#475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games

Demis Hassabis is the CEO of Google DeepMind and Nobel Prize winner for his groundbreaking work in protein structure prediction using AI. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep475-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/demis-hassabis-2-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Demis’s X: https://x.com/demishassabis DeepMind’s X: https://x.com/GoogleDeepMind DeepMind’s Instagram: https://instagram.com/GoogleDeepMind DeepMind’s Website: https://deepmind.google/ Gemini’s Website: https://gemini.google.com/ Isomorphic Labs: https://isomorphiclabs.com/ The MANIAC (book): https://amzn.to/4lOXJ81 Life Ascending (book): https://amzn.to/3AhUP7z SPONSORS: To support this podcast, check out our sponsors & get discounts: Hampton: Community for high-growth founders and CEOs. Go to https://joinhampton.com/lex https://lexfridman.com/s/hampton-ep475-sc Fin: AI agent for customer service. Go to https://fin.ai/lex https://lexfridman.com/s/fin-ep475-sc Shopify: Sell stuff online. Go to https://shopify.com/lex https://lexfridman.com/s/shopify-ep475-sc LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex https://lexfridman.com/s/lmnt-ep475-sc AG1: All-in-one daily nutrition drink. Go to https://drinkag1.com/lex https://lexfridman.com/s/ag1-ep475-sc OUTLINE: (00:00) – Introduction (00:29) – Sponsors, Comments, and Reflections (08:40) – Learnable patterns in nature (12:22) – Computation and P vs NP (21:00) – Veo 3 and understanding reality (25:24) – Video games (37:26) – AlphaEvolve (43:27) – AI research (47:51) – Simulating a biological organism (52:34) – Origin of life (58:49) – Path to AGI (1:09:35) – Scaling laws (1:12:51) – Compute (1:15:38) – Future of energy (1:19:34) – Human nature (1:24:28) – Google and the race to AGI (1:42:27) – Competition and AI talent (1:49:01) – Future of programming (1:55:27) – John von Neumann (2:04:41) – p(doom) (2:09:24) – Humanity (2:12:30) – Consciousness and quantum computation (2:18:40) – David Foster Wallace (2:25:54) – Education and research PODCAST LINKS: – Podcast Website: https://lexfridman.com/podcast – Apple Podcasts: https://apple.co/2lwqZIr – Spotify: https://spoti.fi/2nEwCF8 – RSS: https://lexfridman.com/feed/podcast/ – Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 – Clips Channel: https://www.youtube.com/lexclips

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

• 0:00Lex Fridman?

  The following is a conversation with Demis Hassabis, his second time on the podcast. He is the leader of Google DeepMind and is now a Nobel Prize winner. Demis is one of the most brilliant and fascinating minds in the world today, working on understanding and building intelligence and exploring the big mysteries of our universe. This was truly an honor and a pleasure for me. And now, a quick few second mention of each sponsor. Check them out in the description or at lexfridman.com/sponsors. It's the best way to support this podcast. We've got Hampton for connecting with founders and CEOs, Finn for AI customer service,

• 0:45Lex Fridman?

  Shopify for building e-commerce businesses, Element for daily electrolytes, and AG1 for your health. Choose wisely, my friends. And now on to the full ad reads. I do try to make them interesting, but if you must skip, friends, please still check out our sponsors. I enjoy their stuff, maybe you will too. And also, to get in touch with me, for whatever reason, go to lexfridman.com/contact. All right, let's go.

• 1:11Lex Fridman?

  This episode is brought to you by Hampton, a private community for high-growth founders and CEOs. That's the interesting thing about starting a company and running a company, especially one that's growing really quickly, has to hire a lot, has to scale a lot. It's perhaps a little bit counterintuitive, but for the founder, it can be deeply lonely. I suppose that's one of the reasons they recommend to have a co-founder. But even outside of that, there's just a deep loneliness with putting it all on the line, risking everything, knowing that the chances of success are low. But if you do succeed, the gains are huge. And you have your heart in it, you have your dreams in it, you believe in it. But also, there's a constant roller coaster of fear and doubt

• 1:56Lex Fridman?

  and hope and moments of triumph and moments of failure. All those go back and forth and just it's a constant psychological turmoil. Anyway, through all that, it's just nice to connect with other people that are going through the same thing. And that's what Hampton is about. It does a thing where every month, eight founders face to face have real conversations about their daily struggles. Groups are forming in a bunch of places, New York City, Austin, San Francisco, LA, Miami, Denver, and so on. If you are a founder who's tired of carrying it all alone, visit joinhampton.com/lex to see if it's a fit for you. That's joinhampton.com/lex.

• 2:39Lex Fridman?

  This episode is also brought to you by Finn. It's an AI agent for customer service. So they are focused, laser focused on the customer service application. And they want to do that better than anybody else in the world. In fact, if you measure by the metric of the number of resolutions, so when you have the agent resolve the customer service issue, that's resolution. They have a 59% average resolution rate, which makes it the highest performing customer service agent on the market. It's trusted by over 5,000 customer service leaders and even top AI companies, including Anthropic. The way they design the system is it can continuously improve from the interactions. So you can continuously analyze, train, test and deploy.

• 3:24Lex Fridman?

  Also, probably important to say, they give you a 90 day money back guarantee. Go to fin.ai/lex to learn more about transforming your customer service and scaling your support team. That's fin.ai/lex. This episode is also brought to you by Shopify, a platform designed for anyone to sell anywhere with a great looking online store. Even I figured out how to create an online store at lexfridman.com/store. And I put up a few shirts. I haven't done anything with it since because I'm not a serious person. There's a lot of serious people that build real businesses on top of Shopify. It's a platform that connects you with millions of people that want to buy stuff

• 4:08Lex Fridman?

  and gives you all the tools you need and all the integrations that you need to do just that at scale. As we talked about with DHH, about the incredible beauty and power of Ruby on Rails that Shopify is powered by. I have not yet built a serious sort of medium scale project on Rails. I need to. It's just, I need to actually find things that I need to do web dev type of stuff with to inspire myself to build something useful. I don't want to build some weird variant of a to do list, especially now with the help of LLMs, you can generate so much of the code. So I need to figure out how. To learn a new framework, a new programming languages when LLMs can generate so much of it.

• 4:54Lex Fridman?

  And I don't want to do it exclusively by vibe coding because I feel like that's not a way to learn fully a thing, but vibe coding does remove some of the friction of learning. So balancing that out is a tricky thing to do. Anyway, that's about the programming language and the framework that powers Shopify. But Shopify itself connects buyers and sellers in an incredible scale that's awe inspiring. Sign up for a $1 per month trial period at Shopify.com/lex. That's all lowercase. Go to Shopify.com/lex to take your business to the next level today.

• 5:30Lex Fridman?

  This episode is also brought to you by Element, my daily zero sugar and delicious electrolyte mix. I've been traveling recently and I have a lot of Element packets with me. And I bring that and I bring bands, whatever you call them. I don't know what they're called, like rubber bands for like basic shoulder exercises. So if I have to do a lot of either heavy lifting or heavy jujitsu training, I like to warm up the shoulders really well because probably because I have issues with shoulders from many years of playing tennis and many years of doing stupidly bench press. Anyway, I think of Element as a critical component of my workout routine. Hydrate before, rehydrate after, fully embrace the deliciousness of watermelon salt flavor,

• 6:15Lex Fridman?

  the flavor of champions, the one I recommend. It's been quite a while since I tried the others. They're all good, but for me, I'm a man of focus and dedication.

• 6:25Lex Fridman?

  And I'm dedicated to watermelon salt. I think they have actually, I saw a lemonade flavor. I think a lot of people love lemonade. So maybe that's your thing. For me, I'm sticking to watermelon salt. Get a free Acom sample pack with any purchase. Try it at drinkelement.com/lex.

• 6:43Lex Fridman?

  This episode is also brought to you by AG1, an all-in-one daily drink to support better health and peak performance. I travel with it. It makes me feel like I take a little piece of home with me. I drink it at least once a day, very often twice a day. And they keep innovating, they keep improving it. They recently introduced the AG1 Next Gen, improving every aspect, more vitamins and minerals, and upgraded probiotics. It's funny how a morning routine can be the source of peace and happiness. Because I find that if I check my phone at all in the first couple of hours of the day, I get this weird anxiety that ultimately morphs into unhappiness.

• 7:26Lex Fridman?

  And if I don't, I'm much more likely to sort of maintain that deep focus. And a part of that, early in the morning, is some coffee or caffeinated drink, and then a few hours on is AG1. And it's just many hours of deep focus in between. It makes me feel happy, makes me feel at one with the universe, and it helps me get shit done. Anyway, they'll give you one month's supply of fish oil when you sign up at drinkag1.com/lex. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description or at lexfridman.com/sponsors. And consider subscribing, commenting, and sharing the podcast with folks who might find it interesting.

• 8:09Lex Fridman?

  I promise to work extremely hard to always bring you nuanced and long-form conversations with a wide variety of interesting people from all walks of life. And now, dear friends, here's Demis Hassabis.

• 8:40Lex Fridman?

  In your Nobel Prize lecture, You propose what I think is a super interesting conjecture that, quote, "Any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm." What kind of patterns or systems might be included in that? Biology, chemistry, physics, maybe cosmology?

• 9:03Speaker 01

  Yeah.

• 9:04Lex Fridman?

  Neuroscience, what are we talking about?

• 9:06Speaker 01

  Sure. Well, look, I felt that it's sort of a tradition, I think, of Nobel Prize lectures that you're supposed to be a little provocative and I wanted to follow that tradition. What I was talking about there is if you take a step back and you look at all the work that we've done, especially with the Alpha X projects, so I'm thinking AlphaGo, of course, AlphaFold. What they really are is we're building models of very combinatorily high dimensional spaces that, you know, if you tried to brute force a solution, find the best moving go, or find the the exact shape of a protein, and if you enumerated all the possibilities, there wouldn't be enough time in the, you know, the time of the universe. So you have to do something much smarter. And what we did in both cases was build models of those environments and that guided

• 9:50Speaker 01

  the search in a in a smart way. And that makes it tractable. So if you think about protein folding, which is obviously a natural system, you know, why should that be possible? How does physics do that? You know, proteins fold in milliseconds in our bodies. So somehow physics solves this problem that we've now also solved computationally. And I think the reason that's possible is that in nature, natural systems have structure because they were subject to evolutionary processes that that shaped them. And if that's true, then you can maybe learn what that structure is.

• 10:23Lex Fridman?

  So this perspective, I think, is really interesting one. You've hinted at it, which is almost like crudely stated, anything that can be evolved.

• 10:33Lex Fridman?

  Can be efficiently modeled? Think there's some truth to that?

• 10:37Speaker 01

  Yeah, I sometimes call it survival of the stabilist or something like that because you know, it's, it's of course, there's evolution for life, living things. But there's also, you know, if you think about geological time, so the shape of mountains, that's been shaped by weathering processes, right? Over thousands of years. But then you can even take it cosmological, the orbits of planets, the shapes of asteroids. These have all been survived kind of processes that have acted on them many, many times. So if that's true, then there should be some sort of pattern that you can kind of reverse learn and a kind of manifold really that helps you search to the right solution, to the right shape and actually allow you to predict things about it in an efficient way.

• 11:21Speaker 01

  Because it's not a random pattern, right? So it may not be possible for for man made things or abstract things like factorizing large numbers, because unless there's patterns in the number space, which there might be, but if there's not, and it's uniform, then there's no pattern to learn. There's no model to learn that will help you search. You have to do brute force. So in that case, you know, you maybe you need a quantum computer, something like this. But in most things in nature that we're interested in are not like that. They have structure that evolved for a reason and survived over time. And if that's true, I think that's potentially learnable by a neural network.

• 11:55Lex Fridman?

  It's like nature is doing a search process. And it's so fascinating that it's in that search process is creating systems that can be efficiently modeled.

• 12:05Speaker 01

  Yes, right.

• 12:06Lex Fridman?

  So interesting.

• 12:07Speaker 01

  So they can be efficiently rediscovered or recovered because nature's not random, right? These, everything that we see around us, including like the elements that are more stable, all of those things, they're subject to some kind of selection process, pressure.

• 12:21Lex Fridman?

  Do you think, because you're also a fan of theoretical computer science and complexity, do you think we can come up with a kind of complexity class, like a complexity zoo type of class, where maybe it's the set of learnable systems, the set of learnable natural systems, LNS? This is a demisopis, new class of systems that could be actually learnable by classical systems in this kind of way, natural systems that can be modeled efficiently.

• 12:51Speaker 01

  I mean, I've always been fascinated by the P equals NP question and what is modelable by classical systems, i.e. non-quantum systems, you know, Turing machines in effect. And that's exactly what I'm working on, actually, in kind of my few moments of spare time with a few colleagues about, should there be, you know, maybe a new class of problem that is solvable by this type of neural network process and kind of mapped on to these natural systems. So, you know, the things that exist in physics and have structure. So I think that could be a very interesting new way of thinking about it. And it sort of fits with the way I think about physics in general, which is that, you know, I think information is primary. Information is the most sort of fundamental unit of the

• 13:33Speaker 01

  universe, more fundamental than energy and matter. I think they can all be converted into each other. But I think of the universe as a kind of informational system.

• 13:41Lex Fridman?

  So when you think of the universe as an informational system, then the P equals NP question is a physics question.

• 13:48Speaker 01

  That's right.

• 13:49Lex Fridman?

  It is a question that can help us actually solve the entirety of this whole thing going on.

• 13:54Speaker 01

  Yeah, I think it's one of the most fundamental questions, actually, if you think of physics as informational. And the answer to that, I think, is going to be, you know, very enlightening.

• 14:03Lex Fridman?

  More specific to the P and NP question, this, again, some of the stuff we're saying is kind of crazy right now, just like the Christian Enthony Nobel Prize speech controversial thing that he said sounded crazy. And then you went and got a Nobel Prize for this with John Jump or solve the problem. So let me, let me just stick to the P equals NP. Do you think there's something in this thing we're talking about that could be shown if you can do something like a polynomial time or constant time compute ahead of time and construct this gigantic model, then you can solve some of these extremely difficult problems in a direct computer science kind of way?

• 14:46Speaker 01

  Yeah, I think that there are actually a huge class of problems that could be couched in this way, the way we did AlphaGo and the way we did AlphaFold, where, you know, you model what the dynamics of the system is, the properties of that system, the environment that you're Trying to understand and then that makes the search for the solution or the prediction of the next step efficient. Basically, polynomial time, so tractable by a classical system, which a neural network is, it runs on normal computers, right? Classical computers, churing machines in effect. And I think it's one of the most interesting questions there is, is how far can that paradigm go?

• 15:26Speaker 01

  You know, I think we've proven and the AI community in general, that classical systems, churing machines can go a lot further than we previously thought. You know, they can do things like model the structures of proteins and play go to better than world champion level. And, you know, a lot of people would have thought maybe ten, twenty years ago, that was decades away. Or maybe you would need some sort of quantum machines to to quantum systems to be able to do things like protein folding. And so I think we haven't really even sort of scratched the surface yet of what classical systems, so called, could do. And of course, AGI being built on a on a neural network system, on top of a neural network system, on top of a classical computer, would be the ultimate expression of that.

• 16:12Speaker 01

  And I think the limit, you know, the what the bounds of that kind of system, what it can do, it's a very interesting question and and and directly speaks to the P equals NP question.

• 16:21Lex Fridman?

  What do you think, again, hypothetical, might be outside of this? Maybe emergent phenomena? Like if you look at cellular automata, some of the you have extremely simple systems and then some complexity emerges.

• 16:34Speaker 01

  Yes.

• 16:35Lex Fridman?

  Maybe that would be outside or even would you guess even that might be amenable to efficient modeling by a classical?

• 16:43Speaker 01

  Yeah, I think those systems would be right on the boundary, right? So I think most emergent systems, cellular automata, things like that could be modelable by a classical system. You just sort of do a forward simulation of it and it'd probably be efficient enough. Of course, there's the question of things like chaotic systems where the initial conditions really matter. And then you get to some, you know, uncorrelated end state. Now, those could be difficult to model. So I think these are kind of the open questions, but I think. When you step back and look at what we've done with the systems and the and the problems that we've solved. And then you look at things like VO three on like video generation, sort of rendering physics and lighting and things like that. You know, really core fundamental things in physics.

• 17:29Speaker 01

  It's pretty interesting. I think it's telling us something quite fundamental about how the universe is structured, in my opinion. So, you know, in a way, that's what I want to build AGI for is to help us as scientists answer these questions like P equals MP.

• 17:43Lex Fridman?

  Yeah, I think we might be continuously surprised about what is modelable by classical computers. I mean, Alpha Fold three on the interaction side is surprising that you can make any kind of progress on that direction. Alpha Genome is surprising that you can map the genetic code to the function. Kind of playing with the emergent kind of phenomena. You think there's so many combinatorial options that and then here you go. You can find the kernel that is efficiently modeled.

