Demis Hassabis on Building DeepMind, AlphaFold, and the Final Stretch to AGI

Demis Hassabis, co-founder and CEO of Google DeepMind and 2024 Nobel laureate in chemistry for AlphaFold, joins Sequoia partner Konstantine Buhler at AI Ascent 2026 for a wide-ranging conversation about the path to AGI and what comes after. He explains why he believes AGI is achievable by 2030, why drug discovery could collapse from ten years to days, and why we should think of information, not matter or energy, as the most fundamental substance in the universe. Also: what Einstein would tell us about the limits of today's models, and why the next year or two will be critical for humanity.

• 0:02Speaker 00

  Demis, thank you so much.

• 0:03Speaker 01

  Exciting to be here. Thanks, everyone, for coming. It's great to be here.

• 0:06Speaker 00

  We're so honored to have you at our chocolate factory.

• 0:09Speaker 01

  Yes, I just heard about that. Looking forward to the chocolate afterwards.

• 0:13Speaker 00

  Excellent. Well, Demis, we're going to jump right in. We have one of the OGs in every way. Original thinkers, founders, visionaries in all things AI. A true believer, true scientist in Demis. We're going to spend the beginning of the conversation about the early days, then early days of DeepMind, and then we'll get into the science and open up the room for some questions. So let's jump right in. So, Demis, you are a chess prodigy. You also were a founder of a gaming company. You're a neuroscientist, and you're the founder of DeepMind, and now leader of a really big consequential company. Those seem like pretty unrelated pieces, but you've said that there's a common thread. Can you walk us through that?

• 0:58Speaker 01

  There is a common thread, and maybe I made it into a common thread. So it could be a post hoc, you know, sort of shaping. But I wanted to do AI for a long time. So I kind of decided it was the most important thing I could possibly, and most interesting thing I could do in my teenage years. And then I picked things to study or do that I felt would eventually would help me build a company like DeepMind. So I had that as a plan from about 15, 16 years old. I had a detour into games because, actually, that was in the 90s, this is. That's where all the most cutting-edge technology was being done. Obviously, not just in AI, but in graphics especially, including hardware.

• 1:41Speaker 01

  Of course, the GPUs we all use today, they were designed for graphics engines. And we used the, I mean, I was using the very first GPUs back in the day, in the late 90s. So there was a lot of really cutting-edge technology. And then all the games I made, including all the games I did for Bullfrog, but also my own company, Elixir Studios, all involved AI as the main gameplay component. So probably the most, you know, well-known game I made was Theme Park when I was about seventeen. And that was a simulation of an amusement park. And thousands of little people came into your theme park and played on your rides and decided what to buy from the shops. So there was a whole kind of economics AI model underneath. And it was one of the first games of its type, along with SimCity.

• 2:21Speaker 01

  And when I saw it sold, you know, ten million plus copies, and when I saw how delighted people were to interact with the AI, you know, that was one of the things that made me think about spending my whole career on it. And then, of course, neuroscience is to get inspiration from how the brain works and and different algorithmic ideas from that. And then just bringing all those different things together for the start of DeepMind when the timing, we felt, was right. And then, of course, we use games as the early proving ground for our AI ideas.

• 2:52Speaker 00

  So we've got a room full of founders here. And you can relate because you're a founder not just once, but twice. Take us back to the first time, Elixir Studios. What was that like? It's not the startup that you're most known for, but it was one that you had incredible success with. How did you lead that and what did it teach you about building?

• 3:12Speaker 01

  Well, look, we started, I started Elixir Studios straight out of college. And I was lucky enough to work at Bullfrog Productions, which, for those of you who know Games, was a kind of legendary game studio in the early days of the game industry, probably the best one in the UK, in Europe. And I wanted to do something that combined, pushed AI. So effectively, I was funding AI back in those days through the back door, through games development. And then pushed the forefront of that and combined it with cutting-edge creativity. And I think that's still relevant today with the way we do our blue sky research. But maybe the biggest lesson I learnt was you want to be five years ahead of your time, not fifty years ahead.

