#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 DeepMind and is now a Nobel Prize winner. Demis is one of the most brilliant and fascinating minds in the world today working on understanding and building intelligence and exploring the big mysteries of our universe. This was truly an honor and a pleasure for me. And now a quick few second mention of a sponsor. Check them out in the description or at lexfridman.com/sponsors.

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

neuroscience, what are we talking about?

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

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

So interesting.

Do you think, because you're also a fan of theoretical computer science and complexity, do you think we can come up with a kind of complexity class, like a complexity zoo type of class, where maybe it's the set of learnable systems, the set of learnable natural systems, LNS?

This is a Demis Hassabis new class of systems that could be actually learnable by classical systems in this kind of way, natural systems that can be modeled efficiently.

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

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

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

What do you think, again, hypothetical might be outside of this? Maybe emergent phenomena? Like if you look at cellular automata, some of the extremely simple systems and then some complexity emerges. 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 it will maybe continuously surprise us about what is modelable by classical computers. I mean, AlphaFold 3 on the interaction side is surprising that you can make any kind of progress on that direction. Alpha genome is surprising that you can map the genetic code to the function. Kind of playing with the emergent phenomena, you think there's so many combinatorial options and then here you go. You can find the kernel that is efficiently modelable.

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

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

Yeah, I've been continuously surprised by this aspect of VEO 3. I think a lot of people highlight different aspects including the comedic and the meaning and all that kind of stuff. And then the ultra-realistic ability to capture humans in a really nice way that's compelling and feels close to reality and then combine that with native audio. All of those are marvelous things about VEO 3, but the exactly the thing you're mentioning, which is the physics.

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 if the cynical take with the diffusion models there's no way it understands anything. But it seems I mean I don't think you can generate that kind of video without understanding and then our own philosophical notion of what it means to understand is brought to the surface. To what degree do you think VEO 3 understands our world?

Well, that intuitive physics understanding, that's the base layer. That's the thing people sometimes call common sense. It really understands something. I think that really surprised a lot of people. It blows my mind that I just didn't think it would be possible to generate that level of realism without understanding.

There's this notion that you can only understand the physical world by having an embodied AI system, a robot that interacts with that world. That's the only way to construct an understanding of that world. But VEO 3 is directly challenging that, it feels like.

But VEO 3 is directly challenging that, it feels like.

I have to talk to you about video games. So, you were being a bit trolly.

I think you're having more and more fun on Twitter on X, which is great to see. So, guy named Jimmy Apples tweeted, "Let me play a video game of my VEO 3 videos already. Google cooked so good, playable world models when?" spelled w e n. And then you quote tweeted that with, "Now, wouldn't that be something?" So, how hard is it to build game worlds with AI? Maybe can you look out into the future of video games, 5, 10 years out? What do you think that looks like?

Yeah, so you said a lot of super interesting stuff there. So, one, the open world built into that is a deep personalization, the way you've described it. So, it's not just that it's open world, 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 tried to do that, they give you choice, but it's really just an illusion of choice because you're only like like Stanley Parable is a game I played. It's really there's a couple of doors and it really just takes you down a narrative. Stanley Parable is a great video game, I recommend people play, that kind of in a meta way mocks the illusion of choice and there's philosophical notions of free will and so on. But I do like one of my favorite games of Elder Scrolls is Daggerfall, I believe, that they really played with random generation of the dungeons. If you can step in and they give you this feeling of an open world. And there, you mentioned interactivity, you don't need to interact that 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, it's been interesting, hilarious, and fun to watch you and Elon obviously itching to create games because you're both gamers. And one of the sad aspects of your incredible success in so many domains of science, like serious adult stuff, that you might not have time to really create a game. You might end up creating the tooling that others would create the game and you have to watch others create the thing you've always dreamed of. Do you think it's possible you can somehow in your extremely busy schedule actually find time to create something like Black & White, an actual video game where you could make the childhood dream come become?

I would love to see which post-AGI would you choose, solving the problem that some of the smartest people in human history contended with, so P equals NP, or creating a cool video game?

