Demis Hassabis on AI, game theory, multimodality, and the nature of creativity

26:33Well, I've always been a huge proponent of simulations and simulations and AI. And, you know, it's also interesting to think about what the real world is, right, in terms of a computational system. And so I've always been involved with trying to build very realistic simulations of things. And now, of course, that interacts with AI because you can have an AI that learns a simulator of some real world system just by observing that system or all the data from that system. So I think the current debate is to do with these large foundation models now pretty much use the whole Internet. Right. And so then once you've tried to learn from those, what's left?

27:18Right. That's all the language that's out there. Of course, there's other modalities like video and audio. I don't think we've exhausted all of that kind of multimodal tokens, but even that will reach some limit. So then the question comes with, can you generate synthetic data? And I think that's why you're seeing quite a lot of progress with maths and coding, because in those domains, it's quite easy to generate synthetic data. The problem with synthetic data is, are you creating data that is from the right distribution, the actual distribution, right? Does it mimic the kind of real distribution?

27:51And also, are you generating data that's correct, right? And of course, for things like maths, for coding and for things like gaming, you can actually test the final data and verify if it's correct, right? Before you feed it in as input into the training data for a new system. So it's very amenable, certain areas. In fact, it turns out the more abstract areas of human thinking that you can verify and prove that it's correct. And so therefore, that unlocks the sort of ability to create a lot of synthetic data.

28:39so one of the things that you know is kind of also in addition to the kind of the frequent discussion around you know data how do we get more but one of the the questions is in order to do ai is it important to actually have it embedded in the world yeah well interestingly if we if we talked about this um five years ago or or certainly 10 years ago i would have said that some real world experience you know uh maybe through robotics usually when we talk about embodied intelligence we meaning robotics but it could also be a very accurate simulator right like some kind of ultra realistic game uh environment would be needed to fully understand the, say, the physics of the world around you, right?

29:27And the physical context around you. And there's actually a whole branch of neuroscience that is predicated on this. It's called action in perception. So this is the idea that one can't actually fully perceive the world unless you can also act in it. And the kinds of arguments go is like, how can you really understand the concept of the weight of something, for example, unless you can pick things up and sort of compare them with each other. And then you get this sort of idea of weight, like, can you actually, you know, can you really get that notion just by looking at things? It seems, seems hard, right?

30:01Certainly for, for humans, like, I think you need to act in the world. So this is idea that acting in the world is part of your learning. You're kind of like an active learner. And in fact, reinforcement learning is like that because the decisions you make give you new experiences, but those experiences depend on the actions you took. But also those are the experiences that you'll, you'll then subsequently learn from. So in a sense, reinforcement learning systems are involved in their own learning process, right? Because they're active learners. And I think you can make a good argument that that's also required in the physical world.

30:38Now, it turns out, I'm not sure I believe that anymore, because now, you know, with our systems, especially our video models, if you've seen VO2, you know, our latest video models, completely state of the art, which we released late last year. And it kind of shocked even me that even though we're building this thing, that it can sort of basically by watching YouTube, a lot of YouTube videos, it can figure out, you know, the physics of the world. There's a sort of funny Turing test of, you know, in some sense, Turing test in verb commas of video models, which is, can you chop a tomato? Can you show a video of, you know, a knife chopping a tomato with the fingers and everything in the right place and the tomato doesn't magically spring back together or the knife goes through the tomato without cutting it, et cetera.

31:23And VO can do it. And if you think through the complexity of the physics, to understand what you've got to keep consistent and so on, it's pretty amazing. It's hard to argue that it doesn't understand something about physics and the physics of the world. And it's done it without acting in the world and certainly not acting as a robot in the world. Now, so, you know, it's not clear to me there is a limit now with just sort of passive perception. Now, the interesting thing is that I think this has huge consequences for robots as an embodied intelligence, as an application, because the types of models we've built, Gemini and also now VO, and we'll be combining those things together at some point in the future, is we've always built Gemini, our foundation model, to be multimodal from the beginning.

32:12And the reason we did that, and we still lead on all the multimodal benchmarks, is because for twofold. One is we have a vision for this idea of a universal digital assistant, an assistant that goes around with you on the digital devices, but also in the real world, maybe on your phone or a glasses device, and actually helps you in the real world, like recommend things to you, help you navigate around, help with physical things in the world like cooking, stuff like that. And for that to work, you obviously need to understand the context that you're in. It's not just the language I'm typing into a chatbot.

