Lex Fridman Podcast - Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games

July 23, 2025 - Lex Fridman Podcast

02:28:03

Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games | Lex Fridman Podcast #475

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	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.
	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 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 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.
	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.
	The following is a conversation with Demis Hassabas, his second time on the podcast.
	He is the leader of Google Deep Mind 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 Friedman 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 Hassabas.
	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 of systems might be included in that?
	Biology, chemistry, physics, maybe cosmology?
	Yep.
	Neuroscience?
	What are we talking about?
	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 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 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 try to brute force a solution, find the best moving Go or find the exact shape of a protein.
	And if you enumerated all the possibilities, there wouldn't be enough time in 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 the search in a smart way.
	And that makes it tractable.
	So if you think about protein folding, which is obviously a natural system, why should that be possible?
	How does physics do that?
	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 shape them.
	And if that's true, then you can maybe learn what that structure is.
	This perspective, I think, is a really interesting one.
	You've hinted at it, which is almost like crudely stated.
	Anything that can be evolved can be efficiently modeled.
	Think there's some truth to that?
	Yeah, I sometimes call it survival of the stabilist or something like that, because, you know, 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 because it's not a random pattern, right?
	So it may not be possible 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.
	So you have to do brute force.
	So in that case, you maybe 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.
	It's like nature is doing a search process and it's so fascinating that it's in that search process, it's creating systems that can be efficiently modeled.
	Yes, right.
	Yeah.
	So interesting.
	So they can be efficiently rediscovered or recovered because nature is not random, right?
	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.
	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 demos of this new class of systems that could be actually learnable by classical systems in this kind of way, natural systems that can be modeled efficiently.
	Yeah.
	I mean, I've always been fascinated by the P cause MP 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 is 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 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 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.
	So when you think of the universe as an informational system, then the P equals NP question is a physics question.
	That's right.
	It's a question that can help us actually solve the entirety of this whole thing going on.
	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 very enlightening.
	More specific to the PNP question.
	Again, some of the stuff we're saying is kind of crazy right now.
	Just like the Christian Anthony 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 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 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 kind of way.
	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 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, Turing machines in effect.
	And I think it's one of the most interesting questions there is is how far can that paradigm go?
	You know, I think we've proven 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, 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 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 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, you know, what the bounds of that kind of system, what it can do, it's a very interesting question and directly speaks to the P equals MP question.
	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.
	Yes.
	Maybe that would be outside or even, would you guess even that might be amenable to efficient modeling by a classical machine?
	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.
	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 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, 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 calls MP.
	Yeah, I think 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.
	You can find the kernel that is efficiently modeled.
	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 object 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.
	It's 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 right solution or predict the next step?
	But it turns out that it is possible.
	And of course, reality in nature does do it, right?
	Proteins do fold.
	So 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.e.
	model that process, it should be possible on our classical systems is basically what the conjecture is about.
	And of course, there's non-linear dynamical systems, highly non-linear dynamical systems, everything involving fluid.
	Yes.
	Right.
	You know, I recently had a conversation with Terence 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 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.
	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.
	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.
	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.
	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 memes 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 exactly the thing you're mentioning, which is the physics.
	Yeah.
	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 the cynical take with the diffusion models, there's no way it understands anything.
	But it seems, 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?
	I think to the extent that it can predict the next frames, you know, in a coherent way, 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 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, 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 they will look given where we've come from, you know, the early versions of that one or two years ago.
	And so the rate of progress is incredible.
	And I think I'm like you, it's like a lot of people love all of the stand-up comedians and it actually captures a lot of human dynamics very well 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 opposed to a PhD student really being able to unpack all the equations.
	It's more of an intuitive physics understanding.
	Well, that intuitive physics understanding, that's the base layer.
	That's the thing people sometimes call like common sense.
	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.
	But VO3 is directly challenging that.
	Right.
	It feels like.
	Yes.
	And it's very interesting.
	You know, even if we, 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.
	And again, I think hints at something underlying about the nature of reality, in my opinion, beyond just 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 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.
	I have to talk to you about video games.
	So you're 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?
	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 want to have, I want to scratch that itch 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.
	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 are immersed in it and then you are co-creating it 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, ten years, of having AI systems 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 going to be.
	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.
	Yes.
	But it's really just an illusion of choice because the only like Stanley Parable is the game I recently 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 in a meta way 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 random generation of the dungeons.
	Yeah.
	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.
	Yeah, exactly.
	And so, but what you'd like is a little bit better than just sort of a random generation, right?
	So you'd like, and also better than a simple AB hard coder choice, right?
	That's not really open world, right?
	As you say, it's just giving you the illusion of choice.
	What you want to be able to do is potentially anything in that game environment.
	And I think the only way you can do that is to have generated systems, systems that 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 us back in the 90s when I was working on all these games.
	I think maybe Black and White was the game that I worked on early stages of that that had 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 I've been working on sort of simulations and AI through the medium of games at the beginning of my career.
	And really the whole of what I do today is still a follow-on from those early, more hard-coded ways of doing the AI to now, you know, fully general learning systems that are trying to achieve the same thing.
	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, that you might not have time to really create a game.
	You might end up creating the tooling that others would create the game.
	You have to watch 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, an actual video game where you could make the childhood dream become bigger?
	Well, you know, there's two things, way to think about that is maybe with vibe coding as it gets better.
	And there's possibility that I could, you know, one could do that actually in your spare time.
	So I'm quite excited about that.
	That would be my project if I got the time to do some vibe coding.