I mean, in a really serious way, so video games sometimes are looked down upon. It's just this fun side activity. But especially as AI does more and more of the difficult boring tasks, something we in the modern world called work. Video games is the thing in which we may find meaning, in which we may find 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, 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. And which aspect of that can be mapped into the virtual world?

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

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

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

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

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

Are these kinds of evolution-like techniques promising as a component of future super intelligence systems? So if people don't know, it's kind of LLM-guided evolution search. So the evolutionary algorithms are doing the search and LLMs are telling you where.

But, not to romanticize evolution, 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 able to model, being able to simulate evolution and then using that whatever we understand about that nature-inspired mechanism to then do search better and better and better.

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

combine things out there like sub 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

Yeah, listen, AlphaFold is one of the coolest things I've ever seen. I've On my desk at home, you know, most of my time is spent behind a computer just programming. And next to the three screens is a skull of a Tiktaalik, which is one of the early organisms that crawled out of the water onto land. And I just kind of watch that little guy. [laughter] It's like you whatever the competition mechanism of evolution is is quite incredible. Yeah, it's truly truly incredible. Now, whether that's exactly the thing we need to do to do our search, but never never dismiss the power of nature with what it did here.

So, so many questions I want to ask. 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 about that. I also just for that purpose on the AI scientist front just broadly. So, there's an essay from Daniel Kokotajlo, Scott Alexander, and others that outline steps along the way to get to ASI. And has a lot of interesting ideas in it. One of which is including a superhuman coder and a superhuman AI researcher. And in that there's a term of research taste. That's really interesting. So, in everything you've seen, do you think it's possible for AI systems to have research taste? To help you in the way that AI co-scientist does to help steer human brilliant scientists and then potentially by itself to figure out what are the directions where you want to generate truly novel ideas? Because that seems to be like a really important component of how to do great science.

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

So like it's easy to come up with a thing that's extremely difficult. Yeah. It's easy to come up with a thing that's extremely easy, but that at that very edge

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

So to go to your dream [snorts] of modeling a cell, what are the big challenges that lay ahead for us to make that happen? We should maybe highlight that AlphaFold I mean, there's just so many leaps.

So, AlphaFold solved, if it's fair to say, protein folding, and there's so many incredible things we could talk about there, including the open sourcing. The everything you've released. AlphaFold 3 is doing protein RNA DNA interactions,

which is super complicated and fascinating that's amenable to modeling. AlphaGenome predicts how small genetic changes, like if we think about single mutations, how they link to actual function. So, those are it seems like it's creeping along to a sophisticated to much more complicated things like a cell. But, a cell has a lot of really complicated components.

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

I don't know all the biological mechanisms.

But some of them take a long time. And so, is that that's a level So, the levels of interaction has a different temporal scale that you have to be able to model.

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

Apologies for the pothead questions ahead of time, but will 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.

Bacteria were super happy for a very long time.

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

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

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

As there's no line.

Yeah.

Yeah. It's kind of amazing.

Yeah. [laughter]

Yeah.

Exactly. So we're quite I guess we've developed a lot of mechanisms to cope with these deep 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 it's that's the go-to small talk direction of the weather.

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

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.

So they're it's this [laughter] it's this beautiful mix of like crazy enough to like go into the eye of the storm and like in order to protect your life and predict where the extreme events are going to be, they have to have increasingly sophisticated models of weather.

Yeah, it's a beautiful balance of like being in it as living organisms and the cutting edge of science. So, they actually might be using DeepMind system. So, that's

Exactly. And then also to experience the correct prediction where something will come and how it's going to evolve. It's incredible.

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

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 Terrence Tao, these brilliant experts, they might quickly, in a span of weeks, take for granted all the incredible things it can do and then focus in well, 'Haha, right there.' You know, I consider myself a first of all human.

I identify as human. Is it I you know, some people listen to me talk and they're like, 'That guy's not good at talking. The stuttering, the you know' [laughter] So like even humans have obvious across domains limits. Even just outside of mathematics and physics and so on. I wonder if it will take something like a move 37. So on the positive side versus like a barrage of 10,000 cognitive tasks where it would be one or two where it's like

Holy This is personal.

