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Demis Hassabis

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123

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2025-07-23

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2025-07-23

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1. “Like on the skill tree. Like, literally, that is Yeah, we're quite addicted to this sort of these numbers going up and maybe that's why we made games like that because obviously that is something we're hill climbing systems ourselves, right?”

   2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

2. “The mastery There's nothing more satisfying in a way, is like, oh wow, this thing I couldn't do before, now I can. And again, games and physical sports and mental sports, their ways of measuring, they're beautiful because you can measure that progress.”

   2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

3. “Yeah, and I think the martial arts, as I understand it, but also in things like light chess, at least the way I took it, it's a lot to do with self-improvement, self-knowledge, you know, that, okay, so I did this thing. It's not about really being the other person. It's about maximizing your own potential. If you do it in a healthy way, you learn to use victory and losses in a way. Don't get carried away with victory and think you're just the best in the world and the loss.”

   2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

4. “Life, you know, what job to take, what university to go to. You know, you get maybe, I don't know, a dozen or so key decisions one has to make. And you've got to make those as best as you can. And games is a kind of safe environment, repeatable environment where you can get better at your decision making process. And it maybe has this additional benefit of channeling some energies into more creative and constructive pursuits.”

   2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

5. “And I think that's what my modern sport is. And I love football watching it. And I just feel like, and I used to play it a lot as well. And it's very visceral in its tribal. And I think it does channel a lot of those energies into a, which I think is a kind of human need to belong to some group. But into a fun way, a healthy way and a not destructive way, kind of constructive thing. And I think going back to games again is I think the originally why they're so great as well for kids to play things like chess is they're great little microcosm simulations of the world their simulations of the world too they're simplified versions of some real world situation where it's poker or or go or chess different aspects or diplomacy different aspects of of the real world and it allows you to practice at them too and and because you know how many times do you get to practice a massive decision moment in your life”

   2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

6. “Exactly, because for the first time in human history, we wouldn't be resource constrained. And I think that could be amazing new era for humanity where it's not zero sum, right? I have this land, you don't have it. Or if we take, you know, if the tigers have their forest, then the local villagers can't, what are they going to use? I think that this will help a lot. No, it won't solve all problems because there's still other human foibles that will still exist, but it will at least remove one, I think one of the big vectors, which is scarcity of resources, you know, including land and more materials and energy. And we should be, I sometimes call it like another's call it about this kind of radical abundance era where there's plenty of resources to go around. Of course, the next big question is making sure that that's fairly shared fairly and everyone in society benefits from that.”

   2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

7. “You know, incredible new resources and domains, asteroid mining, I think, will become a thing and a maximum of human flourishing to the stars. That's what I dream about as well is like Carl Sagan's sort of idea of bringing consciousness to the universe, waking up the universe. And I think human civilization will do that in the full sense of time if we get AI right and crack some of these problems with it.”

   2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

8. “I would not be that surprised if it was like a hundred year timescale from here. I mean, I think it's pretty clear if we crack the energy problems in one of the ways we've just discussed fusion or very efficient solar, then if energy is kind of free and renewable and clean, then that solves a whole bunch of other problems. So, for example, the water access problem goes away because you can just use desalination. We have the technology, it's just too expensive, so only, you know, fairly wealthy countries like Singapore and Israel and so on actually use it. But if it was cheap, then all countries that have a coast could. But also you'd have unlimited rocket fuel. You could just separate seawater out into hydrogen and oxygen using energy. And that's rocket fuel. So combined with, you know, Elons, amazing self-landing rockets, then it could be like a bus service to space. So that opens up.”

   2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

9. “I think fusion and solo are the two that I would bet on. Solo, 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 efficient solar material, perhaps eventually in space, these kind of Dyson sphere type ideas and fusion, I think, is definitely doable, seems, if we have the right design of reactor and we can control the plasma and fast enough and so on. And I think both of those things will actually get solved. So we'll probably have at least, those are probably the two primary sources of renewable, clean, almost free, or perhaps free energy.”

