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

The following is a conversation with Demis Hassabis, 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. And now a quick few second mention of e-sponsor, check them out in the description or at lexfremend.com/sponsors. It's the best way to support this podcast. We've got Hampton for connecting with founders and CEOs, Finn for AI customer service, Shopify for building e-commerce businesses, Element for daily electrolytes and AG1 for your health. Choose wise, my friends. And now onto the full adories. I do try to make it interesting, but if you must skip, friends, please still check out our sponsors. I enjoy their stuff, maybe you will too. 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And if I don't, I'm much more likely to so maintain that deep focus. And a part of that early in the morning is some coffee or caffeinated drink and then a few hours on as AG1. And it's just many hours of deep focus in between. It makes me feel happy. It makes me feel at one with the universe and it helps me get shit done. Anyway, they'll give you one month supply of fish oil when you sign up. I drink AG1.com/lex. This is the "lex treatment" podcast to support it. Please check out our sponsors in the description or at "lex treatment.com/sponsors" and consider subscribing, commenting and sharing the podcast with folks who might find it interesting. I promise to work extremely hard to always bring you nuanced and long-form conversations with a wide variety of interesting people from all walks of life. And now, dear friends, here's Demis 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, 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 combinatorially high-dimensional spaces that, you know, if you try to brute force a solution and find the best moving go or find the exact shape of a protein, and if you numerated 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, you know, why should that be possible? how it is physically. physics do that, you know, proteins fold in milliseconds in our bodies. So somehow physics solves this problem that we've now also solved computationally. And I think the reason that's possible is that in nature, natural systems have structure because they were subject to evolutionary processes that shaped them. And if that's true, then you can maybe learn what that structure is. So this perspective, I think, is really interesting one. You've hinted at it, which is almost like crudely stated, anything that can be evolved can be efficiently modeled. Things are 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 know, 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 could be efficiently modeled. Yes, right. So interesting. So they can be efficiently rediscovered or recovered because nature's 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, L-N-S, this is a demonstration 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 peak or the MP question, and what is modulable by classical systems or a non-quantum systems, you know, cheering machines in effect? And that's exactly what I'm working on actually, and kind of my few moments of spare time with a few colleagues about, it should there be, you know, maybe a new class of problem that is solvable by this type of neural network process, and kind of mapped on to these natural systems, so, you know, the things that exist in physics and have structure. So I think that could be a very interesting new way of thinking about it, and it sort of fits with the way I think about physics in general, which is that, you know, I think information is primary, information is the most sort of fundamental unit of the universe, more fundamental than energy a 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 MP question is a physics question. That's right. And 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 it's going to be, you know, very enlightening more specific to the P and P question. This again, some of the stuff we're saying is kind of crazy right now, just like the Christian entrance and Nobel Prize speech controversial thing that he said sounded crazy, and then you went and got a Nobel Prize for this with John Trump or solved the problem. So let me just stick to the P equals MP. Do you think there's something in this thing we're talking about that could be shown if you can do something like a polynomial time or constant time compute ahead of time and construct this gigantic model, then you can solve some of these extremely difficult problems in a theoretical computer science kind of way. Yeah, I think that there are actually a huge cluster 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 in your network is, it runs on normal computers, right? Classical computers, cheering machines in effect, and I think it's one of the most interesting questions there is, is how far can that paradigm go? I think we've proven, and the AI community in general, that classical systems, cheering machines can go a lot further than we previously thought. They can do things like model the structures of proteins and play go to better than world champion level, and 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 new network system, on top of a new 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 that, 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. Now, those could be difficult to model. So I think these are kind of the open questions, but I think when you step back and look at what we've done with the systems, and the 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 in 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 AGI4 is to help us as scientists answer these questions, like Peacles MP. Yeah, I think we might be continuously surprised about what is modelable by classical computers. I mean, Alpha 4.3 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, 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 grading, you can follow, and of course, what neural networks are very good at is following gradients. And so if there's one to follow, and you can specify the objective function correctly, you know, you don't have to deal with all that complexity, which I think is how we maybe have naively thought about it for decades, those problems. If you just enumerate all the possibilities, it looks totally intractable. And there's many, many problems like that. And then you think, well, it's like 10 to the 300, possible protein, structures, 10 to the 170 possible go positions. All of these are way more than atoms in the universe. So how could one possibly find the right solution or predict the next step? But it turns out that it is possible. And of course, reality, nature does do it, right? Proteing to do fault. 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, or a model that process, it should be possible on our classical systems is basically what the conjecture is about. And of course, there's nonlinear dynamical systems, highly nonlinear dynamical systems, everything involving fluid. Yes, right. The recent conversation with Terence Tal, who mathematically contends with a very difficult aspect of systems that have some singularities in them that break the mathematics. And it's just hard for us humans to make any kind of clean predictions about highly nonlinear dynamical systems. But again, to your point, we might be very surprised what classical learning systems might be able to do about even fluid. Yes, exactly. I mean, fluid dynamics and aviastokes equations, these are traditionally thought of as very, very difficult, intractable kind of problems to do on classical systems. They take it enormous amounts of compute, you know, where the prediction systems, you know, these kind of things all involve fluid dynamics calculations. And, um, but again, if you look at something like Vio, 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 it's being squeezed out. I used to write, uh, physics engines and graphics engines and 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 Vio3. I think a lot of people highlight different aspects, including the comedic and the media and all that kind of stuff. And then the ultra realistic ability to capture humans in a really nice way that's compelling and it feels close to reality and then combine that with native audio. All of those are Marvel's things about Vio3, but the 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 to understand anything. But it's seen, I mean, I don't think you can generate that kind of video without understanding. And then our own philosophical notion, when it means to understand, then it's like brought to the surface. To what degree do you think Vio3 understands our world? I think to the extent that it can predict the next frames, you know, in a coherent way, that's some of 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 for the dynamics, you know, put it that way, that they can pretty accurately generate whatever it is, eight seconds of consistent video, that by eye, at least, you know, at a glance is quite hard to distinguish what the issues are. And imagine that in two or three more years time, that's the thing I'm thinking about and how incredible that will, there 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, the, the, the, the standup comedians and the, the actually captures a lot of human dynamics very well and, and body language, but actually the thing I'm most impressed with and fascinated by is the physics behavior, the lighting and materials and liquids and it's pretty amazing that it can do that. And I think that shows it 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, you know, 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 a common sense. Again, it really understands something. I think that really surprised a lot of people. It blows my mind that I just didn't think it would be possible to generate that level of realism without understanding. There's this notion that you can only understand the physical world by having an embodied AI system, a robot that interacts with that world. That's the only way to construct an understanding of that world. Yeah. But VO3 is directly challenging that might feels