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

And you would be in the room for that. And so it would be like probably two or three months before announcing it.

And you would just be sitting there trying not to tweet.

Something like that. Exactly. It's like what is this amazing new, 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 it 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, might miss it?

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

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

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

Yeah, but it could also be sort of to push back against hard takeoff scenario. It could be just a sequence of incremental improvements like matrix multiplication. Like it has to sit there for days thinking how to incrementally improve a thing and that it does so recursively. As you do more and more improvement, it'll slow down. So, there'll be like the path AGI won't be like a it'll be a gradual improvement over time.

And can the same kind of systems provide the breakthroughs also? So, maybe get a bunch of S curves. Like incremental improvement, but also every once in a while leaps.

Yeah. And that would be the move 37 is a

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

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

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

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

Doing a lot with not so much data.

I mean, it's still a lot of data, but I guess enough to get that going.

How crucial is the scaling of compute to building AGI? This is a question that's an engineering question. It's almost a geopolitical question because it also integrated into that is supply chains and energy, a thing that you care a lot about, which is potentially fusion. So, innovating on the side of energy also. Do you think we're going to keep scaling compute?

Yeah, that's one sort of almost meme-y kind of thing, which is like the success in the incredible aspects of VL3. There's people kind of make fun of like the more successful it becomes, the you know, the servers are sweating.

So, if you were to bet, sorry for the ridiculous question, but what is the main source of energy in like 20, 30 years? Do you think it's going to be nuclear fusion?

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

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

A lot of rockets.

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

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

Like the rainforest and all that kind of stuff.

So, there is something about human nature where I go, you know, it's like Borat, like my neighbor, like I would like you start trouble. We do start conflicts. And that's why games throughout, as I'm learning actually more and more, even in ancient history, serve the purpose of pushing people away from war, actually hot war. So, maybe we can figure out increasingly sophisticated video games that pull they give us that scratch the itch of like conflict, whatever that is, but us the human nature and then avoid the actual hot wars that would come with increasingly sophisticated technologies because we're now have 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 with your neighbor. It's better to play a game of chess or football.

Well, I think it's also really important to practice losing and winning. Like losing is a really you know that's why I love games. That's why I love even things like Brazilian Jiu Jitsu where you can get your ass kicked in a safe environment over and over. It reminds you about the way about physics about the way the world works about the sometimes you lose sometimes you win. You can still be friends with everybody. But that feeling of losing I mean it's a weird one for humans to really make sense of. Like that's just part of life. That is a fundamental part of life is losing.

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

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

Yeah, it would be quite sad if we didn't have any mechanism to color belts. All the we do this everywhere, right? Where we just have this thing that it's in a I don't want to dismiss that. There is a source of deep meaning for us humans.

Um so, one of the incredible stories on the business on the leadership side is what Google has done over the past year. So, I think it's fair to say that Google was losing on the LLM product side a year ago with Gemini 1.5. And now it's winning with Gemini 2.5. And you took the helm and you led this effort. What did it take to go from let's say 'losing' to 'winning' in the span of a year?

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

So funny thing you mentioned about like the surface of the billion. I had a conversation with a guy named, brilliant guy, here at the British Museum called Irving Finkel. He's a world expert at cuneiforms, which is an ancient writing on tablets. And he doesn't know about chat GPT or Gemini. He doesn't even know anything about AI. But his first encounter with this AI is AI mode on the He's like, is that what you're talking about this AI mode? And then, it's just a reminder that there's a large part of the world that doesn't know about this AI thing.

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

On the grand scale of the rural India or anywhere across the world. You get to

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 deep scientist and caring about these big research scientific questions. But they also said you're a great product guy. Like how to create a thing that a lot of people would use and enjoy using. So, can you maybe speak to what it takes to create a AI-based product that a lot of people would enjoy using?