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10. “Yes. Can you have the inference? Yeah, yeah, exactly. We did a little video of the servers frying eggs and things. And that's right. And we're going to have to figure out how to do that. There's a lot of interesting hardware innovations that we do. As you know, we have our own TPU line and we're looking at influence only things, inference-only chips and how we can make those more efficient. We're also very interested in building AI systems. And we have done the help with energy usage. So help data center energy like for the cooling systems be efficient, grid optimization. And then eventually things like helping with plasma containment fusion reactors. We've done lots of work on that with Commonwealth Fusion and also one could imagine reactor design and then material design, I think, is one of the most exciting new types of solar material, solar panel material, super room temperature, superconductors has always been on my list of dream breakthroughs and optimal background.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

11. “I think so for several reasons. I think compute there's the amount of compute you have for training. Often it needs to be co-located. So actually even like bandwidth constraints between data centers can affect that. So there's additional constraints even there. And that's important for training obviously the largest models you can. But there's also because now AI systems are in products and being used by billions of people around the world, you need a ton of inference compute now. And then on top of that, there's the thinking systems, the new paradigm of the last year 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 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. It may even become the smaller part.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

12. “I'm not very worried about that, partly because I think there's enough data, and it's been proven to get the systems to be pretty good. And this goes back to simulations again. Do you have enough data to make simulations so that you can create more synthetic data that are from the right distribution? Obviously, that's the key. So you need enough real world data in order to be able to create those kinds of generator, data generators. And I think that we're at that step at the moment.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

13. “Well, I mean, if you look at the history of the last decade or 15 years, it's been, I mean, maybe, I don't know, 80, 90% of the breakthroughs that underpins modern AI field today was from originally Google Brain, Google Research, and DeepMind. So, yeah, I would back that to continue, hopefully.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

14. “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. I would say it's kind of 50-50 whether new things are needed or whether the scaling the existing stuff is going to be enough. And so in true kind of empirical fashion, we're pushing both of those as hard as possible. The new blue sky ideas and, you know, maybe about half our resources are on that. and then scaling to the max the current the current capabilities and um we're still seeing some you know fantastic progress on uh each different version of gemini”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

15. “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 Shazir, who 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 an alpha go or transformers, I would back us to be the place that does that. So I'm actually quite like it when the terrain gets harder, right? Because then it veers more from just engineering to true research. And”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

16. “Yeah, I don't think anyone has systems that can have shown unequivocally those big leaps. We have a lot of systems that do the hill climbing of the S-curve that you're currently on.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

17. “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? Could it have done that back in 2017 when we did it and brain did it? And it's not clear that these systems, something AlphaVolve wouldn't be able to make such a big leap. So for sure, these systems are good. We have systems, I think, that can do incremental hill climbing. And that's a kind of bigger question about, is that all that's needed from here or do we actually need one or two more big breakthroughs?”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

18. “To do with that, how to narrow that down to something tractable. And I think there's similar version of yourself. That's too unconstrained. But we've done it. And as you know, with Alpha Evolve, things like faster matrix multiplication. So when you hone it down to 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.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

19. “I mean, potentially that's possible, I would say. I'm not sure it's even desirable because that's a kind of like hard takeoff scenario. But these current systems like Alpha Evolve, they have 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 conjectural hypothesis. They're good if you give them very specific instructions about what you're trying to do. But if you give them a very vague high-level instruction, that wouldn't work currently. And I think that's related to this idea of invent a game as good as go, right? Imagine that was the prompt. That's pretty underspecified. And so the current systems wouldn't know, I think, what.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

20. “Yeah, and they're going to be pretty complicated to do. But of course, it will be, you can imagine also AI systems that are producing that code or whatever that is. And then human programmers looking at it, but also not unaided with the help of AI tools as well. So it's going to be kind of an interesting, you know, maybe different AI tools to the ones that the more kind of monitoring tools to the ones that generated it.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

21. “Yeah, sure. There could be. But then afterwards they'll figure out with their intuition that why this works. And then empirically, the nice thing about games is one of the great things about games is it's a sort of scientific test. Do you win the game or not win? And then that tells you, okay, that move in the end was good. That strategy was good. And then you can go back and analyze that and explain even to yourself a little bit more why explore around it. And that's how chess analysis and things like that works. So perhaps that's why my brain works like that, because I've been doing that since I was four. And your training, you know, is sort of hardcore training in that way.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

22. “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 best human scientists, but it may be a bit like, for example, in chess, if I was to talk to Gary Kasparov or Magnus Carlson and play a game with them and they make a brilliant move, I might not be able to come up with that move, but they could explain why afterwards that move made sense. And we would be able to understand it to some degree, not to the level they do, but if they were good at explaining, which is actually part of intelligence too, is being able to explain in a simple way that what you're thinking about, I think that that will be very possible for the best human scientists.”

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23. “Not to Like that exactly. It's like, what is this amazing new physics idea? And then we would probably check it with world experts in that domain, right? And validate it and kind of go through its workings. And I guess it would be explaining its workings too. Yeah, be an amazing moment.”

