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Demis Hassabis on Lex Fridman: How AlphaFold Changed Biology

Hassabis conjectures any pattern shaped by nature is learnable by classical systems; alphafold solved protein folding while veo models fluid dynamics passively.

Lex FridmanhostDemis Hassabisguest
Jul 23, 20252h 28mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 1:21

    Episode highlight

    1. LF

      It's hard for us humans to make any kind of clean predictions about highly non-linear dynamical systems. But again, to your point, we might be very surprised what classical learning systems might be able to do about even fluid.

    2. DH

      Yes, exactly. I mean, fluid dynamics, Navier–Stokes equations, these are traditionally thought of as very, very difficult, intractable 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. But again, if you look at something like Veo, 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 s- being squeezed out. I, I used to write, uh, physics engines and graphics engines in, in my early days in gaming. And I know, uh, it's just so painstakingly hard to build programs that can do that, and yet somehow these systems are, you know, reverse engineering from just watching YouTube videos. So th- 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.

  2. 1:21 – 2:06

    Introduction

    1. LF

      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. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description and consider subscribing to this channel. And now, dear friends, here's Demis Hassabis.

  3. 2:06 – 5:48

    Learnable patterns in nature

    1. LF

      In your Nobel Prize lecture, you proposed 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 in- be included in that, biology, chemistry, physics, maybe cosmology-

    2. DH

      Yeah.

    3. LF

      ... neuroscience? What, what are we talking about?

    4. DH

      Sure. Well, look, uh, I felt that it's sort of a tradition, I think, of Nobel Prize lectures that you're supposed to be a little bit 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, um, all the work that we've done, especially with the AlphaX projects, so I'm thinking AlphaGo, of course AlphaFold, what they really are is, we're building models of very combinatorially high dimensional spaces that, you know, if you tried to brute force a solution, find the best move in Go, or find the, the exact shape of a protein, and if you enumerated all the possibilities, you'd... there wouldn't be enough time in the, 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, um, and that guided 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, uh, uh, what that structure is.

    5. LF

      Th- this perspective, I think, is a really interesting one. You've hinted at, at it, which is almost, like, uh, crudely stated. Anything that can be evolved can be efficiently modeled. Think there's some truth to that?

    6. DH

      Yeah, I sometimes call it survival of the stablest or something like that, because, uh, i- you know, it's, it's... of course there's evolution for life, uh, 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, um, 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, um, that you can kind of reverse learn and, uh, a kind of manifold really that helps you, uh, uh, search to the right solution, to the right shape, um, and actually allow you to predict things about it, uh, in an efficient way, because it's not a random pattern, right? So, um, it may not be possible for, for manmade 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, so you have to do brute force. So in that case, you, you know, you maybe need a quantum computer, something like this. But most things in nature that we're interested in, uh, are not like that. They have structure, um, that evolve for a reason and survived over time. And if that's true, I think that's potentially learnable by a neural network.

    7. LF

      It's like nature's doing a search process and it's so fascinating that it's... in that search process, it's creating systems that could be efficiently modeled.

    8. DH

      That's right. Yeah.

    9. LF

      It's so interesting.

    10. DH

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

    11. LF

      Do you think,

  4. 5:48 – 14:26

    Computation and P vs NP

    1. LF

      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.

    2. DH

      Yeah. (laughs)

    3. LF

      This is a demus hablis-

    4. DH

      A new class. (laughs)

    5. LF

      … new class of systems that could be actually learnable by classical systems in this kind of way, natural systems that can be, uh, modeled efficiently.

    6. DH

      Yeah. I mean, I'm, 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 kinda my few moments of spare time with a few colleagues about w- is should there be, you know, maybe a new class or problem that is solvable by this type of neural network process and kind of mapped onto these natural systems, so, you know, the things that exist in physics and have structure. So I think that could be a very interesting, uh, 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 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.

    7. LF

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

    8. DH

      That's right.

    9. LF

      (laughs) And, and it's a question that can help us actually solve the entirety of this whole thing going on.

    10. DH

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

    11. LF

      More specific to the P and NP question, this... Again, some of the stuff we're saying is kinda crazy right now, just like the Christian Anfinsen Nobel Prize speech controversial thing that he said sounded crazy, and then you went and got a Nobel Prize for this with John Jumper, solved 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 g- can do something like, uh, 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 theoretical computer science kinda way?