like. Yes. And it's very interesting, you know, even if we, if you were to ask me five, ten years ago, I would have said, even 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, 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 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, you know, the cool videos that it generates. And of course, there's next stages is maybe even making those videos interactive. So one can actually step into them and move around them, which will be really mind blowing, especially given my games background. So you can imagine. And then, and then I think, you know, you'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 trolley. I think you're having more and more fun on Twitter on X, which is great to see. So guy named Jimmy apples tweeted, let me play a video game of my VO 3 videos already. Google Cook's so good playable world models when spelled WEN question mark. And then you quote tweeted that was not 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 5, 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 each one day and come back to that. So, you know, and I will do, I think. And I think I'd sort of dream about, you know, what would I have done back in the 90s if I'd had access to the kind of AI systems we have today? And I think you could build absolutely mind-blowing games. And I think the next stage is I always used to love making all the games I've made are open world games. So there are 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 use the player 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 a 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 dynamically change the story and story tell 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 ten years and imagine how good it's going to be. Yeah, so you set 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, but you can open any door and there'll be something there. It's that the choice of which door you open in an unconstrained way defines the world you see. So some games try to do that to give you choice. Yes, but it's really just an illusion of choice because you're only like Stanley Parable. 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 you 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 a dagger fall, I believe that they really played with it like random generation of the dungeons. Yeah. Of if you can step in and it gives you this feeling of an open world and there you're much an 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 sort of a random generation. So you'd like and also better than a simple ab hard coded choice. That's not really open world. 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 system systems that will generate that on the fly. Of course, you can't create infinite amounts of game assets. It's expensive enough already how triple A games are made today. 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 the still probably the best AI learning AI in it It was an early reinforcement learning system that you you know You were you were looking after this mythical creature and growing it and nurturing it and depending how you treated it It would treat the villagers in that world in the same way So if you mean to it, it would be mean if you're 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 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 and so many domains of science like serious adult stuff. Yeah, that you might not have time to really create a game You might end up creating the tooling that others will create the game and you have to watch Others create the thing you've always dreamed of do you think it's possible you can somehow and you're extremely busy schedule Actually fine time to create something like black and white some some an actual video game where like you could Make the childhood dream Yeah, well, you know There's two things where to think about that is maybe with vibe coding as it gets better and it's Possibility that I could you know one could do that actually in your spare time So I'm quite excited about that as or as 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 a G.I. Is being safely distributed into the world and delivered into the world You know that and then working on my physics theory as we talked about the beginning those would be the two my two post a G.I. Projects Let's call it that way. I love to see would you help the game post a G.I. Would you choose? Solving the problem that some of the smartest people human history contender with you know P and equals MP Or creating a cool video Well, but they might but in my world they'd be related because it would be an open world simulated game As realistic as possible. So you know, what is what is the universe? That's that's that's speaking to the same question right MP calls MP I think all these things are related at least in my mind. I mean in a really serious way Like video games sometimes are a little down upon. It's just this fun side activity But especially as A.I. does more and more of the difficult boring Tasks something we in a modern world called work You know video games is the thing in which we may find meaning in which we may find like what to do with our time You could create incredibly rich Meaningful Experiences like that's what human life is and then in video games you can create More sophisticated more diverse Ways of living yeah, I think so. I mean those of us who love games and I still do is is is You know, it's almost can let you imagination run wild right like I used to love games I'm working on games so much because it's the fusion especially in the 90s and two early 2000s The sort of gold or nearer and maybe the 80s of of of game of the games industry and it was all being discovered new genres Or being discovered we weren't just making games we felt we were we were creating a new entertainment medium that never existed before Especially with these open world games and simulation games where you were co create you as the player were co-creating the story There's no other media Entertainment media where you do that where you as the audience actually co-create the the story and of course now with multiplayer games as well It can be a very social activity and can explore all kinds of interesting worlds and that but on the other hand, you know, it's very important to Also enjoy and experience the physical world, but the question is then you know I think we're going to have to co-create a confund the question again of what is the fundamental nature of reality? What is the 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 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 You can think about all the different things. I've done that's 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 what my favorite one of all time is civilization? I have to say that that was the Civilization one and civilization two my favorite games of all time I can only assume you've avoided the most recent one because It would probably you would that would be your sabbatical that you would disappear They take a lot of time the 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 a gaming and and being great leaders of AI companies? I don't know I it's interesting one. I mean we both love games and it's interesting he wrote games as well to start off with It's probably especially in the era I grew up in where home computers were just became a thing You know in the late 80s and 90s, especially in the UK I had a spectrum and then a Commodore Mika 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 And then of course I immediately took it in directions of AI and simulations Which so I made was able to express my interest in 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 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 in the 90s was due to gaming So interestingly that was where the forefront of research was going on and it was this incredible fusion with with art You know graphics but also music and just the whole new media of storytelling and I love that for me It's this sort of multi-disciplinary kind of effort is again something. I've enjoyed my whole 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. Yeah, are these kinds of evolution like techniques promising as a component of future Superintelligent system. So people don't know it's kind of I don't know if it's fair to say it's LLM guided Evolution search. Yeah, so evolution algorithms are doing the search. Yes, I'm so telling you where yes Exactly, so LLM is a kind of proposing some possible solutions and then you do you use a evolutionary computing on top to Find some novel part of the of the search base. So actually I think it's an example of very promising directions where you combine LLM's or Foundation models with other computational techniques a evolutionary methods is one But you could also imagine Monte Carlo research 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 where there's a lot of low-hanging fruit of us understanding being being able to model? Being able to simulate evolution and using that whatever We understand about that nature is by mechanism to then do search better and better and better Yes, so if you think about again, breaking down the sort of systems we've built to their really fundamental core You've got like the model of the 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 of the search space and You can do that in a number ways evolutionary computing is one With AlphaGo we just use Monte Carlo research, right? And that's what found move 37 then you a 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's starts to speak about the ideas of creativity. How can these systems create something new, fight, discover something new? Obviously, this is super relevant for scientific discovery or pushing met science and medicine forward, which we wanna 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 would be too big. So you have to have some objective function that you're trying to optimize and heal 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 'cause you can probably generalize to everything. But for example, mutation, it's not just Monte Carlo tree search where it's