How to Build the Future: Demis Hassabis

Demis Hassabis has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads Google DeepMind, pushing toward the same goal he set as a teenager: AGI. On this special live episode of How to Build the Future, he sat down with YC's Garry Tan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve and what the next big scientific breakthroughs might be. Chapters:00:00 — Intro00:46 — Demis Hassabis: From Chess Prodigy to DeepMind01:48 — What’s Missing Before We Get To AGI?03:36 — Why Memory Is Still Unsolved06:14 — How AlphaGo Shaped Gemini08:06 — Why Smaller Models Are Getting So Powerful10:46 — The 1000x Engineer12:40 — Continual Learning and the Future of Agents13:32 — Why AI Still Fails at Basic Reasoning15:33 — Are Agents Overhyped or Just Getting Started?18:31 — Can AI Become Truly Creative?20:26 — Open Models, Gemma, and Local AI22:26 — Why Gemini Was Built Multimodal24:08 — What Happens When Inference Gets Cheap?25:24 — From AlphaFold to the Virtual Cells28:24 — AI as the Ultimate Tool for Science30:43 — Advice for Founders33:30 — The AlphaFold Breakthrough Pattern35:20 — Can AI Make Real Scientific Discoveries?37:59 — What to Build Before AGI ArrivesApply to Y Combinator: https://www.ycombinator.com/applyWork at a startup: https://www.ycombinator.com/jobs

• 0:00Speaker 00

  Continue learning, long-term reasoning, some aspects of memory. These are still unsolved. I think all of these are going to be required for AGI. Depending on what your AGI timeline is, you know, mine's like 2030 or something like this. Then if you start off on a deep tech journey today, you have to just consider AGI appearing in the middle of that journey. It's not bad necessarily, but you have to take that into account. You have to have an active system that can actively solve problems for you to get to AGI. So agents are that path. And I think we're just getting going.

• 0:39Speaker 01

  Demis Hassabis has had one of the most unusual careers in tech. He was a chess prodigy as a kid, then designed his first hit video game, Theme Park, at 17. He then went back to school, got a PhD in cognitive neuroscience, published foundational work on how memory and imagination work in the brain. And then in 2010, co-founded DeepMind with one mission, solve intelligence. And I think they've done it.

• 1:11Speaker 01

  Since then, his lab has gone on to do things most people thought were decades away. AlphaGo beat a world champion at Go. AlphaFold cracked protein structure prediction, a 50-year grand challenge in biology. And they gave it away for free to every scientist on Earth. That work won him the Nobel Prize in Chemistry last year. Today, Demis leads Google DeepMind, where he's building Gemini and pushing toward the same goal he set when he was a teenager, artificial general intelligence. Please welcome Demis Hassabis.

• 1:53Speaker 01

  So you've been thinking about AGI longer than almost anyone. When you look at the current paradigm, large-scale pre-training, RLHF, chain of thought. How much of the final architecture for AGI do you think we already have? And what's fundamentally missing right now?

• 2:08Speaker 00

  Well, first of all, thanks, Gary, for that great introduction. And it's great to be here. Thanks for welcoming here. It's amazing space, actually. I'm going to have to come back here often. Very inspiring that you will get to work in this space. So the question is, I think the components that you just mentioned, I'm pretty sure will be part of the final architecture for AGI. So I think they've come such a long way now. And we've proven out so many things about what they can do. I can't see a world in which we will sort of realize in a couple of years, this was a dead end. That doesn't make sense to me. But there still might be one or two things missing on top of what you've, you know, what we already know works.

• 2:48Speaker 00

  So continual learning, long term reasoning, some aspects of memory, these are still unsolved. And how to get the systems to be more consistent across the board? I think all of these are going to be required for AGI. Now, it might be that the existing techniques can just scale up to that with some innovation and some incremental innovation. But it could be that there's still one or two big ideas left that need to be cracked. I don't think it's more than one or two, if there are out there. And I think, you know, my betting is about 50 50, if that's the case. So of course, at DeepMind, at Google DeepMind, we work on both those things.

• 3:29Speaker 01

  I guess that's so, I mean, working with a bunch of adentic systems, the wildest thing to me is to what degree it's the same weights over and over. So this idea of continual learning is so interesting because like, you know, right now, we're sort of cobbling it together with duct tape.

• 3:43Speaker 00

  Yes.

• 3:44Speaker 01

  These dream cycles at night and things like that.