    12. DH

      Yeah, I think that there are, uh, 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, you model what the dynamics of the system is, the, the, the, 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, uh, classical system, uh, which a neural network is. It runs on normal computers, right, classical computers, uh, Turing machines in effect. And, um, I think it's one of the most interesting questions there is, is how far can that paradigm go?

    13. LF

      Mm-hmm.

    14. DH

      You know, I think we've proven, uh, and the AI community in general, that classical systems, Turing 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, uh, you know, a lot of people would have thought maybe 10, 20 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, uh, even sort of scratched the surface yet of what a classical system, so-called, uh, uh, 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. And I think the limit, the, you know, the, the, what, 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.

    15. LF

      What, what do you think, again, hypothetical, might be outside of this, maybe emergent phenomena? Like if you look at cellular automata-

    16. DH

      Mm-hmm.

    17. LF

      ... some of the, you have extremely simple systems and then some complexity emerges-

    18. DH

      Yes.

    19. LF

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

    20. DH

      Yeah, I think those systems would be right on the boundary, right? So, um, 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. Um, 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 VO3 on like video generation sort of rendering physics and lighting and things like that, you know, really core fundamental things in physics, um, it's pretty interesting. I think it's telling us something quite fundamental about how the universe is structured, in my opinion. Um, so, you know, in, in a way, that's what I want to build AGI for, is to help, uh, us, uh, as scientists answer these questions, uh, like P equals NP.

    21. LF

      Yeah, I think, uh, we might be continuously surprised about what is modelable by classical computers. I mean, AlphaFold 3 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-

    22. DH

      Yeah. (laughs)

    23. LF

      ... you can find the kernel that is efficiently modeled.

    24. DH

      Yes, because, uh, there's some structure, there's some landscape, you know, in the energy landscape or whatever it is that you can follow, some grading you can follow. And of course, what neural networks are very good at is following gradients. And so if there's one to follow, an object- and you can specify the objective function correctly, you know, you don't have to deal with all that complexity-

    25. LF

      Mm-hmm.

    26. DH

      ... 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 pop- possible protein structures. Uh, it's 10 to the 100 and, you know, 70 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? And, and it... 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, uh, in a sense, uh, then... and we could mimic that process, I mean, model that process, uh, it should be possible on our classical systems is, is, is basically what the conjecture is about.

    27. LF

      And of course there's non-linear dynamical systems, highly non-linear dynamical systems. Everything involving fluid-

    28. DH

      Yes. Right.

    29. LF

      You know, I had, recently had a conversation with Terence Tao, who mathematically, uh, it 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 non-linear dynamical systems. But again, to your point, we might be very surprised what classical learning systems might be able to do about even fluid.

    30. DH

      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, um, but again, if you look at something like Veo, 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 s- being squeezed out. I, I used to write, uh, physics engines and graphics engines in, in my early days in gaming, and I know, uh, it's just so painstakingly hard to build programs that can do that, and yet somehow these systems are, you know, reverse engineering from just watching YouTube videos. So f- 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.

  5. 14:26 – 18:50

    Veo 3 and understanding reality

    1. DH

    2. LF

      Yeah, I've been continuously... precisely by this aspect of Veo 3. I think a lot of people highlight different aspects-

    3. DH

      Mm-hmm.

    4. LF

      ... including the comedic and the meme-

    5. DH

      Yes.

    6. LF

      ... and all that kind of stuff, and then th- the ultra-realistic ability to capture humans in a really nice way that's compelling and g- feels close to reality and then combine that with native audio, all of those are marvelous things about Veo 3. But the- exactly the thing you're mentioning, which is the physics-

    7. DH

      Yeah.

    8. LF

      ... it's not perfect, but it's pretty damn good. And then, uh, the really interesting scientific question is what is it understanding about our world-

    9. DH

      Mm-hmm.

    10. LF

      ... in order to be able to do that? Because if... the cynical take with diffusion models, there's no way it understands anything-

    11. DH

      Mm-hmm.

    12. LF

      ... but it seem... uh, 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. Like do... to what degree do you think Veo 3 understands our world?