	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 my two post-AGI projects.
	Let's call it that way.
	I would love to see which game.
	Post-AGI, which you choose, solving the problem that some of the smartest people in human history contended with.
	So P equals MP or creating a cool video.
	Yeah.
	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 is the universe?
	That's speaking to the same question, right?
	MP equals MP.
	I think all these things are related, at least in my mind.
	I mean, in a Really serious way, video games sometimes are looked down upon as just this fun side activity.
	But especially as AI does more and more of the difficult, boring tasks, something we in modern world call 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.
	Yeah.
	I think so.
	I mean, those of us who love games and I still do, it's is, you know, it's almost can let your imagination run wild, right?
	Like I used to love games and working on games so much because it's the fusion, especially in the 90s and early 2000s, the sort of golden era, maybe the 80s 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 creating a new entertainment medium that never existed before, especially with these open world games and simulation games where 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 story.
	And of course, now with multiplayer games as well, it can be a very social activity and can explore all kinds of interesting worlds in 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 going to have to confront the question again of what is the fundamental nature of reality?
	What is going to be the difference between these increasingly realistic simulations and multiplayer ones and emergent and what we do in the real world?
	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?
	Yeah.
	And which aspect of that can be mapped into the virtual world?
	Exactly.
	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?
	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, they're all related in that way.
	This simulation, nature of reality, and what is the bounds of, you know, what can be modeled.
	Sorry for the ridiculous question, but so far, what is the greatest video game of all time?
	What's up there?
	My favorite one of all time is Civilization, I have to say.
	That was the Civilization 1 and Civilization 2, my favorite games of all time.
	I can only assume you've avoided the most recent one because it would probably, that would be your sabbatical.
	You would disappear.
	Yes, exactly.
	They take a lot of time, these Civilization games.
	So I've got to be careful with them.
	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?
	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 just became a thing, you know, in the late 80s and 90s, especially in the UK.
	I had a Spectrum and then a Commodore Amiga 500, which is 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 then, of course, I immediately took it in directions of AI and simulations, which so I was able to express my interest in games and my sort of wider scientific interests all together.
	And then the final thing I think that's great about games is it fuses artistic design, you know, art with the most cutting-edge programming.
	So again, in the 90s, 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 90s was due to gaming.
	So interestingly, that was where the forefront of research was going on.
	And it was this incredible fusion with art, you know, graphics, but also music and just the whole new media of storytelling.
	And I love that.
	For me, it's this sort of multidisciplinary kind of effort is, again, something I've enjoyed my whole life.
	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.
	Are these kinds of evolution-like techniques promising as a component of future superintelligent systems?
	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.
	So evolutionary algorithms are doing the search and LLMs are telling you where.
	Yes, exactly.
	So LLMs are kind of proposing some possible solutions and then you use evolutionary computing on top to find some novel part 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.
	But not to romanticize evolution.
	Yeah.
	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 there's a lot of low-hanging fruit of us understanding, being able to model, being able to simulate evolution and then using that, Whatever we understand about that nature-inspired mechanism to then do search better and better and better.
	Yes.
	So, if you think about again breaking down the solar systems we've built to their really fundamental core, you've got like the model of the underlying dynamics of the system.
	And then, if you want to discover something new, something novel that hasn't been seen before, then you need some kind of search process on top to take you to a novel region of the search space.
	And you can do that in a number of ways.
	Evolutionary computing is one.
	With AlphaGo, we just use Monte Carlo TreeSearch, right?
	And that's what found Move 37, the new 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, discover something new?
	Obviously, this is super relevant for scientific discovery or pushing science and medicine forward, which we want to do with these systems.
	And you can actually bolt on some fairly simple search systems on top of these models and get you into a new region of space.
	Of course, you also have to make sure that you're not searching that space totally randomly.
	It was 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.
	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 everything.
	But, you know, for example, mutation.
	So it's not just Monte Carlo tree search where it's like a search.
	You could every once in a while.
	Combine things, like a component of a thing.
	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.
	So that component is super interesting, especially like with Alpha Evolve and the space of programs.
	Yeah, exactly.
	So you can get a bit of an extra property out of evolutionary systems, which is some new emergent capability may come about.
	But of course, like happen with life.
	Interestingly, with naive sort of traditional evolutionary computing methods without LLMs and the modern AI, the problem with them, they were very well studied in the 90s and early 2000s 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 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.
	Yeah, listen, Alpha Vol is one of the coolest things I've ever seen.
	I've on my desk at home, you know, most of my time is spent on that computer is just programming.
	And next to the three screens is a skull of a tiktalic, 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, whatever the computation mechanism of evolution is, is quite incredible.
	Yes.
	It's truly, truly incredible.
	Now, whether that's exactly the thing we need to do to do our search, but never dismiss the power of nature, what it did here.
	Yeah.
	And it's amazing, which is a relatively simple algorithm, right?
	Effectively.
	And it can generate all of this immense complexity.
	It merges, obviously running over, you know, four billion years of time, but 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.
	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 cell.
	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 Cocotaglio, 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 co-scientist does, to help steer 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.
	Yeah.
	I think that's going to be one of the hardest things to mimic or model is this idea of taste or judgment.
	I think that's what separates the great scientist from the good scientist.
	Like 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 picking the right question is the hardest part of science and making the right hypothesis.
	And that's what today's systems definitely, they can't do.
	So 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.
	Maths Olympiad problems, Alpha proof last year, our system got silver medal in that, really hard problems.
	Maybe eventually we'll better solve a Millennium Prize kind of problem.