• 18:10Speaker 01

  Yes, because there's some structure, there's some landscape, you know, in the energy landscape or whatever it is that you can follow. Some gradient you can follow. And of course, what neural networks are very good at is following gradients. And so if there's one to follow and you can specify the objective function correctly. You know, you don't have to deal with all that complexity, which I think is how we maybe have naively thought about it for decades, those problems. If you just enumerate all the possibilities, it looks totally intractable. And there's many, many problems like that. And then you think, well, it's like 10 to the 300 possible protein structures. 10 to the 170 possible GO positions. All of these are way more than atoms in the universe. So how could one possibly find the the right solution or predict the next step?

• 18:56Speaker 01

  And but it turns out that it is possible. And of course, reality, nature does do it, right? Proteins do fold. So that that gives you confidence that there must be, if we understood how physics was doing that in a sense. Then and we could mimic that process, I model that process. It should be possible on our classical systems is is basically what the conjecture is about.

• 19:18Lex Fridman?

  And of course, there's nonlinear dynamical systems, highly nonlinear dynamical systems, everything involving fluid.

• 19:25Speaker 01

  Yes, right.

• 19:25Lex Fridman?

  You know, I recently had a conversation with Terrence Tao, who mathematically contends with a very difficult aspect of systems that have some singularities in them that break the mathematics. And it's just hard for us humans to make any kind of clean predictions about highly nonlinear dynamical systems. But again, to your point, we might be very surprised what classical learning systems might be able to do about even fluid.

• 19:50Speaker 01

  Yes, exactly. I mean, fluid dynamics, Navier-Stokes equations, these are traditionally thought of as very, very difficult, intractable kind of problems to do on classical systems. They take enormous amounts of compute. You know, weather prediction systems, you know, these kind of things all involve fluid dynamics calculations. And but again, if you look at something like VO, our video generation model, it can model liquids quite well, surprisingly well, and materials, specular lighting. I love the ones where, you know, there's, there's people who generate videos where there's like clear liquids going through hydraulic presses and then being squeezed out. I used to write physics engines and graphics engines in my early days in gaming, and I know it's just so painstakingly hard to build programs that can do that.

• 20:35Speaker 01

  And yet somehow these systems are, you know, reverse engineering from just watching YouTube videos. So presumably what's happening is it's extracting some underlying structure around how these materials behave. So perhaps there is some kind of lower dimensional manifold that can be learned if we actually fully understood what's going on under the hood. That's maybe, you know, maybe true of most of reality.

• 21:00Lex Fridman?

  Yeah, I've been continuously, precisely by this aspect of VO3. I think a lot of people highlight different aspects, including the comedic and the meat and all that kind of stuff. And then the ultra-realistic ability to capture humans in a really nice way that's compelling and feels close to reality, and then combine that with native audio. All of those are marvelous things about VO3. But the exactly the thing you're mentioning, which is the physics.

• 21:26Speaker 01

  Yeah.

• 21:27Lex Fridman?

  It's not perfect, but it's pretty damn good. And then the really interesting scientific question is, what is it understanding about our world in order to be able to do that? Because of the cynical take with diffusion models, there's no way it understands anything. But it seemed, I mean, I don't think you can generate that kind of video without understanding and then our own philosophical notion of what it means to understand then is like brought to the surface. To what degree do you think VO3 understands our world?

• 21:59Speaker 01

  I think to the extent that it can predict the next frames, you know, in a coherent way, that's some, that is a form, you know, of understanding, right? Not in the anthropomorphic version of, you know, it's not some kind of deep philosophical understanding of what's going on. I don't think these systems have that. But they, they certainly have modeled enough of the dynamics, you know, put it that way, that they can pretty accurately generate whatever it is, eight seconds of consistent video that by eye, at least, you know, at a glance, is quite hard to distinguish what the issues are. And imagine that in two or three more years' time. That's the thing I'm thinking about and how incredible that will, they will look, given where we've come from, you know, the early versions of

• 22:39Speaker 01

  that one or two years ago. And so the rate of progress is incredible. And I think I'm like you, is like, a lot of people love all of the, the, the stand-up comedians and the, that actually captures a lot of human dynamics very well and, and body language. But actually, the thing I'm most impressed with and fascinated by is the physics behavior, the lighting and materials and liquids. And it's pretty amazing that it can do that. And I think that shows that it has some notion of at least intuitive physics, right? How things are supposed to work intuitively, maybe the way that a human child would understand physics, right? As

• 23:20Speaker 01

  opposed to a, you know, a PhD student really being able to unpack all the equations. It's more of an intuitive physics understanding.

• 23:27Lex Fridman?

  Well, that intuitive physics understanding, that's the base layer. That's the thing people sometimes call like common sense. Again, it really understands something. I think that really surprised a lot of people. It blows my mind that. I just didn't think it would be possible to generate that level of realism without understanding. There's this notion that you can only understand the physical world by having an embodied AI system, a robot that interacts with that world. That's the only way to construct an understanding of that world.

• 23:57Speaker 01

  Yeah.

• 23:57Lex Fridman?

  But VO3 is directly challenging that, it feels like.

• 24:01Speaker 01

  Yes. And it's very interesting, you know, even if you were to ask me five, 10 years ago, I would have said, even though I was a must in all of this, I would have said, well, yeah, you probably need to understand intuitive physics, you know, like if I push this off the table, this glass, it will maybe shatter, you know, and the liquid will spill out, right? So we know all of these things. But I thought that, you know, and there's a lot of theories in neuroscience, it's called action in perception, where, you know, you need to act in the world to really truly perceive it in a deep way. And there was a lot of theories about you'd need embodied intelligence or robotics or something, or maybe at least simulated action, so that you would understand things like intuitive physics. But it seems like you can understand it through passive observation, which is pretty surprising to me.

• 24:46Speaker 01

  And again, I think hints at something underlying about the nature of reality, in my opinion, beyond just the, you know, the cool videos that it generates. And of course, there's next stages is maybe even making those videos interactive. So one can actually step into them and move around them, which would be really mind blowing, especially given my games background. So you can imagine. And then, and then I think, you know, we're starting to get towards what I would call a world model, a model of how the world works, the mechanics of the world, the physics of the world, and the things in that world. And of course, that's what you would need for a true AGI system.

• 25:24Lex Fridman?

  I have to talk to you about video games. You were being a bit trolly. I think you're having more and more fun on Twitter, on X, which is great to see. So a guy named Jimmy Apples tweeted, "Let me play a video game of my VO3 videos already. Google cooked so good, playable world models when?" Spelled W E N, question mark. And then you quote tweeted that with, now, wouldn't that be something? So how hard is it to build game worlds with AI? Maybe, can you look out into the future? Of video games, five, 10 years out. What do you think that looks like?

• 26:01Speaker 01

  Well, games were my first love, really. And doing AI for games was the first thing I did professionally in my teenage years and was the first major AI systems that I built. And I always wanna have, I wanna scratch that each one day and come back to that. So, you know, and I will do, I think. And I think I'd sort of dream about, you know, what would I have done back in the 90s if I'd had access to the kind of AI systems we have today? And I think you could build absolutely mind blowing games. And I think the next stage is I always used to love making, all the games I've made are open world games. So they're games where there's a simulation and then there's AI characters. And then the player interacts with that simulation and the simulation adapts to the way the player plays.

• 26:45Speaker 01

  And I always thought they were the coolest games because, so games like Theme Park that I worked on, where everybody's game experience would be unique to them, right? Because you're kind of co-creating the game, right? We set up the parameters, we set up initial conditions and then you as the player immersed in it and then you are co-creating it with the, with the simulation. But of course, it's very hard to program open world games. You know, you've got to be able to create content, whichever direction the player goes in and you want it to be compelling, no matter what the player chooses. And so it was always quite difficult to build things like cellular automata, actually, type of those kind of classical systems, which created some emergent behavior. But they're always a little bit fragile, a little bit limited. Now we're maybe on the cusp in the next few years, five, 10 years of having AI systems

• 27:30Speaker 01

  that can truly create around your imagination, can sort of dynamically change the story and storytell the narrative around and make it dramatic, no matter what you end up choosing. So it's like the ultimate choose your own adventure sort of game. And, you know, I think maybe we're within reach if you think of a kind of interactive version of VO and then wind that forward five to 10 years and, you know, imagine how good it's gonna be.

• 27:58Lex Fridman?

  Yeah, so you said a lot of super interesting stuff there. So one, the open world. Built into that is a deep personalization, the way you've described it. So it's not just that it's open world, like you can open any door and there'll be something there. It's that the choice of which door you open in an unconstrained way defines the worlds you see. So some games try to do that, they give you choice.

• 28:22Speaker 01

  Yes.

• 28:23Lex Fridman?

  But it's really just an illusion of choice because the only, uh, like Stanley Parable.

• 28:29Speaker 01

  Yeah.

• 28:29Lex Fridman?

  Game I've played, it's really, there's a couple of doors and it really just takes you down the narrative. Stanley Parable is a great video game. I recommend people play that kind of, uh, in a meta way.

• 28:42Lex Fridman?

  Mocks the illusion of choice and there's philosophical notions of free will and so on. But I do, like, one of my favorite games of Elder Scrolls is Daggerfall. I believe that they really played with a, like, random generation of the dungeons.

• 28:58Speaker 01

  Yeah.

• 28:59Lex Fridman?

  Of if you can step in and they give you this feeling of an open world. And there, you mentioned interactivity, you don't need to interact, that's a first step because you don't need to interact that much. You just, when you open the door, whatever you see is randomly generated for you. And that's already an incredible experience because you might be the only person to ever see that.

• 29:19Speaker 01

  Yeah, exactly. And, and so, but what you'd like is a little bit better than just sort of a random generation, right? So you'd like, uh, and, and also better than a simple A B hard coder choice, right? That's not really an open world, right? As you say, it's just giving you the illusion of choice. What you want to be able to do is, is potentially anything in that game environment. And I think the only way you can do that is to have, uh, generated systems, systems that, uh, will generate that on the fly. Of course, you can't create infinite amounts of game assets, right? It's expensive enough already how AAA games are made today. And that was obvious to, to us back in the nineties when I was working on all these games. I think maybe black and white, uh, was the game that I worked on early stages of that.

• 30:04Speaker 01

  That had the, still probably the best AI, learning AI in it. It was an early reinforcement learning system that you, you know, you were, you were looking after. This mythical creature and growing it and nurturing it. And depending how you treated it, it would treat the villagers in that world in the same way. So if you were mean to it, it would be mean. If you were good, it would be protective. And so it was really a reflection of the way you played it. So actually, all of the, uh, I've been working on sort of simulations and AI through the medium of games at the beginning of my career. And, and really the whole of what I do today is still a follow on from those early more hardcoded ways of doing the AI to now, you know, fully general learning systems that, that are trying to achieve the same thing.

• 30:46Lex Fridman?

  Yeah, it's been interesting, hilarious and fun to watch you and Elon, obviously itching to create games because you're both gamers. And one of the sad aspects of your incredible success in so many domains of science, like serious adult stuff.

• 31:02Speaker 01

  Yeah.

• 31:03Lex Fridman?

  That you might not have time to really create a game. You might end up creating the tooling that others would create the game. And you have to watch.

• 31:12Speaker 01

  Exactly.

• 31:13Lex Fridman?

  Others create the thing you've always dreamed of. Do you think it's possible you can somehow in your extremely busy schedule, actually find time to create something like black and white, some, some, an actual video game where like you could make the childhood dream.

• 31:30Speaker 01

  Yeah.

• 31:30Lex Fridman?

  Come become reality.

• 31:31Speaker 01

  Well, you know, there's two things, ways to think about that is maybe with vibe coding as it gets better. And there's a possibility that I could, you know, one could do that actually in the, in your spare time. So I'm quite excited about that as, as that would be my project. If I got the time to do some vibe coding, um, I'm actually itching to do that. And then the other thing is, you know, maybe it's a sabbatical after AGI has been safely stewarded into the world and delivered into the world. You know, that and then working on my physics theory. As we talked about at the beginning, those would be the two, my, my two post AGI projects. Let's call it that way.

• 32:03Lex Fridman?

  I would love to see which.

• 32:04Speaker 01

  The ultimate game.

• 32:05Lex Fridman?

  Post AGI, which you choose, solving the, the problem that some of the smartest people in human history contend with. So P equals NP. Or creating a cool video.

• 32:16Speaker 01

  Yeah. Well, but they might, but in my world, they'd be related because it would be an open world simulated game. As realistic as possible. So, you know, what, what is, what is the universe? That's, that's, that's speaking to the same question, right? And P equals NP, I think all these things are related, at least in my mind.

• 32:33Lex Fridman?

  I mean, in a really serious way. I think video games sometimes are looked down upon. There's just this fun side activity. But especially as AI does more and more of the difficult, boring tasks, something we in modern world called work. You know, video games is the thing in which we may find meaning, in which we may find like what to do with our time. You could create incredibly rich, meaningful experiences. Like, that's what human life is. And then in video games, you can create more sophisticated, more diverse ways of living.

• 33:14Speaker 01

  Yeah, I think so. I mean, those of us who love games, and I still do, is, is, um, you know, it's almost can let your imagination run wild, right? Like, I, I used to love games and working on games so much because it's the fusion, especially in the nineties and two, early two thousands, the sort of golden era, maybe the eighties of, of, of game, of the games industry. And it was all being discovered, new genres were being discovered. We weren't just making games. We felt we were, we were creating a new entertainment medium that never existed before, especially with these open world games and simulation games where you were co-create, you as the player were co-creating the story. There's no other media, entertainment media where you do that, where you as the audience actually co-create the, the story. And of course, now with multiplayer games as well,

• 33:59Speaker 01

  it can be a very social activity and can explore all kinds of interesting worlds and that. But on the other hand, you know, it's very important to also enjoy and experience the physical world. But the question is then, you know, I think we're gonna have to kind of confront the question again of what is the fundamental nature of reality? What is the gonna be the difference between these increasingly realistic simulations and multiplayer ones and emergent and what we do in the real world?

• 34:29Lex Fridman?

  Yeah, there's clearly a huge amount of value to experiencing the real world, nature. There's also a huge amount of value in experiencing other humans directly in person, the way we're sitting here today. But we need to really scientifically rigorously answer the question, why?

• 34:46Speaker 01

  Yeah.

• 34:47Lex Fridman?

  And which aspect of that can be mapped into the virtual world?

• 34:51Speaker 01

  Exactly.

• 34:51Lex Fridman?

  And it's not enough to say, yeah, you should go touch grass and hang out in nature. It's like, why exactly is that valuable?

• 34:59Speaker 01

  Yes, and I guess that's maybe the thing that's been haunting me, obsessing me from the beginning of my career. If you think about all the different things I've done, that's they're all related in that way. The simulation, nature of reality, and what is the bounds of, you know, what can be modeled?

• 35:14Lex Fridman?

  Sorry for the ridiculous question, but so far, what is the greatest video game of all time? What's up there?

• 35:19Speaker 01

  Well, my favorite one of all time is Civilization. I have to say that that was the Civilization one and Civilization two, my favorite games of all time.

• 35:29Lex Fridman?

  I can only assume you've avoided the most recent one because it would probably, you would, that would be your sabbatical. That you would disappear.

• 35:37Speaker 01

  Yes, exactly. They take a lot of time, these Civilization games, so I've got to be careful with them.

• 35:42Lex Fridman?

  Fun question, you and Elon seem to be somehow solid gamers. Is there a connection between being great at gaming and being great leaders of AI companies?

• 35:53Speaker 01

  I don't know. It's an interesting one. I mean, we both love games and it's interesting. He wrote games as well to start off with. It's probably, especially in the era I grew up in, where home computers were, just became a thing, you know, in the late eighties and nineties, especially in the UK. I had a Spectrum and then a Commodore Mega 500, which was my favorite computer ever. And that's why I learned all my programming. And of course, it's a very fun thing to program is to program games. So I think it's a great way to learn programming, probably still is. And and then, of course, I immediately took it in directions of AI and simulations, which, so I was able to express my interest in in games and my sort of wider scientific interests altogether.

• 36:37Speaker 01

  And then the final thing I think that's great about games is it fuses artistic design, you know, art with the the most cutting edge programming. So again, in the nineties, all of the most interesting. Technical advances were happening in gaming, whether that was AI, graphics, physics engines, hardware, even GPUs, of course, were designed for gaming originally. So everything that was pushing computing forward in the in the 90s was due to gaming. So interestingly, that was where the forefront of research was going on. And it was this incredible fusion with with art. You know, graphics, but also music and just the whole new media of storytelling.

• 37:19Speaker 01

  And I love that. For me, it's this sort of multidisciplinary kind of effort is again, something I've enjoyed my whole my whole life.

• 37:26Lex Fridman?

  I have to ask you, I almost forgot about one of the many, and I would say one of the most incredible things recently that somehow didn't yet get enough attention is Alpha Evolve. We talked about evolution a little bit, but it's the Google Deep Mind system that evolves algorithms.

• 37:43Speaker 01

  Yeah.

• 37:44Lex Fridman?

  Are these kinds of evolution-like techniques promising as a component in a future superintelligent system? So for people who don't know, it's kind of, I don't know if it's fair to say it's LLM-guided evolution search.

• 37:57Speaker 01

  Yeah.

• 37:57Lex Fridman?

  So evolutionary algorithms are doing the search and LLMs are telling you where.