32:53You actually have to understand the 3D world I'm living in. I think to be a really good assistant, you need to do that. But the second thing is, of course, is exactly what you need for robotics as well. and we released our first big sort of Gemini robotics work, which has caused a bit of a stir. And that's the beginning of showcasing what we can do with these multimodal models that do understand physics of the world with a little bit of robotics fine tuning on top to do with the actions and the motor actions and the planning a robot needs to do. It looks like it's going to work. So actually now I think these general models are actually going to transfer to the embodied robotic setting without too much extra sort of special casing or extra data or extra effort, which is probably not what most people, even the top roboticists would have predicted five years ago.

33:44I mean, that's wild. And, you know, thinking about benchmarks and what we're going to need these digital assistants to do, like when we look under the hood of these big AI models, there's, well, some people would say it's attention. So the trade-offs is thinking time versus output quality. We need them to be fast, but of course we need them to be accurate. And so talk about like, what is that trade-off and how is that going in the world right now? Well, look, we, of course, we, we, we sort of, um, pioneered all that area of thinking, uh, systems because that's what our original gaming systems all did, right?

34:21Go alpha go, but actually most famously alpha zero, which was our follow-up system that could play any two player game. And there you always have to think about your time budget, your compute budget. you've got to actually do the planning part, right? So the model you can pre-train, just like we do with our foundation model today. So you can play millions of games offline and then you have your model of chess or your model of Go, whatever it is. And then, but at test time, at runtime, you've only got one minute to think about your move, right? One minute times however many computers you've got running.

34:53So that's still a limited compute budget. So what's very interesting today is there's this trade-off between do you use a more expensive, larger base model, foundation model, right? So in our case, you know, we have different size names like Gemini Flash or Pro or even bigger, which is Ultra, but those models are more costly to run. So they take longer to run. But they're more accurate and they're more capable. So you can run a bigger model with a shorter number of planning steps, or you can run a very efficient, smaller model that's slightly less powerful, but you can run it for many more steps, right?

35:28And it's actually currently what we're finding is it's sort of roughly about equal. But of course, what we want to find is some, is some, the Pareto frontier of that, right? Like actually the exact right trade-off of the size of the model and the expense of that running that model versus the amount of thinking time you want to, and thinking steps that you're, you're able to do per unit of compute time. And I think that's, that's actually fairly cutting edge research right now that I think all the leading labs are probably experimenting on. And I think there's not a clear answer to that yet. You know, all the major labs, DeepMind, others are all working intensely on coding assistance.

36:08And there's, you know, a number of reasons, everything from, you know, like, A, it's one of the things that accelerates productivity across the whole front. It has a kind of good fitness function. It's also, of course, one of the ways that, you know, everyone is going to be enhanced in productivity is having a software, you know, kind of co-pilot agent for helping. There's just a ton of reasons. Now, one of the things that gets interesting here is as you're building these, you know, obviously there's a tendency to start with these computer languages that have been designed for humans. What would be computer languages that would be designed for AIs or an agentic world or designed for this hybrid process of a human plus an AI?

36:49Is that a good world to start looking at those kind of computer languages? How would it change our theory of computation, linguistics, et cetera? I think we are entering a new era in coding, which is going to be very interesting. And, you know, as you say, all the leading labs are pushing on this frontier for many reasons. It's easy to create synthetic data. So that's another reason that everyone's pushing on this vector. And I think we're going to move into a world where, you know, sometimes it's called Vibe coding, where you're basically coding with natural language, really. And we've seen this before with computers.

37:26I remember when I first started programming in the 80s, we were doing Assembler. And then, of course, that seems crazy now. Why would you do machine code? You start with C and then you get Python and so on. And really, one could see is the natural evolution of going higher and higher up the abstraction stack of programming languages and leaving the lower, the more and more of the lower level implementation details to the compiler, in a sense. And now this is just, you know, one could just view this as the natural sort of final step is, well, we just use natural language. And then the whole, you know, everything is high level program, you know, super high level programming language.