like a search. You could every once in a while combine things. Combined things, like a components 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 in the space of programs. Yeah, exactly. So there's a, you can get a bit of an extra property out of evolutionary systems, which is some new merge and capability may come about. Right, of course, like happened with life. Interestingly with naive sort of traditional evolution 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 emerging 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 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 evolve is one of the coolest things I've ever seen. I've on my desk at home, most of my time I spent on that computer is just programming. And next to the three screens is a skull of a tiktolic, 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 competition mechanism of evolution is, it's quite incredible. Yes, 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, effectively, and it can generate all of this immense complexity emerges. Obviously running over four billion years of time, but you can think about that as, again, 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 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 Cocotayo, Scott Alexander, and others that online steps along the way to get to ASI. And as 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-scientists does to help steer human, human brilliant scientists, and then potentially by itself to figure out what are the directions where you want to generate truly novel ideas? Because that seems to be like a really important component of how to do great science. 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 scientists from the good scientists. All professional scientists are good technically, right? Otherwise, it wouldn't have been 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. I am a maths Olympiad problems, alpha-proof last year. Our system got silver metal in that, really hard problems. Maybe eventually we'll be able to solve a millennium prize kind of problem, but could a system come up with a conjecture worthy of study that someone like Terence Tower would have gone, you know what, that's a really deep question about the nature of maths, or the nature of numbers, or the nature of physics. And that is far harder type of creativity. And we don't really know, 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 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 prove. - Yes. - So 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 edge. - That sweet spot, right, of basically advancing the science and splitting the hypothesis space into two, ideally, right? Whether you fit 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 have, you can't really 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 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 a bunch of research. - 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 the experiment well, and your hypotheses are interesting, it should tell you a lot about where to go next. And then it's, 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 alpha, I mean, there's just so many leaps. So alpha fold 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. Alpha fold three is doing protein RNA DNA interactions, which is super complicated, and fascinating, it's 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. - Yes. - It's a 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. 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 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 may have found the quick institute and one of the nowhere prize in 2001. He is, we've been talking about it since, you know, before the, you know, in the 90s. And I come, used to come back to every five years, is 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 experiments, 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 dream. And so, but maybe now finally, so I was trying to build these components, alpha-fold being one, 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 alpha-fold is the solution to the kind of static picture of what does a protein look 3D structure, protein look like, a static picture of it, but we know that biology, all the interesting things happen with the dynamics, the interactions. And that's what alpha-fold 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 tour 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 times. Gills, is that tricky? Like they're, 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 is that, that's the 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 drop up it 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 overmodel. You can skip ahead to just model the really high level things that get you a really good estimate. Yes. 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, of course, that's where alpha-volt 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'd get the emergent behavior. Apologize for the part head 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. 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 they head of us. I think they're most likely in the past, if you read that book, of how unlikely to go 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. So it shows you how hard it was. That's true. We're super happy. We're a very long time before they captured mitochondria somehow. 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 all the chemical soup that you start with, the primordial soup that 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 we'll figure out as they're continuing. 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, it's been the same process. If we can break down that wall that we've constructed in our minds of the actual origin, from non-living to living, and it's not aligned, and it's a continuum that connects physics and chemistry and biology. There's no line. This is my whole reason why I worked on AI and AI my whole life because I think it can be the ultimate tool to help us answer these kind of questions. I don't really understand why the average person doesn't think, like, worry about this stuff more. How can we not have a good definition of life and not living and non-living and the nature of time and let alone consciousness and gravity and all these things? It's just an quantum mechanics witness. It's just, to me, it's, I've always had this, it's sort of screaming at me in my face, and it's getting louder. It's like, how what is going on here? I mean that in the deeper sense, 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 each other and all these living things, all the time we can expect to it, 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 consciousness 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 time. 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 do 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 coat with this, the mysteries that we can't fully, we can see, but we can't fully understand and we 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 it's an extremely difficult system to model. And even that system, Google DeepMine has made progress on it. Yes. We've created the best weather prediction systems in the world, and they're better than traditional fluid dynamics, solar systems that usually calculate 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 weather next 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 have cyclone prediction of where you know, parts of hurricanes might go. Of course, super useful, super important for the world, and it's super important to do that very timely, very quickly, and as well as accurately. And I think it's very promising direction. Again, of, you know, simulating and so they can run forward predictions and simulations of very complicated real-world systems. As you mentioned that I've got 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. Yes. So there it's just it's this beautiful mix of like crazy enough to like go into the eye of the storm. And like in order to protect your life and predict where the extreme events are going to be, they have to have increasingly sophisticated models of of weather. Yeah. Yeah, it's a beautiful balance of like being in it as living organisms and the cutting edge of science. They actually might be using deep mind systems. So that's yeah, hopefully they are. And I love to join them in one of those cases. They look amazing, right? To actually experience it one time. Exactly. And then also to experience the correct prediction for something will come and how it's going to evolve. It's incredible. Yeah. You've estimated that we'll have a GI by 2030. So there's interesting questions around that how will we actually know that we got there and what maybe the move, quote, move 37 of a GI. 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 a GI? Of course, people are arguing about that now. And and mine's quite a high bar and always has been of like, can we match the cognitive functions that the brain has? Right. So we know our brains are pretty much general churing 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 and that's what we currently have with today's systems. They're not consistent. So you'd want that consistency of intelligence across the board. And then we have some missing, I think, capabilities, like sort of the true invention capabilities and creativity that we were talking about earlier. So you'd want to see those. How you test that, I think you just test it one way to do it would be kind of brute force test of tens of thousands of cognitive tasks that, you know, we know that humans can do and maybe also make the system available to a few hundred of the world's top experts, the Terence Tows of each subject area and see if they can find, you know, give them a month or two and see if they can find an obvious floor in the system. And if they can't, then I think you're you're pretty, you know, pretty, you can be pretty confident and 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 Tows, these brilliant experts, they might quickly in a span of weeks take for granted all the incredible things you can do and then focus in, well, ha ha right there. You know, I consider myself a visual human. Yeah, I identify as human. You know, some people listen to me talk and they're like, that guy's not good at talking, the stuttering, the, you know, so even humans have obvious across domains, limits, even just outside of calc 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 tests where it will be one or two where it's like, yes, 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 move 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. 