    13. DH

      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, uh, but they, they certainly have, uh, modeled enough of the dynamics, if we can 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, uh, at a glance it's 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 w- they will look, uh, given where we've come from, you know, the early versions of that, uh, uh, one or two years ago. And so, um, the rate of progress is incredible, and I think, um, I'm like you. It's like a lot of people love all of the, the, the standup comedians and the, the... it 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, uh, that it has some notion of at least intuitive physics, right? Um, how things are supposed to work, uh, intuitively, maybe the way that, uh, a human child would understand physics, right? As opposed to a, you know, a PhD student really, uh, being able to unpack all the equations. It's more of an intuitive physics understanding.

    14. LF

      Well, that intuitive physics understanding, that's the base layer. That's the thing people sometimes call, like, common sense.

    15. DH

      Mm-hmm.

    16. LF

      Like it, it really understands something that I think that really surprises 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.

    17. DH

      Mm-hmm.

    18. LF

      Y- you know, 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.

    19. DH

      Yeah.

    20. LF

      But Veo 3 is directly challenging that-

    21. DH

      Right.

    22. LF

      ... it feels like.

    23. DH

      Yes. And this is very interesting, you know, even, uh, if we... if you were to ask me five, 10 years ago I would have said, even though I was immersed 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, um, and the, 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, 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, uh, so that you would understand things like intuitive physics. But it seems like, um, you can understand it through passive observation, which is pretty surprising to me, and, and again, I think hints at something underlying about the nature of, uh, reality, in, in, in my opinion, beyond, um, just the, you know, the cool videos that it generates. Um, and, and of course there's next stages is maybe even making those videos interactive so, uh, one can actually step into them and move around them, um, which would be really mind-blowing, especially given my games background.

    24. LF

      (laughs)

    25. DH

      So (laughs) you can imagine. Uh, and then, and then I think, you know, you're, 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.

  6. 18:50 – 30:52

    Video games

    1. DH

    2. LF

      I have to talk to you about video games.

    3. DH

      Yes.

    4. LF

      So you, you were being a bit trolley.

    5. DH

      (laughs)

    6. LF

      (laughs) I th- I think you're, 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. Uh, Google cooked so good. Playable world models wen?" Spelled W-E-N question mark. Um, and then you quote tweeted that with, "Now wouldn't that be something."

    7. DH

      (laughs under breath)

    8. LF

      So how, how hard is it to build game worlds with AI? Maybe can you look out into the future, uh, of video games-

    9. DH

      Mm.

    10. LF

      ... five, 10 years out?

    11. DH

      Mm.

    12. LF

      What do you think that looks like?

    13. DH

      Well, games were my first love really, and doing AI for games was the first thing I did professionally in, in my teenage years, and, and was the first, uh, major AI systems that I built. And, uh, I always wanna, I have, I wanna scratch that itch one day and come back to that. So, you know, and I will do, I think, and, um, I think I 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. Um, and I think the next stage is I always used to love making... All the games I've made are open-world games.

    14. LF

      Mm-hmm.

    15. DH

      So they're games where there's a simulation, and then there's AI characters, and then the player, uh, interacts with that simulation, and the simulation adapts to the way the player plays. And I always thought they were the coolest games because, uh, so games like Theme Park that I worked on where everybody's game experience would be unique to them, right, because you're kinda co-creating the game, right? Uh, 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 gotta be able to create, uh, content whichever direction the player goes in, and you want it to be compelling no matter what the player chooses. Um, and so it was always quite difficult to build, uh, things like Saylor Automata actually type of those kinda classical systems which created some emergent behavior. Um, 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 that can truly create around your imagination, um, can nar- and sort of dynamically change the story and storytell the narrative around, uh, 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, uh, you know, I think maybe we're within reach if you think of a kind of interactive version of VO, uh, and then f- wind that forward five to 10 years and, um, you know, imagine how good it's gonna be.

    16. LF

      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.

    17. DH

      Mm.

    18. LF

      So it's not just that it's open world, but 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.

    19. DH

      Yes.

    20. LF

      But it's really just an illusion of choice-

    21. DH

      Yes.

    22. LF

      ... because the only, uh, uh, like, like Stanley Parable, is, is-

    23. DH

      Yeah.