• 38:02Speaker 01

  Yes, exactly. So LLMs are kind of proposing some possible solutions. And then you do, you use evolutionary computing on top to to to find some novel part of the of the search space. So actually, I think it's an example of very promising directions where you combine LLMs or foundation models with other computational techniques. Evolutionary methods is one, but you could also imagine Monte Carlo tree search. Basically, many types of search algorithms or reasoning algorithms sort of on top of or using the foundation models as a basis. So I actually think there's quite a lot of interesting things to be discovered probably with these sort of hybrid systems, let's call them.

• 38:43Lex Fridman?

  But not to romanticize evolution.

• 38:46Speaker 01

  Yeah.

• 38:46Lex Fridman?

  I'm only human, but you think there's some value in whatever that mechanism is? Because we already talked about natural systems. Do you think where there's a lot of low hanging fruit of us understanding being being able to model? Uh being able to simulate evolution and then using that whatever we understand about that nature inspired mechanism to to then do search better and better and better.

• 39:10Speaker 01

  Yes. So if you think about, uh, again, breaking down the sort of systems we've built uh to their really fundamental core, you've got like the model of the of the underlying dynamics of the system. Uh, and then if you want to discover something new, something novel that hasn't been seen before, um, then you need some kind of search process on top to take you to a novel region of the of the of the search space. And um, you can do that in a number of ways. Evolutionary computing is one. Um, with AlphaGo, we just use Monte Carlo tree search, right? And that's what found move thirty seven, the new kind of never seen before strategy in Go. And so that's how you can go beyond potentially what

• 39:52Speaker 01

  is already known. So the model can model everything that you currently know about, right? All the data that you currently have, but then how do you go beyond that? So that starts to speak about the ideas of creativity. How can these systems create something new, find, discover something new? Obviously, this is super relevant for scientific discovery or pushing met science and medicine forward, which we want to do with these systems. And you can actually bolt on some uh fairly simple search systems on top of these models and get you into a new region of space. Of course, you also have to um make sure that uh you're not searching that space totally randomly, it would be too big. So you have to have some objective function that you're trying to optimize and hill climb towards and that guides that search.

• 40:34Lex Fridman?

  But there's some mechanism of evolution that are interesting, maybe in the space of programs, but then the space of programs is an extremely important space because you can probably generalize to uh to everything, you know. But, you know, for example, mutation, this is not just Monte Carlo tree search where it's like a search. You could every once in a while.

• 40:55Speaker 01

  Combine things, yeah.

• 40:56Lex Fridman?

  Combine things, alter like sub, like a components of a thing.

• 41:00Speaker 01

  Yes.

• 41:00Lex Fridman?

  So then, you know, what evolution is really good at is not just the natural selection. It's combining things and building increasingly complex hierarchical systems.

• 41:11Speaker 01

  Yes.

• 41:12Lex Fridman?

  So that component is super interesting.

• 41:13Speaker 01

  Yeah.

• 41:14Lex Fridman?

  Especially like with Alpha evolve in the space of programs.

• 41:16Speaker 01

  Yeah, exactly. So there's a you can get a bit of an extra property out of evolutionary systems, which is some new emergent capability may come about.

• 41:24Lex Fridman?

  Yes.

• 41:25Speaker 01

  Right, of course, like happened with life. Interestingly, with naive sort of traditional evolutionary computing methods without L L M's and the modern A I, the problem with them, they were very well studied in the nineties and and and early two thousands and some promising results. But the problem was they could never work out how to evolve new properties, new emergent properties. You always had a sort of subset of the properties that you put into the system. But maybe if we combine them with these foundation models, perhaps we can overcome that limitation. Obviously, natural evolution clearly did because it did evolve new capabilities, right? So bacteria to where we are now. So clearly that it must be possible with evolutionary systems to generate new patterns, you know, going

• 42:10Speaker 01

  back to the first thing we talked about, and new capabilities and emergent properties. And maybe we're on the cusp of discovering how to do that.

• 42:18Lex Fridman?

  Yeah, listen, Alpha evolve is one of the coolest things I've ever seen. I've I've on my desk at home, you know, most of my time is spent behind that computer is just programming. And next to the three screens is a skull of a tectallic, which is one of the early organisms that crawled out of the water onto land. And I just kind of watch that little guy. It's like you, whatever the computation mechanism of evolution is, is quite incredible.

• 42:50Speaker 01

  Yes.

• 42:51Lex Fridman?

  Truly, truly incredible.

• 42:52Speaker 01

  Yeah.

• 42:52Lex Fridman?

  Now, whether that's exactly the thing we need to do to do our search, but never never dismiss the power of nature what it did here.

• 43:00Speaker 01

  Yeah. And it's amazing, which is a relatively simple algorithm, right, effectively, and it can generate all of this immense complexity emerges, obviously running over, you know, four billion years of time. But but it's it's it's, you know, you can think about that as again, a process, a search process that ran over the physics substrate of the universe for a long amount of computational time. But then it generated all this incredible rich diversity.

• 43:28Lex Fridman?

  So so many questions I want to ask you. So one, you do have a dream. One of the natural systems you want to try to model is a is a cell.

• 43:36Speaker 01

  Yes.

• 43:37Lex Fridman?

  That's a beautiful dream. I could ask you about that. I also, just for that purpose, on the AI scientist front, just broadly. So there's an essay from Daniel Cucatayio, Scott Alexander and others that outlines steps along the way to get to ASI. And has a lot of interesting ideas in it, one of which is including a superhuman coder and a superhuman AI researcher. And in that, there's a term of research taste. That's really interesting. So in everything you've seen, do you think it's possible for AI systems to have research taste? To help you in the way that AI coscientists does, to help steer

• 44:21Lex Fridman?

  human, human brilliant scientists and then potentially by itself to figure out what are the directions where you want to generate truly novel ideas? Because that seems to be like a really important component of how to do great science.

• 44:37Speaker 01

  Yeah, I think that's going to be one of the hardest things to to mimic or model is is this this idea of taste or judgment. I think that's what separates the, you know, the the great scientists from the good scientists, like all all professional scientists are good technically, right? Otherwise, it wouldn't have made it that far in academia and things like that. But then, do you have the taste to sort of sniff out what the right direction is, what the right experiment is, what the right question is? So the is the is picking the right question is is the hardest part of science and and making the right hypothesis. And that's what, you know, today's systems definitely they can't do. So, you know, I often say it's harder to come up with a conjecture, a really good conjecture than it is to solve it.

• 45:21Speaker 01

  So we may have systems soon that can solve pretty hard conjectures. You know, I am in maths Olympiad problems where we, you know, alpha proof last year. Our system got, you know, silver medal in that really hard problems. Maybe eventually we'll be able to solve a millennium prize kind of problem, but could A system come up with a conjecture worthy of study that someone like Terrence Tower would have gone, you know what, that's a really deep question about the nature of maths or the nature of numbers or the nature of physics. And that is far harder type of creativity. And we don't really know, today's systems clearly can't do that. And we're not quite sure what that mechanism would be, this kind of leap of imagination, like Einstein had when he came up with, you know, special relativity and then general relativity with the knowledge he had at the time.

• 46:07Lex Fridman?

  Yes, for conjecture, the, you want to come up with a thing that's interesting, it's amenable to proof.

• 46:15Speaker 01

  Yes.

• 46:15Lex Fridman?

  So like, it's easy to come up with a thing that's extremely difficult.

• 46:18Speaker 01

  Yeah.

• 46:19Lex Fridman?

  It's easy to come up with a thing that's extremely easy, but that at that very edge.

• 46:22Speaker 01

  That sweet spot, right, of basically advancing the science and splitting the hypothesis space into two ideally, right, whether if it's true or not true, you've learned something really useful. And, um, and, and that's hard. And, and, and, and making something that's also, uh, you know, falsifiable and within sort of the technologies that you have, you currently have available. So it's a very creative process, actually, highly creative process that, um, I, I think just a kind of naive search on top of a model won't be enough for that.

• 46:55Lex Fridman?

  Okay, the idea of splitting the hypothesis space into two is super interesting. So, uh, I've heard you say that there's basically no failure in, or failure is extremely valuable if it's done, if you construct the questions right, if you construct the experiments right, if you design them right, that failure or success are both useful. So perhaps because it splits the hypothesis basically too, it's like a binary search.

• 47:18Speaker 01

  That's right. So when you do like, you know, real blue sky research, there's no such thing as failure, really, as long as you're picking experiments and hypotheses that, that, that, that meaningfully spit the hypothesis space. So, you know, and you learn something, you can learn something kind of equally valuable from an experiment that doesn't work, that should tell you, if you've designed the experiment well, and your hypotheses are interesting, it should tell you a lot about where to go next. And, um, and then it's you're effectively doing a search process, um, and using that information in, in, you know, very helpful ways.

• 47:51Lex Fridman?

  So to go to your dream. Of modeling a cell, what are the big challenges that lay ahead for us to make that happen? We should maybe highlight that AlphaFold, I mean, there's just so many leaps. So AlphaFold solved, if it's fair to say, protein folding. And there's so many incredible things we could talk about there, including the open sourcing, everything you've released. AlphaFold 3 is doing protein RNA, DNA interactions, which is super complicated and fascinating. It's amenable to modeling. AlphaGenome predicts how small genetic changes, like if we think about single mutations, how they link to actual function. So those, it seems like it's creeping along.

• 48:36Speaker 01

  Yes.

• 48:37Lex Fridman?

  To sophisticated, to much more complicated things like a cell. But a cell has a lot of really complicated components.

• 48:44Speaker 01

  Yeah. So what I've tried to do throughout my career is I have these really grand dreams. And then I try to, as you've noticed, and then I try to break, but I try to break them down. You know, it's easy to have a kind of a crazily ambitious dream, but the trick is, how do you break it down into manageable, achievable interim steps that are meaningful and useful in their own right? And so Virtual Cell, which is what I call the project of modeling a cell, I've had this idea, you know, of wanting to do that for maybe more like 25 years. And I used to talk with Paul Nurse, who is a bit of a mentor of mine in biology. He runs the, you know, founded the Crick Institute and won the Nobel Prize in 2001.

• 49:25Speaker 01

  It is, we've been talking about it since, you know, before the, you know, in the 90s. And, and I come, used to come back to every five years. It's like, what would you need to model of the full internals of a cell so that you could do experiments on the virtual cell? And what those experiment, you know, in Silico, and those predictions would be useful for you to save you a lot of time in the wet lab, right? That would be the dream. Maybe you could 100x speed up experiments by doing most of it in Silico, the search in Silico, and then you do the validation step in the wet lab. That would be, that's the, that's the dream. And so, but maybe now, finally, so I was trying to build these components, AlphaFold being one, that, that would. Allow you eventually to model the full interaction, a full simulation of a cell.

• 50:10Speaker 01

  And I'd probably start with a yeast cell. And partly that's what Paul Nurse studied because a yeast cell is like a full organism that's a single cell. Right. So it's the kind of simplest, single cell organism. And so it's not just a cell, it's a full organism. And um and yeast is very well understood. And so that would be a good candidate for a, a, a, a kind of full simulated model. Now, AlphaFold is the, is the solution to the kind of static picture of what does a, what does a protein look, 3D structure of protein look like? A static picture of it. But we know that biology, all the interesting things happen with the dynamics, the interactions. And that's what AlphaFold 3 is, is the first step towards is modeling those interactions. So first of all, pair wise, you know, proteins with proteins, proteins with RNA and DNA.

• 50:54Speaker 01

  But then, um, the next step after that would be modeling maybe a whole pathway. Maybe like the tor pathway that's involved in cancer or something like this. And then eventually you might be able to model, you know, a whole cell.

• 51:05Lex Fridman?

  Also, there's another complexity here that stuff in a cell happens at different time scales. Is that tricky? It's like they're, you know, protein, uh, folding is, you know, super fast.

• 51:16Speaker 01

  Yes.

• 51:17Lex Fridman?

  Um, I don't know all the biological mechanisms, but some of them take a long time.

• 51:21Speaker 01

  Yeah.

• 51:21Lex Fridman?

  And so is that, that's a level, so the levels of interaction has a different temporal scale that you have to be able to model.

• 51:28Speaker 01

  So that would be hard. So you'd probably need several simulated systems that can interact at these different temporal dynamics. Or at least, uh, maybe it's like a hierarchical system. So, um, you can drop up and down the, the different temporal stages.

• 51:41Lex Fridman?

  So can you avoid, I mean, one of the challenges here is not avoid simulating, for example, the, the, the quantum mechanical aspects of any of this, right? You want to not over-model. You could skip ahead to just model the really high level things that get you a really good estimate of what's going to happen.

• 52:01Speaker 01

  So you, you've got to make a decision when you're modeling any natural system. What is the cutoff level of the granularity that you're going to model it to that? And then it captures the dynamics that you're interested in. So probably for a cell, I, I would hope that would be the protein. Level and that one wouldn't have to go down to the atomic level. So, you know, of course, that's where alpha fold stuck kicks in. So that would be kind of the basis. And then you'd build these higher level simulations that take those as building blocks. And then you get the emergent behavior.

• 52:34Lex Fridman?

  Apologize for the pothead questions ahead of time, but do you think we'll be able to simulate a model? The origin of life. So being able to simulate the first from from non living organisms, the the birth of a living organism.

• 52:53Speaker 01

  I think that's a one of the, of course, one of the deepest and most fascinating questions. I love that area of biology, you know, these people like there's a great book by Nick Lane, one of the top, top experts in this area called the the ten great inventions of of of evolution. I think it's fantastic and it also speaks to what the great filters might be, but you know, prior or are they ahead of us? I think I think they're most likely in the past, if you read that book of how unlikely to go, you know, have any life at all. And then single cell to multi cell seems an unbelievably big jump that took like a billion years, I think, on earth to do, right? So it shows you how hard it was.

• 53:29Lex Fridman?

  bacteria were super happy for a very long.

• 53:30Speaker 01

  A very long time before they captured mitochondria somehow, right? I don't see why not, why AI couldn't help with that, some kind of simulation again. It's again, it's a bit of a search process through a combinatorial space. Here's like all the, you know, the chemical soup that that you start with, the primordial soup that, you know, maybe was on earth near these hot vents. Here's some initial conditions. Can you generate something that looks like a cell? So perhaps that would be a next stage after the virtual cell project is, well, how how could you actually? Something like that emerge from the chemical soup.

• 54:05Lex Fridman?

  Well, I would love it if there was a move thirty seven for the origin of life.

• 54:08Speaker 01

  Yeah.

• 54:08Lex Fridman?

  I think that's one of the sort of great mysteries. I think ultimately what we will figure out is their continuum. There's no such thing as a line between nonliving and living. But if we can make that rigorous.

• 54:18Speaker 01

  Yes.

• 54:18Lex Fridman?

  That the very thing from the Big Bang to today has been the same process. If we can break down that wall that we've constructed in our minds of the actual origin of from nonliving to living, and it's not a line, that it's a continuum that connects physics and chemistry and biology.

• 54:36Speaker 01

  Yeah.

• 54:36Lex Fridman?

  As there's no line.

• 54:37Speaker 01

  I mean, this is my whole reason why I've worked on AI and AGI my whole life, because I think it can be the ultimate tool to help us answer these kind of questions. And I don't really understand why, um, you know, the average person doesn't think like, worry about this stuff more like, how, how can we not have a good definition of life and not, and not living and nonliving and the nature of time and let alone consciousness and gravity and all these things. It's, it's just, and quantum mechanics weirdness. It's just, to me, it's, I've always had this, this sort of screaming at me in my face. The whole, and that's, it's getting louder. You know, it's like, how, what is going on here? You know, in, in, and I mean that in the deeper sense, like in the, you know, the nature of reality, which has to be the ultimate question.

• 55:20Lex Fridman?

  Yeah.

• 55:21Speaker 01

  That would answer all of these things. It's sort of crazy if you think about it. We can stare at each other and at all these living things all the time. We can inspect it in microscopes and take it apart, uh, almost down to the atomic level. And yet we still can't answer that clearly in a simple way, that question of how do you define living? It's kind of amazing.

• 55:39Lex Fridman?

  Yeah. Living, you can kind of talk your way out of thinking about, but like consciousness, like we have this very obviously subjective conscious experience, like we're at the center of our own world and it, it feels like something. And then how, how are you not screaming?

• 55:54Speaker 01

  Yeah.

• 55:55Lex Fridman?

  At the mystery of it all. Right. I mean, but really humans have been contending with the mystery of the world around them for a long, long, there's a lot of mysteries. Like, what's up with the sun and and the rain?

• 56:09Speaker 01

  Yeah.

• 56:10Lex Fridman?

  Like, what's that about? And then like last year, we had a lot of rain and this year we don't have rain. Like, what did we do wrong? Humans have been asking that question for a long time.

• 56:19Speaker 01

  So we're quite, I guess we've developed a lot of mechanisms to cope with this.

• 56:23Lex Fridman?

  Yeah.

• 56:23Speaker 01

  Uh, these deep mysteries that we can't fully, we can see, but we can't fully understand. And we have to, have to just get on with daily life. And and and we get, we keep ourselves busy, right? In a way, do we keep ourselves distracted?

• 56:35Lex Fridman?

  I mean, weather is one of the most important questions of human history. We still, that's, that's the go to small talk direction of the weather.

• 56:43Speaker 01

  Especially in England.