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? That's 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 a GI level. And then maybe to fill that out, you would also check the consistency, you know, make sure there's no holes in that system either. 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 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 out to tweet. Like that exactly. Like what is this amazing you, you know, physics idea. And then we would probably check it with world experts in that domain, right? And validate it and kind of go through its workings. And I guess it would be explaining its workings too. Yeah, be an amazing moment. Do you worry that we as humans, even expert humans like you might miss it? Well, 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 to the best human scientists, but it may be a bit like, for example, in chess, if I was to talk to Gary Casparo for 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 better understand it to some degree, not to the level they do, but you know, if they were good at explaining, which is actually part of intelligence too, is being able to explain in a simple way that what you're thinking about. I think that that will be very possible for the best human scientists. But I wonder maybe you can you can educate me on the side of go. I wonder if there's moves from Magnus or Gary where they at first will dismiss it as a bad move. Yeah, sure. It could be. But then afterwards they'll figure out with their intuition that this why this works. 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. Does it do you win the game or not win? And then that tells you, okay, that move in the end was good. That strategy was good. And then you can go back and analyse 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 your train, you know, train sort of hardcore training in that way. But even even now like when I generate code that 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 tell that it's like, what, what, what is this a bug or a feature where 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 programme is looking at 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 they're more that, you know, not kind of monitoring tools are the ones that generated it. So if we look at a AI system, sorry to bring it back up, but actually evolve. Super cool. So if evolve enables on the programming side, something like recursive self-improvement potentially, like what if we can imagine what that AI 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 programme in 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 take off scenario. But you, these current systems like alpha evolve. They have, you know, human in the loop deciding on various things, their separate hybrid systems that interact. One could imagine eventually doing that end-to-end. I don't see why that wouldn't be possible. But right now, you know, I think the systems are not good enough to do that in terms of coming up with the architecture of the code. And again, it's a little bit reconnected to this idea of coming up with a new conjecture 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 like invent a game as good as go. Imagine that was the prompt. That's pretty underspecified. And so the current systems wouldn't know, I think, what to do with 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, without revolve, 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 know, if you wanted a big leap in understanding, you'd need a much larger advance. Yeah, but it could also be sort of the pushback against hard takeoffs in area. It could be just sequence of incremental improvements, like matrix multiplication, like it has to sit there for days to get how to incrementally improve a thing. And that it does so recursively. And as you do more and more improvement, you'll slow down. Right. So there would be like a, like, the path to AGI won't be like a, it would be a gradual improvement over time. Yes. 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? Right. Could it have done that back in 2017 when, you know, we did it and brain did it. And it's, it's not clear that that these systems, something our alphabet 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 heel 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 leap. Yeah. I don't think anyone has systems that can have shown unequivocally those big leaps that, that, that, that, right, we have a lot of systems that do the heel climbing of the S curve that you're currently on. Yeah. And that would be the move 37 is a yeah, I think would be a leap as something like that. Do you think the scaling laws are holding strong and the pre-training post-training test line 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 again there, it's about how innovative you can be and we pride ourselves on having the broadest and deepest research bench. We have amazing incredible researchers and people like Noam Shazoe came up with Transformers and Dave Silver, who led the AlphaGo project and so on and that research base means that if some new breakthrough is required, like in 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 because then there's more from just engineering to true research and research plus engineering and that's our sweet spot. And I think that's harder, it's harder to invent things than to fast follow and so we don't know. So 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 maybe about half our resources 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 deep mind as well, Google deep mind as well, position 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, I know, maybe I don't know, 80/90% of the breakthroughs that underpins more than AGI feel today was from, you know, originally Google brain, Google research and deep minds. 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. If you do have enough data to make simulations or so that you can create more synthetic data that are from the right distribution, obviously that's the key. So you need enough real world data in order to be able to create those kinds of generator, data generators, and I think that we're at that step at the moment. Yeah, you don't have a lot of kind of stuff on the set of science and biology, doing a lot with not so much data. Yeah. I mean, still a lot of data, but I guess enough takes off. Get that going. Exactly. Yeah. Exactly. How crucial is the scaling of compute to building a GI? This is a question that's an engineering question. It's a almost geopolitical question because it also integrated into that is supply chains and energy. Yes. I think that you care a lot about, which is potentially fusion, innovating on the side of energy also. Do you think we're going to keep scaling compute? I think so. For several reasons. There's the amount of compute you have for training, often it needs to be co-located. So actually, even like bandwidth constraints between data centers can affect that. So there's additional constraints even there. 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 they'll be more demand for them. So both from the training side, the training side actually is only just one part of that. They even become the smaller part of what's needed in the overall compute that's required. Yeah, that's one sort of almost meami 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, the you know, the servers are sweating. Yes. Did you have the inference? 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 is 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 one of the most exciting new types of solar material, solar panel material, super good room temperature, super conductors has always been on my list of dream breakthroughs and optimal batteries. And I think a solution to any, you know, one of those things would be absolutely revolutionary for, you know, climate and energy usage and we're probably close, you know, and again in the next five years to having AI systems that can materially help with those problems. So if you were to bet, sorry for the ridiculous question, 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 disinsphere 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 type one car shaft scale civilization? I would not be that surprised if there's like a hundred year time scale from here. I mean, I think it's pretty clear if we crack the energy problems in one of the ways we've just discussed fusion or 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, like actually use it. But if it was cheap, then you know, all countries that have a coast could. But also you'd have unlimited rocket fuel. You could just separate sea water out into hydrogen and oxygen using energy. And that's rocket fuel. So combined with, you know, Elon's amazing self-landing rockets, then it could be like you sort of like a bus service to space. So that opens up, you know, incredible new resources and domains, asteroid mining, I think will become a thing and maximum of human flourishing to the stars. That's what I dream about as well as 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 just a tourist flying through space, you would probably notice Earth, because if you saw the energy problem, you would see a lot of space rockets probably. It would be like traffic here in London, but in space, that's a lot of rockets. Yes. And then you would probably see floating in space, some kind of source of energy like solar. Yeah. Potentially. So Earth would just look more on the surface, more technological. And then you would use the power of energy than to preserve the natural, yes, like the rainforest and all that stuff. And for the first time in