    24. LF

      ... a game I recently (laughs) played. It's, it's, it's really there's a couple doors and it really just takes you down a narrative. Stanley Parable is a great video game I recommend people play.

    25. DH

      Yeah.

    26. LF

      That kinda, uh, in a meta way, uh, (laughs) mocks the illusion of choice and there's philosophical notions of free will and so on. But, uh, 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-

    27. DH

      Yeah.

    28. LF

      ... of if you could step in and they give you this-

    29. DH

      Yes.

    30. LF

      ... feeling of an open world, and there, you mentioned interactivity. You don't need to interact. That, that's a first step 'cause you don't need to interact that much. You just... When you open the door, whatever you see is randomly generated for you.

  7. 30:52 – 36:53

    AlphaEvolve

    1. DH

    2. LF

      I have to ask you, I almost forgot about o- one of the many and I would say one of the most incredible things recently, uh, that somehow didn't yet get enough attention is AlphaGo.

    3. DH

      Mm-hmm.

    4. LF

      We talked about evolution a little bit, but it's the Google DeepMind system that evolves algorithms.

    5. DH

      Yeah.

    6. LF

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

    7. DH

      Yeah.

    8. LF

      So it's ev- uh, evolutionary algorithms are doing the search, and-

    9. DH

      Yes.

    10. LF

      ... LLMs are telling you where.

    11. DH

      Yes, exactly. So LLMs are kinda 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, uh, things to be discovered probably with these sort of hybrid systems, let's call them.

    12. LF

      But not to romanticize evolution-

    13. DH

      Yeah.

    14. LF

      ... and I'm only human, but you, you think there's some value in whatever that mechanism is? 'Cause we already talked about natural systems. Do you think where there's a lot of low-hanging fruit of us understanding being mo- being able to model, um, 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?

    15. DH

      Yes. So if you think about, uh, again, br- a b- uh, breaking down the sys- 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 wanna 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 used Monte Carlo tree search, right? And that's what found Move 37, the new, uh, kind of never-seen-before strategy in Go. And so that's how you can go beyond potentially what 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, fi- discover something new? Obviously, this is super relevant for scientific discovery or pushing med... science and medicine forward, which we wanna 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 or 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.

    16. LF

      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 'cause you can probably generalize to, uh, to everything, you know. Uh, but, you know, for example, mutation. So it's not just Monte Carlo tree search where it's like a search.

    17. DH

      Mm-hmm.

    18. LF

      You could, every once in a while alt-

    19. DH

      Combine things, yeah.

    20. LF

      ... combine things-

    21. DH

      Yeah.

    22. LF

      ... alter c- like, sub, like, uh, components of a thing.

    23. DH

      Yes.

    24. LF

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

    25. DH

      Yes.

    26. LF

      So that component's super interesting-

    27. DH

      Yeah.

    28. LF

      ... especially like with AlphaEvolve and this base of programs.

    29. DH

      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.

    30. LF

      Mm-hmm. Yes.

  8. 36:53 – 41:17

    AI research

    1. DH

    2. LF

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

    3. DH

      Yes.

    4. LF

      That's a beautiful dream. Uh, I could ask you about that. I also just, for that purpose, on the AI scientist front, just broadly. So there's a essay, uh, from Daniel Cocotaio, Scott Alexander, and others that outlines steps along the way to get to ASI and has a lot of interesting ideas i- in it, one of which is, uh, 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 co-scientist does to help steer human, um, human brilliant scientists and then bo- potentially by itself to figure out what are the directions, eh, where you want to gen- generate truly novel ideas? 'Cause that seems to be like a really important component of how to do great science.

    5. DH

      Yeah. I think that's gonna be one of the hardest things to, to, uh, mimic or model is, is this, this idea of taste or, 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 they wouldn't have, be- made it, uh, that far in, 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 qu- it's the, it's... Picking the right question is, is the hardest part of science, um, and, and making the right hypothesis and, um, 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. So we may have systems soon that can solve pretty hard conjectures. Um, you know, I, I... Um, in math Olympiad problems where we, we... you know, Alpha proved last year our system got

    6. NA

      (laughs)

    7. DH

      ... 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 Terence Tao 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, like Einstein had when he came up with, you know, special relativity and then general relativity with the knowledge he had at the time.