• 56:45Lex Fridman?

  And then it's, which is, you know, famously is an extremely difficult system to model. And even that system, uh, Google Deep Mind has made progress on.

• 56:56Speaker 01

  Yes, we've, we have, we've created the, the best weather prediction systems in the world. And they're better than traditional fluid dynamics sort of systems that usually calculated on massive supercomputers. Takes days to calculate it. We've managed to model a lot of the weather dynamics with neural network systems with our WeatherNext system. And again, it's interesting that those kinds of dynamics can be modeled, even though they're very complicated, almost bordering on chaotic systems in some cases. A lot of the interesting aspects of that can be modeled by these neural network systems, including very recently, we had, you know, cyclone prediction of where, you know, paths of hurricanes might go. Of course, super useful, super important for the world.

• 57:37Speaker 01

  And and and it's super important to do that very timely and very quickly and as well as accurately. And I think it's a very promising direction, again, of, you know, simulating and so that you can run forward predictions and simulations of very complicated real world systems.

• 57:52Lex Fridman?

  I should mention that I've got a chance in Texas to meet a community of folks called the Storm Chasers.

• 57:58Speaker 01

  Yes.

• 57:59Lex Fridman?

  And what's really incredible about them, I need to talk to them more, is they're extremely tech savvy. Because what they have to do is they have to use models to predict where the storm is. So there, it's this, it's this beautiful mix of like crazy enough to like go into the eye of the storm. And like, in order to protect your life and predict where the extreme events are going to be, they have to have increasingly sophisticated models of of weather.

• 58:23Speaker 01

  Yeah.

• 58:24Lex Fridman?

  Yeah, it's a beautiful balance of like. Being in it as living organisms and the cutting edge of science. So they actually might be using deep mind systems. So that's.

• 58:35Speaker 01

  Yeah, they are. Hopefully they are, and I'd love to join them in one of those checks. They look amazing, right? To actually experience it one time.

• 58:41Lex Fridman?

  Exactly. And then also to experience the correct prediction.

• 58:45Speaker 01

  Yeah.

• 58:45Lex Fridman?

  Where something will come and how it's going to evolve. It's incredible.

• 58:48Speaker 01

  Yeah.

• 58:49Lex Fridman?

  You've estimated that we'll have AGI by 2030. So there's interesting questions around that. How will we actually know that we got there? And what may be the move, quote, move 37 of AGI?

• 59:06Speaker 01

  My estimate is sort of 50% chance by in the next five years. So, you know, by 2030, let's say. And so I think there's a good chance that that could happen. Part of it is what is your definition of AGI? Of course, people are arguing about that now. And, and mine's quite a high bar and always has been of like, can we match the cognitive functions that the brain has? Right? So we know our brains are pretty much general Turing machines, approximate. And of course, we created incredible modern civilization with our minds. So that also speaks to how general the brain is. And for us to know we have a true AGI, we would have to like, make sure that it has all those capabilities. It isn't kind of a jagged intelligence where some things it's really good at, like today's

• 59:51Speaker 01

  systems, but other things it's really flawed at. And, and that's what we currently have with today's systems. They're not consistent. So you'd want that consistency of intelligence across the board. And then we have some missing, I think, capabilities, like sort of the true invention capabilities and creativity that we were talking about earlier. So you'd want to see those. How you test that? I think you just test it. One way to do it would be kind of brute force test of tens of thousands of cognitive tasks that, you know, we know that humans can do. And maybe also make the system available to a few hundred of the world's top experts, the Terrance Towers of each, each subject area and see if they can find, you know, give

• 1:00:33Speaker 01

  them, give them a month or two and see if they can find an obvious flaw in the system. And if they can't, then I think you're, you're pretty, you know, pretty, you can be pretty confident we have a, a fully general system.

• 1:00:45Lex Fridman?

  Maybe to push back a little bit, it seems like humans are really incredible. As the the intelligence improves across all domains to take it for granted. Uh, like you mentioned, Terrence Tao, uh, these brilliant experts, they might quickly, in a span of weeks, take for granted all the incredible things it can do and then focus in while, ha ha, right there. You know, I I consider myself, uh, hu- first of all, human.

• 1:01:10Speaker 01

  Yeah.

• 1:01:12Lex Fridman?

  I identify as human.

• 1:01:16Lex Fridman?

  The I, you know, some people listen to me talk and they're like, that guy is not good at talking, the stuttering, the, you know. So like even humans have obvious across domains limits, uh, even just outside of

• 1:01:30Speaker 01

  Of course.

• 1:01:30Lex Fridman?

  calc mathematics and physics and so on. It I I wonder if it will take something like a move thirty seven. So on the positive side versus like a barrage of ten thousand cognitive tasks. Where it would be one or two where it's like.

• 1:01:46Speaker 01

  Yes.

• 1:01:47Lex Fridman?

  Holy shit.

• 1:01:47Speaker 01

  So I think there are exactly. So I think there's the sort of blanket testing to just make sure you've got the consistency. But I think there are the sort of lighthouse moments like the move thirty seven that will I would be looking for. So one would be inventing a new conjecture or a new hypothesis about physics like Einstein did. So maybe you could even run the back test of that very rigorously, like have a cutoff of knowledge, cutoff of nineteen hundred. And then give the system everything that was, you know, that was written up to nineteen hundred and then and then see if it could come up with special relativity and general relativity, right? Like Einstein did, that that would be an interesting test. Another one would be, can it invent a game like Go?

• 1:02:29Speaker 01

  Not just come up with move thirty seven, a new strategy, but can it invent a game that's as deep, as aesthetically beautiful, as elegant as Go? And those are the sorts of things I would be looking out for. Uh, and probably a system being able to do, uh, several of those things, right? For it to be very general, um, not just one domain. And so I think that would be the signs, at least that I would be looking for, that we've got a system that's AGI level. And then maybe to fill that out, you would also check the consistency, you know, make sure there's no holes in that system either.

• 1:03:01Lex Fridman?

  Yeah, something like a new conjecture or scientific discovery, that would be a cool feeling.

• 1:03:05Speaker 01

  Yeah, that would be amazing. So it's not not just helping us do that, but actually coming up with something brand new.

• 1:03:12Lex Fridman?

  And you would be in the room for that.

• 1:03:14Speaker 01

  I feel it.

• 1:03:14Lex Fridman?

  It would be like probably two or three months before announcing it. And you would just be sitting there trying not to tweet.

• 1:03:23Speaker 01

  Something like that, exactly. It's like, what is this amazing new, you know, physics idea? And then we would probably check it with world experts in that domain, right? And validate it and kind of go through its workings. And I guess it would be explaining its workings too. Yeah, be an amazing moment.

• 1:03:41Lex Fridman?

  Do you worry that we as humans, even expert humans like you, might miss it?

• 1:03:46Speaker 01

  Well, it may be pretty complicated. So it could be, the analogy I give there is, I don't think it will be

• 1:03:53Speaker 01

  totally mysterious to the, to the best human scientists. But it may be a bit like, for example, in chess, if I was to talk to Gary Kasparov or Magnus Carlsen and play a game with them and they make a brilliant move, I might not be able to come up with that move. But they could explain why afterwards that move made sense. And we were better to understand it to some degree, not to the level they do. But, you know, if they were good at explaining, which is actually part of intelligence too, is being able to explain in a simple way that what you're thinking about.

• 1:04:23Speaker 01

  I, I think that that will be very possible for the best human scientists.

• 1:04:26Lex Fridman?

  But I wonder, maybe you can, you can educate me on the side of go. I wonder if there's moves from Magnus or Gary where they at first will dismiss it as a bad move.

• 1:04:36Speaker 01

  Yeah, sure, it could be. But then afterwards, they'll figure out with their intuition that, that this why this works. And then, and then, and then empirically, the nice thing about games is, one of the great things about games is you can, it's, it's a sort of scientific test. Does it, do you win the game or not win? And then that tells you, okay, that move in the end was good. That strategy was good. And then you can go back and analyze that and, and, and, and explain even to yourself a little bit more why, explore around it. And that's how chess analysis and things like that work. So perhaps that's why my brain works like that, because I, I've been doing that since I was four. And you're trained, you know, it's sort of hardcore training in that way.

• 1:05:13Lex Fridman?

  But even, even now, like when I generate code.

• 1:05:18Lex Fridman?

  There is this kind of nuanced, fascinating contention that's happening where I might at first identify as a set of generated code as incorrect in some interesting nuanced ways. But then I'm always have to ask the question, is there a deeper insight here that I'm the one who's incorrect? And that's going to, as the systems get more and more intelligent, you're gonna have to contend with that. It's like, what, what, what do you, is this a bug or a feature where you just came up with?

• 1:05:48Speaker 01

  Yeah, and they're gonna be pretty complicated to do, but of course, it will be, you can imagine also AI systems that are producing that code or whatever that is, and then human programmers looking at it, but also not unaided with the help of AI tools as well. So it's gonna be kind of an interesting, you know, maybe different AI tools to the ones that the more, you know, kind of monitoring tools are the ones that generated it.

• 1:06:10Lex Fridman?

  So if we look at an AGI system, sorry to bring it back up, but Alpha evolve, super cool. So Alpha evolve enables, on the programming side, something like recursive self-improvement, potentially. Like what, if you can imagine what that AGI system, maybe not the first version, but a few versions beyond that, what does that actually look like? Do you think it will be simple? You think it will be something like a self-improving program in a simple one?

• 1:06:40Speaker 01

  I mean, potentially, that's possible, I would say. I'm not sure it's even desirable, because that's a kind of like hard take-off scenario. But, but you, these current systems like Alpha evolve, they have, you know, human in the loop deciding on various things, they're separate hybrid systems that interact. One could imagine eventually doing that end to end. I don't see why that wouldn't be possible, but right now, you know, I think the systems are not good enough to do that in terms of coming up with the architecture of the code. And again, it's a little bit reconnected to this idea of coming up with a new conjectural hypothesis. How, like, they're good if you give them very specific instructions about what you're trying to do. But if you give them a very vague, high-level instruction, that wouldn't work currently.

• 1:07:24Speaker 01

  Like, and I think that's related to this idea of like, invent a game as Good as go, right? Imagine that was the prompt. That's that's pretty underspecified. And so the current systems wouldn't know, I think, what to do with that, how to narrow that down to something tractable. And I think there's similar like, look, just make a better version of yourself. That's too, that's too unconstrained. But we've done it in, you know, and as you know, with Alpha evolve, like things like faster matrix multiplication. So when you, when you hone it down to a very specific thing you want, it's very good at incrementally improving that. But at the moment, these are more like incremental improvements, sort of small iterations. Whereas if, you know, i- if you wanted a big leap in understanding, you'd need a, you'd need a much larger advance.

• 1:08:08Lex Fridman?

  Yeah, but it could also be sort of to push back against hard takeoff scenario. It could be just a sequence of incremental improvements, like matrix multiplication. Like it has to sit there for days to get how to incrementally improve a thing. And that it does so recursively. And as you do more and more improvement, it'll slow down.

• 1:08:29Speaker 01

  Right.

• 1:08:29Lex Fridman?

  There'll be like a, like the path to AGI won't be like a, it'd be a gradual improvement over time.

• 1:08:37Speaker 01

  If it was just incremental improvements, that's how it would look. So the question is, could it come up with a new leap, like the Transformers architecture?

• 1:08:44Lex Fridman?

  Yeah.

• 1:08:45Speaker 01

  Could it have done that back in 2017, when, you know, we did it and Brain did it? And it's, it's not clear that, that these systems, something like Alpha evolve, wouldn't be able to do, make such a big leap. So for sure, these systems are good. We have systems, I think, that can do incremental hill climbing. And that's a kind of bigger question about, is that all that's needed from here? Or do we actually need one or two more big breakthroughs?

• 1:09:08Lex Fridman?

  And can the same kind of systems provide the breakthroughs also? So make it a bunch of S-curves, like incremental improvement, but also every once in a while leaps.

• 1:09:18Speaker 01

  Yeah, I don't think anyone has systems that can have shown unequivocally those big leaps. That, that, right? We have a lot of systems that do the hill climbing of the S curve that you're currently on.

• 1:09:28Lex Fridman?

  Yeah. And that would be the move thirty seven is a.

• 1:09:31Speaker 01

  Yeah, I think would be a leap, something like that.

• 1:09:35Lex Fridman?

  Do you think the scaling laws are holding strong on pre-training, post-training, test time, compute? Do you, on the flip side of that, anticipate AI progress hitting a wall?

• 1:09:47Speaker 01

  We certainly feel there's a lot more room just in the scaling. So actually, all steps, pre-training, post-training, and inference time. So there's sort of three scalings that are happening concurrently. And we, again, there, it's about how innovative you can be. And we, you know, we pride ourselves on having the broadest and deepest research bench. We have amazing, you know, incredible researchers and people like Noam Chazir, who, you know, came up with Transformers and Dave Silver, you know, who led the AlphaGo project and so on. And it's, it's, it's, that research base means that if some new, new breakthrough is required,

• 1:10:28Speaker 01

  like an AlphaGo or Transformers, I would back us to be the place that does that. So I'm actually quite like it when the terrain gets harder, right? Because then it veers more from just engineering to, to true research and, you know, research plus engineering, and that's our sweet spot. And I think that's harder. It's harder to invent things than to, than to, you know, fast follow. And so, you know, we don't know. I would say it's a, it's kind of 50/50 whether new things are needed or whether the scaling the existing stuff is going to be enough. And so in true kind of empirical fashion, we're pushing both of those as hard as possible. The new Blue Sky ideas and, you know, maybe about half our resources are on that.

• 1:11:10Speaker 01

  And then, and then scaling to the max, the current, the current capabilities. And we're still seeing some, you know, fantastic progress on each different version of Gemini.

• 1:11:22Lex Fridman?

  That's interesting, the way you put it, in terms of the deep bench. That if progress towards AGI is more than just scaling compute, so the engineering side of the problem, and is more on the scientific side where there's breakthroughs needed, then you feel confident DeepMind as well, Google DeepMind as well positioned to

• 1:11:45Speaker 01

  Yes.

• 1:11:45Lex Fridman?

  kick ass in that domain.

• 1:11:47Speaker 01

  Well, I mean, if you look at the history of the last decade or 15 years, it's been, I mean, maybe, I don't know, 80, 90% of the breakthroughs that underpins modern AI field today was from, you know, originally Google Brain, Google Research and DeepMind. So, yeah, I would back that to continue, hopefully.

• 1:12:05Lex Fridman?

  So on the data side, are you concerned about running out of high-quality data, especially high-quality human data?

• 1:12:10Speaker 01

  I'm not very worried about that, partly because I think there's enough data, and it's been proven to get the systems to be pretty good. And this goes back to simulations again. If you have, do you have enough data to make simulations so that you can create more synthetic data that are from the right distribution? Obviously, that's the key. So you need enough real-world data in order to be able to create those kinds of generator, data generators. And I think that we're at that step at the moment.

• 1:12:39Lex Fridman?

  Yeah, you've done a lot of incredible stuff on the side of science and biology, doing a lot with not so much data.

• 1:12:45Speaker 01

  Yeah.

• 1:12:46Lex Fridman?

  I mean, it's still a lot of data, but I guess enough takeoff.

• 1:12:49Speaker 01

  Get that going, exactly.

• 1:12:50Lex Fridman?

  Yeah.

• 1:12:51Speaker 01

  Exactly.

• 1:12:52Lex Fridman?

  How crucial is the scaling of compute to building AGI? This is a question that's an engineering question. It's a almost geopolitical question because it also integrated into that is the supply chains and energy.

• 1:13:07Speaker 01

  Yes.

• 1:13:07Lex Fridman?

  A thing that you care a lot about, which is potentially fusion. So innovating on the side of energy also. Do you think we're going to keep scaling compute?

• 1:13:15Speaker 01

  I think so, for several reasons. I think compute, there's the amount of compute you have for training. Often, it needs to be co-located. So actually, even like, you know, bandwidth constraints between data centers can affect that. So it's, it's, it's, there's additional constraints even there. And that, that's important for training, obviously, the largest models you can. But there's also Because now AI systems are in products and being used by billions of people around the world, you need a ton of inference compute now. Um, and then on top of that, there's the thinking systems, the new paradigm uh of the last year that uh where they get smarter, the longer amount of inference time you give them at test time. So all of those things need a lot of compute and I don't really see that slowing down.

• 1:14:00Speaker 01

  Um, and as AI systems become better, they'll become more useful and there'll be more demand for them. So both from the training side, the training side actually is is only just one part of that, may even become the smaller part of of what's needed um uh uh in the overall compute that that's required.

• 1:14:16Lex Fridman?

  Yeah, that's one sort of almost meme-y kind of thing, which is like the success and the incredible aspects of VO three. There's uh people kind of make fun of like the more successful it becomes, the you know, the servers are sweating.

• 1:14:29Speaker 01

  Yes.

• 1:14:29Lex Fridman?

  To do the difference.