	But could a system have 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.
	Those 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.
	And for conjecture, you want to come up with a thing that's interesting.
	It's amenable to proof.
	Yes.
	So like it's easy to come up with a thing that's extremely difficult.
	Yeah.
	It's easy to come up with a thing that's extremely easy.
	But at that very end.
	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 that's hard.
	And making something that's also, you know, falsifiable and within sort of the technologies that you currently have available.
	So it's a very creative process, actually, highly creative process that I think just a kind of naive search on top of a model won't be enough for that.
	Okay, the idea of splitting the hypothesis space into is super interesting.
	So 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 space into two, it's like a binary search.
	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 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 an experiment well and your hypotheses are interesting, it should tell you a lot about where to go next.
	And then you're effectively doing a search process and using that information in, you know, very helpful ways.
	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, amenable to modeling.
	Alpha Genome predicts how small genetic changes, like if we think about single mutations, how they link to actual function.
	So those are, it seems like it's creeping along to a sophisticated, to much more complicated things like a cell, but a cell has a lot of really complicated components.
	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.
	We've been talking about it since, you know, before the, you know, in the 90s.
	And I 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, 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, alpha fold being one, that that would 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.
	There'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 yeast is very well understood.
	And so that would be a good candidate for a kind of full simulated model.
	Now, AlphaFold is the solution to the kind of static picture of what does a protein look 3D structure, a 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 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 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.
	Also, there's another complexity here that stuff in a cell happens at different time scales.
	Is that tricky?
	Like there, you know, protein folding is, you know, super fast.
	Yes.
	I don't know all the biological mechanisms, but some of them take a long time.
	And so that's a level.
	So the levels of interaction has a different temporal scale that you have to be able to model.
	So that would be hard.
	So you'd probably need several simulated systems that can interact at these different temporal dynamics, or at least maybe it's like a hierarchical system.
	So you can jump up and down the different temporal stages.
	So can you avoid, I mean, one of the challenges here is not avoid simulating, for example, the quantum mechanical aspects of any of this, right?
	You want to not over model, you can skip ahead, just model the really high-level things that get you a really good estimate of what's going to happen.
	So 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 then captures the dynamics that you're interested in?
	So, probably for a cell, 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.
	I 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 non-living organisms, the birth of a living organism?
	I think that's one of the, of course, one of the deepest and most fascinating questions.
	I love that area of biology.
	You know, there's people like there's a great book by Nick Lane, one of the top experts in this area called The 10 Great Inventions of Evolution.
	I think it's fantastic.
	And it also speaks to what the great filters might be, prior or are they ahead of us?
	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.
	Exterior were super happy for a very long time.
	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, you know, the chemical soup 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 could you actually something like that emerge from the chemical soup?
	Well, I would love it if there was a move 37 for the origin of life.
	I think that's one of the great mysteries.
	I think ultimately what we will figure out is their continuum.
	There's no such thing as a line between non-living and living.
	But if we can make that rigorous, 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 non-living to living, and it's not a line, that it's a continuum that connects physics and chemistry and biology.
	Yes.
	Because there's no line.
	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, 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 non-living 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 sort of screaming at me in my face.
	And that's it's getting louder.
	You know, it's like, how, what is going on here?
	You know, 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 that would answer all of these things.
	It's sort of crazy if you think about it.
	We can stare at each other and all these living things all the time.
	We can inspect it with microscopes and take it apart, 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.
	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 feels like something.
	And then how are you not screaming at the mystery of it all?
	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 the rain?
	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.
	Exactly.
	So we're quite, I guess we've developed a lot of mechanisms to cope with this, 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 we keep ourselves busy, right?
	In a way, do we keep ourselves distracted?
	I mean, weather is one of the most important questions of human history.
	We still, that's the go-to small talk direction of the weather.
	Especially in England.
	And then it's, which is, you know, famously is an extremely difficult system to model.
	And even that system, Google Deep Mind has made progress on.
	Yes.
	We've created 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 Weathernex 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.
	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 simulating and so that you can run forward predictions and simulations of very complicated real world systems.
	I should mention that I've gotten a chance in Texas to meet a community of folks called the Storm Chasers.
	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 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 weather.
	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 a deep mind system.
	So that's.
	Yeah, but hopefully they are.
	And I'd love to join them on one of those chases.
	they look amazing, right?
	To actually experience it one time.
	Exactly.
	And then also to experience the correct prediction of where something will come and how it's going to evolve.
	It's incredible.
	Yeah.
	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?
	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 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 systems, but other things it's really flawed at.
	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 we know that humans can do and maybe also make the system available to a few hundred of the world's top experts, the Terence Taos of each subject area, and see if they can find, 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 can be pretty confident we have a fully general system.
	Maybe to push back a little bit, it seems like humans are really incredible as the intelligence improves across all domains to take it for granted.
	Like you mentioned, Terence Tao, 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, haha, right there.
	You know, I consider myself, first of all, human.
	I identify as human.
	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, even just outside of mathematics and physics and so on.
	I wonder if it will take something like a move 37.
	So on the positive side versus like a barrage of 10,000 cognitive tasks where it would be one or two where it's like, holy shit.
	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 Mooth 37 that I would be looking for.
	So one would be inventing a new conjecture or 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 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.
	That would be an interesting test.
	Another one would be, can it invent a game like Go?
	Not just come up with Move 37, 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 and probably a system being able to do several of those things, right?
	For it to be very general, 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 their consistency, you know, make sure there's no holes in that system either.