    8. LF

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

    9. DH

      Yes.

    10. LF

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

    11. DH

      Yeah.

    12. LF

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

    13. DH

      That sweet spot, right?

    14. LF

      Yeah.

    15. DH

      Of, of basically advancing the science and splitting the hypothesis space into two ideally, right? Whether if it's true or not true, you, you've learned something really useful and, um, and, and that's hard. 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 naïve search on top of a model won't be enough for that.

    16. LF

      Okay. The idea of splitting the hypothesis space in two is super interesting. So, uh, I've heard you say that there's basically no failure and... Or failure's extremely valuable if it's done... you know, if you construct the questions right, if you construct the experiments right, if you design them right, that failure and success are both useful. So-

    17. DH

      Yes.

    18. LF

      ... perhaps because it splits the hypothesis base in two, it's like a binary-

    19. DH

      Yes.

    20. LF

      ... search?

    21. DH

      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 split the hypothesis space. So, you know, and you learn something... You can learn something kind of equally valuable from, uh, an experiment that doesn't work, that should tell you... If you've designed an experiment well and your hypotheses are, are interesting, it should tell you a lot about where to go next. And, um, and then it's... You're, you're effectively doing a search process, um, and using that information in, in, you know, very helpful ways.

  9. 41:17 – 46:00

    Simulating a biological organism

    1. DH

    2. LF

      So to go to your dream of, uh, modeling a cell, um, 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.

    3. DH

      Yeah.

    4. LF

      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, uh, the... everything you've released. AlphaFold3 is doing protein RNA/DNA interactions-

    5. DH

      Mm-hmm.

    6. LF

      ... which is super complicated and, and fascinating. There's a amenable to modeling. Alpha Genome, uh, predicts how small genetic changes, like if you think about single mutations, how they link to actual, uh, function. So, um, those are... It seems like it's creeping along (laughs) to a sophistic-

    7. DH

      Yes.

    8. LF

      ... to, to much more complicated, um, things like a cell, but a cell has a lot of...... really complicated components.

    9. DH

      Yeah. So what I've tried to do throughout my career is I have these m- 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 i- at any... You know, it's easy to have a kind of a, a c- crazy, ambitious dream, but the c- the, the trick is how do you break it down into manageable, achievable, uh, 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, uh, I've had this idea, you know, of wanting to do that for maybe more, like 25 years. And, uh, I used to talk with Paul Nurse, who is a bit of a mentor of mine in biology. He runs the, the, you know, he founded the Crick Institute and, and won the Nobel Prize in, in 2001. Uh, i- i- is, is... We've been talking about it since, you know, t- before the, uh, you know, in the '90s. And, um, and I come, used to come back to it 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 p- 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, um, but maybe now, finally... Uh, so I was trying to build these components, AlphaFold being one, that, that would, uh, allow you eventually to model the full interaction, a full simulation of a cell. 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 a 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, 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 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 AlphaFold3 is, is the first step towards, is modeling those interactions. So first of all pairwise, you know, proteins with proteins, proteins with RNA and DNA. 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.

    10. LF

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

    11. DH

      Yes.

    12. LF

      Um, I don't know all the biological mechanisms-

    13. DH

      Yeah.

    14. LF

      ... but some of them take a long time.

    15. DH

      Yeah.

    16. LF

      And so is that... That's a level... So the levels of interaction has a different temporal scale-

    17. DH

      Yeah.

    18. LF

      ... that you have to be able to model.

    19. DH

      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 jump up or down the, the different temporal stages.

    20. LF

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

    21. DH

      Yes, so you a- you got to make a decision when you're modeling any natural system, what is the cutoff level of the granularity that you're gonna model it to that, a- 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, uh, and that one wouldn't have to go down to the atomic level. Um, so, you know, and of course that's where AlphaFold's stuff kicks in, so that would be kind of the basis, and then you'd build these, um, uh, higher-level simulations that, um, take those as building blocks, and then you get the emergent behavior.

  10. 46:00 – 52:15

    Origin of life

    1. DH

    2. LF

      Apologize for the pothead questions ahead of time but, uh, will-

    3. DH

      (laughs)

    4. LF

      ... do you think, uh, we'll be able to simulate or model the origin of life? So being able to simulate the first... from, from non-living organisms, the, the birth of a living organism.