• 3:53Speaker 01

  So we tried to do a game called Republic at Elixir Studios, which simulated a whole country. And then the idea of the game is you could sort of over Throw, I think there was a dictator in charge of the country in any number of different ways. And we basically simulated living, breathing cities. And this is bearing in mind, this is like the late nineties on a pentium. So we had to get all the graphics and all the AI for a million people. Um, working on, uh, you know, home PC at the time. So it was a little bit ambitious and, um, uh, and maybe it was too ambitious. And it caused, it caused some, some issues. And I took that lesson with me of like, you want to be ahead of your time? You don't want to be, obviously, when it's obvious to everyone, it's too late.

• 4:33Speaker 01

  But, um, if you're fifty years ahead, then there's probably no way you can get it to be successful.

• 4:39Speaker 00

  All right. So speaking of not being too far ahead of your time, it was two thousand nine and you decided there would be AGI.

• 4:45Speaker 01

  Mm hmm. Yes.

• 4:48Speaker 01

  Maybe it was only, you know, ten years ahead of our time at that time, better than fifty years.

• 4:52Speaker 00

  So, so tell us about, again, room full of founders here. Tell us about oh nine. How did you convince the first few brilliant talents? Cause you pulled in really high caliber employees, early team members. How do you convince them to believe in what seemed like total sci fi at the time?

• 5:09Speaker 01

  Well, there were some interesting threads that I think we picked up on. I, I, I think we thought we were five years ahead, but maybe we were more like ten. But it was, you know, deep learning had just been invented by Jeff Hinton and colleagues sort of in academia. But almost no one had really realized it was a big deal. Uh, we, we knew a lot about reinforcement learning and we felt there was huge, um, progress to be made by combining those two techniques, which almost had never been mixed together, really, certainly not at any kind of anything other than toy problems. Uh, in the academic subjects, they were quite two quite siloed parts of AI. Um, then we could see the compute, uh, the GPUs at the time were going to be really useful.

• 5:48Speaker 01

  Of course, we use TPUs now, um, but the accelerated computing industry was, was, was going to be very helpful. Uh, and then we also felt at the end of my PhD and postdoc and some of the other people I got together were computational neuroscientists, that we had enough ideas and principles, um, from the brain that could be useful, including the idea that reinforcement learning, uh, could eventually scale, uh, to AGI. So, um, so we felt we had these ingredients and we almost felt like we were keepers of a secret because no one, uh, either in academia or industry really believed that any big progress was possible. In fact, a lot of the people in academia. Uh, used to roll out, you know, literally roll their eyes up at us when we were sort of suggested we would work on.

• 6:31Speaker 01

  AGI or strong AI, it was sometimes called at the time.

• 6:34Speaker 00

  Hmm.

• 6:34Speaker 01

  Um, because it was like, well, we know this doesn't work. So, you know, everyone tried it in the nineties. And I did my postdoc at MIT, which was the, which was the sort of center uh, uh, uh, point for expert systems.

• 6:46Speaker 00

  Hmm.

• 6:47Speaker 01

  Uh, and first of all, the logic language systems. I mean, it seems amazing to think that now. But I was already feeling like that was okay then, but they, they, you know, that's still how it was done, both in Cambridge in the UK. And also in MIT, these big centers of traditional AI.

• 7:01Speaker 00

  Hmm.

• 7:02Speaker 01

  Um, and it felt like, but actually that convinced me even more that we were on to something because. At least if we were gonna fail, we would fail in a different way.

• 7:09Speaker 00

  Hmm.

• 7:10Speaker 01

  Then people had failed, you know, to get to AGI in the nineties. So that felt like it was worth doing. Uh, no matter what, even if obviously it was researched, we didn't know for sure it would be successful. But at least we would, we would fail in an original way if it didn't work.

• 7:25Speaker 00

  Was there any common sticking point in that early belief? Was there something that you had to? Prove either to yourself or to your early followers to get them on board?

• 7:33Speaker 01

  Well, it was, uh, uh, I mean, we had, we put it this way, I, I would have been spent my life on AI. Um, no matter what had happened. So as it's turned out, it's gone. You, you know, it's sort of gone on the absolutely amazing side of the optimistic side of what we thought. Still actually within what we were predicting in twenty ten, we thought it would be a twenty year mission.

• 7:52Speaker 00

  Hmm.

• 7:53Speaker 01

  Uh, and I think we're basically exactly on track as a field for that. And obviously we played our part in that.

• 7:58Speaker 00

  Hmm.