38:05And I think eventually that's maybe what we'll get to. And the exciting thing there is that, of course, it will make accessible coding to a whole new range of people, creatives, right, who normally would, you know, designers, game designers, app writers, that would normally would not have been able to implement their ideas without the help of, you know, teams of programmers. So that's going to be pretty exciting, I think, from a creativity point of view. But it may also be very good, certainly in the next few years, for coders as well, because I think there's, and I think this in general with these AI tools is I think that the people that are going to get most benefit out of them initially will be the experts in that area who also know how to use these tools in precisely the right way, you know, whether that's prompting or interfacing with your existing code base, you know, there's going to be this sort of interim period where I think the current experts who embrace these new tools, whether that's filmmakers game designers or coders are going to be like superhuman for uh in terms of what they're able to do and i see that with some you know film uh you know uh directors and uh film designer uh friends of mine who are able to you know create pitch decks for example for new film ideas in a day on their own you know and then they can but it's very high quality pitch deck that they can pitch for a 10 million dollar budget for and normally they would have had to spend a few tens of thousands of dollars just to get to that pitch deck, which is a huge risk for them.

39:36So it becomes, you know, I think there's going to be a whole new incredible set of opportunities. And then there's the question of like, if you think about creative, the creative arts, whether there'll be new ways of working, much more fluid, instead of doing, you know, Adobe Photoshop or something, you're actually co-creating this thing with this fluid, responsive tool. And that could be kind of feel more like minority report or something, you know, I imagine with the kind of interface and there's this thing swirling around you and you're and you're kind of but it will require people to get used to a very new workflow to take like maximum advantage of that.

40:15But I think when they do, it will be probably incredible for those people. They'll be like 10x more productive. So I want to go back to the the world of multimodal that we were talking about before with sort of robots in the real world. And so right now, most AI doesn't need to be multimodal in real time because the internet is not multimodal. And for our listeners, that means absorbing many types of input, voice, text, vision at once. And so can you go deeper in what you think the benefits of truly real-time multimodal AI will be? And what are the challenges to get to that point? I think, first of all, we live in a multimodal world, right?

40:56And we have our five senses, and that's what makes us human. So if we want our systems to be brilliant tools or fantastic assistants, I think in the end, they're going to have to understand the world, the spatial temporal world that we live in, not just our linguistic maths world, right? Abstract thinking world. I think that they'll need to be able to act in and plan in and process things in the real world and understand the real world. I think that computer, you know, sort of the potential for robotics is huge. I don't think it's had its chat GPT or its alpha fold moment yet, say in science and language, right, or alpha go moment.

41:36I think that's to come. But I think we're close. And as we talked about before, I think in order for that to happen, I think that the shortest path I see that happening on now is these general multimodal models being eventually good enough. And maybe we're not very far away from that to sort of install on a robot, perhaps a humanoid robot with the cameras. Now there's additional challenges of you've got to fit it locally on maybe on the local chips to have the latency fast enough and so on. But, you know, as we all know, just wait a couple of years and those systems that stay of the art today will fit on a little mobile chip tomorrow.

42:13So I think it's very exciting multimodal from that point of view, robotics, assistance. And then finally, I think also for creativity, I think we're the first model in the world, Gemini 2.0, that you can try now in AI Studio that allows native image generation. So not calling a separate program, you know, in this separate model, in our case, Imogen 3, you know, which you can try separately, but actually Gemini itself natively coming up in the chat flow of images. And I think people seem to be really enjoying using that. So it's sort of like you're now talking to a multimodal chatbot, right? And so you can get it to express emotions in pictures, or you can give it a picture and then tell it to modify it and then continue to work on it with word descriptions.

43:02Can you remove that background? Can you do this? So this goes back to the earlier thing we said about programming or any of these creative things in a new workflow. I think we're just seeing the glimpse of that if you try out this new Gemini 2 experimental model of how that might look in image creation. And that's just the beginning. Of course, it will work with video and coding and all sorts of things. so in the land of the real world multimodal one of the things that you know frequently people speculate is you know geolocation of ai work and obviously in the u.s we intensely track everything that's happening on the west coast we also intensely track deep mind and then somewhat less mistral you know and others what's some of the stuff that's really key for the world to understand what's coming out of Europe, what's the benefit of having there be multiple major centers of innovation and invention, you know, not just within the West Coast, but also obviously DeepMind in London and Mistral and Paris and others.