• 1:14:31Speaker 01

  Yeah, yeah, exactly. We did a little video of of of the servers frying eggs and things and um, that's right, and and and we're gonna have to figure out how to do that. Um, there's a lot of interesting hardware innovations that we do. As you know, we have our own TPU line and we're looking at like inference only things, inference only chips, and how we can make those more efficient. We're also very interested in building AI systems and we have done the help with energy usage. So help um data center energy like for the cooling systems be efficient, um grid optimization. Um, and then eventually things like helping with uh plasma containment fusion reactors. We've done lots of work on that with Commonwealth Fusion and also uh one could imagine reactor design. Um, and then material design, I think, is one of the most exciting.

• 1:15:15Speaker 01

  New types of solar materials, solar panel material, super room temperature superconductors has always been on my list of dream breakthroughs and um optimal batteries. And I think a solution to any, you know, one of those things would be absolutely revolutionary for, you know, climate and energy usage. And we're probably close, you know, and again, in the next five years to having AI systems that can materially help with those problems.

• 1:15:38Lex Fridman?

  If you were to bet, sorry for the ridiculous question, but what what is the main source of energy? Eh in like twenty, thirty, forty years, you think it's gonna be nuclear fusion?

• 1:15:49Speaker 01

  I think fusion and solar are the two that I I would bet on. Um solar, I mean, you know, it's the fusion reactor in the sky, of course. And I think really the the problem there is is is batteries and transmission. So, you know, as well as more efficient, more more efficient solar material, perhaps eventually, you know, in space. You know, these kind of Dyson sphere type ideas. And fusion, I think, is definitely doable, seems. Uh if we have the right design of reactor and we can control the plasma and uh fast enough and so on. And I think both of those things will actually get solved. So we'll probably have at least, those are probably the two primary sources of renewable, clean, almost free, or perhaps free energy.

• 1:16:32Lex Fridman?

  What a time to be alive. If I uh traveled into the future with you a hundred years from now. How much would you be surprised if we've passed a type one Carter Chef scale civilization?

• 1:16:45Speaker 01

  I would not be that surprised if there's a like a hundred year time scale from here. I mean, I think it's pretty clear if we crack the energy problems in one of the ways we've just discussed fusion or or very efficient solar. Um then if energy is kind of free and renewable and clean. Um then that solves a whole bunch of other problems. So, for example, the water access problem goes away because you can just use desalination. We have the technology, it's just too expensive. So only, you know, fairly wealthy countries like Singapore and Israel and so on like actually use it. But but if it was uh cheap, then every then, you know, all countries that have a coast could. But also you'd have unlimited rocket fuel. You could just separate seawater out into hydrogen and oxygen using energy, and that's rocket fuel.

• 1:17:30Speaker 01

  So uh combined with, you know, Elon's amazing self-landing rockets. Then it could be like you sort of like a bus service to to space. So that opens up, you know, incredible new resources and domains. Uh asteroid mining, I think, will become a thing and maximum human flourishing to the stars. Like, that's what I uh dream about as well, is like Carl Sagan's sort of idea of bringing consciousness to the universe, waking up the universe. And I I think human civilization will do that in the full sense of time if we get AI right. And uh and and and crack some of these problems with it.

• 1:18:04Lex Fridman?

  Yeah, I wonder what it would look like if you're just a tourist flying through space. You would probably notice Earth, because if you solve the energy problem, you would see a lot of space rockets, probably. So it would be like traffic here in London. But in space, it's just a lot of rockets.

• 1:18:21Speaker 01

  Yes.

• 1:18:22Lex Fridman?

  And then you would probably see floating in space some kind of source of energy like solar.

• 1:18:27Speaker 01

  Yep.

• 1:18:28Lex Fridman?

  Potentially. So Earth would just look more on the surface, more technological. And then you would use the power of that energy then to preserve the natural.

• 1:18:39Speaker 01

  Yes.

• 1:18:39Lex Fridman?

  Like the rainforest and all that kind of stuff.

• 1:18:41Speaker 01

  Because for the first time in in human history, we wouldn't be resource constrained. And I think that could be amazing new era for humanity where it's not zero sum. Right? I have this land, you don't have it. Or if we take, you know, if the tigers have their forest, then the the local villagers can't. What are they going to use? I I I think that this will help a lot. No, it won't solve all problems. Because there's still other human foibles that will will will still exist. But it will at least remove one, I think, one of the big vectors, which is scarcity of resources. You know, including land and more materials and energy. And we know we should be, as some of us call it, like and others call it about this kind of radical abundance era.

• 1:19:24Speaker 01

  Where there's plenty of resources to go around. Of course, the next big question is making sure that that's fairly, you know, shared fairly. And everyone in society benefits from that.

• 1:19:34Lex Fridman?

  So there is something about human nature where I go, you know, it's like Borat, like my neighbor. Like I like you start trouble. We we do start conflicts. And that's why games throughout, as I'm learning actually more and more, even in ancient history, serve the purpose of pushing people away from war, actually, hot war. So maybe we can figure out increasingly sophisticated video games that pulls, they they give us that. Uh, that scratch the itch of like conflict, whatever that is, but about us, the human nature,

• 1:20:12Lex Fridman?

  and then avoid the actual hot wars that would come with increasingly sophisticated technologies. Because we're now, we've long passed the stage where the weapons we're able to create can actually just destroy all of human civilization. So it's no longer, um, that's no longer a great way to, uh, start shit with your neighbor. It's better to play a game of chess.

• 1:20:37Speaker 01

  Or football.

• 1:20:37Lex Fridman?

  Or football.

• 1:20:38Speaker 01

  Yeah.

• 1:20:38Lex Fridman?

  Yeah.

• 1:20:39Speaker 01

  And I think, I mean, I think that's what my modern sport is so, and I love football, watching it. And I just feel like, uh, and I used to play it a lot as well. And it's, it's, it's, it's, it's very visceral and it's tribal. And I think it does channel a lot of those energies into a, which I think is a kind of human need to belong to some, some group and, um, but into a, into a, into a fun way, a, a healthy way and, and a not, a not destructive way, kind of constructive, uh, thing. And I think going back to games again is, I think they're originally why they're so great as well for kids to play, things like chess is they're great little microcosm simulations of the world. They're, they're simulations of the world too. They're simplified versions of some real world situation, whether it's poker or, or go or chess, different aspects or diplomacy,

• 1:21:24Speaker 01

  different aspects of, of the real world. And it allows you to practice at them too. And, and cause you know, how many times do you get to practice a massive decision moment in your life? You know, what job to take, what university to go to, you know, you, you get maybe, I don't know, a dozen or so key decisions one has to make. And you've got to make those as best as you can. Um, and games is a kind of safe environment, repeatable environment where you can get better at your decision-making process. Um, and it maybe has this additional benefit of channeling some energies into, uh, into more creative and constructive pursuits.

• 1:21:58Lex Fridman?

  Well, I think it's also really important to practice, um, losing and winning.

• 1:22:02Speaker 01

  Right.

• 1:22:03Lex Fridman?

  Like losing is a really, you know, that's why I love games. That's why I love even, um, things like, uh, Brazilian Jiu-Jitsu.

• 1:22:09Speaker 01

  Yeah.

• 1:22:09Lex Fridman?

  Where you can get your ass kicked in a safe environment over and over. It reminds you about the way, about physics, about the way the world works, about sometimes you lose, sometimes you win, you can still be friends with everybody. But that, that feeling. Of losing, I mean, it's a weird one for us humans to like, really like make sense of like, that's just part of life. That is a fundamental part of life is losing.

• 1:22:33Speaker 01

  Yeah, and I think in martial arts, as I understand it, but also in things like light chess, at least the way I took it, it's a lot to do with self-improvement, self-knowledge, you know, that, okay, so I did this thing. It's not about really being the other person. It's about maximizing your own potential. If you do it in a healthy way, you learn to use victory and losses in a way. Don't get carried away with victory and think you're just the best in the world. And the losses keep you humble and always knowing there's always something more to learn. There's always a bigger expert that you can mentor you. You know, I think you learn that. I'm pretty sure in martial arts and I think that's also the way that at least I was trained in chess. And so in the same way, and it can be very hardcore and very important. And of course you want to win,

• 1:23:18Speaker 01

  but you also need to learn how to deal with setbacks in a healthy way that, and wire that feeling that you have when you lose something into a constructive thing of, next time I'm going to improve this, right? Or get better at this.

• 1:23:31Lex Fridman?

  There is something that's a source of happiness, a source of meaning, that improvement step. It's not about the winning or losing.

• 1:23:38Speaker 01

  Yes, the mastery. There's nothing more satisfying in a way. It's like, oh, wow, this thing I couldn't do before, now I can. And again, games and physical sports and mental sports, they're ways of measuring. They're beautiful because you can measure that progress.

• 1:23:53Lex Fridman?

  Yeah, I mean, there's something about, that is why I love role-playing games, like the number go up of like on the skill tree. Like literally, that is a source of meaning for us humans, whatever our.

• 1:24:03Speaker 01

  Yeah, we're quite addicted to this sort of, yeah, these numbers going up. And maybe that's why we made games like that, because obviously that is something, we're hill climbing systems ourselves, right?

• 1:24:15Lex Fridman?

  Yeah, it would be quite sad if we didn't have any mechanism.

• 1:24:19Speaker 01

  Different color belts. We do this everywhere, right? Where we just have this thing that.

• 1:24:24Lex Fridman?

  And I don't want to dismiss that. There is a source of deep meaning for us humans.

• 1:24:29Lex Fridman?

  So one of the incredible stories on the business on the leadership side is what Google has done over the past year. So I think it's fair to say that Google was losing on the LLM product side a year ago with Gemini one five and now it's winning with Gemini two five and you took the helm and you led this effort. What did it take to go from, let's say, quote unquote, losing to quote unquote, winning in the span of a year?

• 1:24:56Speaker 01

  Yeah, well firstly, it's absolutely incredible team that we have, you know, led by Corey and Jeff Dean and Oriole and the amazing team we have on Gemini, absolutely world class. So you can't do it without the best talent. And of course, you have, you know, we have a lot of great compute as well, but then it's the research culture we created, right? And basically coming together, both different groups in Google, you know, there was Google Brain, world-class team and then the old DeepMind and pulling together all the best people and the best ideas and gathering around to make the absolute greatest system we could. And it was being hard, but we're all very competitive

• 1:25:38Speaker 01

  and we, you know, love research. This is so fun to do and we, you know, it's great to see our trajectory. It wasn't a given, but we're very pleased with the, where we are and the rate of progress is the most important thing. So if you look at where we've come to from two years ago to one year ago to now, you know, I think our, we call it relentless progress, along with relentless shipping of that progress, is being very successful. And, you know, it's unbelievably competitive, the whole space, the whole AI space, with some of the greatest entrepreneurs and leaders and companies in the world all competing now because everyone's realized how important AI is. And it's very, you know, been pleasing for us to see that progress.

• 1:26:21Lex Fridman?

  You know, Google's a gigantic company. Can you speak to the natural things that happen in that case? Is the bureaucracy that emerges? Like, you want to be careful, like? You know, like the the the natural kind of there's there's meetings and there's managers and that. Like, what what are some of the challenges from a leadership perspective, breaking through that in order to, like you said, ship? Like, the the number of products.

• 1:26:43Speaker 01

  Yeah.

• 1:26:44Lex Fridman?

  Gemini related products that have been shipped over the past year is just insane.

• 1:26:47Speaker 01

  Right, it is. Yeah, exactly. That's that's what relentlessness looks like. Um, I think it's it's a question of like any big company, you know, ends up having uh, a lot of layers of management and things like that. It's sort of the nature of how it works. Um, but I still operate and I was always operating with old DeepMind as a as a startup still. Large one, but still as a startup. And that's what we still act like today with Google DeepMind. And acting with decisiveness and the energy that you get from the best smaller organizations. And we try to get the best of both worlds, where we have this incredible billions of users, surfaces, uh, incredible products that we can power up with our AI and our and our research.

• 1:27:31Speaker 01

  Um, and that's amazing. And you can, you know, that's very few places in the world you can get that. Do incredible world-class research on the one hand, and then plug it in and and improve billions of people's lives the next day. Uh, that's a pretty amazing combination. And we're continually fighting and cutting away bureaucracy to allow the research culture and the relentless shipping culture to flourish. And I think we've got a pretty good balance whilst being responsible with it. You know, as you have to be as a large company and also uh, with a number of, you know, uh, huge products, surfaces that we have.

• 1:28:04Lex Fridman?

  Uh, so a funny thing you mentioned about like the the surface of the billion. I I had a conversation with a guy named, um, brilliant guy, uh, here at the British Museum called Erwin Finkel. He's a world expert at cuneiforms, which is a ancient writing on tablets. And he doesn't know about chat GPT or Gemini. He doesn't even know anything about AI. But his first encounter with this AI is AI mode on Google.

• 1:28:31Speaker 01

  Yes.

• 1:28:32Lex Fridman?

  He's like, is that what you're talking about? This AI mode? And you know, it's just it's just a reminder that there's a large part of the world that doesn't know about this AI thing.

• 1:28:41Speaker 01

  Yeah, I know. It's funny because if you live on uh, X and Twitter and I mean, it's sort of, at least my feed, it's all AI. And and there's certain places where, you know, in the valley and certain pockets where everyone's just all they're thinking about is AI, but. A lot of the normal world hasn't hasn't come across it yet.

• 1:28:58Lex Fridman?

  But that's a great responsibility to their first interaction.

• 1:29:02Speaker 01

  Yep.

• 1:29:03Lex Fridman?

  The grand scale of the rural India or anywhere across the world.

• 1:29:07Speaker 01

  Right, right. And we want it to be as good as possible. And in a lot of cases, it's just under the hood powering, making something like maps or search work better. And ideally, for a lot of those people, should just be seamless. It's just new technology that makes their lives more productive and helps them.

• 1:29:24Lex Fridman?

  A bunch of folks on the Gemini product and engineering teams spoken extremely highly of you on another dimension that I almost didn't even expect because I kind of think of you as the like deep scientist and caring about these big research scientific questions. But they also said you're a great product guy, like how to create a thing that a lot of people would use and enjoy using. So can you maybe speak to what it takes to create an AI based product that a lot of people enjoy using?

• 1:29:52Speaker 01

  Yeah, well, I mean, again, that comes back from my game design days where I used to design games for millions of gamers. People forget about that. I've had experience with cutting edge technology in product. That is how games was in the 90s. And so I love actually the combination of cutting edge research and then being applied in a product and to power a new experience. And so I think it's the same skill really of imagining what it would be like to use it viscerally and having good taste. Coming back to earlier, the same thing that's useful in science, I think is can also be useful in in product design. And I've just had a very, you know, always been a sort of multi-disciplinary person.

• 1:30:36Speaker 01

  So I don't see the boundaries really between arts and sciences or product and research. It's a continuum for me. I mean, I only work on, I like working on products that are cutting edge. I wouldn't be able to have cutting edge technology under the hood. I wouldn't be excited about them if they were just run of the mill products. So it requires this invention creativity capability.

• 1:30:57Lex Fridman?

  What are some specific things you kind of learned about when you, even on the LLM side, you're interacting with Gemini? You're like, this doesn't feel like the layout, the interface, maybe the trade off between the latency, like how?

• 1:31:13Lex Fridman?

  How to present to the user, how long to wait? And how that waiting is shown or the reasoning capabilities? There's some interesting things, because like you said, it's a very cutting edge. We don't know.

• 1:31:23Speaker 01

  Yeah.

• 1:31:24Lex Fridman?

  How to present it, how to present it correctly. So is there some specific things you've learned?

• 1:31:29Speaker 01

  I mean, it's such a fast evolving space. We're evaluating this all the time. But where we are today is that you want to continually simplify things. The, whether that's the interface or the what you build on top of the model. You kind of want to get out of the way of the model. The model train is coming down the track and it's improving unbelievably fast. This relentless progress we talked about earlier. You know, you look at 2.5 versus 1.5 and it's just a gigantic improvement. And we expect that again for the future versions. And so the models are becoming more capable. So you've got the interesting thing about the design space in today's world, these AI first products is you've got to design not for what the thing can do today, the technology can do today, but in a year's time.

• 1:32:11Speaker 01

  So you actually have to be a very technical product person because you've got to kind of have a good intuition for and feel for, okay, that thing that I'm dreaming about now can't be done today. But is the research track on schedule to basically intercept that in six months or a year's time? So you've kind of got to intercept where this highly changing technology is going. As well as the new capabilities are coming online all the time that you didn't realize before that can allow like deep research to work. Or now we've got video generation. What do we do with that? This multimodal stuff, you know, is it? One question I have is, is it really going to be the current UI that we have today? These text box chats?

• 1:32:53Speaker 01

  Seems very unlikely, given, once you think about these super multimodal systems. Shouldn't it be something more like Minority Report where you're, you're sort of vibing with it in a, in a, in a kind of collaborative way, right? It seems very restricted today. I think we'll look back on today's interfaces and products and systems as quite archaic in, maybe in just a couple of years. So I think there's a lot of space actually for innovation to happen on the product side as well as the, the research side.

• 1:33:21Lex Fridman?

  And then we were offline talking about this keyboard is the, the open question is how, when? And how much will we move to audio? As the primary way of interacting with the machines around us versus typing stuff.