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 to present to the user how long to wait and how that waiting is shown or the reason capabilities. There's some interesting things because it like you said it's the very cutting edge. We don't know how to present it correctly. So is there some specific things you've learned?

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

I think that, you know, under appreciated art form is the interface design. And 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. It's such an interesting question of how to do that. So, how how it you would think like getting out of the way is an real art form.

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

Well, I'm glad you have a Steve Jobs mode in you as well. This is great. Einstein mode, Steve Jobs mode. All right, let me try to trick you into answering a question. When will Gemini 3 come out? Is it 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 in performance. So, what does it even mean to go to a new version? Is it about performance? Is it about a complete different flavor of an experience?

So, behind the version changes, there is a big hero run. 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 each step you take, you realize there might be a cool product and the side quests. And then you also don't want to take too many side quests because then you have a million versions of million products. It's very unclear. But you also get super excited because it's super cool. Like how does even it look at VOs? Very cool. How does it fit into the bigger thing?

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

Yeah, because ultimately you want to measure the usefulness, but it's so hard to convert that into a number. It's really vibe-based benchmarks across a large number of users and it's hard to know. And I would it would be just terrifying to me to you know you have a much smarter model, but it's just something vibe-based. It's not quite working. That's just scary 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 of a sudden solving programming problems it's never been able to solve before, but now it's crappy at poetry or something. 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.

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.

So, what's the probability of Google DeepMind winning?

I agree with you. I think that's beautifully put. You've said that you talk to and are on good terms with the leads of some of these labs. As the competition heats up, how hard is it to maintain those relationships?

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

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

Well, from the bottom of my heart thank you for pushing the scientific efforts forward with with rigor, with fun, with humility, all of that. I just love to see it and still talking about P equals NP. I mean it's just incredible. So I love it. There have 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 DeepMind as 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, when you're facing such gigantic consequences and gigantic fascinating scientific questions.

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

I think that's right. That anytime where there's a lot of disruption and change, you know, we've had this not just this time. We've had this in many times in human history with the internet, mobile, but before that was the Industrial Revolution. And it's going to be one of those eras where there will be a lot of change. I think there'll be new jobs we can't even imagine today just like the internet created and then those people with the right skill sets to ride that wave will become incredibly valuable. Right, those skills. But maybe people will have to relearn or adapt a bit their current skills. And it's the thing that's going to be harder to deal with this time around is the 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 100 years, it takes 10 years. And so, that's going to make it, you know, it's like a 100x the impact and the speed combined. So, that's what I think going to make it more difficult for society to deal with and it's good 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 should society be affected by this and what should we do including things like universal basic provision or something like that where a lot of the increased productivity get 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 the 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 fueling the division and using that to get elected, defining the other and then saying that's bad and based on that I think that's often counterproductive to leveraging a rapidly changing technology 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.

Uh, you mentioned to me the book The Maniac by Benjamin Labatut, a book on, first of all, about you. There's a bio about you.

It's unclear how much is fiction, how much is reality. Um, but I think the central figure is John von Neumann. I would say it's a haunting and beautiful exploration of madness and genius and the double-edged sword of discovery. And for people who don't know, John von Neumann is a legendary mind. He contributed to quantum mechanics, was on the Manhattan Project, and is widely considered to be the father of or pioneered the modern computer and AI. So many people say he's one of the smartest humans ever. It's fascinating. And what's also fascinating is that he was a person who saw nuclear science and physics become the atomic bomb. He saw ideas become a thing that has a huge impact on the world. He also foresaw the same thing for computing.

He's, and that's the beautiful and haunting aspect of the book. Then taking a leap forward and looking at AlphaGo and AlphaZero, that big moment, maybe John von Neumann's thinking was brought to reality. So the question is, what do you think if you got to hang out with John von Neumann now? What would he say about what's going on?

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

And I think one of the takeaways from the book is that reason, as said in the book Mad Dreams of Reason, is not enough for guiding humanity as we build these super powerful technologies. That there's something else, that there's also a religious component. Whatever God, whatever religion gives, it pulls at something in the human spirit that raw, cold reason doesn't give us.

