And if you want to different levels. And if you want to different levels. And if you want to also have a basic understanding of what also have a basic understanding of what also have a basic understanding of what it is from the ground up that came to be it is from the ground up that came to be it is from the ground up that came to be colloquially referred to as AI or LLMs, colloquially referred to as AI or LLMs, colloquially referred to as AI or LLMs, this is a great book for that. It really this is a great book for that. It really this is a great book for that. It really lays out kind of the nuts and bolts and lays out kind of the nuts and bolts and lays out kind of the nuts and bolts and how this evolved over time in a way that how this evolved over time in a way that how this evolved over time in a way that I think is intelligible to I think is intelligible to I think is intelligible to non-engineers. So everybody should check non-engineers. So everybody should check non-engineers. So everybody should check out the Infinity Machine. Sebastian, is out the Infinity Machine. Sebastian, is out the Infinity Machine. Sebastian, is there anywhere else you would like to there anywhere else you would like to there anywhere else you would like to point people or anything else you'd like point people or anything else you'd like point people or anything else you'd like to say as we wind to a close? to say as we wind to a close? to say as we wind to a close? Well, um, Well, um, Well, um, yeah, you stopped me on that one. yeah, you stopped me on that one. yeah, you stopped me on that one. [laughter] [laughter] [laughter] I've enjoyed the conversation. I'm happy I've enjoyed the conversation. I'm happy I've enjoyed the conversation. I'm happy to leave it there. Thank you for doing to leave it there. Thank you for doing to leave it there. Thank you for doing it, Tim. It's been great. it, Tim. It's been great. it, Tim. It's been great. >> Absolutely. I'll I'll give one one more >> Absolutely. I'll I'll give one one more >> Absolutely. I'll I'll give one one more link for folks if they want to find you link for folks if they want to find you link for folks if they want to find you on X. That's SC Malib on X. That's SC Malib on X. That's SC Malib Malibby. Well, Sebastian, thank you so Malibby. Well, Sebastian, thank you so Malibby. Well, Sebastian, thank you so much for the time. Really enjoyed the much for the time. Really enjoyed the much for the time. Really enjoyed the conversation. And for people listening, conversation. And for people listening, conversation. And for people listening, we will include links to everything we will include links to everything we will include links to everything we've discussed, all the characters and we've discussed, all the characters and we've discussed, all the characters and everything else at tim.blog/mpodcast. everything else at tim.blog/mpodcast. everything else at tim.blog/mpodcast. Just search Sebastian. I'm pretty sure Just search Sebastian. I'm pretty sure Just search Sebastian. I'm pretty sure that Oh, actually, we have Sebastian that Oh, actually, we have Sebastian that Oh, actually, we have Sebastian Younger. So, there are two Sebastians. Younger. So, there are two Sebastians. Younger. So, there are two Sebastians. But if you search Malib, M A L L A B Y, But if you search Malib, M A L L A B Y, But if you search Malib, M A L L A B Y, it'll be very easy to find this. And it'll be very easy to find this. And it'll be very easy to find this. And until next time, be just a bit nicer until next time, be just a bit nicer until next time, be just a bit nicer than is necessary, a little bit kinder than is necessary, a little bit kinder than is necessary, a little bit kinder than is necessary to others, but also to than is necessary to others, but also to than is necessary to others, but also to yourself and prepare your mind. Thanks yourself and prepare your mind. Thanks yourself and prepare your mind. Thanks for tuning in.