44:10And what are some of the things to, for people to pay attention to why it's important and what's happening, especially within the UK and European AI ecosystem? We started DeepMind in London and still headquartered here for several reasons. I mean, this is where I grew up. That's what I know. It's where I had all the contacts that I had. But the competitive reasons were that we felt that the talent in the UK and in Europe was the coming out of universities was the equivalent of the top US ones. You know, Cambridge, my alma mater and Oxford, they're up there with the MITs and Harvard's and the Ivy Leagues, right?

44:50I think they're sort of, you know, they're always in the top 10 there together on the university world tables. But if you, this is certainly true in 2010, if you were coming, say you had a PhD in physics out of Cambridge, and you didn't want to work in finance at a hedge fund in the city, but you wanted to stay in the UK and be intellectually challenged, there weren't that many options for you, right? There were not that many deep tech startups. So we were the first really, and to prove that could be done. And actually we were a big draw for the whole of Europe. So we got the best people from the technical universities in Munich and in Switzerland and so on.

45:23And for a long while, that was a huge competitive advantage. And also salaries were cheaper here and then in the West Coast and you weren't competing against the big incumbents. And also it was conducive. The other reason I chose to do that was I knew the AGI, which was our plan from the beginning, solve intelligence and then use it to solve everything else. That was where we articulated our mission statement. And I still like that framing of it. It was a 20-year mission. And if you're on a 20-year mission, and we're now 15 years in, and I think we're sort of on track unbelievably, right, which is strange for any 20-year mission, but you don't want to be too distracted on the way in a deep technology, deep scientific mission.

46:08So one of the issues I find with Silicon Valley is lots of benefits, obviously, contacts and support systems and funding and amazing things and the amount of talent there, the density of talent. But it is quite distracting, I feel. Like everyone and their dog is trying to do a startup, you know, that they think is going to change the world, but it's just a photo app or something. And then, you know, the cafes are filled with this. Of course, it leads to some great things, but it's also a lot of noise if one actually wants to commit to a long-term mission that you think is the most important thing ever.

46:38And you don't want to be too, you know, you and your staff and want to be too distracted and like, oh, I could make a, maybe I could make a hundred million though, if I jumped and did this, you know, quickly did this gaming app or something. Right. And, and I think that's sort of the, the, the milieu that you're in, uh, in the Valley, at least, at least back then, maybe this is less true now that there's probably more mission focused, uh, startups now, but I also, I kind of also wanted to prove it could be done elsewhere. And then the final reason I think it's important is that AI is going to affect the whole world, right it's going to affect every industry it's going to affect every country it's it's going to be the most transformative technology ever in my opinion so so if that's true and it's going to be like electricity or fire you know more impactful than even the internet or or or mobile then i think it's important that the whole world participates in its design and and with the different value systems that we think are out there that are you know philosophies that are you know, are good philosophies.

47:39And, you know, from democratic values, you know, Western Europe, US, you know, I think it's important that it's not just a hundred square miles of, you know, a patch of California, right? I do actually think it's important that we get these other inputs, the broader inputs, not just geographically, but also, and I know you agree with this, Reid, like different subjects, philosophy, social sciences, economists, economists, academia, civil society, not just the tech companies, not just the scientists involved in deciding how this gets built and what it gets used for. And I feel that I've always felt that very strongly from the beginning.

48:16And I think having some European involvement and some UK involvement at the top table of the innovation is a good thing. So Demas, one of the areas of AI that when anyone asks me like, hey, Aria, I know you're interested in AI, but like, well, you can write my emails? Like, why is it so special? I just say, no, think about what it can do in medicine. I always talk about AlphaFold. I tell them about what Reed is doing. Like, I'm just so excited for those breakthroughs. Can you give us just a little bit? You had this seminal breakthrough in AlphaFold and what is it going to do for the future of medicine?

48:51I've always felt that what are the most important things AI can be used for? Right. And I think there are two. One is how human health, that's number one trying to solve and cure terrible diseases uh and then number two is to help with you know energy sustainability and climate the planet the planet's health let's call it right so there's humans health and then there's a planet's health and those are the two areas that we have focused on in our science group which i think is you know fairly unique amongst the ai labs actually in terms of how much we push that from the beginning and then and protein folding specifically was this canonical for me.