	Yeah, something like a new conjecture or a scientific discovery.
	That would be a cool feeling.
	Yeah, that would be amazing.
	So it's not just helping us do that, but actually coming up with something brand new.
	And you would be in the room for that.
	So it would be like probably two or three months before announcing it.
	And you would just be sitting there trying not to tweet something like that.
	Exactly.
	It's like, what is this amazing new 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.
	It'd be an amazing moment.
	Do you worry that we as humans, even expert humans like you, might miss it?
	Might miss.
	It may be pretty complicated.
	So it could be the analogy I give there is I don't think it will be totally mysterious 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 Carlson 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 would be able to understand it to some degree.
	Not to the level they do, but if they were good at explaining, which is actually part of intelligence too, is being able to explain in a simple way what you're thinking about.
	I think that that would be very possible for the best human scientists.
	But I wonder, maybe you can educate me on the side of Go.
	I wonder if there's moves from Agnes or Gary where they at first will dismiss it as a bad move.
	Yeah, sure.
	It could be.
	But then afterwards, they'll figure out with their intuition that this why this works.
	And then and then empirically, the nice thing about games is one of the great things about games is you can, it's a sort of scientific test.
	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 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've been doing that since I was four.
	And you're training, you know, it's sort of hardcore training in that way.
	But even now, like when I generate code, 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 going to have to contend with that.
	It's like, what, what do you, is this a bug or a feature of what you just came up with?
	Yeah.
	And they're going to 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 going to be kind of an interesting, you know, maybe different AI tools to the ones that the more, you know, kind of monitoring tools to the ones that generated it.
	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 we could 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?
	Do you think it will be something like a self-improving program and a simple one?
	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 takeoff scenario.
	But 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, 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.
	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.
	And I think that's related to this idea of invent a game as good as go, right?
	Imagine that was the prompt.
	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 unconstrained.
	But we've done it in, you know, and as you know with Alpha Volve, like things like faster matrix multiplication.
	So 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 wanted a big leap in understanding, you'd need a much larger advance.
	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 thinking how to incrementally improve a thing.
	And it does so recursively.
	And as you do more and more improvement, it'll slow down.
	So there'll be like a like the path to AGI won't be like a it'll be a gradual improvement over time.
	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?
	Like, could it have done that back in 2017 when we did it and brain did it?
	And it's not clear that these systems, something like AlphaVol 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?
	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.
	Yeah, I don't think anyone has systems that have shown unequivocally those big leaps.
	Right.
	We have a lot of systems that do the hill climbing of the S curve that you're currently on.
	Yeah.
	And that would be the move 37.
	Yeah, I think would be a leap, something like that.
	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?
	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 Shazir who, you know, came up with Transformers and Dave Silver, you know, who led the AlphaGo project and so on.
	And it's that research base means that if some new breakthrough is required, 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 true research and, you know, or 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 fast follow.
	And so, you know, we don't know.
	I would say 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.
	And then scaling to the max the current capabilities.
	And we're still seeing some fantastic progress on each different version of Gemini.
	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 kick ass in that domain.
	Well, I mean, if you look at the history of the last decade or 15 years, it's been, you know, 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.
	So on the data side, are you concerned about running out of high-quality data, especially high-quality human data?
	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.
	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 data generators.
	And I think that we're at that step at the moment.
	Yeah, you've done a lot of incredible stuff on the side of science and biology, doing a lot with not so much data.
	Yeah.
	I mean, still a lot of data, but I guess enough take-that going.
	Exactly.
	So exactly.
	How crucial is the scaling of compute to building AGI?
	This is a question that's an engineering question.
	It's almost a geopolitical question because it also integrated into that is the supply chains and energy.
	Yes.
	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?
	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 there's additional constraints even there.
	And 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.
	And then on top of that, there's the thinking systems, the new paradigm of the last year that 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.
	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 only just one part of that.
	It may even become the smaller part of what's needed in the overall compute that's required.
	Yeah, that's one sort of almost meme-y kind of thing, which is like the success and the incredible aspects of VO3.
	People kind of make fun of like the more successful it becomes, you know, the servers are sweating.
	Yes.
	Yeah, yeah, exactly.
	We did a little video of the servers frying eggs and things.
	And that's right.
	And we're going to have to figure out how to do that.
	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 data center energy like for the cooling systems be efficient, grid optimization, and then eventually things like helping with plasma containment fusion reactors.
	We've done lots of work on that with Commonwealth Fusion and also one could imagine reactor design and then material design, I think, is one of the most exciting new types of solar material, solar panel material, room temperature superconductors has always been on my list of dream breakthroughs and optimal batteries.
	And I think a solution to any one of those things would be absolutely revolutionary for climate and energy usage.
	And we're probably close again in the next five years to having AI systems that can materially help with those problems.
	If you were to bet, sorry for the ridiculous question, but what is the main source of energy in like 20, 30, 40 years?
	Do you think it's going to be nuclear fusion?
	I think fusion and solar are the two that I would bet on.
	Solar, I mean, you know, it's the fusion reactor in the sky, of course.
	And I think really the problem there 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, if we have the right design of reactor and we can control the plasma and 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.
	What a time to be alive.
	If I traveled into the future with you 100 years from now, how much would you be surprised if we've passed a Type 1 Karashev scale civilization?
	I would not be that surprised if there was 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 very efficient solar, then if energy is kind of free and renewable and clean, 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 actually use it.
	But if it was cheap, then 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 combined with Elon's amazing self-landing rockets, then it could be sort of like a bus service to space.