human history, we wouldn't be resource constrained. And I think that could be an 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 there for us, then the local villages 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 more materials and energy. And we, you know, we should be at something that's called it, like another's called 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, "Boar at 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 in color. increasingly sophisticated video games 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 with 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 yeah. And I think I mean I think that's what my modern sport is so and I love football watching it and I just feel like and I used to play it a lot as well and it's it's it's it's it's very visceral and it's tribal and I think it does channel a lot of those energies into a which I think is a kind of human need to belong to some some group and um but into a into a into a fun way a healthy way and and not 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 their great little microcosm simulations of the world. They're simulations of the world too they're simplified versions of some real world situation whether it's poker or or go or chess different aspects or diplomacy different aspects of of the real world and it allows you to practice out them too and and because you know how many times you get to practice a massive decision moment in your life you know what job to take what university go to you know you get maybe I don't know a dozen or so key decisions one has to make and you've got to make those as best as you can um and games is a kind of safe environment repeatable environment where you can get better at your decision making process um and it maybe has as an additional benefit of channeling some energies into uh into more creative and constructive pursuits. Well I think it's also really important to practice um losing and winning. Right. Like losing is a really you know that's why I love games that's why I love even um things like Brazilian jiu jitsu yeah 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 yeah that that feeling of losing I mean it's a weird one for us humans to like really like make sense of like that's just part of life that is a fundamental part of life is losing. Yeah and I think the martial arts as I understand it but also in things like light chess is 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're doing a healthy way you learn to use victory and losses in a way don't get carried away with victory and and think you're the just the best in the world key and and and the losses keep you humble and always knowing there's always something more to learn there's always a bigger expert that you can mentor you you know I think you learn that I'm pretty sure in martial arts and and 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 you of course you want to win but you also need to learn how to deal with setbacks in a healthy way that and and and why are that 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's something that's a source of happiness a source of meaning that improvements that it's not about the winning or losing yes the mastery yeah there's nothing more satisfying in a way it's like oh wow this thing I couldn't do before now I can and and and again games and physical sports and mental sports they're what their ways of measuring they're beautiful because you can measure that that progress yeah 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 whatever are yeah we're quite we're quite addicted to this sort of yeah these numbers going up and and and and and maybe that's why we made games like that because obviously that is something we're we're heel climbing systems ourselves right it would be quite sad if we didn't have any mechanism but we call a bell we do this everywhere right where we just have this thing that it's right and I don't want to dismiss that there's a source of deep meaning yeah it's 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 I think it's fair to say that Google was losing on the LLM product side a year ago with Gemini at 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 in in spend of a year yeah well firstly it's absolutely incredible team there we have you know led by Coray and Jeff Dean and and Oriole and the amazing team we have on Gemini absolutely world class so you can't do it without the best talent and of course you have you know we have a lot of great compute as well but then it's the research culture we created right and basically coming together both different groups in in Google you know those Google brain world class team and 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 was 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 after jetty wasn't a given but we're very pleased with that that where we are in the rate of progress is the most important thing so if you look at where we've come to from two years ago to one year ago to now you know I think our we call it relentless progress along with relentless shipping of that progress is being very successful and you know it's unbelievably competitive the whole space the whole AI space with some of the greatest entrepreneurs and leaders and companies in the world all competing now because everyone's realized how important AI is and it's very you know been pleasing for us to see that progress you know Google is a gigantic company can you speak to the natural things that happen in that case is the bureaucracy that emerges like you want to be careful like you know like the natural kind of there's there's meetings and there's managers and that like what what are some of the challenges from a leadership perspective breaking through that in order to like you said ship like the number of products yeah Gemini related products has been shipped over the past years just insane right it is yeah exactly that's that's what relentlessness looks like um I think it's it's a question of like any big company you know ends up having a lot of layers of management and things like that is sort of the nature of how it works um but I can still operate and I was always operating with old deep mind as a as a startup still large one but still as a startup and that's what we still act like today as with Google deep mind and acting with the size of nurse 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 and credible products that we can power up with our AI and our and our research and that's amazing and you can you know that's very few places in the world you can get that do incredible world-class research on the one hand and then plug it in and 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 uh with a number of you know uh huge product surfaces that we have uh so a funny thing you mentioned about like the the surface of the billion I had a conversation with a guy named um brilliant guy uh here at the British Museum called Irvin Finkel she's a world expert at Kineha Forms which is a ancient writing on tablets and he doesn't know about Chad Gbt or Gemini he doesn't even know anybody I but his first encounter with this AI is AI mode on yeah yeah he's like is that what you're talking about this AI mode and you know it's just it's just a reminder that there's a large part of the world that doesn't know about this AI thing yeah I know it's funny because if you live on uh X and Twitter and I mean it's sort of at least my feed it's all AI and and there's certain places where you know in the valley and certain pockets where everyone's just all they're thinking about is AI but a lot of the normal world hasn't hasn't come across it yet but that's a great responsibility to the their first interaction yep on the the grand scale of the rural India or anywhere across the world they're good to 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 um and it's ideally for a lot of those people should just be seamless it's just new technology that makes their lives more you know productive and 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 scientists 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 a AI based product that a lot of people would enjoy using yeah well I mean that again that comes back from my game design days where I used to design games for millions of gamers people forget about that I've had experience with cutting edge technology in product that that 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 of you know imagining what it would be like to use it viscerally and having good taste coming back to earlier. The same thing that's useful in science I think is can also be useful in in product design. And I've just had a very you know always being a sort of multi-disciplinary 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 know like this doesn't feel like the layout the interface maybe the trade-off between the latency like how how to present to the user how long to wait and how that waiting is shown or the reason capability is there 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 false 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 whatever 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 a 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 you didn't realize before that can allow like de-repe search 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 give once you think about these super multimodal systems shouldn't it be something more like minority report where you're you're sort of vibing with it in a in a in a kind of collaborative way right seems very restricted to that I think we'll look back on today's interfaces and products and systems as quite archaic in maybe in just a couple of years so I think there's a lot of space actually for innovation to happen on the product side as well as the the research side and then we're offline talking about this keyboard is the open questions how when and how much will we move to audio as the primary way of attracting with the machines around us versus typing stuff yeah I mean typing is a very low bandwidth way of doing even if you're very fast you know type and I think we're going to have to start utilizing other