• 3:46Speaker 00

  Yeah, it's pretty cool, the dream cycles. And we used to think about this with consolidation with episodic memory. Actually, that's what I studied for my PhD is how the hippocampus works and integrates, you know, new knowledge gracefully into the existing knowledge base. So the brain does that amazingly well. It does it, you know, during sleep, especially things like REM sleep, replaying back episodes that are important so that you can learn from it. In fact, our very first Atari program, DQN, one of the ways it was able to master Atari games was by doing experience replay. So we sort of borrowed that from neuroscience and replayed successful trajectories many times. You know, there's Way back in 2013 now, in the in the dark ages of AI.

• 4:31Speaker 00

  It was a really important thing and and I agree with you. We're kind of using duct tape right now. So like shove it all in the context window. This but this seems a bit unsatisfying, right? And actually, even though we're working on machines, not biological brains. And so potentially you could have, you know, millions or tens of millions size context window or memory. And it can be perfect. There's still a cost to looking it up and finding the right thing. That that's actually relevant for the specific decision you've got to make right now. And that's non trivial, that cost, even if you can potentially store it all. I think there's actually a lot of room for innovation in in areas like memory.

• 5:12Speaker 01

  Yeah, I mean, the wild thing is it feels like a million token context ones is actually bigger than. I mean, it's plenty big, honestly, you can do so.

• 5:19Speaker 00

  Well, it's it's plenty big for for for most things that it should be useful. I mean, if you think about the context windows sort of equivalent to working memory. You know, humans have, we have like a few digits, you know, it's like a dozen digits, maybe, you know, average of seven. We got a million or, you know, 10 million context windows. But the problem is, is that we're trying to store everything in that. You know, things that aren't and not important, things that are wrong. It's pretty brute force currently, and that doesn't seem right. And then the problem is, if you're now trying to try and process live video. And you're just going to naively record all the tokens, then actually a million tokens isn't that much. It's only like 20 minutes.

• 5:59Speaker 00

  So actually, you need more if you want something that's going to understand your, you know, your what's going on in your life over maybe a month or two.

• 6:07Speaker 01

  DeepMind has historically leaned into reinforcement, learning and search. AlphaGo, AlphaZero and MuZero. How much of that philosophy is actually embedded in? How you're building Gemini today is RL still underrated?

• 6:23Speaker 00

  Yeah, I think potentially it is. It sort of goes in ebbs and waves. We've worked on agents since the beginning of DeepMind. In fact, that's what we said we were working on. So all of the Atari work and AlphaGo, most specifically, their agent systems. And what we meant by that is systems that are able to accomplish goals on their own and make active decisions and make plans. And so, of course, we were doing it in the domain of games to make it tractable and then doing increasingly complex games, things like StarCraft after AlphaGo, AlphaStar. So we basically did all the games that are out there. And then, of course, the question is, can you generalize those models to be world models

• 7:07Speaker 00

  or models of language, not just models of simple games or even complex games? And that's what the last few years has been about. But really, you can think of a lot of the things we're doing today, all the leading models, with thinking modes and chain of thought reasoning as aspects of what was pioneered with AlphaGo coming back now. And I actually think there's a lot of work we did back then that is relevant today. And we're relooking at some of those old ideas at scale today in a more general way, including things like Monte Carlo tree search and other ways of augmenting the RL on top of the reinforcement learning we're ready to do today. And I think a lot of those ideas, both from AlphaGo and AlphaZero,

• 7:50Speaker 00

  are really, really relevant to where we are with today's foundation models. And I think a lot of that is what we're going to see of the advances the next few years.

• 7:59Speaker 01

  One question I would have, obviously, today you need bigger and bigger models to be smarter and smarter, but then we're also seeing distillation working. And then smaller models can be quite a bit faster. I think you guys have incredible flash models that are like 90... You're finding that they're 95% as good as the Frontier and at like 1/10 the price, is that right?

• 8:22Speaker 00

  I think that's one of our core strengths is, I mean, you have to build the biggest models to have the Frontier capabilities. But I think one of our biggest strengths has been distilling and packing that power into smaller and smaller models very quickly. Obviously, we invented the kind of distillation process and people like Jeff and Oreo and others.

• 8:44Speaker 00

  Still world experts in that. And we also have a huge need to do it because we've got to serve the biggest probably AI surfaces there are. Obviously, there's search with AI overviews and AI mode, then there's Gemini app. And now increasingly, every single product at Google has, you know, maps and YouTube and so on, has some aspect of Gemini or Gemini related technology in it. And so that's billions of users, a dozen, more than a dozen billion user products. And they have to be served extremely fast, extremely efficiently and cheaply and with low latency. So that that gives us a really important incentive to to make these flash and even smaller models, flashlight models, extremely efficient. And

• 9:29Speaker 00

  hopefully that ends up then being really useful for many of the workloads that all of you do use for.