	So that opens up incredible new resources and domains.
	Asteroid mining, I think, will become a thing and maximum human flourishing to the stars.
	That's what I dream about as well is like Carl Sagan's sort of idea of bringing consciousness to the universe, waking up the universe.
	And I think human civilization will do that in the full sense of time if we get AI right and crack some of these problems With it, 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, yes.
	And then you probably see floating in space some kind of source of energy, like solar, 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 rainforest and all that.
	Exactly, because for the first time 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 local villagers can't, what are they going to use?
	I think that this will help a lot.
	No, it won't solve all problems because there's still other human foibles that 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, you know, we should be, sometimes call it like an others call it about this kind of radical abundance era 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.
	So there is something about human nature where I go, you know, it's like Borat, like my neighbor, like you start trouble.
	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 pull us, that give us that scratch the itch of like conflict, whatever that is about us, the human nature, and then avoid the actual hot wars that would come with increasingly sophisticated technologies because we're now long past the stage where the weapons we're able to create can actually just destroy all of human civilization.
	So it's no longer, that's no longer a great way to start shit with your neighbor.
	It's better to play a game of chess or football.
	Or football.
	And I think, I mean, I think that's what my modern sport is.
	And I love football, watching it.
	And I just feel like, and I used to play it a lot as well.
	And 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 group.
	But into a fun way, a healthy way and a not destructive way, kind of constructive 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 are simulations of the world too.
	They're simplified versions of some real world situation, whether it's poker or go or chess, different aspects or diplomacy, different aspects of the real world.
	And it allows you to practice at them too.
	And because, 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.
	And games is a kind of safe environment, repeatable environment where you can get better at your decision-making process.
	And it maybe has this additional benefit of channeling some energies into more creative and constructive pursuits.
	Well, I think it's also really important to practice losing and winning.
	Right.
	Like losing is a really, you know, that's why I love games.
	That's why I love even things like Brazilian Jiu-Jitsu, 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 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.
	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, but you also need to learn how to deal with setbacks in a healthy way 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.
	There is something that's a source of happiness, a source of meaning, that improvement step.
	It's not about the winning or losing.
	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.
	Yeah.
	I mean, there's something about, I guess, 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.
	And maybe that's why we made games like that, because obviously that is something we're hill climbing systems ourselves, right?
	Yeah, it would be quite sad if we didn't have any mechanism by color belts.
	We do this everywhere, right?
	Where we just have this thing that I don't want to dismiss that.
	That is a source of deep meaning for us humans.
	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 1.5, and now it's winning with Gemini 2.5.
	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?
	Yeah, well, firstly, it's absolutely incredible team that we have, you know, led by Core and Jeff Dean and Aurel 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've created, right?
	And basically coming together, both different groups in Google, you know, there was Google Brain, world-class team, and then the old deep mind and pulling together all the best people and the best ideas and gathering around to make the absolute greater system we could.
	And it has been hard, but we're all very competitive 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 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 been 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.
	You know, Google's a gigantic company.
	Can you speak to the natural things that happen in that case, the bureaucracy that emerges?
	Like, you want to be careful?
	Like, you know, like the natural kind of there's meetings and there's managers and that.
	Like what are some of the challenges from a leadership perspective, breaking through that in order to, like you said, ship?
	Like the number of products, Gemini-related products that's been shipped over the past year is just insane.
	Right.
	It is.
	Yeah, exactly.
	That's, that's what relentlessness looks like.
	I think it's, it's a question of like any big company, you know, ends up having a lot of layers of management and things like that.
	It's sort of the nature of how it works.
	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, incredible products that we can power up with our AI and our research.
	And that's amazing.
	And you can, you know, there'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 improve billions of people's lives the next day.
	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 with a number of, you know, huge product surfaces that we have.
	So a funny thing you mentioned about like the surface with the billion.
	I had a conversation with a guy named Brilliant Guy here at the British Museum called Irvin Finkel.
	He's a world expert at Cuneaforms, which is a ancient writing on tablets.
	And he doesn't know about Chad GPT or Gemini.
	He doesn't even know anything about AI.
	But his first encounter with this AI is AI mode on Google.
	He's like, is that what you're talking about?
	This AI mode.
	And, you know, it's just a reminder that there's a large part of the world that doesn't know about this AI thing.
	Yeah.
	I know, it's funny because if you live on X and Twitter, and I mean, it's sort of, at least my feed, it's all AI.
	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 come across it yet.
	And that's a great responsibility to their first interaction on the grand scale of the rural India or anywhere across the world.
	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 it's 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.
	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 don't enjoy using?
	Yeah, well, I mean, again, that comes back from my game design days where I used to design games for millions of gamers.
	People would 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 can also be useful in product design.
	And I've just had a very, you know, always been a sort of multidisciplinary person.
	So I don't see the boundaries really between, you know, 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, you know, 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.
	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 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 the very cutting edge.
	We don't know how to present it, how to present it correctly.
	So is there some specific things you've learned?
	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, whether that's the interface or 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.
	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 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 we didn't realize before that can allow like the 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?
	Seems very unlikely once you think about these super multimodal systems.
	Shouldn't it be something more like Minority Report where you're sort of vibing with it 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 research side.
	And then we were offline talking about this keyboard.
	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?
	Yeah, I mean, typing is a very low bandwidth way of doing it, even if you're a very fast typer.
	And I think we're going to have to start utilizing other devices, whether that's smart glasses, audio, earbuds, and eventually maybe some sorts of neural devices where we can increase the input and the output bandwidth to something maybe 100x of what is today.