    5. DH

      I think that's, uh, one of the... of course, one of the deepest and most fascinating questions. Um, I love that area of biology. You know, uh, there's people like... There's a great book by Nick Lane, one of the top, top experts in this area called th- The Ten Great Inventions of, of, of Evolution. I think it's fantastic, and it also speaks to what the great filters might be be- 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-

    6. LF

      Yeah.

    7. DH

      ... a- on Earth to do, right? So it shows you how hard it was, right? (laughs)

    8. LF

      Bacteria were super happy for a very long time.

    9. DH

      For 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 chem- 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, uh, 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, um... something like that emerge from the chemical soup?

    10. LF

      Well, I would love it if there was a Move 37 for the origin of life.

    11. DH

      Yeah. (laughs)

    12. LF

      I think that's one of the sort of s- great mysteries.I think ultimately what we'll figure out is there a continuum, there's no such thing as a line between non-living and living. But if we can make that rigorous-

    13. DH

      Yes.

    14. LF

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

    15. DH

      Yeah.

    16. LF

      And so there's no line.

    17. DH

      I mean, this is my whole reason why I 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.

    18. LF

      (laughs)

    19. DH

      Like how, how, how can we not have a good definition of life and not, and not living and non-living, and-

    20. LF

      (laughs)

    21. DH

      ... 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.

    22. LF

      (laughs)

    23. DH

      The whole, and that scream is 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-

    24. LF

      Yeah.

    25. DH

      ... uh, that would answer all of these things. It's sort of crazy if you think about it. We can stare at each other and a- all these living things all the time, we can inspect it with microscopes and take it apart, uh, almost down to the atomic level, and yet we still can't answer that clearly-

    26. LF

      Yeah.

    27. DH

      ... in a simple way, that question of, well, how do you define living?

    28. LF

      Yeah.

    29. DH

      It's kind of amazing.

    30. LF

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

  11. 52:15 – 1:03:01

    Path to AGI

    1. DH

      Yeah.

    2. LF

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

    3. DH

      My estimate is sort of 50% chance by, in the next five years, so, you know, by 2030 let's say. And, uh, so I think there's a good chance that that could happen. Part of it is what, what is your definition of AGI? Of course people are arguing about that now and, and, uh, 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 sh- also speaks to how general the brain is. And, um, 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 systems, but other things it's really, uh, 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, uh, the true invention capabilities and creativity that we were talking about earlier. So you'd want to see those. How you test that, um, I think you just test it. One way to do it would be a kind of brute force test of tens of thousands of cognitive tasks that-

    4. LF

      Mm-hmm.

    5. DH

      ... um, you know, we know that humans can do, uh, and maybe also make the system available to, uh, a few hundred of the world's top experts, uh, the Terence Tows of each- each subject area, and see if they can find... you know, give th- give them a month or two, and see if they can find, uh, an obvious flaw in the system. And if they can't, then I think you're- you're pretty, uh, you know, pretty- you can be pretty confident we have a- a fully general system.

    6. LF

      Maybe to push back a little bit, it seems like humans are really incredible as th- the intelligence improves across all domains, to take it for granted.

    7. DH

      Mm-hmm.

    8. LF

      Uh, like you mentioned Terence Tow, uh, th- 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 a h- first of all, human.

    9. DH

      Yeah. (laughs)

    10. LF

      (laughs) Uh, so-

    11. DH

      Good.

    12. LF

      ... I- I identify-

    13. DH

      Yeah.

    14. LF

      ... as human. Um, (laughs) is this... I, you know, some people listen to me talk and they're like, "That guy is not good at talking."

    15. DH

      (laughs)

    16. LF

      The stuttering, the, you know... (laughs) So, like, e- even humans have obvious, across domains, limits. Uh, even just outside of-

    17. DH

      Of course.

    18. LF

      ... calc- mathematics and physics and so on, it- I- I- I wonder if it will take something like a Move 37, so on the positive side-

    19. DH

      Yeah.

    20. LF

      ... versus, like, a barrage of 10,000 cognitive tasks-

    21. DH

      Yeah.