• 9:34Speaker 01

  I'm curious about how much smarter these smaller models can actually be. Like, are there limits to the distillation process? Like, could a 50B or 400B model be as smart as like a Mythos for today?

• 9:48Speaker 00

  Yeah, I don't I don't see any, I don't think we've got to any kind of, or at least none of us know yet if we've got to any kind of informational limit. I mean, maybe at some point that will be the case where there's just an information density that can't, we can't get beyond. But I think for now, there's the the assumption we make is that, you know, a year later, after one of our leading, you know, pro models or frontier models goes out, half a year later, a year later, you'll have them in the the really tiny, almost edge models. And you also see some of that goodness in our Gemma models, which hopefully all enjoying our Gemma four models, which I think are really amazing power for their sizes. So again, that uses a lot of this, these distillation techniques and and the idea of

• 10:31Speaker 00

  how to make things really efficient in these very small models. So I don't really see any limit yet in terms of like some kind of theoretical limit. I think we're still pretty far off of that.

• 10:39Speaker 01

  That's amazing. I mean, that is really good. You know, one of the weirder things that we're seeing right now is like engineers can do like 500 to 1,000 times the amount of work that they were doing like six months ago, I guess. I mean, the people in this room, there are people who are doing about like 1,000x the work that like, Steve Yegi talks about this, it's like 1,000x the work that a Google engineer from the 2000s was doing.

• 11:04Speaker 00

  I think it's very exciting. I mean, I think the small models have many uses. One is obviously cost, but the speed can allow, you know, if you think about coding even or other things, you can iterate a lot faster also, especially if there's, if you're collaborating with the system. I think there's a, there's a, a, a lot of need for having fast systems that maybe are not quite frontier, like you said, like 95%, 90%, but that's plenty good enough. And actually you gain back more than the 10% on the, the iteration speed. So, and then the other big thing I think is running these things on the edge. Again, for efficiency reasons, but also for privacy and security reasons too. If you think about different devices that you might run these systems on,

• 11:46Speaker 00

  that, that, you know, process very personal information, you can also think about robotics as well. You know, robots in your house, I think you're going to want very efficient, very powerful local models, which maybe are orchestrated, you know, with some bigger models, frontier models that are in the, in the cloud. But you only delegate to that in certain circumstances. And perhaps you, you know, you process all of the audio visual feed, let's say locally, and that stays local. I could imagine that would be a very good sort of end state.

• 12:18Speaker 02

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• 12:33Speaker 01

  Going back to context and memory, models currently stateless, but you know, continual, like, what would the developer experience even be like for someone who's using a continual learning model? Like, you know, any idea like how you'd steer it?

• 12:46Speaker 00

  I think it's really interesting. I think that's one of the not having continual learning currently is one of the things holding back agents from doing full tasks. You know, I think they're really useful for aspects of tasks right now, and you can patch them together and do some really cool things, but they don't adapt well with the context that you're in. And I think that's the missing piece for them being really kind of fire and forget, and they'll figure it out themselves. You know, I think they need to be able to learn about the specific context that you're gonna put them in. So I think we have to crack that to get full general intelligence.

• 13:26Speaker 01

  Where are we on reasoning? So, models can do really impressive chain of thought now, but they still fail on things a smart undergrad wouldn't. What specifically needs to change and what progress do you expect in reasoning?

• 13:37Speaker 00

  There's a lot of innovation left in the thinking paradigms, I would say. Again, I think we're fairly, we're doing fairly simplistic things, fairly brute force. One could imagine, I think there's a lot of scope, for example, in monitoring the chain of thought, maybe interjecting midway through a thought process. I often get the impression with our systems and our competitor systems that they're almost overthinking, they're almost getting into sort of loops of things. Like, one thing I sometimes like to do is play chess against Gemini, and you know, it's, all the leading foundation models are pretty poor at games, which is quite interesting. It's very cool to kind of look at the thinking

• 14:18Speaker 00

  traces, because obviously these can be a well-understood, you know, I can tell quite quickly if it's going off on a tangent, and it's very sort of provable what the, what the thinking is doing, whether it's useful or not. And so, what we see is that, you know, sometimes it will, it will, it will consider a move, it will realize it's a blunder, but it can't find anything better, so it kind of goes back to that move and does it anyway. So, you know, you just shouldn't Be seeing that happening in a in a very precise reasoning system. So there's just sort of huge gaps, I think, still, but it may only be one or two tweaks that are required to fix those kind of gaps, just to be clear. But I think that's pretty, pretty obvious there are there. And that's why you get this kind of jagged intelligence.

