Audio · 2025-07-23 · 2h 35m · 6 moments

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

Demis Hassabis is the CEO of Google DeepMind and Nobel Prize winner for his groundbreaking work in protein structure prediction using AI. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep475-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/demis-hassabis-2-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, video ✦ AI generated

Apple Podcasts ↗

GUEST Demis Hassabis

timeline · colored by role

Claim◆

Any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm.

Demis Hassabis proposes that natural systems shaped by evolutionary processes have structure that can be learned by neural networks, making computationally intractable problems like protein folding solvable by classical systems.

transcript

Demis Hassabis: 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 AlphaX projects, so I'm thinking AlphaGo, of course, AlphaFold, what they really are is we're building models of very combinatorially high dimensional spaces that, you know, if you try to brute force a solution, find the best moving go, or find the exact shape of a protein, and if you enumerated all the possibilities, there wouldn't be enough time in the, time of the universe. So you have to do something much smarter. And what we did in both cases was build models of those environments. And that guided the search in a smart way. And that makes it tractable. So if you think about protein folding, which is obviously a natural system, you know, why should that be possible? How does physics do that? You know, proteins fold in milliseconds in our bodies. So somehow physics solves this problem that we've now also solved computationally. And I think the reason that's possible is that in nature, natural systems have structure because they were subject to evolutionary processes that shaped them. And if that's true, then you can maybe learn what that structure is. ... I sometimes call it survival of the stabilist or something like that, because, it's, of course, there's evolution for life, living things, but there's also, 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. You have to do brute force. So in that case, you maybe need a quantum computer, something like this. But in most things in nature that we're interested in are not like that. They have structure that evolved for a reason and survived over time. And if that's true, I think that's potentially learnable by a neural network.

provides context · 1

Mechanism◆

Video generation models like Veo are reverse-engineering intuitive physics from passive observation, challenging the theory that embodied interaction is necessary for understanding the physical world.

Hassabis describes how Veo 3 models liquids, lighting, and materials from YouTube videos, suggesting it extracts underlying physical structure without embodied interaction — something he previously thought would require robotics or simulation.

transcript

Demis Hassabis: I love the ones where, you know, there's people who generate videos where there's like clear liquids going through hydraulic presses and then it's being squeezed out. I used to write physics engines and graphics engines in my early days in gaming. And I know it's just so painstakingly hard to build programs that can do that. And yet somehow these systems are reverse engineering from just watching YouTube videos. So presumably what's happening is it's extracting some underlying structure around how these materials behave. ... The thing I'm most impressed with and fascinated by is the physics behavior, the lighting and materials and liquids. And it's pretty amazing that it can do that. And I think that shows that it has some notion of at least intuitive physics, right? How things are supposed to work intuitively, maybe the way that a human child would understand physics, right? As opposed to a PhD student really being able to unpack all the equations. It's more of an intuitive physics understanding. ... 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. And this is very interesting, even if you were to ask me 5, 10 years ago, I would have said, even though I was a must in all of this, I would have said, you probably need to understand intuitive physics, like if I push this off the table, this glass, it will maybe shatter, and the liquid will spill out, right? So we know all of these things. But I thought that, and there's a lot of theories in neuroscience, it's called action in perception, where, you need to act in the world to really truly perceive it in a deep way. And there was a lot of theories about you'd need embodied intelligence or robotics or something, or maybe at least simulated action so that you would understand things like intuitive physics. But it seems like you can understand it through passive observation, which is pretty surprising to me. And again, I think hints at something underlying about the nature of reality, in my opinion, beyond just the cool videos that it generates.

explains mechanism · 1

Prediction

The next frontier in video games is AI systems that dynamically generate content and narrative around whatever the player chooses, creating truly open worlds rather than illusions of choice.

Hassabis reflects on his roots building AI for games like Theme Park and Black & White, and envisions a future where generative AI creates games that adapt in real-time to player actions, making every playthrough unique.

transcript

Demis Hassabis: 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 scratch that itch one day and come back to that. So, and I will do, I think. And I think I'd sort of dream about, what would I have done back in the 90s if I'd had access to the kind of AI systems we have today? And I think you could build absolutely mind-blowing games. And I think the next stage is I always used to love making all the games I've made are open world games. So they're games where there's a simulation and then there's AI characters and then the player interacts with that simulation, and the simulation adapts to the way the player plays. And I always thought they were the coolest games because, so games like Theme Park that I worked on where everybody's game experience would be unique to them, right? Because you're kind of co-creating the game, right? We set up the parameters, we set up initial conditions, and then you as the player immersed in it, and then you are co-creating it with the simulation. But of course, it's very hard to program open-world games. You know, you've got to be able to create content whichever direction the player goes in and you want it to be compelling no matter what the player chooses. ... Now we're maybe on the cusp in the next few years, 5, 10 years of having AI systems that can truly create around your imagination. Can now sort of dynamically change the story and storytell the narrative around and make it dramatic no matter what you end up choosing. So it's like the ultimate choose your own adventure sort of game.