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24. “Several of those things, right, for it to be very general, not just one domain. And so I think that would be the signs, at least that I would be looking for, that we've got a system that's AGI level. And then maybe to fill that out, you would also check the consistency, you know, make sure there's no holes in that system either.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

25. “So, I think 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 a 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 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 says 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.”

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26. “And then we have some missing, I think, capabilities like sort of the true invention capabilities and creativity that we were talking about earlier. So you'd want to see those. How you test that, I think you just test it one way to do it would be kind of brute force test of tens of thousands of cognitive tasks that We know that humans can do and maybe also make the system available to a few hundred of the world's top experts, the Terence Towers of E.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

27. “My estimate is sort of 50% chance by in the next five years. So, you know, by 2030, let's say. And so I think there's a good chance that that could happen. Part of it is, what is your definition of AGI? Of course, people are arguing about that now. And mine's quite a high bar and always has been of like, can we match the cognitive functions that the brain has, right? So we know our brains are pretty much general Turing machines approximate. And of course we created incredible modern civilization with our minds. So that also speaks to how general the brain is. And for us to know we have a true AGI, we would have to like make sure that it has all those capabilities. It isn't kind of a jagged intelligence where some things that's really good at like today's systems, but other things it's really flawed at. And that's what we currently have with today's systems. They're not consistent. So you'd want that consistency of intelligence across the board.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

28. “Yeah, hopefully they are. And I love to join them on one of those checks. They look amazing, right? To actually experience it one time.”

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29. “Yes, we've created the best weather prediction systems in the world, and they're better than traditional fluid dynamics sort of systems that usually 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 neck system. And again, it's interesting that those kinds of dynamics can be modeled, even though they're very complicated, almost bordering on chaotic systems in some cases. A lot of the interesting aspects of that can be modeled by these neural network systems, including very recently we had cyclone prediction of where paths of hurricanes might go. Of course, super useful, super important for the world. And it's super important to do that very timely and very quickly, and as well as accurately. And I think it's very promising direction, again, of simulating so that you can run forward predictions and simulations of very complicated real world systems.”

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30. “Question for a long time. Exactly. So we're quite, I guess we've developed a lot of mechanisms to cope with this 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, did we keep ourselves distracted?”

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31. “I mean, this is my whole reason why I've worked on AI and AGI my whole life, because I think it can be the ultimate tool to help us answer these kind of questions. And I don't really understand why 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, and quantum mechanics weirdness, it's just, to me, I've always had this sort of screaming at me into my face. And that's getting louder. You know, it's like, how, what is going on here? And I mean that in the deeper sense, like in the nature of reality, which has to be the ultimate question that would answer all of these things. It's sort of crazy if you think about it. We can stare at each other and all these living things all the time we can inspect it with microscopes and take it apart almost down to the atomic level and yet we”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

32. “A very long time before they captured mitochondria somehow, right? I don't see why not why AI couldn't help with that, some kind of simulation. Again, it's again, it's a bit of a search process through a combinatorial space. Here's like all the 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?”

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33. “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, you know, of course, that's where alpha fold stuck kicks in. So that would be kind of the basis. And then you'd build these higher level simulations that take those as building blocks and then you get the emergent behavior”

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34. “So that would be hard. So, you'd probably need several simulated systems that can interact at these different temporal dynamics, or at least maybe it's like a hierarchical system. So you can jump up or down the different temporal stages.”

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35. “To the kind of static picture of what is 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 TOR pathway that's involved in cancer or something like this. And then eventually you might be able to model a whole cell.”

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36. “What those experiments 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's the dream. And so, but maybe now, finally, so I was trying to build these components, Alpha Fole 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 the yeast cell is like a full organism that'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.”

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37. “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 crazy ambitious dream. But the trick is, how do you break it down into manageable, achievable interim steps that are meaningful and useful in their own right? And so Virtual Cell, which is what I call the project of modeling a cell, I've had this idea, you know, of wanting to do that for maybe more like 25 years. And I used to talk with Paul Nurse, who is a bit of a mentor of mine in biology. He runs the, you know, founded the Quick Institute and won the NOAA Prize in 2001. We've been talking about it since before the, you know, in the 90s. And I used to come back to it every five years. It's like, what would you need to model the full internals of a cell so that you could do experiments on the virtual cell?”

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38. “That's right. So when you do 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, and you learn something, you can learn something kind of equally valuable from an experiment that doesn't work, that should tell you if you've designed an experiment well and your hypotheses are interesting, it should tell you a lot about where to go next. And then you're effectively doing a search process and using that information in very helpful ways.”