	I think that underappreciated art form is the interface design.
	I think you can not unlock the power of the intelligence of a system if you don't have the right interface.
	The interface is really the way you unlock its power.
	It's such an interesting question of how to do that.
	You would think getting out of the way isn't real art form.
	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 that 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 going to 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 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.
	Yeah, because some people are power users and they want every single parameter on screen, everything, everything based, like perhaps me with a keyboard-based navigation.
	I like to have shortcuts for everything.
	And some people like the minimalism.
	Just hide all of that complexity.
	Exactly.
	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 will Gemini 3 come out?
	Before or after DTA 6?
	The world waits for both.
	And what does it take to go from 2.5 to 3.0?
	Because it seems like there's been a lot of releases of 2.5, which are already leaps in performance.
	So what does it even mean to go to a new version?
	Is it about performance?
	Is this about a completely different flavor of an experience?
	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 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.
	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?
	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 experimenting 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 2.5, 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 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, you know, on the one hand, the Y-axis is, you know, like performance.
	And then the X-axis is, you know, cost or latency and speed, basically.
	And we have models that completely define the frontier.
	So, whatever your trade-off is that you want as an individual user or as a developer, you should find one of our models that satisfies that constraint.
	So, behind diversion changes, there is a big hero run.
	Yes.
	And then there's just an insane complexity of productization.
	Then there's the distillation of the different sizes along that Pareto front.
	And then as with each step you take, you realize there might be a cool product.
	There's side quests.
	Yes.
	Exactly.
	And then you also don't want to take too many side quests because then you have a million versions of a million products.
	Yes.
	It's very unclear.
	But you also get super excited because it's super cool.
	Like how does even you look at VOs?
	Very cool.
	How does it fit into the bigger thing?
	Exactly.
	Exactly.
	And then you constantly, this process of converging upstream, we call it, you know, ideas from the product surfaces or from the post-training and even further downstream than that, you kind of upstream that into the core model training for the next run.
	So then the main model, the main Gemini track becomes more and more general.
	And eventually, you know, AGI.
	One hero run.
	Yes, exactly.
	A few hero runs later.
	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?
	You need them.
	But 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 LM Arena, or it used to be called Elemsys that's one of them that turned out sort of organically to be one of the main ways people like to test these systems, at least the chatbots.
	Obviously, there's loads of academic benchmarks that 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 optimization problem, right?
	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 improve in like, you know, coding, 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, I don't know, gaming data in, but then does it make worse your language system or your translation systems and other things that you care about?
	So you've got to kind of continually monitor this increasingly larger and larger suite of benchmarks.
	And also there's 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 the average person using the chat interfaces.
	Yeah, because ultimately you want to measure the usefulness.
	It's so hard to convert that into a number.
	It's really vibe-based benchmarks across a large number of users and it's hard to know.
	And 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 quite working.
	That's just scary.
	And everything you just said, it has to be smart and useful across so many domains.
	So you get super excited because it's all of a sudden solving programming problems it'd 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 to balance.
	And because you can't really trust the benchmarks, you really have to trust the end users.
	Yeah.
	And then other things that are even more esoteric come into play, like, you know, the style of the persona of the system, you know, how it, you know, is it verbose?
	Is it succinct?
	Is it humorous?
	You know, and different people like different things.
	So, you know, it's very interesting.
	It's almost like cutting edge part of psychology research or 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 we're going to sort of rapidly have to deal with now.
	I think it's a super fascinating space, developing the character of the thing.
	And in so doing, it puts a mirror to ourselves, what are the kind of things that we like?
	Because 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?
	Yeah, exactly.
	So what's the probability of Google Deep Mind winning?
	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 about, in my view, all of us, 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 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, that that's what's needed.
	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.
	How hard is it to maintain sort of those relationships?
	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 helping solve with AI.
	So I just want that technology to exist in the world and be used for the right things.
	And the kind of the benefits of that, the productivity benefits Of that being shared for 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.
	And I think that's going to be important 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.
	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 collaborate on creating a video game, that kind of thing.
	I think that enables camaraderie and good terms.
	And also, you two are legit gamers, so it's just fun to find together.
	Yeah, that would be awesome.
	And we've talked about that in the past, and it may be a cool thing that we can do.
	And I agree with you.
	It'd be nice to have kind of side projects in a way where one can just lean into the collaboration aspect of it.
	And it's a sort of win-win for both sides.
	And it kind of builds up that collaborative muscle.
	I see the scientific endeavor as that kind of side project for humanity.
	And I think Google Deep Mind has been really pushing that.
	I would love to see other labs do more scientific stuff and then collaborate because it just seems like easier to collaborate on the big scientific questions.
	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 how AI can be clearly used in a very concrete way for the benefit of humanity.
	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 can think of build additional AlphaFold systems to go into chemistry space to help accelerate drug design.
	And the examples I think we need to show and society needs to understand are where AI can bring these huge benefits.
	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.
	There's been 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 the heating up of this battle for talent?
	I should say that I think a lot of people see DeepMind as a really great place to do cutting edge work for the reasons that you've outlined.
	There's this vibrant scientific culture.
	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 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 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 just money.
	Of course, one has to pay, you know, people their market rates and all of these things.
	And that continues to go up.
	And I was expecting this because more and more people are finally realizing, leaders of companies, what I've always known for 30 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.
	And I remember the days where we used, I used to have to work for free 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 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.