• 7:58Speaker 01

  But, um, even if it hadn't transpired that way and it was still now a niche subject. That's all, that's what I would still be doing because I felt it was the most uh, important technology ever. If it was obvious to me, you know, our original state, uh, mission statement at DeepMind was. Step one, solve, solve intelligence, i.e. build AGI. Step two, use it to solve everything else. So it was always, I always thought it was, um, the most important technology that could ever be invented. But also the most interesting one.

• 8:25Speaker 00

  Hmm.

• 8:26Speaker 01

  So as a tool for science, as an interesting artifact in itself. And actually as one of the best ways to understand our own minds. You know, like the nature of consciousness, dreaming, creativity, all of these questions I had as a neuroscientist. I felt one of the things that was missing was an analysis tool like AI, but also a comparison. A con for that you could do sort of a controlled experiment, study and compare uh, uh, uh, two different systems against each other.

• 8:51Speaker 00

  Hmm. Let's talk about AI for science. You've been early to that, you've been a believer, and you've been really a purist about this. This is the driving mission. What about the way you set up DeepMind and set the culture? Has positioned it to be on the constant forefront of AI for science?

• 9:08Speaker 01

  Well, that was the ultimate goal, at least for me, my personal passion. There's my own drive to build AI, which was to advance science and medicine and our understanding of the world. It's my expression of that mission was to sort of do it in a meta way, right? Build the ultimate tool and then come back when that was ready and use it to make breakthroughs in science. Things like AlphaFold that we've done and I think many more things. So we've always had that at the heart of what we've been trying to do at DeepMind. So actually, we've had an AI for Science group, division, led by Pushmeet Kholi, that has existed for nearly a decade now. Actually, pretty much the day after we got back from Seoul and the AlphaGo match, which is sort of 10 years to the month now,

• 9:53Speaker 01

  is when we started, formally started the AI for Science efforts. Because I was waiting for the algorithms to be powerful enough and the ideas to be general enough. And for me, you know, cracking Go was that point, that time, that we thought, okay, now we're ready to really apply these ideas to important real-world problems, starting with these big scientific challenges. So we've always had that in mind as the most beneficial use of AI. Like, what could be better than using it to, you know, cure diseases and give us healthier lifespans? And to help with medicine, followed obviously by other really important areas like material science and the environment and energy and these kinds of topics, which I think AI is also going to be, have a huge part to play in the next few years.

• 10:37Speaker 00

  And how does AI break through in biology? You're deeply involved with isomorphic, this is an area of deep passion. You have been a purist on the potential of AI to cure diseases from the very beginning. When do we have the type of moment that we've had in language and coding, but in biology?

• 10:56Speaker 01

  Yeah, well, I mean, I'd argue we've already had one of those moments with AlphaFold. So, you know, it's a fifty year grand challenge, protein folding. And the three D structure of proteins is incredibly important thing to know about if you want to design medicines or if you want to understand biology. Of course, it's only one part of the drug discovery process. It's an important part, but it's only one part. So isomorphic labs, which is our, you know, latest spin out, having a lot of fun running that as well. Is to build adjacent technologies in more biochemistry and chemistry space that can actually design the compounds automatically to kind of fit and bind to the right part of the protein. So we now know the protein, the shape of the protein. We know that what's on the on the surface of the protein and what we have to target.

• 11:42Speaker 01

  But now we've got to build the right compounds that, of course, bind strongly to where you want it to bind on the target of interest, but doesn't bind to anything else ideally, because that would be a toxic side effect. So the dream is to do almost all the exploration, which is ninety nine percent of the of the work and the time in Silico, and then save the the wet lab step just for the validation step. Right? So that would be, you know, I think if we can do that, and I think we can get there in the next few years. I think we could reduce drug discovery times instead of that for taking like, you know, an average of ten years down to months, maybe even weeks, and perhaps even days one day. And and then I think then all disease could be in reach.

• 12:26Speaker 01

  And I think things like personalized medicine will become possible, you know, like personalized variations off of base medicines. So I think the whole of the whole medical area, drug discovery areas, is going to be revolutionized in the next in next few years.

• 12:41Speaker 00

  Brilliant. You you talked a lot about AI for science. Do you think that at some point AI will create new sciences? All industrial revolution and thermodynamics. Will there be something new taught fundamentally in our education system? And if so, what would it be like?