• 1:33:34Speaker 01

  Yeah, I mean, typing is a very low bandwidth way of doing, even if you're very fast, you know, typer. And I think we're gonna have to start utilizing other devices, whether that's smart glasses, you know, audio, earbuds, and eventually maybe some sorts of neural devices where we can increase the the input and the output bandwidth to something, you know, maybe a hundred X of what is today.

• 1:33:58Lex Fridman?

  I think that, you know, under appreciated art form is the interface design. But I think you can not unlock the power of the intelligence of a system if you don't have the right interface. Their interface is really the way you unlock its power.

• 1:34:12Speaker 01

  Yeah.

• 1:34:12Lex Fridman?

  It's such an interesting question of how to do that.

• 1:34:15Speaker 01

  Yeah.

• 1:34:15Lex Fridman?

  So how you would think like getting out of the way isn't real art form.

• 1:34:20Speaker 01

  Yes, you know, it's the sort of thing that I guess Steve Jobs always talked about, right? It's simplicity, beauty and elegance that we want, right? And we're not there, nobody's there yet, in my opinion. And that's what I would like us to get to. Again, it sort of speaks to like go again, right? As a game, the most elegant, beautiful game. Can you, you know, can you make an interface as beautiful as that? And actually, I think we're gonna enter an era of AI-generated interfaces that are probably personalized to you so it fits the way that you, your aesthetic, your feel, the way that your brain works. And and and and the AI kind of generates that depending on the task, you know, that feels like that's probably the direction we'll end up in.

• 1:34:58Lex Fridman?

  Yeah, because some people are power users and they want every single parameter on the screen, everything. And everything based like perhaps me with a keyboard, keyboard-based navigation and like to have shortcuts for everything. And some people like the minimalism.

• 1:35:11Speaker 01

  Just hide all of that complexity. Yeah, exactly.

• 1:35:13Lex Fridman?

  Yeah, well, I'm glad you have a Steve Jobs mode in you as well. This is great Einstein mode, Steve Jobs mode. All right, let me try to trick you into answering a question. When when will Gemini three come out? This is before or after DTS six? The world waits for both. And what does it take? To go from two five to three O because it seems like there's been a lot of releases of two five, which are already leaps in performance. So what, what does it even mean to go to a new version? Is it about performance? This is about a completely different flavor of an experience?

• 1:35:49Speaker 01

  Yeah, well, so the way it works with our different version numbers is we, you know, we try to collect, so maybe it takes, you know, roughly six months or something to, to do a new kind of full run and the full productization of a new version. And during that time, lots of new interesting research iterations and ideas come up and we sort of collect them all together that, you know, you could imagine the last six months worth of interesting ideas on the architecture front. Maybe it's on the data front. It's like many different possible things and we collect, package that all up, test which ones are likely to be useful for the next iteration and then bundle that all together. And then we start the new, you know, giant hero training run, right?

• 1:36:34Speaker 01

  And, and then, and then of course that gets monitored. And then at the end, then there's the, of the pre-training, then there's all the post-training. There's many different ways of doing that, different ways of patching it. So there's a whole experiment and phase there, which you can also get a lot of gains out. And that's where you see the version numbers usually referring to the base model, the pre-trained model. And then the interim versions of two point five, you know, and the different sizes and the different little additions, they're often patches or post-training ideas that can be done afterwards off the same basic architecture. And then of course, on top of that, we also have different sizes, pro and flash and flashlight that are often distilled from the biggest ones, you know, the flash model from the pro model. And that means we have a range of different choices.

• 1:37:19Speaker 01

  If you are the developer of do you want to prioritize performance or speed, right? And cost. And we like to think of this Pareto frontier of, of, you know, on the one hand, the Y-axis is, you know, like performance and then the, the X-axis is, you know, cost or latency and, and speed, basically. And we, we have models that completely define the frontier. So whatever your trade-off is that you want as an individual user or as a, as a developer, you should find one of our models satisfies that constraint.

• 1:37:51Lex Fridman?

  So behind the version changes, there is a big hero run.

• 1:37:55Speaker 01

  Yes.

• 1:37:55Lex Fridman?

  And then. There's just an insane complexity of productization. Then there's the distillation of the different sizes along that parade of front. And then as with each step you take, you realize there might be a cool product. There's side quests.

• 1:38:13Speaker 01

  Yes, exactly.

• 1:38:14Lex Fridman?

  But and then you also don't want to take too many side quests because then you have a million versions of a million products.

• 1:38:19Speaker 01

  Yes, precisely.

• 1:38:20Lex Fridman?

  It's very unclear.

• 1:38:21Speaker 01

  Yeah.

• 1:38:21Lex Fridman?

  But you also get super excited because it's super cool.

• 1:38:23Speaker 01

  Yeah.

• 1:38:24Lex Fridman?

  Like, how does even you look at Vios, very cool. How does it fit into the bigger thing?

• 1:38:28Speaker 01

  Exactly, exactly. And then you're constantly, this process of converging upstream, we call it, you know, ideas from the, from the product surfaces or, or from the post-training and, and even further downstream than that, you, you kind of upstream that into the, the core model training for the next run. Right? So then the main model, the main Gemini track becomes more and more general. And eventually, you know, AGI.

• 1:38:54Lex Fridman?

  One hero.

• 1:38:55Speaker 01

  Yes, exactly. A few hero runs later.

• 1:38:57Lex Fridman?

  Yeah, so sometimes when you release these new versions or every version, really, are benchmarks productive or counterproductive for showing the performance of a model?

• 1:39:10Speaker 01

  You need them and, and I bet it's important that you don't overfit to them. Right? So there shouldn't be the end with a be all and end all. So there's, there's LM Arena or it used to be called ELEMSIS. That's one of them that turned out sort of organically to be one of the, the main ways people like to test these systems, at least the chatbots. Obviously, there's loads of academic benchmarks on, from, from the test mathematics and coding ability, general language ability, science ability and so on. And then we have our own internal benchmarks that we care about. It's a kind of multi-objective, you know, optimization problem, right? You, you don't want to be good at just one thing. We're trying to build general systems that are good across the board. And you try and make no regret improvements. So where you're improving like, you know, coding,

• 1:39:54Speaker 01

  but it doesn't reduce your performance in other areas, right? So that's the hard part because you can, of course, you could put more coding data in or you could put more, um, I don't know, gaming data in. But then does it make worse your language system? Or, or in your translation systems and other things that you care about? So it's, you've got to kind of continually monitor this increasingly large and larger suite of, of benchmarks. And also there's, uh, when you stick them into products, these models, you also care about the direct usage and the direct stats and the signals that you're getting from the end users, whether they're coders or, or, or the average person using, uh, using the chat interfaces.

• 1:40:34Lex Fridman?

  Yeah, because ultimately you want to measure the usefulness. It's so hard to convert that into a number.

• 1:40:39Speaker 01

  Right.

• 1:40:39Lex Fridman?

  It's, it's really vibe based benchmarks across a large number of users and it's hard to know. And I, it would be just terrifying to me to, you know, you have a much smarter model, but it's just something vibe based. It's not, not, not quite working. That's such a scary, because, and everything you just said, it has to be smart and useful across so many domains. So you, you get super excited because it's all of a sudden solving programming problems that never been able to solve before. But now it's crappy poetry or something. And it's just, I don't know, that's a stressful, that's so difficult.

• 1:41:16Speaker 01

  To balance, yeah.

• 1:41:17Lex Fridman?

  To balance it because you can't really trust the benchmarks. You really have to trust the end users.

• 1:41:21Speaker 01

  Yeah. And then other things that are even more esoteric come into play like, um, you know, the style of the persona of the, the, the system, you know, how it, you know, is it verbose? Is it succinct? Is it humorous? You know, and, and different people like different things. So, um, you know, it's very interesting. It's almost like cutting edge part of psychology research or person, personality research. You know, I used to do that in my PhD, like five factor personality. What do we actually want our systems to be like? And different people will like different things as well. So these are all just sort of new problems in product space that I don't think have ever really been tackled before, but, um, we're going to sort of rapidly have to deal with now.

• 1:42:01Lex Fridman?

  I think it's a super fascinating space, developing the character of the thing.

• 1:42:05Speaker 01

  Yeah.

• 1:42:05Lex Fridman?

  And in so doing, it puts a mirror to ourselves. What are the kind of things, um, that we like? Cause prompt engineering allows you to control a lot of those elements, but can the product? Make it easier for you to control the different flavors of those experiences, the different characters that you interact with.

• 1:42:25Speaker 01

  Yeah, exactly. So.

• 1:42:27Lex Fridman?

  So what's the probability of Google DeepMind winning?

• 1:42:30Speaker 01

  Well, I don't see it as sort of winning. I mean, I think we need to think winning is the wrong way to look at it, given how important and consequential what it is we're building. So funnily enough, I don't, I try not to view it like a game or competition, even though that's a lot of my mindset. It's, it's about, in my view, all of us have, those of us at the leading edge have a responsibility to steward this unbelievable technology that could be used for incredible good, but also has risks. Steward it safely into the world for the benefit of humanity. That's always what I've dreamed about and what we've always tried to do. And I hope that's what eventually the community, maybe the international community, will rally around when it becomes obvious that as we get closer and closer to AGI,

• 1:43:14Speaker 01

  that that's what's needed.

• 1:43:17Lex Fridman?

  I agree with you. I think that's beautifully put. You've said that you talk to and are on good terms with the leads of some of these labs as the competition heats up.

• 1:43:29Lex Fridman?

  How hard is it to maintain sort of those relationships?

• 1:43:32Speaker 01

  It's been okay so far. I try to pride myself in being collaborative. I'm a collaborative person. Research is a collaborative endeavor. Science is a collaborative endeavor, right? It's all good for humanity in the end. If you cure incredible, you know, terrible diseases and you come with an incredible cure, this is net win for humanity. And the same with energy, all of the things that I'm interested in, in, in helping solve with AI. So I just want that technology to exist in the world and be used for the right things. And, and, and the, the kind of the benefits of that, the productivity benefits of that being shared for every, the benefit of everyone. So I try to maintain good relations with all the leading lab people. They're very interesting characters, many of them, as you might expect. But yeah, I'm on good terms, I hope, with pretty much all of them.

• 1:44:17Speaker 01

  And I, I think that's going to be important when, when things get even more serious than they are now, that there are those communication channels. And that's what will facilitate cooperation or collaboration, if that's what is required, especially on things like safety.

• 1:44:34Lex Fridman?

  Yeah, I hope there's some collaboration on stuff that's sort of less high stakes. And in so doing, serves as a mechanism for maintaining friendships and relationships. So for example, I think the internet would love it if you and Elon somehow collaborated on creating a video game, that kind of thing. That I think that enables camaraderie and good terms. And also you two are legit gamers, so it's just fun to.

• 1:44:56Speaker 01

  Yeah.

• 1:44:56Lex Fridman?

  Fun to.

• 1:44:57Speaker 01

  Yeah, that would be awesome. And we've talked about that in the past and it may be a cool thing that, that, you know, we can do. And I agree with you, it'd be nice to have um kind of side projects in a way where, where one can just lean into the collaboration aspect of it. And it's a sort of uh win-win for both sides. And it's um, and it kind of builds up that, that, that collaborative muscle.

• 1:45:18Lex Fridman?

  I see the scientific endeavor as that kind of side project for humanity.

• 1:45:23Speaker 01

  Yeah.

• 1:45:23Lex Fridman?

  And I, I think deep, Google Deep Mind has been really pushing that. Uh, I would love it if to see other labs do more scientific stuff and then collaborate because it just seems like easier to collaborate on the big scientific questions.

• 1:45:35Speaker 01

  I agree, and I would love to see a lot of people, a lot of the other labs talk about science, but I think we're really the only ones using it for science and doing that. And that's why projects like AlphaFold are so important to me. And I think to our mission is to show uh how AI can this, you know, be clearly used in a very concrete way for the benefit of humanity. And, and also we spun out companies like Isomorphic off the back of AlphaFold to do drug discovery. And it's going really well and build sort of, you know, you can think of build additional AlphaFold type, type systems to go into chemistry space to help accelerate drug design. And the examples I think we need to show uh and society needs to understand are what AI can bring, these huge benefits.

• 1:46:16Lex Fridman?

  Well, from the bottom of my heart, thank you for pushing the scientific efforts forward with rigor, with fun, with humility, all of it. I just love to see it. And still talking about P equals NP, I mean, it's just incredible. So I love it. Uh, there, there's been uh seemingly a war for talent. Some of it is meme, I don't know. What do you think about Meta buying up talent with huge salaries and, and the heating up of this battle for talent? And I, I should say that I think a lot of people see Deep Mind as a really great place to do uh cutting-edge work for the reasons that you've outlined.

• 1:46:50Speaker 01

  Yeah.

• 1:46:51Lex Fridman?

  Is like, there's this vibrant scientific culture.

• 1:46:55Speaker 01

  Yeah, well, look, of course, you know, there's a strategy that Meta is taking right now. I think that from my perspective at least, I think the people that are real believers in the mission of AGI and what it can do and understand the real consequences, both good and bad from that, and what's what that responsibility entails. I think they're mostly doing it to be like myself, to be on the frontier of that research. So, you know, they can help influence the way that goes and steward that technology safely into the world. And, you know, Meta right now are not at the frontier. Maybe they'll they'll manage to get back on there. And, you know, it's probably rational what they're doing from their perspective because they're behind and they need to do something. But I think there's more important things than than just money.

• 1:47:37Speaker 01

  Of course, one has to pay, you know, people their market rates and all of these things, and that continues to go up. But as and and I was expecting this because more and more people are finally realizing leaders of companies, what I've always known for thirty plus years now, which is that AGI is the most important technology probably that's ever going to be invented. So in some senses, it's rational to be doing that. But I also think there's a much bigger question. I mean, people in AI these days are very well paid. You know, I remember when we were starting out back in 2010, you know, I didn't even pay myself a couple of years because it wasn't enough money, we couldn't raise any money. And these days interns are being paid, you know, the amount that we raised as our first entire seed round. So it's pretty funny.

• 1:48:20Speaker 01

  And I remember the days where we used, I used to have to to work for free and and almost pay my own way to do an internship. Right now, it's all the other way around. But that's just how it is, it's the new world. And but I think that, you know, we've been discussing like what happens post AGI and energy systems are solved and so on. What is even money going to mean? So I think, you know, and the economy and and we're going to have much bigger issues to work through. And how does the economy function in that world and companies? So I think, you know, it's a little bit of a side issue about salaries and things of like that today.

• 1:48:53Lex Fridman?

  Yeah, when you're facing such gigantic consequences and gigantic fascinating scientific questions.

• 1:48:59Speaker 01

  Which maybe are only a few years away, so.

• 1:49:01Lex Fridman?

  So on the practical sort of pragmatic sense, if we zoom in on jobs, we can look at programmers because it seems like AI systems are currently doing incredibly well at programming and increasingly so. So a lot of people that. Program for a living, love programming, are worried they will lose their jobs. How worried should they be, do you think? And what's the right way to sort of adjust to the new reality and ensure that you survive and thrive as a human in the programming world?

• 1:49:32Speaker 01

  Well, it's interesting that programming, and it's again count intuitive to what we thought years ago, maybe that some of the skills that we think of as harder skills are turned out maybe to be the easier ones for various reasons. But you know, coding and maths, because you can create a lot of synthetic data and verify if that data is correct. So because of that nature of that, it's easier to make things like synthetic data to train from. It's also an area, of course, we're all interested in because as programmers, right? To help us and get faster at it and more productive. So I think the, for the next era, like the next five, 10 years, I think what we're going to find is people who are kind of embrace these technologies, become almost at one with them, whether that's in the creative industries or the technical industries, will become sort of superhumanly productive, I think.

• 1:50:17Speaker 01

  So the great programmers will be even better, but they'll be even 10x even what they are today. And because there you'll be able to use their skills to utilize the tools to the maximum, exploit them to the maximum. And so I think that's what we're going to see in the next domain. So that's going to cause quite a lot of change, right? And so that's coming, a lot of people benefit from that. So I think one example of that is if coding becomes easier, it becomes available to many more creatives to do more. And but I think the top programmers will still have huge advantages as terms of specifying, going back to specifying what the architecture should be, the question should be how to guide these coding assistants

• 1:51:00Speaker 01

  in a way that's useful, you know, check whether the code they produce is good. So I think there's plenty of headroom there for the foreseeable, you know, next few years.

• 1:51:10Lex Fridman?

  So I think there's several interesting things there. One is there's a lot of imperative to just get better and better consistently of using these tools. So they're riding the wave of the improvement, improving models versus like competing against them. But sadly, but that's the nature of life on earth. There could be a huge amount of value to certain kinds of programming at the cutting edge and less value to other kinds. For example, it could be like, you know, front end web design. Might be more amenable to, as you mentioned, to generation by AI systems and maybe,

• 1:51:52Lex Fridman?

  for example, game engine design or something like this, or backend designers, or guiding systems in high-performance situations, high-performance programming type of design decisions. That might be extremely valuable, but it will shift where the humans are needed most, and that's scary for people to address.