49:29I sort of came across it when I was an undergrad in Cambridge, you know, 30 years ago. And it's always stuck with me as this fantastic puzzle that would unlock so many possibilities. You know, the structure of proteins, everything in life depends on proteins and we need to understand the structure so we know their function. And if we know the function, then we can understand what goes wrong in disease and we can design drugs and molecules that will bind to the right part of the surface of the protein. If you know the 3D structure. So it's a fascinating problem. It goes to all of the computational things we were discussing earlier as well.

50:02Can you enumerate, can you see through this forest of possibilities, you know, all these different ways a protein could fold? You know, some people estimate that Levantel very famously in the 1960s estimated an average protein can fold in 10 to the 300 possible ways, right? So how do you, how do you get, you know, enumerate those astronomical possibilities? And yet it is possible with these learning systems. And that's what we did with our Hufferfold. And then we spun out a company, Isomorphic, and I know Reed's very interested in this area too with his new company of like, if we can reduce the time it takes to discover a protein structure from, it used to take a PhD student, their entire PhD as a rule of thumb to discover one protein structure.

50:43So four or five years. And there's 200 million proteins known to science and we folded them all in one year. So we did a billion years of PhD time in one year is another way you can think of it. And then gave it to the world, you know, freely to use. And, you know, two million researchers around the world have used it. And we spun out a new company, Isomorphic, to try and go further downstream now and develop the drugs needed and try and reduce that time. I mean, it's just amazing. I mean, Demis, there's a reason they give you the Nobel Prize. Thank you so much for all of your work in this area.

51:16It's truly amazing.

51:24And now to Rabbit Fire. Is there a movie, song or book that fills you with optimism for the future? There's lots of movies that I've watched that have been super inspiring for me. Things like, even like Blade Runner is probably my favorite sci-fi movie. But maybe it's not that optimistic. So if you want an optimistic thing, I would say the Culture series by Ian Banks. I think that's the best depiction of a post-AGI universe where, you know, AIs and you've basically got societies of AIs and humans and kind of alien species actually, and sort of maximum human flourishing across the galaxy. That's a kind of amazing, compelling future that I would hope for humanity.

52:13What is a question that you wish people asked you more often? The questions I sort of often wonder why people don't discuss a lot more, including with me, are some of the really fundamental properties of reality that actually drove me in the beginning when I was a kid to think about building AI, to help us sort of as ultimate tool for science. So for example, I don't understand why people don't worry more about what is time, what is gravity, or basically the fundamental fabric of reality, like which is sort of staring us in the face all the time all these very obvious things that impact us all the time and we we don't really have any idea how it works and i don't know why that it doesn't trouble people more it troubles me and uh and and you know i'd love to have more debates with people uh about those things but uh actually most people don't seem to you know they seem to sort of shy away from those topics where do you see progress or momentum outside of your industry that inspires you?

53:14That's a tough one because AI is so general. It's almost touching what industry is outside of the AI industry. I'm not sure there's many. Maybe the progress going on in quantum is kind of interesting. I still believe AI is going to get built first and then will maybe help us perfect our quantum systems. But I have ongoing bets with some of my quantum friends like Hartman-Nevin on, they're going to build quantum systems first, and then that will help us accelerate AI. So I always keep a close eye on the advances going on with quantum computing systems. Final question. Can you leave us with a final thought on what is possible over the next 15 years if everything breaks humanity's way?

53:58And what's the first step to get there? Well, what I hope for next 10, 15 years is what we're doing in medicine to really have new breakthroughs. And I think maybe in the next 10, 15 years, we can actually have a real crack at solving all disease, right? That's the mission of isomorphic. And I think with AlphaFold, we showed what the potential was to sort of do what I like to call science at digital speed. And why couldn't that also be applied to finding medicines? And so my hope is 10, 15 years time, we'll look back on the medicine we have today, a bit like how we look back on medieval times and how we used to do medicine then.

54:41And that would be, I think, the most incredible benefit we could imagine from AI.

54:56Possible is produced by Wonder Media Network. It's hosted by Aria Finger and me, Reid Hoffman. Our showrunner is Sean Young. Possible is produced by

55:14Jenny Kaplan is our executive producer and editor. Special thanks to And a big thanks to And a big thanks to Thank you.