• 13:01Speaker 01

  Well, I think there's several things uh that along those lines that I think is gonna happen. So first of all, um the understanding and the uh analysis of AI systems themselves, I think is gonna become a whole science, a kind of engineering science. These are incredible, incredibly interesting artifacts that we are building. And um they're incredibly complex as well. As complex, eventually they'll be as complex as the human mind and the brain. And so they'll need to be studied so we can understand fully way beyond where we are today, how these systems work. So I think there's a whole kind of field, Mekin Turp is part of that. But there's a lot more, I think, that we can do to analyze uh these systems. So that would be a science.

• 13:45Speaker 01

  But I think also um AI itself will maybe unlock new sciences, which is maybe what you're getting at. Um I the one I'm particularly excited about is AI for simulations. So I I love simulations or my all the games I wrote not only had AI, but they were simulations. And I think simulations is the way we can address some of the um what we maybe think of social sciences, uh like economics um and and other more humanistic subjects. Because um it's very difficult to do control studies in that, you know, why aren't they just sciences like physics today? Because the problem is they're emergent systems, um just like biology, actually. And it's very hard to do repeated controlled experiments. You know, if you're raising stress rates by half a percent, you have to do it in the real world and then see what happens.

• 14:30Speaker 01

  You can have theories, but you can't run it thousands of times. But if you could simulate things uh really accurately, then maybe there's sort of new sciences to be done where you can sort of rigorously sample uh from a very accurate simulator. Um and then I think that will turn that give allow us to make much better decisions in these today, what are very uncertain uh domains.

• 14:54Speaker 00

  What will it take to get to those extremely accurate simulations? World models, what kind of science is necessary and engineering?

• 15:00Speaker 01

  Yeah, well, look, I I mean, I'm thinking a lot about that in, we do a ton of that work, A like learning simulators, basically what it'd be. So, you know, these are in domains where you can't, we don't know the mathematics of it well enough or it's perhaps too complex. We can't just write, we can't just write a a a directly down a special case simulator. Um it's just not accurate enough, doesn't capture all the variables. Um. We're doing that, uh, it was done it with weather. Um, uh, we have the most accurate kind of weather simulator in the world, uh, WeatherNext. And it's far faster than what.

• 15:32Speaker 00

  Can you change the weather yet?

• 15:34Speaker 01

  No, we can't, we can't, no, and I'm not sure that would be a good idea. But the fir- the first step is to understand it better. Um, and but then even biology, you know, we're, we're working on a kind of what I call a virtual cell. So you know hugely um, dynamical emergent system. And I think biology is uh, uh, is perfect, uh, uh, sort of em- machine learning is perfect description language for biology in the same way maths is for physics. Because I think in biology and in lots of these natural systems, you have loads of weak signals, weak correlations, tons of data, far too much that any the hu- human mind can analyze. But there are connections and correlations and interesting causalities within that mass of data. So I think it's sort of, it's always struck me that machine learning is the perfect tool

• 16:19Speaker 01

  uh, uh, to describe those kinds of systems where until today, you know, mathematics hasn't been able to do that. Uh, either because we can't manage it as, as top mathematicians because too complex. Or the expressive power of maths is not enough for uh, uh, to, to understand these sort of highly emergent dynamical systems.

• 16:38Speaker 00

  Is it also because of the messiness and stochastic nature?

• 16:41Speaker 01

  Yeah, sure. And, and I mean, eventually you could, by the way, once you learn these simulators, it may be there's another, this is maybe is another branch of new branch of science. You could maybe extract some equations from the, once you have the simulator. So you have this sort of implicit simulator or intuitive simulator. And then maybe you could extract explicit equations from that. Um, because you, partly because you could also sample it as many times as you want.

• 17:07Speaker 00

  As fundamental as Maxwell's or something like that.

• 17:10Speaker 01

  If, if, I don't know if that exists for such emergent systems. Um, but if they do exist, I don't see why we won't be able to find them in this, with this, with this methods.

• 17:18Speaker 00

  That would be amazing. You've talked about this theory that the basic building block of, of everything in the universe could be information like. This is more theoretical. How do you think about that and what does that mean for? A traditional classical Turing computer.