Yeah, because they force the technologists to have the human conversation. That's the hopeful aspect of it. It's a dual-use technology that we're forcefully integrating the entire humanity into the discussion about AI, because ultimately AGI will be used for things that states use technologies for, which is conflict and so on. The more we integrate humans into this picture by having chats with them, the more it will guide...

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

Yeah, I personally believe on the education side and immigration side, it would be great if both directions people from the West immigrated to China and China to the West. There is a family human aspect of people just intermixing.

And thereby those ties grow strong so you can't divide against each other in that kind of old school way of thinking. Multi-cultural, multi-disciplinary research teams working on scientific questions, that's the hope. Don't let the warmongers divide us. Science is the ultimately beautiful connector.

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

What would be the source of worry for you more? Would it be human caused or AI AGI caused? Humans abusing the technology versus AGI itself through mechanisms of deception or this kind of stuff getting better and better secretly?

Yeah, I tend to worry more about the human bad actors. There it could be how do you not put destructive technology in the hands of bad actors, but also from a geopolitical perspective, how do you reduce the number of bad actors in the world? That's also an interesting human problem.

I have to ask you about the book The Maniac. There's the hand of God moment, Lee Sedol's move 78. That perhaps the last time a human did a move of pure human genius and beat AlphaGo, or broke its brain. It's an interesting moment because in so many domains it will keep happening.

Yeah, as the AI becomes smarter and smarter, one of the interesting questions we can ask ourselves is what makes humans special. It feels, 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 outside the mad dreams of reason.

Yeah, what I cannot create I do not understand. That's somebody you deeply admire, Richard Feynman. You also reach for the Wigner's dreams of universality that he saw in constrained domains, but also broadly in mathematics and so on. So many aspects you're pushing towards.

Not to start trouble at the end, but Roger Penrose.

Do you think consciousness, this hard problem of consciousness, how information feels? Do you think consciousness is a computation? And if it is, if it's information processing like you said, is it something that can be modeled by a classical computer?

Or is it of a quantum mechanical nature?

Yeah, that's a huge test for radical empathy. It's to empathize with a different substrate.

Yeah, maybe through brain-computer interfaces be able to truly empathize what it feels like to be a computer.

I mean, that's deeply exciting. Some people think about that with plants, other life forms, which is different, similar substrate but sufficiently far on the evolutionary tree that it requires radical empathy. But to do that with a computer...

What gives you hope for the future of human civilization?

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

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.