• 15:02Speaker 00

  You know, on the one hand, it can solve gold medal problems in IMO, which is super hard. But on the other hand, as we've all seen, it can still make basic elementary maths errors if you pose the question in a certain way. Right. So or elementary reasoning errors. So there's just something to me about the almost an introspection about its own thought process that I feel like there's, there's something maybe missing there.

• 15:26Speaker 01

  Agents are really big. Some would say they're hyped. I personally think they're just getting started. Totally insane. What does DeepMind's internal research tell you about where agent capabilities actually are right now versus, you know, sort of the hype out there?

• 15:40Speaker 00

  I think we are. I agree with you. I think we're just at the beginning. You have to have an active system that can actively solve problems for you to get to AGI. That was always clear to us. So agents are that path. And I think we're just getting going. I think all of us are getting used to how do we best work? And you're leading the way in a lot of this in your own personal experiments. I saw many of you are doing that. I think how do you incorporate it into your workflow in a way that isn't just sort of a nice to have, but actually starting to do fundamental things? My impression is at the moment, we're all, you know, we're experimenting a lot of things, but we're only in the maybe the last couple of months, starting to find the really valuable places. And the technology is probably only getting good enough for that to be the case, right?

• 16:23Speaker 00

  Where that it's not a kind of toy, nice demonstration, but actually really adding value to your, to your, to your time inefficiency. I often wonder, I see a lot of people working on like setting off, you know, dozens of agents for like 40 hours. But I'm not sure I've seen the output that yet of that quite justify that level of input going in. But I think it will come. So I still think we're in the experimentation phase. We haven't seen a AAA game that tops the App Store. Charts that was sort of vibe coded yet, right? I've seen and I've programmed and I'm sure many, we've all done little nice demonstrations and it's like amazing. I can do a prototype of theme park in half an hour now, which took me six months back when I was seventeen.

• 17:07Speaker 00

  It's kind of mind blowing and I, and I wish I, I got this feeling if I spent the whole summer working on it. You could make something really incredible, but it still needs craft and you know, human sort of soul into it and taste. I think that's, that's something that can, that's, you have to make sure you still bring that to, to whatever it is you're building. And I think it still shows like it's not quite there yet because why haven't we seen a, a kid making a hit game? That's, that sells ten million copies, right? That should be possible, given the effort that's gone in. So something's still somehow missing. Maybe it's to do with the process or maybe it's to do with the tools. I'm not quite sure. You all probably know better than me because I'm sure you're all experimenting on that.

• 17:48Speaker 00

  But I haven't seen the result yet, which I would expect once this is really delivering that full value. Which I think will come in the next six to twelve months.

• 17:57Speaker 01

  Some of it is like, how much of it will be autonomous versus, I mean, I don't think we'd see autonomous first. We would actually probably see people in this room operating a thousand X and then.

• 18:07Speaker 00

  That's what you should see first. And then many of you, you know, there'll be like games companies or, you know, other types of companies that have built some kind of best selling app, best selling game using these tools. That's what you should see first and then more of that will get automated.

• 18:25Speaker 01

  I mean, some of it is like, there's a human in there and then the human doesn't want to say that the, the, the agents did it yet.

• 18:31Speaker 00

  I think part of it might be though, that this, we want to discuss like creativity. What I often say about that is like, if we look at the things we've done like AlphaGo. So obviously very famously, you all know about the move thirty seven in game two. And for me, I was waiting for a moment like that to start the science projects like AlphaFold. So we started AlphaFold like the day we got back from Seoul, which is ten years ago now. I'm going to Korea after this to celebrate the ten year anniversary of AlphaGo. But it's not enough to come up with move thirty seven. Like that's pretty cool, very useful, but can it invent go? That's what I want. I want a system that can invent go if you give it a high level description.

• 19:13Speaker 00

  You know, like a game you can learn the rules of in five minutes, but it takes many lifetimes to master. It's beautiful aesthetically, but you can play it in a few hours in an afternoon. So, you know, maybe you could imagine that would be the high level description I would give. And then I'd want the the return, the thing I get back is go, right? And clearly today's systems, I think, can't do that. So the question is why? And I think there's something still missing there.

• 19:42Speaker 01

  Well, someone in this room might might make it.

• 19:44Speaker 00

  Then the answer would be there's nothing missing. It just was the way we were using the systems and that might actually be the answer. It might be that our today's systems are capable of that with a brilliant enough creative person using it and providing that impetus, that the soul of the project and being able to probably being O'fay enough with the tools to like almost be at one with the tools. I could imagine that would be happening if you experimented with the tools all day and all night, like probably many of you are doing that. And you combine that with proper deep creativity, something, you know, more incredible could be done.