	Yeah, when you're facing such gigantic consequences and gigantic, fascinating scientific questions.
	Which maybe are only a few years away.
	So on the practical 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 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?
	Well, it's interesting that programming, and it's again counterintuitive to what we thought years ago, maybe that some of the skills that we think of as harder skills turned out maybe to be the easier ones for various reasons.
	But, you know, coding and math, 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 as programmers, right, to help us and get faster at it and more productive.
	So I think 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.
	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 will 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.
	But I think the top programmers will still have huge advantages in terms of specifying, going back to specifying what the architecture should be, the questions should be, how to guide these coding assistants in a way that's useful and check whether the code they produce is good.
	So I think there's plenty of headroom there for the foreseeable next few years.
	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 way for the improvement, improving models versus 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 front-end web design might be more amenable to, as you mentioned, to generation by AI systems and maybe, for example, game engine design or something like this or backhand design 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.
	I think that's right.
	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 was the 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 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 it, 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 deal with.
	And 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,
	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 there's a basic provision that is provided.
	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.
	The other, defining the other and then saying that's bad.
	And sort of 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.
	Definitely.
	And 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.
	You mentioned to me the book The Maniac by Benjamin Lebatut, a book on, first of all, about you.
	There's by About You.
	It's strange, yeah.
	It's unclear.
	Yes, sir.
	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.
	And, 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 pioneered the modern computer and AI and so on.
	So many people say he's like one of the smartest humans ever, which is 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.
	And that's a little bit, again, beautiful and haunting aspect of the book.
	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 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?
	Well, that would be an amazing experience.
	You know, he's a fantastic mind.
	And I also love where 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 AlphaGo 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 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.
	This the fruition of what I think he already foresaw in the 1950s.
	I wonder what advice you would give.
	You 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 not enough of picking up the phone and talking to people that are called enemies by the said politicians.
	There might be some deep wisdom that we just may have lost from that time, actually.
	Yeah, I'm sure there is.
	I mean, we, you know, study, I read a lot of books for that time as well, Chronicle Time, and some brilliant people involved.
	But I agree with you.
	I think maybe there needs to be more dialogue and understanding.
	I hope we can learn from 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 all diseases, help with energy and scarcity.
	These incredible things.
	This is why all of us and myself, I worked, started on this journey 30 plus years ago.
	But of course, there are risks too.
	And probably von Neumann, my guess is he foresaw both.
	And I think he sort of said, I think it's to his wife 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 10 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.
	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.
	And I agree with that.
	I think we need to approach it with whatever you want to call it, a spiritual dimension or humanist dimension.
	It doesn't have to be to do with religion, right?
	But this idea of a soul, what makes us human, this spark that we have, perhaps it's 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 evolve like that, right?
	That just makes it more beautiful, 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 try 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.
	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 will 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.
	Yeah, because they force the technologists to have the human conversation.
	Yeah, for sure.
	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.
	Yeah, be able to adapt.
	Society will be able to adapt to these technologies, like we've always done in the past with the incredible technologies we've invented in the past.
	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?
	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 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 level, it's important for the researchers to keep in touch and keep close to each other on, at least on those kinds of topics.
	Yeah, and I personally believe on the education side and immigration side, it would be great if both directions, people from the West immigrated to China and China back.
	I mean, there is some like family human aspect of people just intermixing.
	Yeah.
	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 multicultural, multidisciplinary research teams working on scientific questions.
	That's like the hope.
	Don't let the leaders that are warmongers divide us.
	I think science is the ultimately really beautiful connector.
	Yeah, science has always been, I think, quite a very collaborative endeavor.
	And, you know, scientists know that 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.
	What's your ridiculous question?
	What's your P doom?
	Probability that human civilization destroys itself.
	Well, look, I don't have a, you know, I don't have a P doom number.
	The reason I don't is because I think it 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 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 are they going to be.
	Some things may turn out to be and hopefully like way easier than we thought, right?
	But it may be there's some really hard problems that are harder than we guess today.
	And I think we don't know that for sure.
	And so under those conditions of a lot of uncertainty, but huge stakes both ways, you know, on the one hand, we could solve all diseases, energy problems, 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 benefits, of course, and all of the amazing things that AI can bring.
	And actually, I would be really worried for humanity given the other challenges that we have, climate, you know, aging, 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.
	So I think it could be amazingly transformative for good.
	But on the other hand, there are these risks that we know are there, but we can't quite quantify.
	So the best thing to do is to use the scientific method to do more research to try and more precisely define those risks and, of course, address them.
	And I think that's what we're doing.
	I think there probably needs to be 10 times more effort on that than there is now as we're getting closer and closer to the AGI line.
	What would be the source of worry for you more?
	Would it be human-caused or AI, AGI-caused?
	Humans abusing their technology versus AGI itself through mechanism that you've spoken about, which is fascinating, deception or this kind of stuff.
	Yes.
	Getting better and better and better secretly.
	I think they operate over different time scales and they're equally important to address.
	So there's just the common gardenal valority of like, you know, bad actors using new technology, 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 for the benefit of the scientific community.
	But how does one restrict bad actors access to these powerful systems, whether they're individuals or even rogue states, but enable access at the same time to good actors to maximally build on top of?
	It's a pretty tricky problem that I've not heard a clear solution to.
	So there's the bad actor use case problem.
	And then there's obviously as the systems become more agentic and closer to AGI and more autonomous, how do we ensure the guardrails and they stick to what we want them to do and under our control?
	Yeah, I tend to, maybe my mind is limited, worry more about the humans, the bad actors.