Thanks for listening to this conversation with Demis Hassabis. 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'd like to submit questions, including in audio and video form, go to lexfridman.com/ama. I got a lot of amazing questions, thoughts, and requests from folks. I'll keep trying to pick some randomly and comment on it at the end of every episode. I got a note on May 21st this year that said, 'Hi, Lex. 20 years ago today, David Foster Wallace delivered his famous This Is Water speech at Kenyon 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. 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. His writing was his notes from the front lines of that battle. Now, onto the speech. Let me quote some parts. There's of course the parable of the fish and the water that goes, 'There are these two young fish swimming along and they happen to meet an older fish swimming the other way who nods at them and says, Morning, boys. How's the water? And the two young fish swim on for a bit, and then eventually, one of them looks over at the other and goes, What the hell is water? In the speech, David Foster Wallace goes on to say, 'The point of the fish story is merely that the most obvious, important realities are often the ones that are hardest to see and talk about.' Stated as an English sentence, this is just a banal platitude. But the fact is that in the day-to-day trenches of adult existence, banal platitudes can have a life or death importance. Or so I wish to suggest to you in this dry and lovely morning. I have several takeaways from this parable and the speech that follows. First, I think we must question everything, and in particular the most basic assumptions about our reality, our life, and the very nature of existence. And 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, 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. Second takeaway is that the central spiritual battles of our life are not fought on a mountaintop somewhere at a meditation retreat, but it is fought in the mundane moments of daily life. Third takeaway is that we too easily give away our time and attention to the multitude of distractions that the world feeds us. The insatiable black holes of attention. David Foster Wallace's call to action is to be deeply aware of the beauty in each moment and to find meaning in the mundane. I often quote David Foster Wallace in his advice that the key to life is to be unborable. And I think this is exactly right. Every moment, every object, every experience when looked at closely enough contains within it infinite richness to explore. And since Demis Hassabis of this very podcast episode and I are such fans of Richard Feynman, let me quote Mr. Feynman on this topic as well. Quote, 'I have a friend who's an artist and has sometimes taken a view which I don't agree with very well. He'll hold up a flower and say, Look how beautiful it is. And I'll agree. Then he says, I as an artist can see how beautiful this is, but you as a scientist take this all apart and it becomes a dull thing. And I think that's kind of nutty. First of all, the beauty that he sees is available to other people and to me too, I believe. Although I may not be quite as refined aesthetically as he is, I can appreciate the beauty of a flower. At the same time, I see much more about the flower than he sees. I can imagine the cells in there, the complicated actions inside which also have beauty. I mean, it's not just beauty at this dimension, at one centimeter. There's also beauty at the smaller dimensions. Their inner structure, also the processes. The fact that the colors in the flower evolved in order to attract insects to pollinate it is interesting. It means that the insects can see the color. It adds the question, does this aesthetic sense also exist in lower forms? Why is it aesthetic? All kinds of interesting questions which science knowledge only adds to the excitement, the mystery, and the awe of a flower. It only adds.' All right, back to David Foster Wallace's speech. He has a great story in there that I particularly enjoy. It goes, 'There are these two guys sitting together in a bar in the remote Alaskan wilderness. One of the guys is religious, the other is an atheist. And the two are arguing about the existence of God with that special intensity that comes after about the fourth beer. And the atheist says, Look, it's not like I don't have actual reasons for not believing in God. It's not like I haven't ever experimented with the whole God and prayer thing. Just last month I got caught away from the camp in that terrible blizzard. And I was totally lost, and I couldn't see a thing, and it was 50 below. So I tried it. I fell to my knees in the snow and cried out, Oh God, if there is a God, I'm lost in this blizzard and I'm going to die if you don't help me.' And now back in the bar, the religious guy looks at the atheist all puzzled. 'Well, then you must believe now,' he says. 'After all, there you are, alive.' The atheist just rolls his eyes. 'No, man. All that happened was a couple of Eskimos happened to be wondering by and showed me the way back to the camp.' All this teaches us that everything is a matter of perspective, and that wisdom may arrive if we have the humility to keep shifting and expanding our perspective on the world. Thank you for allowing me to talk a bit about David Foster Wallace. He's one of my favorite writers and he's a beautiful soul. If I may, one more thing I wanted to briefly comment on. I found myself in this strange position of getting attacked online often from all sides, including being lied about, sometimes through selective misrepresentation, but often through downright lies. I don't know how else to put it. This all breaks my heart, frankly. But I've come to understand that it's the way of the internet and the cost of the path I've chosen. There's been days when it's been rough on me mentally. It's not fun being lied about, especially when it's about things that have been a source of happiness and joy for me. But again, that's life. I'll continue exploring the world of people and ideas with empathy and rigor, wearing my heart on my sleeve as