• 20:19Speaker 01

  Switching gears to open source, I mean, or open, open and open weights. I mean, the recent release of Gemma, you're making highly capable, open and accessible ones that can actually run locally. What do you think that means for, will AI be something that is in the hands of the users instead of primarily in the cloud? And does that change who gets to, you know, build with these models?

• 20:42Speaker 00

  We're huge proponents of, in general, of open source and open science. And you mentioned Alpha Fold at the beginning, you know, we put that all out there for free. And all of our science work, even still today, we publish in, you know, the big journals. We wanted to create world leading models for their their sizes, right? And so that's what hopefully we've done with Gemma and we're, you know, very committed to that path. And hopefully you all experiment and build and enjoy using Gemma. I think it's been like 40 million downloads now and just in, you know, two and a half weeks. So we're really excited about that. And I also think it's important for there to be Western stacks. On open source, you know, obviously, a lot of the Chinese models are excellent and and they're currently what we're leading in open source.

• 21:25Speaker 00

  And we think Gemma is very competitive for its sizes uh in in all those respects. And for us, I mean, there is a question of resources, talent and compute, like, nobody has enough spare compute to just make two, you know, uh frontier models at maximum size, right, with different attributes. So that's pretty difficult. But also, for what for now, what we've we've decided is that our edge models, the things we want to use for Android and glasses and robotics, um it's best that they're open models because they're vulnerable anyway on the surf, once you put them out on the surfaces. So they might as well be actually fully open, right? So we've sort of made a decision to kind of unify that uh at the at the kind of, we call it nano size level.

• 22:10Speaker 00

  And so that actually works for us strategically as well. Um and, you know, we hope as many people as possible build on it. And of course, we'll be building on that, too.

• 22:19Speaker 01

  Earlier, uh before we came on, I got to show you a demo of uh my version of Samantha from her, which is

• 22:25Speaker 00

  Yes.

• 22:25Speaker 01

  Uh harrowing for me to try to demo something to you.

• 22:28Speaker 00

  Yeah.

• 22:28Speaker 01

  Um and it worked, which is amazing. Gemini was built multimodal and I spent a lot of time with a bunch of the models and I mean, the depth of the context and the tool use with speech directly to model.

• 22:41Speaker 00

  Yeah.

• 22:41Speaker 01

  There is nothing like, bar none, like the best one, actually.

• 22:44Speaker 00

  Yeah. Yeah, I think I think that's just sort of still a slightly underappreciated aspect of of of the Gemini series is we we started it being multimodal from the start. That made it a little bit more difficult, actually, to begin with, because then just focusing on text, for example. But I we believe we're going to gain from that in the long run. And I think we're seeing that now for uh uh things like world model building, so stuff like Genie that we build on top of Gemini. I think it's going to be really important for things like robotics. So this is why Gemini Robotics, which many of you probably played around with, I think it's going to be built on multimodal foundation models, the robotics models. And uh we think we have a sort of competitive advantage with with Gemini being so strong at multimodal. We're using

• 23:28Speaker 00

  It increasingly in things like Waymo. But also, if you imagine devices and assistants, that digital assistants that come with you into the real world, you know, maybe on your phone or glasses or some other device. It needs to understand the physical world around you and intuitive physics and the physical context you're in. And that's what our systems are extremely good at. And I think you found that's why you've enjoyed using it in your setup. We're planning to continue on that. And I think we're far and away the strongest models on those types of problems.

• 24:01Speaker 01

  So the cost of inference is dropping fast. What becomes possible when inference is essentially free? And how does that change what your team is actually optimizing for?

• 24:11Speaker 00

  Yeah, I'm not sure inference will ever be essentially free. I mean, there's sort of Jevon's paradox and other things about like, I think we'll just end up using, all of us will end up using whatever we can get our hands on. And you could imagine millions of agents, swarms of agents working together on things. So that's one way to use the inference. Or you could imagine single agents or groups, smaller groups of agents thinking for in multiple directions and then ensembling that. So we're experimenting with all these things, probably many of you are. All of that will use up any inference, I think, that's available. I mean, one day, maybe it can be almost cost zero. Certainly the energy, if we solve fusion or, you know, superconductors

• 24:54Speaker 00

  or, you know, optimal batteries or some set of those things, which I think we will do with material science. Energy costs will be essentially zero, but there'll still be the physical creation of the chips and other things. There'll be some bottleneck, at least for the next few decades, I think. And so if that's the case, they'll still be rationing on the inference side. You'll still have to use it, I think, efficiently.