    22. LF

      ... where it would be one or two where it's like-

    23. DH

      Yes.

    24. LF

      ... "Holy shit, this is-"

    25. DH

      So I think there are-

    26. LF

      "... special."

    27. DH

      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-

    28. LF

      Mm-hmm.

    29. DH

      ... like the Move 37, that w- 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 backtest of that very rigorously, like, l- have a cutoff of- knowledge cutoff of 1900, and then give the system everything that was, you know, that was written up to 1900, and then- and then see if it could come up with special relativity and general relativity, right? Like Einstein did.

    30. LF

      Mm-hmm.

  12. 1:03:01 – 1:06:17

    Scaling laws

    1. DH

    2. LF

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

    3. DH

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

    4. LF

      (laughs) Yeah.

    5. DH

      ... to, to true research and, you know, re- or research plus engineering, and that's our sweet spot. And I, I think that's harder. It's harder to invent things than to, than to, um, you know, fast follow. And, um, 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 gonna 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, and then, and then, uh, scaling to the max the, the current, the current capabilities. And, um, we're still seeing some, you know, fantastic progress on, uh, each different version of Gemini.

    6. LF

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

    7. DH

      Yes.

    8. LF

      ... to ki- kick ass in that domain.

    9. DH

      Well, I mean, if you look at the history of the last decade or 15 years-

    10. LF

      Yeah.

    11. DH

      ... um, it's been, uh, we know, maybe, I don't know, 80, 90% of the breakthroughs that mo- 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. (laughs)

    12. LF

      (laughs) Uh, so on the data side, are you concerned about running out of high-quality data, especially high-quality human data?

    13. DH

      I'm not very worried about that, partly because I think there's enough data, uh, on, and it's b- been proven to get the systems to be pretty good. And this goes back to simulations again. If you do have enough data to make simulations or... 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, uh, uh, create those kinds of generator, data generators. And, um, I think that we're at that step at the moment.

    14. LF

      Yeah, you've done a lot of incredible stuff on the side of science and biology.

    15. DH

      Mm-hmm.

    16. LF

      D- doing a lot with not so much data.

    17. DH

      Yeah.

    18. LF

      I mean, it's still a lot of data, but, uh, I guess enough take off-

    19. DH

      Get that going, exactly.

    20. LF

      Yeah,

  13. 1:06:17 – 1:09:04

    Compute

    1. LF

      yeah.

    2. DH

      Yes, exactly.

    3. LF

      Uh, how crucial is the scaling of compute to building AGI? This is a question that's an engineering question. It's a almost a geopolitical question.

    4. DH

      Mm-hmm.

    5. LF

      Because it also integrated into that is the supply chains and energy.

    6. DH

      Yes.

    7. LF

      A thing that you care a lot about, which is, um, potentially fusion.

    8. DH

      Yes.

    9. LF

      So innovating on the side of energy also.

    10. DH

      Yeah.

    11. LF

      Do you think we're gonna keep scaling compute?

    12. DH

      I think so, for several reasons. I think compute, there's, there's the amount of compute you have for training. Uh, often it needs to be co-located, so actually even, like, you know, uh, 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. Um, and, uh, 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-

    13. LF

      (laughs) Yeah.

    14. DH

      ... um, uh, uh, in the overall compute that, that's required.

    15. LF

      Yeah, that's one sort of almost meme-y kind of thing, which is, like, the success and the incredible aspects of VO3. There's like (laughs) ... uh, people kind of make fun of, like, the more successful it becomes, the, you know, the servers are sweating.

    16. DH

      Yes (laughs) . Exactly. They're melting.

    17. LF

      To, to do inference (laughs) .

    18. DH

      Yeah, yeah, exactly. We did a little video of, of the se- of the servers frying eggs and things.

    19. LF

      Yeah.

    20. DH

      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, that 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 new types of solar material, solar panel material, superco- 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, in, again, in the next five years, to having AI systems that can materially help with those

  14. 1:09:04 – 1:13:00

    Future of energy

    1. DH

      problems.

    2. LF

      So if you were to bet... sorry for the ridiculous question-

    3. DH

      Yeah.