• 1:52:11Speaker 01

  Yeah, I can't, I think that's right, that any time where there's a lot of disruption and change, you know, and we've had this, it's not just this time, we've had this in many times in human history with the internet, mobile, but before that, we see industrial revolution. And it's going to be one of those eras where there will be a lot of change. I think there'll be new jobs we can't even imagine today, just like the internet created. And then those people with the right skill sets to ride that wave will become incredibly valuable, right, those skills. But maybe people will have to relearn or adapt a bit their current skills. And it's the thing that's going to be harder to deal with this time around is that I think what we're going to see is something like

• 1:52:52Speaker 01

  probably 10 times the impact the industrial revolution had, but 10 times faster as well, right? So instead of 100 years, it takes 10 years. And so that's going to make, you know, it's like 100x the impact and the speed combined. So that's what's, I think, going to make it more difficult for society to, to, to deal with. And it's, there's a lot to think through, and I think we need to be discussing that right now. And I, you know, encourage top economists in the world and philosophers to start thinking about, how is society going to be affected by this and what should we do, including things like, you know, universal basic provision or something like that,

• 1:53:31Speaker 01

  where a lot of the increased productivity gets shared out and distributed to society, and maybe in the form of services and other things, where if you want more than that, you still go and get some incredibly rare skills and things like that and make yourself unique. But, but there's a basic provision that is provided.

• 1:53:53Lex Fridman?

  And if you think of government as a technology, there's also interesting questions, not just in economics, but just politics. How do you design a system that's responding to the rapidly changing times such that you can represent? The different pain that people feel from the different groups and how do you reallocate resources in a way that addresses that pain and represents the hope and the pain and the fears of different people in a way that doesn't lead to division because politicians are often really good at sort of fueling the division and using that to get elected,

• 1:54:32Lex Fridman?

  the other, defining the other and then saying that's bad. And so based on that, I think that's often counterproductive to leveraging a rapidly changing technology, how to help the world flourish. So we almost need to improve our political systems as well rapidly, if you think of them as a technology.

• 1:54:53Speaker 01

  Definitely. And I think, I think we'll need new governance structures, institutions, probably to help with this transition. So I think political philosophy and political science is going to be key to that. But I think the number one thing, first of all, is to create more abundance of resources, right? Then there's the, so that's the number one thing, increase productivity, get more resources, maybe eventually get out of the zero sum situation. Then the second question is how to use those resources and distribute those resources. But yeah, you can't do that without having that abundance first.

• 1:55:27Lex Fridman?

  You mentioned to me the book The Maniac by Benjamin Levittut, a book on, first of all, about you, there's a bio about you.

• 1:55:37Speaker 01

  Strange, yeah.

• 1:55:39Lex Fridman?

  It's unclear, yes, sure. It's unclear how much is fiction, how much is reality. But I think the central figure that is John von Neumann, I would say it's a haunting and beautiful exploration of madness and genius and let's say the double edged sword of discovery.

• 1:55:59Lex Fridman?

  You know, for people who don't know, John von Neumann is a kind of legendary mind. He contributed to quantum mechanics. He was on the Manhattan Project. He is widely considered to be the father of or pioneer of the modern computer and AI and so on. So many people say he is like one of the smartest humans ever. So it's just fascinating. And what's also fascinating is as a person who saw nuclear science and physics become the atomic bomb. So you got to see ideas become a thing that has a huge amount of impact on the world. He also foresaw the same thing for computing. He's here and that's the little bit, again, beautiful and haunting aspect of the book.

• 1:56:44Lex Fridman?

  Then taking a leap forward and looking at this, at least at all, Alpha Zero, Alpha Go, Alpha Zero, big moment that maybe John von Neumann's thinking was brought to to to reality. So I guess the question is, what do you think if you got to hang out with John von Neumann now? What would he say about what's going on?

• 1:57:09Speaker 01

  Well, that would be an amazing experience. You know, he's a fantastic mind and I also love the way he spent a lot of his time at Princeton, at the Institute of Advanced Studies, a very special place for thinking. And it's amazing how much of a polymath he was and the spread of things he helped invent, including, of course, the von Neumann architecture that all the modern computers are based on. And he had amazing foresight. I think he would have loved where we are today and he would have, I think he would have really enjoyed Alpha Go. Being a, you know, game, he also did game theory. I think he foresaw a lot of what would happen with learning machines, systems that that are kind of grown, I think he called it, rather than programmed. I'm not sure how even, maybe he wouldn't even be that surprised.

• 1:57:54Speaker 01

  There's the fruition of what I think he already foresaw in the 1950s.

• 1:57:58Lex Fridman?

  I wonder what advice he would give. He got to see the building of the atomic bomb with the Manhattan Project. I'm sure there's interesting stuff that maybe is not talked about enough. Maybe some bureaucratic aspect, maybe the influence of politicians, maybe. Maybe not enough of picking up the phone and talking to people that are called enemies by the said politicians. There might be some like deep wisdom that we just may have lost from that time, actually.

• 1:58:22Speaker 01

  Yeah, I'm sure, I'm sure there is. I mean, I've, we, you know, study, I read a lot of books for that time as a world chronicle time and some brilliant people involved. I, I agree with you. I think maybe there needs to be more dialogue and understanding. I hope we can learn from those, those times. I think the difference here is that the AI has so many, it's a multi-use technology. Obviously, we're trying to do things like that, like solve, you know, all diseases,

• 1:58:49Speaker 01

  help with energy and scarcity. These incredible things, this is why all of us and myself, you know, I worked, started on this journey thirty plus years ago. And, but of course, there are risks too. And probably von Neumann, my guess is he foresaw both. And, and I think he sort of said, I think he is to his wife, that, that it would be, this is, computers would be even more impactful in the world. And as we just discussed, you know, I think that's right. I think it's going to be ten times at least of the Industrial Revolution. So I think he's right. So I think he would have been, I imagine, fascinated by where we are now.

• 1:59:27Lex Fridman?

  And I think one of the, maybe you can correct me, but one of the takeaways from the book is that reason, as said in the book, mad dreams of reason, it's not enough for guiding humanity as we build these super powerful technology, that there's something else. I mean, there's also like a religious component. Whatever God, whatever religion gives, it pulls at something in the human spirit that raw, cold reason doesn't give us.

• 1:59:56Speaker 01

  And I, I agree with that. I think we need to approach it with whatever you want to call it, the spiritual dimension or humanist dimension, doesn't have to be to do with religion, right? But this idea of, of a soul, what makes us human, this spark that we have, perhaps is to do with consciousness when we finally understand that. I think that has to be at the heart of the endeavor. And technology, I've always seen technology as the enabler, right? The tools that enable us. To flourish and to understand more about the world. And I'm sort of with Feynman on this, and he used to always talk about science and art being companions. Right. You can understand it from both sides, the beauty of a flower, how beautiful it is. And also understand why the colors of the flower evolved like that.

• 2:00:39Speaker 01

  Right. That just makes it more beautiful, that just the intrinsic beauty of the flower. And I've always sort of seen it like that. And maybe, you know, in the Renaissance times, the great discoverers then, like people like Da Vinci. You know, they were, I don't think he saw any difference between science and art and perhaps religion, right? Everything was, it's just part of being human and being inspired about the world around us. And that's what I, the philosophy I tried to take. And one of my favorite philosophers is Spinoza. And I think he combined that all very well, you know, this idea of trying to understand the universe and understanding our place in it. And that was his kind of way of understanding religion. And I think that's quite beautiful.

• 2:01:20Speaker 01

  And for me, all of these things are related, interrelated, the technology and what it means to be human. And I think it's very important, though, that we remember that as when we're immersed in the technology and the research. I think a lot of researchers that I see in our field are a little bit too narrow and only understand the technology. And I think also that's why it's important for this to be debated by society at large. And I'm very supportive of things like the AI summits that happen and governments understanding it. And I think that's one good thing about the chatbot era and the product era of AI is that everyday person can actually feel and interact with cutting edge AI and feel it for themselves.

• 2:02:04Lex Fridman?

  Yeah, because they force the technologists to have the human conversation. Yeah, for sure.

• 2:02:08Speaker 01

  Yeah.

• 2:02:08Lex Fridman?

  That's the hopeful aspect of it. Like you said, it's a dual use technology. That we're forcefully integrating the entire humanity into it by, into the discussion about AI. Because ultimately, AI, AGI will be used for things that states use technologies for, which is conflict and so on. And the more we integrate humans into this picture by having chats with them, the more we will guide.

• 2:02:35Speaker 01

  Yeah, be able to adapt. Society will be able to adapt to these technologies, like we've always done in the past with with the incredible technologies we've invented in the past.

• 2:02:44Lex Fridman?

  Do you think there will be something like a Manhattan Project? Where there will be an escalation of the power of this technology and states in their old way of thinking will try to use it as weapons technologies and there will be this kind of escalation.

• 2:03:01Speaker 01

  I hope not. I think that would be very dangerous to do. And I think also, you know, not the right use of the technology. I hope we'll end up with more, something more collaborative if needed, like more like a like a CERN project, you know, where it's research focused and the best minds in the world come together to carefully complete the final steps and make sure it's responsibly done before, you know, like deploying it to the world. We'll see. I mean, it's difficult with the current geopolitical climate, I think, to see cooperation, but things can change. And I think at least on the scientific

• 2:03:42Speaker 01

  level, it's important for the researchers to to to to keep in touch and and and keep close to each other on at least on those kinds of topics.

• 2:03:50Lex Fridman?

  Yeah, and I personally believe on the education side and immigration side, it would be great if both directions, people from the West immigrated China and China back. I mean, there is some like family human aspect of people just intermixing.

• 2:04:06Speaker 01

  Yeah.

• 2:04:06Lex Fridman?

  And thereby those ties grow strong. So you can't sort of divide against each other, this kind of old school way of thinking. And so multi multicultural, multidisciplinary research teams working on scientific questions, that's like the hope. Don't don't let the the warm leaders that are warmongers because it divide us. I think science is the ultimately really beautiful connector.

• 2:04:29Speaker 01

  Yeah, science has always been, I think, quite a very collaborative endeavor. And you know, scientists know that it's it's a it's a collective endeavor as well. And we can all learn from each other. So perhaps it could be a vector to get a bit of cooperation.

• 2:04:42Lex Fridman?

  What's your ridiculous question? What's your P doom? Probability the human civilization destroys itself?

• 2:04:48Speaker 01

  Well, look, I don't have a. It's a, you know, I don't have a P doom number. The reason I don't is because I think it's would imply a level of precision that is not there. So like, I don't know how people are getting their P doom numbers. I think it's a kind of a little bit of a ridiculous notion because what I would say is it's definitely non-zero and it's probably non-negligible. So that in itself is pretty sobering and my my view is it's just hugely uncertain. Right? What these technologies are going to be able to do, how fast are they going to take off, how controllable they're going to be. Some things may turn out to be, and hopefully, like way easier than we thought, right?

• 2:05:29Speaker 01

  Um, but it may be there's some really hard um, uh, uh, problems that are harder than we guessed today. And I think uh, we don't know that for sure. And so in under those conditions of a lot of uncertainty, but huge stakes both ways. You know, on the one hand, we we could solve all diseases, energy problems, the not the the the scarcity problem and then travel to the stars and consciousness of the stars and maximum human flourishing. On the other hand, is this sort of P doom scenarios? So given the uncertainty around it and the importance of it, it's clear to me the only rational, sensible approach is to proceed with cautious optimism. So we want the outcome, we want the um, uh, the benefits, of course, uh, and uh, all of the the amazing things that AI can bring.

• 2:06:13Speaker 01

  And actually, I would be really worried for humanity if I, if given the other challenges that we have, climate, disease, you know, aging, uh, resources, all of that. If I didn't know something like AI was coming down the line, right? How would we solve all those other problems? I think it's hard. Um, so I think we've, you know, it could be amazingly transformative for good. But on the other hand, you know, there are these risks that we know are there, but we can't quite quantify. So the the best thing to do is to use the scientific method to do more research, to try and uh, more precisely define those risks.

• 2:06:49Speaker 01

  And of course, address them. Um, and I think that's what we're doing. I think there probably needs to be uh, ten times more effort of that than there is now as we're getting closer and closer to the. To the AGI line.

• 2:07:01Lex Fridman?

  What would be the source of worry for you more? Would it be human caused or AI AGI caused?

• 2:07:09Speaker 01

  Yeah.

• 2:07:09Lex Fridman?

  Humans abusing that technology versus AGI itself through mechanism that you've spoken about, which is fascinating deception or this kind of stuff.

• 2:07:16Speaker 01

  Yes.

• 2:07:16Lex Fridman?

  Getting better and better and better secretly and then.

• 2:07:19Speaker 01

  I think they operate over different time scales and they're equally important to address. So there's just the the the common garden of variety of like, you know, bad actors using new technology. Uh, in this case, general purpose technology and repurposing it for harmful ends. And that's a huge risk. And I think that has a lot of complications because generally, you know, I'm in huge favor of open science and open source. And in fact, we did it with all our science projects like AlphaFold and all of those things uh, for the benefit of of of the scientific community. Um, but how does one restrict bad actors access to these powerful systems? Whether they're individuals or even rogue states uh, and but enable access at the same time to good actors

• 2:08:04Speaker 01

  to to maximally build on top of. It's a pretty tricky problem that there's I've not heard a clear solution to. So there's the bad actor use case problem. And then there's obviously uh, as the systems become more agentic and closer to AGI um, uh, and more autonomous, how do we ensure the guardrails and they stick to what we want them to do uh, and under our control?

• 2:08:26Lex Fridman?

  Yeah, I tend to maybe on my mind is limited worry more about the humans, the bad actors. And there it could be uh, in part, how do you not put destructive technology in the hands of bad actors? But in another part, from again geopolitical technology perspective, how do you reduce the number of bad actors in the world? That's that's also an interesting human problem.

• 2:08:48Speaker 01

  Yeah, it's a hard problem. I mean, look, we we we can um, maybe also use the technology itself to help um, uh, uh, early warning on some of the bad actor use cases, right? Whether that's bio or nuclear or whatever it is, like AI could be potentially helpful there as long as the AI that you're using is itself reliable, right? So it's a sort of interlocking problem and that's what makes it very tricky. And and again, it may require some agreement internationally, at least between China and the and and the US of of of some uh, basic standards, right?

• 2:09:24Lex Fridman?

  I have to ask you about the book, The Maniac. There's this, the hand of God moment, Lisa Doll's move seventy eight. That perhaps the last time a human did a move of sort of pure human genius and beat AlphaGo or like broke its brain.

• 2:09:42Speaker 01

  Yes.

• 2:09:42Lex Fridman?

  If, sorry to anthropomorphize, but it's an interesting moment because I think in so many domains it will keep happening.

• 2:09:48Speaker 01

  Yeah, it's a special moment and, you know, it was great for Lisa Doll and, you know, I think it's, in a way, there was sort of inspiring each other. We as a team were inspired by Lisa Doll's brilliance and nobleness. And then maybe he got inspired by, you know, what AlphaGo was doing to then conjure this incredible inspirational moment. It's all, you know, captured very well in the, in the documentary about it. And I think that'll continue in many domains where there's this, at least for the, for the, again, for the foreseeable future of like the humans bringing in their ingenuity and asking the right question, let's say, and then utilizing these tools in a way that then cracks a problem.

• 2:10:32Lex Fridman?

  Yeah, what is the AI becomes smarter and smarter. One of the interesting questions we can ask ourselves is what makes humans special? It does feel, I'm perhaps biased, that we humans are deeply special. I don't know if it's our intelligence. It could be something else, that, that other thing that's outside the mad dreams of reason.

• 2:10:54Speaker 01

  I think that's what I've always imagined when I was a kid and starting on this journey of like, I was of course fascinated by things like consciousness, did a neuroscience PhD to look at how the brain works, especially imagination and memory. I focused on the hippocampus and it's sort of going to be interesting. I always thought the best way, of course, one can philosophize about it and have thought experiments and maybe even do actual experiments like you do in neuroscience on, on real brains. But in the end, I always imagined that building AI, a kind of intelligent artifact, and then comparing that to the human mind and seeing what the differences were, would be the best way to uncover what's special about the human mind, if indeed there is anything special.

• 2:11:34Speaker 01

  And I suspect there probably is, but it's going to be hard to, you know, I think this journey we're on will help us uh understand that and define that. And, you know, there may be a difference between carbon based substrates that we are and silicon ones when they process information. You know, one of the best definitions I like of of of consciousness is it's the way information feels when we process it, right? Um, it could be, I mean, it doesn't have, it's not a very helpful scientific explanation, but I think it's kind of interesting intuitive one. And um, and so, you know, on this, this, this journey, this scientific journey we're on will, I think, um, help uncover that mystery.

• 2:12:10Lex Fridman?

  Yeah, what I cannot create, I do not understand. That's uh, somebody you deeply admire, Richard Feynman, like you mentioned. You also reach um for the, the Wigner's dreams of universality that he saw in constrained domains, but also broadly, generally in in mathematics and so on. So so many aspects on which you're pushing towards. Not to start trouble at the end, but uh Roger Penrose.

• 2:12:34Speaker 01

  Yes.

• 2:12:36Lex Fridman?

  So uh, you know, do you, do you think consciousness, there's this hard problem of consciousness, how information feels?

• 2:12:45Lex Fridman?

  Do you think consciousness, first of all, is a computation? And if it is, if it's information processing, like you said, everything is. Is it something that could be modeled by a classical computer?