• 25:17Speaker 01

  Yeah. Well, luckily, the smaller models are getting smarter and smarter, which is fantastic. We got a lot of bio and biotech founders in the audience. I can see a few. Alpha 3 took us beyond proteins to a broad spectrum of biomolecules. How close are we to modeling full cellular systems? Or is that still a fundamentally harder problem in a class of its own?

• 25:38Speaker 00

  Well, isomorphic labs, which we spun out from DeepMind after we did AlphaFold 2, which is going amazingly well, it's trying to build out not just AlphaFold, it's just one piece of the drug discovery process, as many of you know, but we're trying to do the adjacent biochemistry and chemistry to design the right compounds, with the right properties and so on. We'll have some big announcements very soon to talk about on that front. I think that's going really well. Eventually, you want a whole virtual cell. So I've talked about this in many of my science talks, about a full working simulation of a cell that you can perturb, and then the outputs of that would be close enough to experimental that it's useful, right?

• 26:23Speaker 00

  You could skip out a lot of the search steps and generate lots of synthetic data to train other models that then would predict things about, you know, real cells. And I think we're about 10 years away probably from something like a virtual cell, like a full virtual cell. You know, we're starting out, this is we're working on the DeepMind side, science side, on a, you know, virtual nucleus, cell nucleus first, because relatively self-contained. The trick with all of these things is can you pick a slice of the complexity? You know, eventually you want to model a human body, but can you model it down to the right level of detail? And what slice can you take out of it that will be self-contained enough?

• 27:04Speaker 00

  You can kind of model and approximate the inputs and outputs into that self-contained system, and then just focus on the self-contained system. So a nucleus is quite interesting from that perspective. Then the other issue is just there's not enough data yet. So you need data, and I talked to various, you know, top scientists about who work on electron microscopes and other imaging things. If we could image a live cell without killing the cell, that would be game-changing, obviously, because then you could convert it into a vision problem, which we would know how to solve. But at the moment, there are, at least I'm not aware of any techniques that can give you a kind of, you know, nanometer resolution, but without destroying, but in, you know, in a live dynamic cell.

• 27:49Speaker 00

  So you can see all the interactions, right? You can take static images at that resolution, obviously, really detailed now, and that's quite exciting. But it's not enough to turn it just into, just into a complex vision problem. So that's one way it could be solved. So it could be a hardware driven, data driven solution, or it could be that we build better learned simulators of these dynamical systems. So that's, that's the more modeling way of solving it.

• 28:17Speaker 01

  You've been looking at all kinds of science, not just bio. There's material science, drug discovery, climate modeling, mathematics. If you had to rank which scientific domain will transform the most dramatically the next five years, what's in your list?

• 28:30Speaker 00

  Well, they're also exciting. And that's why, I mean, that for me has been my main passion and always the reason why I've worked on AI for my whole career for thirty plus years now is to use AI as the ultimate tool. I always thought AI would be the ultimate tool for science and to advance scientific understanding, scientific discovery and things like medicine and just our understanding of the universe around us. So actually, when you mentioned our original way we used to articulate our mission statement, which is still the way we think about it is there was two steps to it. One was step one was solve intelligence, i.e. build AGI. And then step two was use it to solve everything else. We had to change that a bit over time because people were like, do you really mean solve everything else? And we did mean that. And I think people are sort of understanding what that means today.

• 29:14Speaker 00

  But specifically, I was meaning solve other what I call root node problems in science. So areas of science that would unlock whole new branches or avenues of discovery. And AlphaFold is the prototypical example of what we want to do. So over three million researchers around the world, pretty much every biology researcher in the world uses AlphaFold now. And I was told by some of my, you know, pharma executive friends that, you know, almost every drug discovered from now on will have used AlphaFold at some point in it, in the drug discovery process. So that's something we're very proud of. And it's the sort of impact that we hope to have with, with AI. But I do think it's just the beginning. I don't really see any area of science or engineering that this won't be

• 29:57Speaker 00

  able to be helpful with. And the ones you mentioned, I think we're almost like an AlphaFold one moment. So it's, we've got very promising results, but it's not quite solved the, the grand challenge yet in that domain. But I think we're going to have a lot to talk about in the next couple of years on all those areas you mentioned, materials, which I think is very exciting, all the way to mathematics.

• 30:18Speaker 01

  In science, I mean, it feels Promethean. It's like, here is this capability.