• 17:33Speaker 01

  Well, look, I think you can, of course, all the famous, you know, equals M C squared and all the stuff Einstein did and energy and matter are kind of equivalent. But I actually think information has a kind of equivalency in the same way. So you can think of, you know, the organization of matter and structure. And especially things like biology that are resisting entropy as basically information processing systems at their heart. So I think one can convert all of those three kind of quantities into each other, but I have this feeling information is most fundamental. So it's a little bit the opposite way around to the classic physicist thought in the nineteen twenties and things where, you know, it's sort of energy and matter primary.

• 18:13Speaker 01

  I actually think it's a better, a better way to understand the world, the universe is to think about it as information first. And if that's true, and I think there's quite a lot of evidence for that, then of course A I is even more sort of profound in a sense.

• 18:28Speaker 00

  Totally.

• 18:28Speaker 01

  Than we think, and it's already pretty profound because it's also about organizing information and understanding information. And constructing informational objects. So A I, in my opinion, is all about information processing. So I think there's something sort of very deeply connected with with these different areas, if you think of it through the lens of information processing as the primary way to think about it.

• 18:54Speaker 00

  And do you think a classical Turing machine will be able to compute everything?

• 18:58Speaker 01

  Well, I sometimes think, you know, I sometimes sort of think about what we're doing and refer to ourselves as Turing's champion because Turing machines. I think Alan Turing is one of my all time, you know, favorite scientific heroes. I think what he did obviously laid the foundations for the computers, for computer science, but also A I. And I think it's one of the most profound results ever is the Turing machine result. You know, everything that is computable can be computed by a relatively simple description of a machine. So I think our brains are likely to be approximate Turing machines. And I think it's interesting to think about the connection between Turing machines and quantum. Computers and quantum systems.

• 19:43Speaker 01

  But I think at least what we've shown with things like AlphaGo and especially AlphaFold.

• 19:47Speaker 00

  Hmm.

• 19:47Speaker 01

  Uh, is that a a classical Turing machine, uh, obviously in the guise of a modern neural network. Uh, it can model what was thought to be in the case of protein folding, it's a quantum system. You know, in a, at some sense, it's very, you know, it's dealing with very small, uh, uh, particles. Um, and one might think you'd have to take into account all the quantum effects. Uh, of the water bonds and all sorts of things. But it turns out you can get to um, approximate, um, optimal sort of solution uh, on a classical system. So it may turn out there are a lot of things that we think a quant- that would need a quantum system to model or run. Uh, might be modelable on a classical system if thought about in the right way.

• 20:29Speaker 00

  So you've talked about AI consistently as a tool. Well, like a telescope or a microscope, uh, astrolabe through the centuries. But when you think about a machine that can model almost anything.

• 20:42Speaker 01

  Mm hmm.

• 20:43Speaker 00

  Uh, let's say it can even model quantum systems like you've pointed out. When does it stop becoming a tool and will that ever happen?

• 20:51Speaker 01

  Well, I my my my feeling, strong feeling is we should, uh, in this sort of mission to and journey to build AGI.

• 20:58Speaker 00

  Mm hmm.

• 20:58Speaker 01

  Uh, those of us on that journey, many of people in this room. You know, I feel like it would be best to build a tool first. Uh, an incredibly intelligent and useful and precise tool. And then cross the the the next sort of rubicon, that's already profound enough. And um, has, you know, of course, the tool could start becoming more and more autonomous and agent-like that we're all. Uh, seeing we're in the midst of that, the agent era now. Um, but then there's a further step of like, you know, does it have agency? Is it conscious? These sorts of uh, questions, which are also going to be questions we're going to need to address. But I would, um, I would recommend we do that as a second step. Perhaps using the tool in the first step to help us, uh, uh, with those next profound questions.

• 21:43Speaker 01

  And ideally also, we could understand, uh, our own brain and and minds better. And define things like consciousness a lot more precisely than we can today.

• 21:53Speaker 00

  Do you have estimations of what that definition of consciousness might look like?

• 21:56Speaker 01

  No, I mean, I haven't got much to add beyond that thousands of years of philosophy hasn't said already. But I mean, it's very clear to me that it's obvious some components are going to be needed. They're probably necessary but not sufficient, like things like self-awareness and, you know, the idea of self and other, some kind of continuity over time. So some of these things are clearly needed for anything that might look like consciousness. But I mean, obviously, it's an open question as to as to what the full definition is. And I've talked to many of the great philosophers about that. Daniel Dennett, obviously sadly passed away recently, but we had a long conversation a few years back about this. And I think, you know, one of the issues is how does a system behave?