much as I can. For me, that's the only way to live. Anyway, a common attack on me is about my time at MIT and Drexel, two great universities I love and have tremendous respect for. Since a bunch of lies have accumulated online about me on these topics to a sad and at times hilarious degree, I thought I would once more state the obvious facts about my bio for the small number of you who may care. TLDR, 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 for my bachelor's, master's, and doctorate degrees. Second, I am a research scientist at MIT and have been there in a paid research position for the last 10 years. Allow me to elaborate a bit more on these two things now, but please skip if this is not at all interesting. So, like I said, a common attack on me is that I have no real affiliation with MIT. The accusation is that I'm falsely claiming an MIT affiliation because I taught a lecture there once. Nope. That accusation against me is a complete lie. I have been at MIT for over 10 years in a paid research position from 2015 to today. To be extra clear, I'm a research scientist at MIT working in LIDS, the Laboratory for Information and Decision Systems in the College of Computing. For now, since I'm still at MIT, you can see me in the directory and on the various lab pages. I have indeed given many lectures at MIT over the years, a small fraction of which I posted online. Teaching for me always has been just for fun and not part of my research work. I personally think I suck at it, but I have always learned and grown from the experience. It's like Feynman spoke about, if you want to understand something deeply, it's good to try to teach it. But my main focus has always been on research. I published many peer-reviewed papers that you can see in my Google Scholar profile. For my first four years at MIT, I worked extremely intensively. Most weeks were... After that, in 2019, I still kept my research scientist position, but I split my time taking a leave to pursue projects in AI and robotics outside MIT and to dedicate a lot of focus to the podcast. I've been continuously surprised just how many hours preparing for an episode takes. There are many episodes of the podcast for which I have to read, write, and think for 100, 200, or more hours across multiple weeks and months. Since 2020, I have not actively published research papers. Just like the podcast, I think it's something that's a serious full-time effort. But not publishing and doing full-time research has been eating at me because I love research and I love programming and building systems that test out interesting technical ideas, especially in the context of human-AI or human-robot interaction. I hope to change this in the coming months and years. What I've come to realize about myself is if I don't publish or if I don't launch systems that people use, I definitely feel like a piece of me is missing. It legitimately is a source of happiness for me. Anyway, I'm proud of my time at MIT. I was and am constantly surrounded by people much smarter than me, many of whom have become lifelong colleagues and friends. MIT is a place I go to escape the world, to focus on exploring fascinating questions at the cutting edge of science and engineering. This again makes me truly happy. And it does hit pretty hard on a psychological level when I'm getting attacked over this. Perhaps I'm doing something wrong. If I am, I will try to do better. In all this discussion of academic work, I hope you know that I don't ever mean to say that I'm an expert at anything. In the podcast and in my private life, I don't claim to be smart. In fact, I often call myself an idiot and mean it. I try to make fun of myself as much as possible and in general to celebrate others instead. Now, to talk about Drexel University, which I also love, am proud of, and am deeply grateful for my time there. I went to Drexel for my bachelor's, master's, and doctorate degrees in computer science and electrical engineering. I've talked about Drexel many times, including, as I mentioned, at the end of a recent podcast, the Donald Trump episode, funny enough, that was listened to by many millions of people, where I answered a question about graduate school and explained my own journey at Drexel and how grateful I am for it. If it's at all interesting, please go listen to the end of that episode or watch the related clip. At Drexel, I met and worked with many brilliant researchers and mentors from whom I've learned a lot about engineering, science, and life. There are many valuable things I gained from my time at Drexel. First, I took a large number of very difficult math and theoretical computer science courses. They taught me how to think deeply and rigorously and also how to work hard and not give up even if it feels like I'm too dumb to find a solution to a technical problem. Second, I programmed a lot during that time, mostly C, C++. I programmed robots, optimization algorithms, computer vision systems, wireless network protocols, multimodal machine learning systems, and all kinds of simulations of physical systems. This is where I really developed a love for programming, including, yes, Emacs and the Kinesis keyboard. I also during that time read a lot. I played a lot of guitar, wrote a lot of crappy poetry, and trained a lot in Judo and Jiu-Jitsu, which I cannot sing enough praises to. Jiu-Jitsu humbled me on a daily basis throughout my 20s, and it still does to this very day whenever I get a chance to train. Anyway, I hope that the folks who occasionally get swept up in the chanting online crowds that want to tear down others don't lose themselves in it too much. In the end, I still think there's more good than bad in people. But we're all, each of us, a mixed bag. I know I am very much flawed. I speak awkwardly, I sometimes say stupid things, I can get irrationally emotional, I can be too much of a dick when I should be kind, I can lose myself in a biased rabbit hole before I wake up to the bigger, more accurate picture of reality. I'm human, and so are you, for better or for worse. And I do still believe we're in this whole beautiful mess together. I love you all.