• 2:12:57Speaker 01

  Yeah.

• 2:12:58Lex Fridman?

  Or is it a quantum mechanical in nature?

• 2:13:00Speaker 01

  Well, look, Penrose is an amazing thinker, one of the greatest of the modern era, and he, we've had a lot of discussions about this. Of course, we cordially disagree, which is, you know, I, I feel like, um, I mean, he collaborated with a lot of good neuroscientists to see if he could find mechanisms for quantum mechanics behavior in the brain. And they, to my knowledge, they haven't found anything convincing yet. So my betting is there is, is that, that it's mostly, you know, it is just classical computing that's going on in the brain, which suggests that all the phenomena are modelable or mimicable by a classical computer. But we'll see, you know, there, there may be this final mysterious things of the feeling of consciousness, the qualia, these kinds of things that philosophers debate where.

• 2:13:44Speaker 01

  It's unique to the substrate. We may even come towards understanding that when if we do things like Neuralink and and or have neural interfaces to the AI systems, which I think we probably will eventually, um, maybe to keep up with the AI systems. We might actually be able to feel for ourselves what it's like to compute on silicon, right? So, um, and maybe that will tell us. So I think it's it's going to be interesting. And I had a debate once with the late Daniel Dennett about why do we think each other are conscious? Okay, so it's for two reasons. One is you're exhibiting the same behavior that I am. So that's one thing. Behaviorally, you seem like a conscious being if I am. But the second thing, which is often overlooked, is that we're running on the same substrate.

• 2:14:27Speaker 01

  So if you're behaving in the same way and we're running on the same substrate, it's most parsimonious to assume you're feeling the same experience that I'm feeling. But with an AI uh that's on silicon, we won't be able to rely on the second part. Even if it exhibits the first part, that behavior looks like a behavior of a conscious being. It might even claim it is. But we but but we wouldn't know how it actually felt. And it probably couldn't know we what we felt, at least in the first stages. Maybe when we get to superintelligence and the technologies that builds, perhaps we'll we'll be able to bridge that.

• 2:15:00Lex Fridman?

  No, I mean, that's a huge test for radical empathy is to empathize with a different substrate.

• 2:15:06Speaker 01

  Right. Exactly. We never had to confront that before.

• 2:15:09Lex Fridman?

  Yeah, so maybe maybe to bring computer interfaces be able to truly empathize what it feels like to be a computer.

• 2:15:16Speaker 01

  Well, for information to be computed, not on a carbon system.

• 2:15:20Lex Fridman?

  I mean, that's deeply exciting. I mean, some people kind of think about that with plants, with other life forms, which are different.

• 2:15:25Speaker 01

  It could be, exactly.

• 2:15:26Lex Fridman?

  Similar substrate, but sufficiently far enough on the evolutionary tree.

• 2:15:32Speaker 01

  Yeah.

• 2:15:32Lex Fridman?

  That it's requires a radical empathy, but to do that with a computer.

• 2:15:36Speaker 01

  I mean, no, we sort of, there are animal studies on this of like, of course, higher animals like, you know, killer whales and dolphins and dogs and and monkeys, you know, they have some and elephants, you know, they have some aspects certainly of consciousness, right? Even though they're not might not be that that that smart on an IQ sense. So so we can already empathize with that. And maybe even some of our systems one day, like we built this thing called Dolphin Gemma, you know, which can one a version of our system was trained on dolphin and whale sounds. And maybe we will be able to build a an interpreter or translator at some point. Should be pretty cool.

• 2:16:09Lex Fridman?

  What gives you hope for the future of human civilization?

• 2:16:12Speaker 01

  Well, what gives me hope is I think our almost limitless ingenuity, first of all. I think the best of us and the best human minds are incredible. And, you know, I love, you know, meeting and watching any human that's the top of their game, whether that's sport or science or art, you know, it's, it's, it's just nothing more wonderful than that, seeing them in their element in flow. I think it's almost limitless, you know, our brains are general systems, intelligent systems. So I think it's almost limitless what we can potentially do with them. And then the other thing is our extreme adaptability. I think it's gonna be okay in terms of there's gonna be a lot of change.

• 2:16:53Speaker 01

  But, but look where we are now with our effectively our hunter gatherer brains. How is it we can, you know, we can cope with the modern world, right? Flying on planes, doing podcasts, you know, playing computer games and virtual simulations. I mean, it's already mind blowing, given that our mind was, was developed for, you know, hunting buffaloes on the, on the tundra. And, and so I think this is just the next step. And, and it's actually kind of interesting to see how society is already adapted to this mind blowing AI technology we have today already. It's sort of like, oh, I talk to chatbots, totally fine.

• 2:17:28Lex Fridman?

  And it's very possible that this very podcast activity, which I'm here for, will be completely replaced by AI. I'm very replaceable and I'm waiting for it.

• 2:17:36Speaker 01

  Not to the level that you can do it, Lex, I don't think.

• 2:17:38Lex Fridman?

  Ah, thank you. That's, that's what we humans do to each other, we complement. All right, and I'm deeply grateful for us humans to have this infinite capacity for curiosity, adaptability, like you said, and also compassion and ability to love.

• 2:17:52Speaker 01

  Exactly.

• 2:17:52Lex Fridman?

  All of those human things.

• 2:17:53Speaker 01

  All the things that are deeply human.

• 2:17:55Lex Fridman?

  Well, this is a huge honor, Demis. You're one of the truly special humans in the world. Thank you so much for doing what you do and for talking today.

• 2:18:03Speaker 01

  Thank you very much, Lex.

• 2:18:06Lex Fridman?

  Thanks for listening to this conversation with Demis Kasabas. To support this podcast, please check out our sponsors in the description and consider subscribing to this channel.

• 2:18:16Lex Fridman?

  And now, let me answer some questions and try to articulate some things I've been thinking about. If you'd like to submit questions, including in audio and video form, go to lexfridman.com/ama. I got a lot of amazing questions, thoughts, and requests from folks. I'll keep trying to pick some randomly and comment on it at the end of every episode. I got a note on May 21st this year that said, "Hi Lex, 20 years ago today, David Foster Wallace delivered his famous 'This is Water' speech at Kenyan College. What do you think of this speech?" Well, first, I think this is probably one of the greatest and most

• 2:19:01Lex Fridman?

  unique commencement speeches ever given. But of course, I have many favorites, including the one by Steve Jobs. And David Foster Wallace is one of my favorite writers and one of my favorite humans. There's a tragic honesty to his work, and it always felt as if he was engaging in a constant battle with his own mind. And the writing, his writing, were kind of his notes from the front lines of that battle.

• 2:19:33Lex Fridman?

  Now, onto the speech. Let me quote some parts. There's, of course, the parable of the fish and the water that goes, "There are these two young fish swimming along and they happen to meet an older fish swimming the other way, who nods at them and says, 'Morning, boys. How's the water?' And the two young fish swim on for a bit, and then eventually, one of them looks over at the other and goes, 'What the hell is water?'

• 2:20:03Lex Fridman?

  In the speech, David Foster Wallace goes on to say, "The point of the fish story is merely that the most obvious, important realities are often the ones that are hardest to see and talk about." Stated as an English sentence, of course, "This is just the banal platitude. But the fact is that in the day-to-day trenches of adult existence, banal platitudes can have a life-or-death importance, or so I wish to suggest to you in this dry and lovely morning." I have several takeaways from this parable and the speech that follows. First, I think we must question everything, and in particular, the most basic assumptions about our reality, our life, and the very nature of existence.

• 2:20:48Lex Fridman?

  And that this project is a deeply personal one. In some fundamental sense, nobody can really help you in this process of discovery. The call to action here, I think, from David Foster Wallace, as he puts it, is to, quote, "to be just a little less arrogant, to have just a little more critical awareness about myself and my certainties." Because a huge percentage of the stuff that I tend to be automatically certain of is, it turns out, totally wrong and deluded. All right, back to me, Lex, speaking. The second takeaway is that the central spiritual battles of our life

• 2:21:31Lex Fridman?

  are not fought on a mountaintop somewhere at a meditation retreat, but it is fought in the mundane moments of daily life. Third takeaway is that we too easily give away our time and attention to the multitude of distractions that the world feeds us, the insatiable black holes of attention. David Foster Wallace's call to action in this case is to be deeply aware of the beauty in each moment and to find meaning in the mundane. I often quote David Foster Wallace in his advice that the key to life is to be un-borable.

• 2:22:15Lex Fridman?

  And I think this is exactly right. Every moment, every object, every experience, when looked at closely enough, contains within it infinite richness to explore.

• 2:22:28Lex Fridman?

  And since Demis Chasabis of this very podcast episode and I are such fans of Richard Feynman, allow me to also quote Mr. Feynman on this topic as well. Quote, "I have a friend who's an artist and has sometimes taken a view which I don't agree with very well. He'll hold up a flower and say, 'Look, how beautiful it is,' and I'll agree. Then he says, 'I, as an artist, can see how beautiful this is, but you, as a scientist, take this all apart and it becomes a dull thing.'" And I think that's kind of nutty.

• 2:23:07Lex Fridman?

  First of all, the beauty that he sees is available to other people and to me too, I believe. Although I may not be quite as refined aesthetically as he is, I can appreciate the beauty of a flower. At the same time, I see much more about the flower than he sees. I can imagine the cells in there, the complicated actions inside, which also have beauty. I mean, it's not just beauty at this dimension, at one centimeter. There's also beauty at the smaller dimensions, the inner structure, also the processes. The fact that the colors in the flower evolved in order to attract insects to pollinate it is interesting. It means that the insects can see the color.

• 2:23:51Lex Fridman?

  It adds a question, "Does this aesthetic sense also exist in lower forms? Why is it aesthetic?" All kinds of interesting questions which the science knowledge only adds to the excitement, the mystery, and the awe of a flower. It only adds. All right, back to David Foster Wallace's speech. He has a great story in there that I particularly enjoy.

• 2:24:19Lex Fridman?

  It goes, "There are these two guys sitting together in a bar in the remote Alaskan wilderness. One of the guys is religious, the other is an atheist. And the two are arguing about the existence of God with that special intensity that comes after about the fourth beer. And the atheist says, "Look, it's not like I don't have actual reasons for not believing in God. It's not like I haven't ever experimented with the whole God and prayer thing. Just last month, I got caught away from the camp in that terrible blizzard. And I was totally lost and I couldn't see a thing and it was 50 below. And so I tried it.

• 2:24:59Lex Fridman?

  I fell to my knees in the snow and cried out, "Oh, God, if there is a God, I'm lost in this blizzard and I'm gonna die if you don't help me."

• 2:25:09Lex Fridman?

  And now, back in the bar, the religious guy looks at the atheist all puzzled. "Well, then you must believe now," he says. "After all, there you are, alive."

• 2:25:20Lex Fridman?

  The atheist just rolls his eyes. "No, man. All that happened was a couple of Eskimos happened to be wandering by and show me the way back to the camp." All this, I think, teaches us that everything is a matter of perspective and that wisdom may arrive if we have the humility to keep shifting and expanding our perspective on the world. Thank you for allowing me to talk a bit about David Foster Wallace. He's one of my favorite writers and he's a beautiful soul.

• 2:25:54Lex Fridman?

  If I may, one more thing I wanted to briefly comment on.

• 2:25:58Lex Fridman?

  I find myself to be in this strange position of getting attacked online, often, from all sides, including being lied about, sometimes through selective misrepresentation, but often through downright lies. I don't know how else to put it. This all breaks my heart, frankly. But I've come to understand that it's the way of the internet and the cost of the path I've chosen. There's been days when it's been rough on me mentally. It's not fun being lied about, especially when it's about things that are usually, for a long time, have been a source of happiness and joy for me. But again... That's life. I'll continue exploring the world of people and ideas with empathy and

• 2:26:43Lex Fridman?

  rigor, wearing my heart on my sleeve as much as I can. For me, that's the only way to live. Anyway, a common attack on me is about my time at MIT and Drexel, two great universities I love and have tremendous respect for. Since a bunch of lies have accumulated online about me on these topics, to a sad and at times hilarious degree, I thought I would once more state the obvious facts about my bio for the small number of you who may care. TLGR, two things. First, as I say often, including in a recent podcast episode that somehow was

• 2:27:23Lex Fridman?

  listened to by many millions of people, I proudly went to Drexel University for my bachelor's, master's, and doctorate degrees. Second, I am a research scientist at MIT and have been there in a paid research position for the last 10 years. Allow me to elaborate a bit more on these two things now, but please skip if this is not at all interesting. So like I said, a common attack on me is that I have no real affiliation with MIT. The accusation, I guess, is that I'm falsely claiming an MIT affiliation because I taught a lecture there once. Nope,

• 2:28:04Lex Fridman?

  that accusation against me is a complete lie. I have been at MIT for over 10 years in a paid research position from 2015 to today. To be extra clear, I'm a research scientist at MIT, working in LIDS, the Laboratory for Information and Decision Systems, in the College of Computing. For now, since I'm still at MIT, you can see me in the directory and on the various lab pages. I have indeed given many lectures at MIT over the years, a small fraction of which I posted

• 2:28:45Lex Fridman?

  online. Teaching, for me, always has been just for fun and not part of my research work. I personally think I suck at it, but I have always learned and grown from the experience. It's like Feynman spoke about, if you want to understand something deeply, it's good to try to teach it.

• 2:29:06Lex Fridman?

  But like I said, my main focus has always been on research. I published many peer-reviewed papers that you can see in my Google Scholar profile. For my first four years at MIT, I worked extremely intensively. Most weeks were 80 to 100-hour work weeks. After that, in 2019, I still kept my research scientist position, but I split my time taking a leap to pursue projects in AI and robotics outside MIT and to dedicate a lot of focus to the podcast. As I've said, I've been continuously surprised just how many hours preparing for an episode takes. There are many episodes of the podcast for which I have to read, write, and think

• 2:29:48Lex Fridman?

  for 100, 200 or more hours across multiple weeks and months. Since 2020, I have not actively published research papers. Just like the podcast, I think it's something that's a serious full-time effort. But not publishing and doing full-time research has been eating at me because I love research and I love programming and building systems that test out interesting technical ideas, especially in the context of human-AI or human-robot interaction. I hope to change this in the coming months and years.

• 2:30:26Lex Fridman?

  What I've come to realize about myself is if I don't publish or if I don't launch systems that people use, I definitely feel like a piece of me is missing. It legitimately is a source of happiness for me.

• 2:30:40Lex Fridman?

  Anyway, I'm proud of my time at MIT. I was and am constantly surrounded by people much smarter than me, many of whom have become lifelong colleagues and friends. MIT is a place I go to escape the world, to focus on exploring fascinating questions at the cutting edge of science and engineering. This, again, makes me truly happy. And it does hit pretty hard on a psychological level when I'm getting attacked over this.

• 2:31:12Lex Fridman?

  Perhaps I'm doing something wrong. If I am, I will try to do better. In all this discussion of academic work, I hope you know that I don't ever mean to say that I'm an expert at anything. In the podcast and in my private life, I don't claim to be smart. In fact, I often call myself an idiot and mean it. I try to make fun of myself as much as possible and in general to celebrate others instead. Now, to talk about Drexel University, which I also love, am proud of, and am deeply grateful for my time there. As I said, I went to Drexel for my bachelor's, master's, and doctorate degrees in computer

• 2:31:56Lex Fridman?

  science and electrical engineering. I've talked about Drexel many times, including, as I mentioned, at the end of a recent podcast, the Donald Trump episode, funny enough, that was listened to by many millions of people, where I answered a question about graduate school and explained my own journey at Drexel and how grateful I am for it. If it's at all interesting to you, please go listen to the end of that episode or watch the related clip. At Drexel, I met and worked with many brilliant researchers and mentors from whom I've learned a lot about engineering, science, and life. There are many valuable things I gained from my time at Drexel.

• 2:32:39Lex Fridman?

  First, I took a large number of very difficult math and theoretical computer science courses. They taught me how to think deeply and rigorously, and also how to work hard and not give up, even if it feels like I'm too dumb to find a solution to a technical problem. Second, I programmed a lot during that time, mostly C, C++. I programmed robots, optimization algorithms, computer vision systems, wireless network protocols, multimodal machine learning systems, and all kinds of simulations of physical systems. This is where I really developed a love for programming, including, yes, Emacs and the Kinesis keyboard.

• 2:33:22Lex Fridman?

  I also during that time, read a lot. I played a lot of guitar, wrote a lot of crappy poetry, and trained a lot of jujitsu and jujitsu, which I cannot sing enough praises to. Jujitsu humbled me on a daily basis throughout my twenties, and it still does to this very day, whenever I get a chance to train. Anyway, I hope that the folks who occasionally get swept up in the chanting online crowds that want to tear down others, don't lose themselves in it too much. In the end, I still think there's more good than bad in people. But we're all, each of us, a mixed bag.

• 2:34:07Lex Fridman?

  I know I am very much flawed. I speak awkwardly. I sometimes say stupid shit. I can get irrationally emotional. I can be too much of a dick when I should be kind. I can lose myself in a biased rabbit hole before I wake up to the bigger, more accurate picture of reality. I'm human, and so are you, for better or for worse. And I do still believe we're in this whole beautiful mess together. I love you all.