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39. “That sweet spot, right? Of basically advancing the science and splitting the hypothesis space into two, ideally, right? Whether if it's true or not true, you've learned something really useful. And that's hard and making something that's also falsifiable and within sort of the technologies that you have you currently have available. So it's a very creative process actually, highly creative process that I think just a kind of naive search on top of a model won't be enough for that.”

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40. “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, they 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 heartest part of science and making the right hypothesis. And that's what today's systems definitely, they can't do. So, you know, I often say it's harder to come up with a conjecture, a really good conjecture than it is to solve it. So we may have systems soon that can solve pretty hard conjectures. I am Mass Olympiad problems, alpha proof last.”

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41. “And it's amazing, which is a relatively simple algorithm, right? 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.”

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42. “Yeah, exactly. So you can get a bit of an extra property out of evolutionary systems, which is some new emergent capability may come about. Of course, like to happen 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 emergent properties. You always had a sort of subset of the properties that you put into the system. But maybe if we combine them with these foundation models, perhaps we can overcome that limitation. Obviously, naturally evolution clearly did because it did evolve new capabilities, right? So bacteria to where we are now. So clearly it must be possible with evolutionary systems to generate new patterns going back to the first thing we talked about.”

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43. “You. Obviously, this is super relevant for scientific discovery or pushing science and medicine forward, which we want to do with these systems. And you can actually bolt on some fairly simple search systems on top of these models and get you into a new region of space. Of course, you also have to make sure that you're not searching that space totally randomly. It would be too big. So you have to have some objective function that you're trying to optimize and heel climb towards and that guides that search.”

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44. “Yes. So if you think about breaking down the solar systems we've built to their really fundamental core, you've got like the model of the underlying dynamics of the system. And then if you want to discover something new, something novel that hasn't been seen before, then you need some kind of search process on top to take you to a novel region of the search space. And you can do that in a number of ways, evolutionary computing is one. With AlphaGo, we just use Monte Carlo Tree Search, right? And that's what found MOVE 37, the new kind of never seen before strategy in Go. And so that's how you can go beyond potentially what is already known. So the model can model everything that you currently know about, right? Or the data that you currently have. But then how do you go beyond that? So that starts to speak about the ideas of creativity. How can these systems create something new, discover something?”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

45. “Yes, exactly. So LLMs are kind of proposing some possible solutions, and then you use evolutionary computing on top to find some novel part of the search space. So actually, I think it's an example of very promising directions where you combine LLMs or foundation models with other computational techniques, evolutionary methods is one, but you could also imagine Monte Carlo tree search. Basically, many types of search algorithms or reasoning algorithms sort of on top of or using the foundation models as a basis. So I actually think there's quite a lot of interesting things to be discovered probably with these sort of hybrid systems, let's call them.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

46. “Again, in the 90s, all of the most interesting technical advances were happening in gaming, whether that was AI, graphics, physics engines, hardware, even GPUs, of course, were designed for gaming originally. So everything that was pushing computing forward in the 90s was due to gaming. So interestingly, that was where the forefront of research was going on. And it was this incredible fusion with art, graphics, but also music and just the whole new media of storytelling. And I love that. For me, it's this sort of multidisciplinary kind of effort is, again, something I've enjoyed my whole life.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

47. “I don't know. It's an interesting one. I mean, we both love games, and it's interesting he wrote games as well to start off with. It's probably, especially in the era I grew up in where home computers just became a thing in the late 80s and 90s, especially in the UK. I had a spectrum and then a Commodore Amiga 500, which was my favorite computer ever. And that's why I learned all my programming. And of course, it's a very fun thing to program is to program games. So I think it's a great way to learn programming probably still is. And then, of course, I immediately took it in directions of AI and simulations, which, so I was able to express my interest in games and my sort of wider scientific interests all together. And then the final thing I think that's great about games is it fuses artistic design, art with the most cutting edge programming.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

48. “What's up there? Well, my favorite one of all time is civilization, I have to say that was the civilization one and civilization two, my favorite games of all time”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

49. “Exactly. Is that valuable? Yes. And I guess that's maybe the thing that's been haunting me, obsessing me from the beginning of my career. If you think about all the different things I've done, they're all related in that way. The simulation, nature of reality, and what is the bounds of what can be modeled.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

50. “Also, enjoy and experience the physical world. But the question is then, you know, I think we're going to have to kind of confront 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.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source