	And there it could be 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 also an interesting human problem.
	Yeah, it's a hard problem.
	I mean, look, we can maybe also use the technology itself to help 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 again, it may require some agreement internationally, at least between China and the US, of some basic standards, right?
	I have to ask you about the book, The Maniac.
	There's this the hand of God moment, Lucy Dahl's move 78, that perhaps the last time a human did a move of sort of pure human genius and beat AlphaGo or like broke its brain.
	Sorry to anthropomorphize, but it's an interesting moment because I think in so many domains it will keep happening.
	Yeah, it's a special moment and it was great for Lisa Doll.
	And I think it's in a way they were 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, you know, captured very well 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.
	Yeah, as the AI becomes smarter and smarter, one of the interesting questions we can ask ourselves is what makes humans special?
	It does feel, perhaps biased, that we humans are deeply special.
	I don't know if it's our intelligence, but it could be something else that other thing that's outside the mad dreams of reason.
	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 focus 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 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.
	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 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 consciousness is it's the way information feels when we process it, right?
	It could be.
	I mean, it doesn't help.
	It's not a very helpful scientific explanation, but I think it's kind of interesting, intuitive one.
	And so, you know, on this, this, this journey, this scientific journey we're on will, I think, help uncover that mystery.
	Yeah, what I cannot create, I do not understand.
	That's somebody you deeply admire, Richard Feynman, like you mentioned.
	You also reach for the Wigner's dreams of universality that he saw in constraint domains, but also broadly generally in mathematics and so on.
	So so many aspects on which you're pushing towards not to start trouble at the end, but Roger Penrose.
	Yes.
	Okay.
	So, you know, do you think consciousness, there's this hard problem of consciousness, how information feels?
	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?
	Yeah.
	Or is it a quantum mechanical in nature?
	Well, look, Penrose is an amazing thinker, one of the greatest of the modern era.
	And we've had a lot of discussions about this.
	Of course, we cordially disagree, which is, you know, I feel like, 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 to my knowledge, they haven't found anything convincing yet.
	So my betting is there is 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 may be this final mysterious things of the feeling of consciousness, the qualia, these kinds of things that philosophers debate where it's unique to the substrate.
	We may even come towards understanding that when if we do things like Neuralink and have neural interfaces to the AI systems, which I think we probably will eventually, 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 and maybe that will tell us.
	So I think it's going to be interesting.
	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.
	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 that's on silicon, we won't be able to rely on the second part, even if it exhibits the first part, the behavior looks like a behavior of a conscious being.
	It might even claim it is.
	But we wouldn't know how it actually felt.
	And it probably couldn't know what we felt, at least in the first stages.
	Maybe when we get to superintelligence and the technologies that builds, perhaps we'll be able to bridge that.
	No, I mean, that's a huge test for radical empathy is to empathize with a different substrate.
	Right.
	Exactly.
	We've never had to confront that before.
	Yeah.
	Maybe through brain computer interfaces, be able to truly empathize what it feels like to be a computer.
	But information to be computed, not on a carbon system.
	I mean, that's deeply exciting.
	I mean, some people kind of think about that with plants, with other life forms, which are a similar substrate, but sufficiently far enough on the evolutionary tree that it requires a radical empathy.
	But to do that with a computer.
	I mean, 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 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 smart on an IQ sense.
	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 an interpreter or translator at some point, which would be pretty cool.
	What gives you hope for the future of human civilization?
	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 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 going to be okay in terms of there's going to be a lot of change, but look where we are now without 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 developed for hunting buffaloes on the tundra.
	And so I think this is just the next step.
	And it's actually kind of interesting to see how society has already adapted to this mind-blowing AI technology we have today already.
	It's sort of like, oh, I talk to chatbots.
	Totally fine.
	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 you.
	Not to the level that you can do it, Lex, I don't think.
	Thank you.
	That's what we humans do to each other with compliments.
	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.
	Exactly.
	All of those humans.
	All the things that are deeply human.
	Well, this is a huge honor, Demos.
	You're one of the truly special humans in the world.
	Thank you so much for doing what you do and for talking today.
	Thank you very much.
	Thanks.
	Thanks for listening to this conversation with Demos Cassabas.
	To support this podcast, please check out our sponsors in the description and consider subscribing to this channel.
	And now, let me answer some questions and try to articulate some things I've been thinking about.
	If you would like to submit questions, including in audio and video form, go to lexfrema.com slash 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 Kenya and College.
	What do you think of this speech?
	Well, first, I think this is probably one of the greatest and most 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.
	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?
	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 a 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, 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.
	Second takeaway is that the central spiritual battles of our life 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 unborable.
	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.
	And since Demis Gasabas 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.
	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.
	It adds the question, does this aesthetic sense also exist in the 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.
	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.
	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 going to die if you don't help me.
	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.
	The atheist just rolls his eyes.
	No, man, all that happened was a couple of Eskimos happened to be wandering by and showed 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.
	If I may, one more thing I wanted to briefly comment on.
	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 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 listened to by many millions of people, I proudly went to Druckson 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, 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 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.
	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 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.
	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.
	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.
	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 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.
	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 develop a love for programming, including, yes, Emacs and the Kinesis keyboard.
	I also, during that time, read a lot.
	I played a lot of guitar, wrote a lot of crappy poetry, and trained a lot in judo and jiu-jitsu, which I cannot sing enough praises to.
	Jiu-Jitsu humbled me on a daily basis throughout my 20s, 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.
	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.

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