devices whether that's smart glasses you know audio earbuds and eventually maybe some sorts of neural devices where we can increase the input and the output bandwidth to something you know maybe a hundred X of what is today I think that you know under appreciated art form is the interface design that I think you can not unlock the power of the intelligence of a system if you don't have the right interface their interface is really the way you unlock its power yeah it's such an interesting question of how to do that yeah how you would think like 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 that 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 you're aesthetic you'll feel the way that your brain works and and and and the AI kind of generates that depending on the task you know that feels like that's probably the direction we'll end up in yeah because some people are power users and they want every single parameter on screen everything and everything based like perhaps me with the keyboard keyboard based navigation like that shortcuts for everything and some people like the minimalism just by all of that complexity yeah exactly yeah well I'm glad you have a Steve Jobs mode in you as well this is great Einstein most Steve Jobs mode all right let me try to trick you into answering a question when when will Gemini 3 come out this is before after GTA 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 and performance so what what does it even mean to go turn you version is the performance this is 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 to do a new kind of full run and the full productization of a new version and during that time lots of new interesting research iterations and ideas come up and we sort of collect them all together that you know you could imagine the last six months worth of interesting ideas on the architecture front maybe it's on the data front it's like many different possible things and we collect package that all up test which ones are likely to be useful for the next iteration and then bundle that all together and then we start the new you know giant hero training run right and and then and then of course that gets monitored and then at the end then there's there of the pre training then there's all the post-training there's many different ways of doing that different ways of patching here so there's a whole experiment and phase there which you can also get a lot of gains out and that's where you see the version numbers usually referring to the base model the pre-trained model and then the interim versions of 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 promote prioritise performance or speed right and cost and we like to think of this Pareto Frontier well 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 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 the side quests yes exactly but and then you also don't want to take too many side quests because then you have a million versions of a million products yes sorry it's very unclear yeah but you also get super excited because it's super cool yeah like how does even you look at vehicles very cool how does it fit into the bigger thing exactly yeah exactly and then you constantly this process of converging upstream we call it you know ideas from the from the product surfaces or or from the post-training and and even further downstream than that you you kind of upstream that into the the core model training for the next run right so then the main model the main gem and eye track becomes more more general and eventually you know AGI one hero run yeah 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 and I bet it's important that you don't overfit to them right so there shouldn't be the end with a be all an end all so there's there's LM arena or you should be called a lemsis that's one of them that turned out sort of organically to be one of the the main ways people like to test these systems at least the chat box obviously there's loads of academic benchmarks on from from the test mathematics and coding ability general language ability science ability and so on and then we have our own internal benchmarks that we care about it's a kind of multi objective you know optimization problem right you what 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 you can of Of course, you could put more coding data in or you could put more 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, 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 using the chat interfaces. Yeah, because ultimately you want to measure the usefulness, it's so hard to convert that into a number. Right. 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. And that's just scary because everything you just said, it has to be smart and useful across so many domains. So you get super excited because it's all a sudden solving programming problems, it never been able to solve before, but now it's crappy of poetry or something. Yes. And it's just, I don't know, that's a stressful, that's so difficult to balance. 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 even more so Terry 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. Yeah. 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 did mind-winning? Well, I don't see it sort of winning. I mean, I think we need to, I think winning is the wrong way to look at it, given how important and consequential what it is we're building. So, finally 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've dreamed about and what we've always tried to do. And I hope that's what eventually the community, maybe the international community, will rally around when it becomes obvious that as we get closer and closer to AGI, that's what's needed. I agree with you. I think that's beautifully put. You've said that you talk to and are in good terms with the leads of some of these labs as the competition heats up. How hard is it to maintain those relationships? It's been okay. So, if I try to pry myself in being collaborative, I'm a collaborative person, research is a collaborative endeavor. Science is a collaborative endeavor. It's all good for humanity in the end. If you cure incredible, you know, terrible diseases and you can't read 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 AGI. 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've very interesting characters, many of them, as you might expect. But yeah, I'm in 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. There are those communication channels and that's what will facilitate cooperation or collaboration if that's what was 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, some Hulk elaborate on creating a video game, that kind of thing. I think that enables camaraderie in good terms and also you two are legit gamers. So, it's just fun to. Yeah. Yeah, that would be awesome. And we've talked about that in the past and it may be a cool thing that, you know, we can do. 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's kind of builds up that collaborative muscle. I see the scientific endeavor as that kind of side project for humanity. Yeah. And I think Google DeepMind has been really pushing that I would love it if to see other labs do more scientific stuff and then collaborate because it just seems like easier to collaborate on the big scientific questions. I agree. And I would love to see a lot of people, a lot of 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 Alpha Fold are so important to me. And I think to our mission is to show how AI can this, you know, 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 Alpha Fold to do drug discovery and it's going really well and build sort of, you know, you can think of build additional Alpha Fold type systems, so going to chemistry space to help accelerate drug design. And the examples I think we need to show and society needs to understand a well 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 PE equals that, I mean, it's just incredible. 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? And I should say that I think a lot of people see deep minds are a really great place to do. Cutting edge work for the reasons that you've outlined is like 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's what that responsibility entails. I think they're mostly doing it to be like myself to be on the frontier of that research. So, you know, they can help influence the way that goes and steward that technology safely into the world. And you know, meta right now are not at the frontier. Maybe they'll 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. But as prop 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 for a couple of years because it was enough money. We couldn't raise money and these days in turns are being paid, you know, the amount that we raised as our first entire sea round. So it's pretty funny. And I remember the days were we used to have to work for free and almost pay my own way to do it in turns. 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, in 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 question lines, which maybe only a few years away. So on the practical sort of pragmatic sense of zooming on jobs, we can look at programmers because it seems like AI systems are currently doing incredibly well at programming and increasingly so. So a lot of people that program for a living love programming are worried they will lose their jobs. How worried should they be do you think and what's the right way to sort of adjust to the new reality and ensure that you survive and thrive as a human in the programming world? 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 are turned out maybe to be the easier ones for various reasons, but coding a maths because you can create a lot of synthetic data and verify if that data is correct. So because of that nature of that, it's easier to make things like synthetic data to train from. It's also an area, of course, we're all interested in because as programmers, right, to help us and get faster at it. 