Definition

The hardest part of great science is not solving problems but having the taste to pick the right question — and this kind of research taste is currently beyond AI systems.

Hassabis argues that what separates great scientists from good ones is the ability to identify the right question and hypothesis. Current AI can solve hard problems but cannot make creative leaps like Einstein's relativity or invent deep games like Go.

transcript

Demis Hassabis: 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, you know, the great scientists from the good scientists, like all professional scientists are good technically, right? Otherwise, it wouldn't have made it that far in academia and things like that. But then do you have the taste to sort of sniff out what the right direction is, what the right experiment is, what the right question is? So it's picking the right question is the hardest part of science and making the right hypothesis. And that's what today's systems definitely, they can't do. So, I often say it's harder to come up with a conjecture, a really good conjecture, than it is to solve it. So we may have systems soon that can solve pretty hard conjectures. Maths Olympiad problems, Alpha proof last year, our system got a silver medal in that, really hard problems. Maybe eventually we'll better solve a Millennium Prize kind of problem. But could a system come up with a conjecture worthy of study that someone like Terence Tao would have gone, you know what? That's a really deep question about the nature of maths or the nature of numbers or the nature of physics. And that is a 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.

supports · 1

Prediction

AGI will likely be achieved by 2030 with a 50% chance within five years, and we will know we have it when a system can make creative leaps like inventing a new physics theory or inventing a game as deep as Go.

Hassabis gives his AGI timeline estimate and describes the 'lighthouse moments' he would look for: a system inventing a new physics conjecture or a game with the depth and beauty of Go, backed by consistent performance across thousands of cognitive tasks.

transcript

Demis Hassabis: My estimate is sort of 50% chance in the next five years. So, you know, by 2030, let's say. And so I think there's a good chance that could happen. Part of it is what is your definition of AGI? Of course, people are arguing about that now. And mine's quite a high bar and always has been of like, can we match the cognitive functions that the brain has. Right. So we know our brains are pretty much general Turing machines, approximate. And of course, we created incredible modern civilization with our minds. So that also speaks to how general the brain is. And for us to know we have a true AGI, we would have to 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 flaw that. 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. ... 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 cut off of knowledge cut off of 1900 and then give the system everything that was written up to 1900 and then see if it could come up with special relativity and general relativity, right, like Einstein did. That would be an interesting test. Another one would be, can it invent a game like Go? Not just come up with Move 37, a new strategy, but can it invent a game that's as deep as aesthetically beautiful, as elegant as go.

provides context · 2rebuts · 1

Prediction

If we crack fusion and solar energy, we enter an era of radical abundance where resource scarcity ceases to be a driver of human conflict.

Hassabis envisions a future where cheap fusion and efficient solar energy solve water access, enable space travel, and end resource scarcity — opening up asteroid mining and allowing human civilization to spread consciousness to the universe.

transcript

Demis Hassabis: I think fusion and solar are the two that I would bet on. Solar, I mean, it's the fusion reactor in the sky, of course. And I think really the problem there is batteries and transmission. So, as well as more efficient, more and more efficient solar material, perhaps eventually, in space, these kind of Dyson sphere type ideas. And fusion, I think, is definitely doable, it 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. ... 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. ... 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 seawater 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 sort of like a bus service to space. So that opens up incredible new resources and domains. Asteroid mining, I think, will become a thing and maximum human flourishing to the stars. That's what I dream about as well as like Carl Sagan's sort of idea of bringing consciousness to the universe, waking up the universe. ... 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?

Highlight slides

The Core Claim: Nature Has Learnable Structure✦ from: Any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm.From Proteins to Planets — Patterns Everywhere✦ from: Any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm.Boundary: When Learning Fails✦ from: Any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm.Veo 3 reverse-engineers intuitive physics from passive video✦ from: Video generation models like Veo are reverse-engineering intuitive physics from passive observation, challenging the theory that embodied interaction is necessary for understanding the physical world.Intuitive physics, not formal physics✦ from: Video generation models like Veo are reverse-engineering intuitive physics from passive observation, challenging the theory that embodied interaction is necessary for understanding the physical world.

Related episodes

Video · Machine Learning Street Talk

Intelligence is collective, not artificial — Prof. Michael I. Jordan (UC Berkeley / Inria)

→

Video · Machine Learning Street Talk

He won a Nobel here for AlphaFold. Then he left. - John Jumper

→

Video · Lenny's Podcast

OpenAI’s Head of Design: This is the best time in history to be a designer | Ian Silber

→