• 30:23Speaker 00

  I think so. I mean, of course, along with that, including the parable of Prometheus, we have to also be careful with how we use that and what we use it for. And also the misuse that can happen with those same tools.

• 30:36Speaker 01

  A lot of people in this room are trying to build companies applying AI to science. For them, what's the difference between a startup that actually advances the frontier in your view versus one that's just wrapping an API around a foundation model and calling it AI for science?

• 30:49Speaker 00

  Well, look, I think that's one of the things I would recommend. I'm trying to think about, and I think you mentioned this to me before. What would I do today myself if I was sitting in your place and Y Combinator, you know, looking at things? One thing you have to do is obviously intercept where the AI tech is going. So that's one hard part of it. But I do think there's huge scope for combining where AI is going with some other deep technology area. I just think that that sweet spot is whether it's materials or medicine or other really hard areas of science. I think that those kinds of interdisciplinary teams, especially if it involves the world of atoms as well, there's not going to be a shortcut to that, at least in the foreseeable future.

• 31:31Speaker 00

  Those are areas that are pretty safe from just getting swarmed by whatever the next update is to the foundation models. So I think if you're looking for things like that, that's one of the more defensible areas, I would say. And I've always loved deep tech, so I'm kind of biased towards deep tech things. I think nothing that's really long lasting and worthwhile is easy. And so I'm always being drawn to deep technologies. Obviously, AI was like that back in 2010 when we started out, right? It was, it was thought to just, we know, we know it doesn't work kind of thing is what I was told by investors. And even in academia, it was considered to be a very niche subject that we sort of tried in the 90s and we know doesn't work.

• 32:14Speaker 00

  But if you, you know, if you have belief and conviction in your idea why it's different this time or what special combination from your background that you had, ideally, you're expert in both those areas, both the machine learning and the other area you're applying it to. Or you can create a founding team with that expertise. I think there's huge impact to be made there and huge value to be built there.

• 32:34Speaker 01

  That's a really important message. I mean, even. I mean, it's hard, it's easy to forget, like, basically, once you've done it, you've done it. But before you've done it, people are arrayed against you.

• 32:43Speaker 00

  Oh, sure. I mean, no one believes in it, which is why I think you've got to, you've also got to work in things that you're genuinely passionate about. Like, for me, I would have worked on AI no matter what happened. I just decided from a very young age, it was the thing that could be the most consequential thing I could think of. It's turned out that way, but it might not, maybe we would have been fifty years too early. And it was also the most interesting thing I could think of working on. And so I would still be working on AI today, even if we were still, you know, in a little garage somewhere and it still wasn't quite working. I would have still been trying to find, maybe I'd have been back in academia or something, but I would have found some way of continuing to work on it.

• 33:23Speaker 01

  So, I mean, AlphaFold was like an example of a spike that you pursued and it worked. You know, what makes the scientific domain ripe for an AlphaFold-style breakthrough? And is there a pattern, a certain objective function, like?

• 33:35Speaker 00

  The way I, I should write this up at some point when I have five minutes spare. But the lesson I've learned from all the Alpha projects we've done, specifically AlphaGo and AlphaFold, is I think the techniques we have and the problems I like to look for are great in, if the situation can be described as massive combinatorial search space. The more massive, the better in some ways. So no brute force or special case algorithm will solve it. And that's true of Go moves and of, you know, different configurations of proteins, far more than the atoms in the universe, both of those. And then you have a clear objective function. So, you know, you could think of it as minimizing the free energy in the proteins or, you know, winning the game of Go.

• 34:18Speaker 00

  So you need to specify your objective function clearly so you can hill climb. And then enough data and/or simulator that can generate you lots of in-distribution synthetic data. If those things are true, then I think with today's methods, you can go a long way into tackling and finding the kind of needle in the haystack that you need for the solution that you're trying to look for. And I think of just drug discovery, by the way, in the same way. Right, there is a compound out there that would solve this disease if one could find it, if one could only find it, right? And that wouldn't have any side effects and so on. And as long as the laws of physics allows it, then the only question is how do you find it in an efficient way, in a tractable way?

• 35:04Speaker 00

  I think we showed for the first time, actually with AlphaGo, that these systems could find those kinds of needles in a haystack. In that case, you know, the perfect Go move.

• 35:13Speaker 01

  I guess to get a little meta, I mean, we were talking about humans using these methods to create AlphaFold. But then there's a meta level, which is humans using AI to explore the space of possible hypotheses. How close are we to AI systems that can do genuine scientific reasoning? Not just pattern matching on data.

• 35:32Speaker 00

  I think we're close.