    4. LF

      ... but what, what is the main source of energy, uh, in like 20, 30, 40 years? Do you think it's gonna be nuclear fusion?

    5. DH

      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 and 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, it 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.

    6. LF

      What a time to be alive. If I, uh, traveled into the future with you 100 years from now, how much would you be surprised if we've passed a type I Kardashev scale civilization?

    7. DH

      I would not be that surprised if there's a h- like a 100-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, uh, fairly wealthy countries like Singapore and Israel and so on, like, actually use it. But, but if it was, uh, cheap, then ev- 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. 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.

    8. LF

      Yeah, I wonder what it would look like if you're just, uh, tourists flying through space. You would probably notice Earth... because if you solve the energy problem, you would see a lot of space rockets probably.

    9. DH

      Mm-hmm.

    10. LF

      So it would be like traffic (clears throat) here in London-

    11. DH

      Yeah.

    12. LF

      ... but in space (laughs) .

    13. DH

      Yes, exactly.

    14. LF

      It's just a lot of rockets.

    15. DH

      Yes.

    16. LF

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

    17. DH

      Yeah.

    18. LF

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

    19. DH

      Yes.

    20. LF

      ... like the rainforest and all that kind of stuff.

    21. DH

      Exactly, because for the first time in, in human history, we wouldn't be, uh, resource constrained.

    22. LF

      Mm-hmm.

    23. DH

      And I think that could be amazing new era for humanity where it's not...... zero sum.

    24. LF

      Mm-hmm.

    25. DH

      Right? I have this land, you don't have it. Or if we take... Y- you know, if the tigers have their forest, then th- the local villagers can't... What are they gonna use? I- I- I think that this will help a lot. Now, it won't solve all problems, because there's still other human, uh, foibles that 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 raw materials and energy. And, um, we know we should be... Some of us call it, like others call it, about this kind of radical abundance era, where, um, 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, uh, and everyone in society benefits

  15. 1:13:00 – 1:17:54

    Human nature

    1. DH

      from that.

    2. LF

      So there is something about human nature where I go... You know, (laughs) it's, it's like Borat, like, "My neighbor." Like... (laughs) Like, you start trouble. We, we, we do start conflicts. And that's why games throughout... as I'm learning, actually, more and more... e- even in ancient history, serve the purpose of pushing people away from war-

    3. DH

      Yes.

    4. LF

      ... actual hot war. So maybe we can figure out increasingly sophisticated video games that pull us... They, they give us that, uh-

    5. DH

      Visceral.

    6. LF

      They scratch the itch-

    7. DH

      Yeah.

    8. LF

      ... of, like, conflict, whatever that is, ab- above us, the human nature, and then avoid the actual hot wars that would come with increasingly sophisticated technology. 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.

    9. DH

      Yeah.

    10. LF

      So, it's no longer, um... That's no longer a great way t- to, uh, start shit with your neighbor. It's better to play a game of chess, a-

    11. DH

      Or football.

    12. LF

      ... or football.

    13. DH

      Or, or... Yeah.

    14. LF

      Yeah.

    15. DH

      And I think... I mean, I think that's what, why modern sport is so... And I love football, watching it and, 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 very visceral and it's tribal.

    16. LF

      Mm-hmm.

    17. DH

      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 a, and 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.

    18. LF

      Mm-hmm.

    19. DH

      They're, they are 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. 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 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 a- additional benefit of channeling some energies into, uh, into more creative and constructive pursuits.

    20. LF

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

    21. DH

      Right.

    22. LF

      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-

    23. DH

      Yeah.

    24. LF

      ... 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.

    25. DH

      Yeah.

    26. LF

      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.

    27. DH

      Yeah. And I think in martial arts, as I understand it, but also in things like, like chess, there's a lo-... 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, and think you're the, just the best in the world, but kee-... And, and, 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- I- I'm pretty sure, in martial arts, and, and I think that's also, uh, the way that at least I was trained in chess. And so, v-... In the same way. And it can be very hardcore and very important, and, of course, you wanna win, but you also need to learn how to deal with setbacks, uh, in a, in a healthy way that... And, and, and, and wire that, that feeling that you have when you lose something into a constructive thing of, "Next time, I'm gonna improve this," right? Or, "get better at this."

Episode duration: 2:28:14

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