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 programs will be even better, but they'll be even 10X, even what they are today. And because there you'll be able to use their skills to utilize the tools to the maximum, exploit them to the maximum. And so I think that's what we're going to see in the next domain. So that's going to cause quite a lot of change, right? And so that's coming. A lot of people benefit from that. So I think one example of that is, if coding becomes easier, it becomes available to many more creatives to do more. But I think the top programmers will still have huge advantages as terms of specifying, going back to specifying what the architecture should be, the question should be how to guide these coding assistants in a way that's useful, you know, check whether the code they produce is good. So I think there's plenty of headroom there for the foreseeable, you know, next few years. 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 are, they're regretting the way for the improvement, improving models versus like competing against them. But sadly, but that's the nature of life on earth. There could be a huge amount of value to certain kinds of programming at the cutting edge and less value to other kinds. For example, it could be like, you know, front and web design might be more amenable to, as you mentioned, to generation by AI systems and maybe, for example, game engine design or stuff 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 access. I can't, I think that's right. At any time where there's a lot of disruption and change, you know, we've had this, it's not just this time, we've had this in many times in human history with the internet, mobile, but before that, we see industrial revolution. And it's going to be one of those errors 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 write that wave will become incredibly valuable, write 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, and but 10 times faster as well. Right. So instead of a hundred years, it takes 10 years. And so that's going to make, you know, it's like a hundred X, the impact and the speed combined. So that's what 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 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, a 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 surface 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 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? So that's the number one thing, increased 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 Louis Tute, a book on, first of all, about you. There's a buy about you. It's strange. Yeah, it's unclear. Yes, it's unclear how much is fiction, how much is reality. But I think the central figure that is John Van 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 for people who don't know, John Van Neumann is a kind of legendary mind. He contributed to quantum mechanics. He was on the Manhattan project. He is widely considered to be the father of or pioneer the modern computer in AI and so on. So as many people say, he's like one of the smartest humans ever, so he's just fascinating. And what's also fascinating is as a person who saw nuclear science and physics become the atomic bomb. So you got to see ideas become a thing that has a huge amount of impact on the world. He also foresaw the same thing for computing. And that's a little bit, again, beautiful and haunting aspect of the book than taking a leap forward and looking at this at least at all Alpha Zero, Alpha Go Alpha Zero big moment that maybe John Van 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 Van Neumann now? What would he say about what's going on? Well, that would be an amazing experience. He's a fantastic mind. And I also love the way he spent a lot of his time at Princeton at the Institute of Advanced Studies. He's a very special place for thinking. And it's amazing how much of a polymath he was in the spread of things he helped invent, including of course the Van Neumann architecture that all the modern computers are based on. And he had amazing foresight. I think he would have loved where we are today. And he would have, I think he would have really enjoyed Alpha Go being a game theory. I think he foresaw a lot of what would happen with learning machine systems that that that 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. There's the fruition of what I think he already foresaw in the 1950s. I wonder what advice he would give. He got to see the building of the atomic bomb with them ahead in project. Yeah, I'm sure there's interesting stuff that maybe he's not talked about enough. Maybe some bureaucratic aspect. Maybe the influence of politicians, maybe maybe not enough of picking up the phone and talking to people that are called enemies by the said politicians. There might be some like deep wisdom that we just may have lost from that time actually. Yeah, I'm sure there is. I mean, I've read a lot of books for that time. It's a world chronicle time and some brilliant people involved. 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 that solve all diseases, help with energy and scarcity. These incredible things. This is why all of us and myself, you know, I worked started on this journey 30 plus years ago. But of course, there are risks too. Probably, Von Neumann, my guess is he foresaw both. And I think he sort of said, I think he's to his wife that it would be, this is, the 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 has 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 it something in the human spirit that raw, called reason doesn't give us. And I agree with that. I think we need to approach it with whatever you at the spiritual dimension or humanist dimension doesn't have to be to do with religion, but this idea of a soul, what makes us human, this spark that we have perhaps is to do with consciousness when we finally understand that. I think that has to be at the heart of the endeavor. And technology, I've always seen technology as the enabler, right? The tools that enable us to flourish and to understand more about the world. And I'm sort of with Feynman on this and he used to always talk about science and art being companions, right? You can understand it from both sides, the beauty of a flower, how beautiful it is, and also understand why the colors of the flower evolve like that, right? That just makes it more beautiful, that just the intrinsic beauty of the flower. And I've always sort of seen it like that. And maybe, you know, in the Renaissance times, the great discoverers then, like people like Da Vinci, you know, I don't think he saw any difference between science and art and perhaps religion, right? Everything was, it's just part of being human and being inspired about the world around us. And that's what I, the philosophy I tried to take, and one of my favorite philosophers is Spinoza. And I think he combined that all very well, you know, this idea of trying to understand the universe and understanding our place in it. And that was his kind of way of understanding religion. And I think that's quite beautiful. 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 error and the product error of AI is that every day person can actually feel and interact with cutting AI and feel feel it for themselves. Yeah, because they force the technologist to have the human conversation. Yeah, for sure. That's the whole aspect of it, like you said, is the dual use technology that we're forcefully integrating the entire humanity into it by into the discussion about AI, because ultimately AI, AI will be used for things that states use technologists for, which is conflict and so on. And the more we integrate humans into this picture by having some chats with them, the more we will guide. Yeah, be able to adapt society or 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 the technology in states in their old way of thinking we'll 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 not the right use of the technology. I hope we'll end up with more, something more collaborative, if needed, like more like a like a certain 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 are people from the West, from a greater 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 at this kind of old school way of thinking. And so multi multi cultural, multidisciplinary research teams working on scientific questions that's like the hope don't let the the warm leaders that are warm on girls because they 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 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-dume? Probability of the human civilization destroys itself. Well, look, I don't have a it's a you know, I don't have a p-dume number. The reason I don't is because I think it's would imply a level of precision that is not there. So like I don't know how people are getting their p-dume 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. What these technologies are going to be able to do, how fast are they going to take off, how controllable they're going to be, some things may turn out to be and hopefully like way easier than we thought. But it may be there some really hard problems that are harder than we guess today. And I think we don't know that for sure. And so in under those conditions of a lot of uncertainty but huge stakes both ways. You know, on the one hand we 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-dumes 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 the benefits of course and all of the amazing things that AI can bring and actually I would be really worried for humanity if I if given the other challenges that we have, climber, dizzy, 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 we've you know, it could be amazingly transformative for good. But on the other hand, you know, there are these risks that we know are there, but we can't quite quantify. So the best things 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 of that than there is now as we're getting closer and closer to the to the to the AI 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. And then I think they operate over different time scales and they're equally important to address. So there's just the common garden of reliability of like, you know, bad actors using new technology. In this case, general purpose technology and we purpose it for harmful ends. And that's a huge risk. And I think there has a lot of complications because generally, you know, I mean, huge favor of open science and open source. And in fact, we did it with all our science projects like alpha fold 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 and but enable access at the same time to good actors to to maximally build on top of? It's a pretty tricky problem that there's I've not heard a clear solution to. So there's the bad actor use case problem. And then there's obviously 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 on my mind is limited worry more about the humans to 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, whether that's bio or nuclear or whatever it is, like AGI could be potentially helpful there, as long as the AGI that you're using is itself reliable. 