• 35:35Speaker 00

  We're working on these general systems like that, like we have this system called Co-Scientist. And we have other algorithms like AlphaVol that can go a little bit beyond what the basic Gemini will do. And obviously, all the frontier labs are experimenting in this way. I've yet to seen anything so far. And we all tinker with the same things, you know, some math problems that are a little bit harder than IMO and so on. I haven't seen anything yet that is a true, genuine, you know, massive discovery. That's my personal opinion. I think it's coming. I think it may be related to this earlier, this thing we discussed about creativity and actually going on beyond the bounds of what's known. So clearly, that's just not pattern matching at that point because there is no pattern to match to.

• 36:20Speaker 00

  And it's a bit more than extrapolation. It's some kind of analogical reasoning. And I don't think these systems have that, or at least we're not using them in the right way to do that. So the way I often say that in science is can it come up with a hypothesis that's really interesting? Not just solve one. When I say just, we're now talking about just like solving the Reiman hypothesis or something. This would be obviously amazing, or one of the Millennium Prize problems. And maybe we're a couple of years out from doing that. But I'd like to solve P equals NP. That's my favorite one. But can you, but even harder than that would be to come up with a new set of Millennium Prize problems.

• 36:57Speaker 00

  That were regarded by top mathematicians to be as, you know, deep and meaningful and worthy of lifetime of study and effort to solve. But I think that's another level harder and we don't have, you know, I still don't think we know how to do that. I don't think it's it's magical, though. I do think these systems will be eventually be able to do that. Maybe we're missing one or two things. And then the way we would test that is, you know, sometimes call it my Einstein test, which is, you know, can you train a system with the knowledge of cutoff of nineteen oh one? And then will it come up with, you know, what Einstein did in nineteen oh five, including special relativity, you know, his annus merablis? Can it can it do that? Right? And then I think we could run that test.

• 37:43Speaker 00

  Maybe we should just run that test and keep seeing if that's possible. And once that is, then I think we're on the verge of these systems being able to invent something new, truly novel.

• 37:52Speaker 01

  So last, last question for the people who are deeply technical in this room, who want to work on something, you know, even close to the scale that what you've created with, you know, it's one of the largest AI efforts in the world and you've been a pioneer for all these years. So for that, I think everyone in this room, thanks you and the folks at DeepMind very, very deeply from the bottom of our hearts. Thank you. What's the thing that you know now about building at the frontier that you wish you'd known at twenty five?

• 38:20Speaker 00

  I think we covered some of it in terms of actually you you work out that going after hard problems and deep problems.

• 38:28Speaker 00

  It's no more difficult in some ways than than going after a shallower, simpler, more superficial problem. They're they're just differently difficult. There's different things that are hard about each of those things. But I think given life's very short and you you know, you only have so much time and energy, you might as well put your life force into something that will really make a a difference if you hadn't done it, if you hadn't been there to push it. So I would just think of it through that lens. And then the other thing is, if you're if you are, and we talked about deep tech, and I love interdisciplinary work, and I think that's going to be even more prevalent in the next few years, in combinations of fields and finding the the the connections between those fields. And it's going to be even easier to do that with AI.

• 39:13Speaker 00

  And then the only other thing I would say is if, you know, if you have your, depending on what your AGI timeline is, you know, mine's like twenty thirty or something like this, then if you start off on a deep tech journey today. Usually that you're talking about a 10 year journey for for true deep tech, in my opinion. So then now you have to just consider AGI appearing in the middle of that journey. So what does that mean? It doesn't, it's not bad necessarily, but you have to take that into account. Right to will it be able to leverage it? What will the AGI system do with it? And it goes a little bit back to what you said earlier about AlphaFold and general AI systems. So one thing I can think see happening is Gemini Claude or one of these general systems

• 39:56Speaker 00

  making use of AlphaFold like specialized systems as tools. I don't think we're going to have it just in one giant brain because it will have too much regression. And if I put all the proteins into, you know, our Gemini, that wouldn't make sense. We don't need Gemini to do protein folding. Going back to your information efficiency, it will definitely affect its language skills or something like that, right? In a bad way. So much better, I think, is to have really good general purpose tool usage models that will then maybe they could even train those specific tools, but they would be in a separate system. So I think that's kind of interesting to think through the implications of that. And then what you might build today, also physical things too, like what kinds of factories would you build?

• 40:38Speaker 00

  What sorts of, you know, finance systems and so on? So I just think you need to really take that seriously. And in on the one hand is like, and imagine what that world would look like. And then build something that would be useful if that comes in halfway through.

• 40:53Speaker 01

  Demis Sabas, everyone.