• 22:41Speaker 01

  Does it behave like a conscious system? So that's, you know, you could argue some of the AI systems might end up being able to do that as they get close to AGI. But then there's still the question of why do you, you know, why do we think each other are conscious? One is the way we're behaving, we're behaving like conscious beings. But the other thing is we're running on the same substrate. So I think if both those things are true, then it's parsimonious to imagine you're experiencing the same thing I'm experiencing, which is why we don't have that debate about, you know, normally about are each other conscious? But I think we'll obviously we'll never have the substrate equivalence with an artificial system. So there'll always be a, I think it'll be hard to completely close that gap. So you can look at it behaviorally, but what about experientially?

• 23:26Speaker 01

  There are probably some ways to do that post AGI, but maybe it's a bit out of scope today, even for AI for science discussion.

• 23:33Speaker 00

  Brilliant. So we're going to open the room to questions in just a moment, get your questions ready. But you brought up philosophers. You've mentioned Kant and Spinoza as two of your favorite philosophers. Kant is this, you know, deontological, highly duty-driven philosopher. Spinoza almost has this deterministic view of the universe. How do you kind of connect those two beliefs? And where is your thinking of how the world works?

• 23:59Speaker 01

  Well, the reason I like those two, they stuck out for me is that I think Kant, when I was doing my PhD in neuroscience, you know, his his sort of statements about the mind creates reality, right? I think that's basically true. And and so another reason to study the mind, right? And how the brain works. And I'm interested ultimately in the nature of reality. So we have to understand how the mind is interpreting that. And so I think that's for me what I took from Kant. And then Spinoza, it's more about the you could almost call spiritual dimension of like, well, if you're trying to understand the universe, using science, in my case, as the tool, you're sort of understanding some deep mystery about how

• 24:44Speaker 01

  the universe works, right? In a in a really kind of deep way. And that's what I feel I'm we're doing and I'm doing when, you know, I do my science and we work on AI and we're building these tools is somehow we're kind of reading the language of of the universe.

• 25:02Speaker 00

  Beautiful. What a beautiful way to say what you do every day. Demis, scientist, orator, and philosopher. We will, before we wrap, do a couple of rapid fire questions. Thank you for fitness. And he's not seen these yet. Over under on distribution year of AGI?

• 25:22Speaker 01

  Oh, wow.

• 25:23Speaker 00

  Or reject premise of question?

• 25:25Speaker 01

  No, twenty thirty. I've been pretty consistent about that.

• 25:28Speaker 00

  Okay, twenty thirty. Must read book, poem, or paper for when we achieve AGI?

• 25:34Speaker 01

  Oh, wow. For when we achieve, once we achieve it. Well, my favorite book is The Fabric of Reality by David Deutsch. So I think that still holds. I'd hope to answer the questions in that book with with the AGI. That's my post AGI work.

• 25:50Speaker 00

  Brilliant. Proudest moments so far in Deep Mind?

• 25:53Speaker 01

  Oh, wow. We've been lucky to have a lot. I mean, probably Alpha Fold.

• 25:58Speaker 00

  Okay. Now, a couple of games questions. If you were engaged in a high stake strategy game, turn based strategy game,

• 26:07Speaker 01

  Okay.

• 26:08Speaker 00

  Civ, Polytopia series games. And you could select one scientist from history. We're thinking the Einsteins, the Turrings,

• 26:17Speaker 01

  Oh, wow.

• 26:18Speaker 00

  Newtons. Who would you select to be on your team?

• 26:19Speaker 01

  On my team?

• 26:21Speaker 00

  On your team?

• 26:22Speaker 01

  Oh, gosh.

• 26:26Speaker 01

  Um, probably Von Neumann, I think. Great choice. He won a game theorist, I think. And I think, I think, I think, I think he's the best. Makes sense. I feel like he'd be a good teammate. Yeah. All right.

• 26:41Speaker 00

  Well, Demis, you do it all. Thank you so much for being with us. Please join me in thanking Demis. You're so welcome.