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 U.S. of some basic standards, right? I have to ask you about the book, the Maniac, there's this, the hand of God moment, we said all this moves 78, that perhaps the last time a human did a move 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 you know, it was great for Lisa Dol and you know, I think it's in a way they were sort of inspiring each other, we as a team were inspired by Lisa Dol's brilliance and nobleness. and then maybe he got inspired by, you know, what AlphaGo was doing to then conjure this incredible inspiration or moment. It's all, you know, captured very well in the documentary about it. And I think I'll continue in many domains where there's this, at least 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 utilising these tools in a way that then cracks a problem. Yeah, what is the AI becomes smarter and smarter? One of the interesting questions we can ask ourselves is what makes humans special? Does feel, perhaps biased, that we humans are deeply special? I don't know if it's our intelligence. 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 neuroscience PhD to look at how the brain works, especially imagination and memory I focused on the hippocampus. And it's sort of going to be interesting. I always thought the best way, of course, one can come, philosophise about it and have thought experiments. And maybe even do actual experiments like you do neuroscience on, on real brains. But in the end, I always imagined that building AI, a kind of intelligent artifact, and then comparing that to the human mind and seeing what the differences were, would be the best way to uncover what's special about the human mind. If indeed there is anything special. And I suspect there probably is, but it's going to be hard to, you know, I think this journey where Ron 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, it's not a very helpful scientific explanation. I think it's kind of interesting and intuitive one. And so, you know, on this journey, this scientific journey where Ron will, I think, help uncover that mystery. Yeah, what I cannot create, I do not understand, that's somebody that you deeply admire, Richard Feynman, like you mentioned. You also reach, for the Wigner dreams of universality that he saw in constrained domains, but also broadly generally in mathematics and so on. 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 is 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, or is it a quantum mechanical nature? Well, look, Penrose is an amazing thinker, one of the greatest stuff for 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 collaborates 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 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 modulable or mimicable by 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. 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 that behavior looks like a behavior of a conscious being it might even claim it is. But we, 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 super intelligence and the technologies that builds, perhaps we'll be able to bridge that. No, I mean, that's a huge test for the radical empathy is to empathize with a different substrate. Right. Exactly. We 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, I mean, some people kind of think about that with plants with other life forms, which is definitely similar substrate, but sufficiently far enough. And the evolutionary tree that it requires a radical empathy, but to do that with the computer. I mean, no, we sort of there are animal studies on this of like, of course, higher animals like, you know, killer whales and dolphins and dogs and monkeys, you know, they have some an elephants, you know, they have some aspects, certainly of consciousness. Right. Even though they're not might not be that that that smart on an IQ sense. So 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 have won a version of our system was trained on dolphin and whale sounds. And maybe we'll be able to build an interpreter or translator at some point should be pretty cool. What gives you hope for the future of human civilization? Well, what gives me hope is that 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 with our effectively our hunter-gatherer brains. How is it we can, you know, we can cope with the modern world, right? Flying on planes, doing podcasts, you know, playing computer games and virtual simulations. I mean, it's already mind-blowing given that our mind was developed for, you know, 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's already adapted to this mind-blowing AI technology we have today already. It's sort of like, oh, I talk to chatbots, really 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 it. Not to the level that you can do it like so don't think. All right, thank you. That's what we humans do to each other with compliments. All right. And I'm deeply grateful for us humans that have this infinite capacity for curiosity, adaptability. Like you said, and also compassion and ability to love. Exactly. All of those human things that are deeply human. Well, this is a huge honor. Demis, you're one of the truly special humans in the world. Thank you so much for doing what you do and for talking today. Well, thank you very much. Thanks. Thanks for listening to this conversation with Demis. 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 like to submit questions, including in audio and video form, go to lexfrueme.com/amay. I got a lot of amazing questions, thoughts and requests from folks. I'll keep trying to pick some randomly and comment on it at the end of every episode. I got a note on May 21st this year that said hi, lex. 20 years ago today, David Foster Wallace delivered his famous "This is Water" speech at Kenyan College. What do you think of this speech? Well, first, I think this is probably one of the greatest and most 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, "Morning boys, how's the water?" 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 the banal platitude, but the fact is that in the day-to-day trenches of adult existence banal platitudes can have a life for 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." Alright, back to me, what's speaking. Second takeaway is that the central spiritual battles of our life are not fought on a mountain tops 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 unborrowable. 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 the demos 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 could imagine the cells in there, the complicated actions inside which also have the 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 and the flower evolved in order to attract insects to pollinate it is interesting. It means that the insects can see the color. It adds a question, does this aesthetic sense also exist in lower forms? Why is it aesthetic? All kinds of interesting questions which the science knowledge only adds to the excitement, the mystery, and the awe of a flower. It only adds. All right, back to David Foster Wallace's speech. He has a great story in there that I particularly enjoy. He 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. Next 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 felt in 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 showing 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, if I 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, I mean 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, wiring 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 Drexel University from my bachelor's masters and doctorate degrees. Second, I am a research scientist at MIT and have been there and it paid research position for the last ten years. Call 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 ten 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 Scala 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. 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 more smarter than me, many of whom have become lifelong colleagues and friends. MIT is a place I go to escape the world. I'm focused 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 Druxy University, which I also love and am proud of and am deeply grateful for my time there. As I said, I want to Druxy from my bachelor's master's and doctor degrees in computer science and electrical engineering. I've talked about Druxy a lot of times, including 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 of Druxy 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. A Druxy 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 for my time at Druxy. 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, EMEX 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 of 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 will 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 a rational emotional, I can be too much of a dick when I should be kind. I can lose myself in a biased rabbit hole before I wake up to the bigger, more accurate picture of reality. I'm human, and so are you, for better or for worse. And I do still believe we're in this whole beautiful mess together. I love you all. [MUSIC]