I would say about 90% of the breakthroughs that underpin the modern AI industry were done either by Google Brain, Google Research, or DeepMind. So for one of our groups, the returns are kind of still very substantial, although they're a bit less than they were obviously at the start of all of this scaling.
We have amazing guests on the show, but very few, honestly, will be considered in the same realm as Newton, Turing, or Einstein. Our guest today is one of the greatest minds on the planet, and I consider myself incredibly lucky to have had the chance to sit down with him.
Those labs that have the capability to invent new algorithmic ideas are going to start having a bigger advantage over the next few years, as the last set of ideas—all the juice is being wrung out of them.
This is a truly special one, and one that I'll remember for a very long time.
I think we could probably get 30-40% more efficiency out of our national grids.
Enjoy the episode, and I so appreciate the time we had with a very special human being.
I sometimes quantify the coming of AGI as 10 times the Industrial Revolution at 10 times the speed. Thrilled to welcome Demis Hassabis at DeepMind. Ready to go.
Demis, I'm so excited to be doing this. Thank you so much for joining me today.
Great to be here.
Now, there are many places that we could have started, but I was watching the documentary that you did, which was fantastic, and I wanted to start on AGI. Definitions are very varying. You've been very thoughtful about what it means to you. And so I wanted to start—can you explain to me how you think about it today so we get that as a kind of ground center?
Yeah. Well, we've always been very consistent in how we define AGI: as a system that exhibits all the cognitive capabilities the human mind has. And that's important because the brain is the only existence proof we have, that we know of in the universe, that general intelligence is possible. So that, for me, is the bar for what AGI should be.
It's the worst question: how close are we? Everyone says different things, and it's very difficult when you have very prominent figures saying it could be as early as 2026 or 2027.
Yeah, I think, look—I've got a probability distribution around the timings, but I would say there's a very good chance of it being within the next 5 years. So that's not long at all.
Is that closer than you thought? Has that changed over time?
Not really. It's funny—my co-founder Shane Legg, who's Chief Scientist here, when we started out DeepMind back in 2010, he used to write blog posts sort of predicting when AGI would happen. And bearing in mind, in 2010 when we started, almost nobody was working in AI, and everyone thought AI was a dead end. But they're still there on the internet for people to check. And we used to do this extrapolation of compute and algorithmic progress, and we predicted it would take around 20 years from when we started out. And I think we're pretty much on track.
What are the biggest bottlenecks when you look today? In the documentary, you said you just never have enough compute. What are the biggest bottlenecks when you look at where we are today?
I think compute is the big one. Not just for the obvious reason of scaling up your ideas and your systems—as you know, the "scaling laws" as they're called, keeping on building bigger and bigger architectures with more and more parameters. As you do that, you get more intelligent systems. But the other thing you need a lot of compute for is for doing experiments. The cloud is our workbench. So if you have a new algorithmic idea but you want to test it, you've kind of got to test it at a reasonable scale, otherwise it won't hold when you put it into the main system. So you need quite a lot of compute if you have a lot of researchers with lots of new ideas.
You mentioned the word "scaling laws." A lot of people suggest that we're hitting scaling laws and we're starting to see that plateauing effect. Do you think that's true?
No, I don't think so. I think it's a bit more nuanced than that. Of course, when the leading companies all started building these large language models, you were getting enormous jumps with each generation of new system—maybe they're almost like doubling in performance. At some point, that had to slow down. So it's not continuing to be exponential, but that doesn't mean there aren't still great returns for scaling the existing systems up further. We and the other frontier labs are getting a lot of great returns on that kind of compute expansion. So I would say the returns are still very substantial, although they're a bit less than they were at the start of all of this scaling.
Where are we behind where you thought we would be?
I think in most areas, we are ahead of where I thought we would be. If you think about things like the video models or even now with our newest systems like Genie, they're interactive world models, which I think is kind of incredible if you sort of step back and think about it. I think if you'd shown me that 5 or 10 years ago, I would have been pretty amazed. So I think in most domains, we are ahead of where the field thought.
There's still some big things missing, though, like continual learning. These systems don't learn after you finish training them and put them out into the world; they're not very good at learning further things. And I think some critical capabilities...
I'm sorry to ask blunt and basic questions. Why do we not have continuous learning today?
Well, people haven't quite figured out yet—and all the leading labs are working on this—how to integrate new learning into the existing systems that you spent months training. Of course, the brain does this very elegantly, probably through things like sleep and reinforcement learning. You get what's called "consolidation" in the brain, where your memories during the day are replayed and then some of that information is elegantly incorporated into your existing knowledge base. I've thought for a while that maybe we need something like that to incorporate new information along with the existing information base.
You mentioned video models, you mentioned media and image. It seems that DeepMind has progressed very quickly and caught up slash overtaken other providers. I think I've tweeted—I think you liked it—but I tweeted what I used and how it's changed over time, and DeepMind now is my number one for research for new shows. It wasn't that way before. What has led to the acceleration and progression of DeepMind in a way that wasn't maybe there 2 to 3 years ago?
Yeah. Well, we made some organizational changes. I think we've always had the deepest and broadest research bench at Google and at DeepMind. If you look at the last decade or plus—15 years—I would say about 90% of the breakthroughs that underpin the modern AI industry were done either by Google Brain, Google Research, or DeepMind. If you think of things like AlphaGo and reinforcement learning, and of course transformers, these are all the key breakthroughs. So I would back us to make those breakthroughs in the future if there are any missing ones.
I think we've helped put together all the talent from around the company, pushing in one direction. And then, we talked earlier about compute resources—it was also about combining all of our resources together so we could build the biggest models rather than having two or three versions around the company. So I think a lot of it was assembling together all the ingredients we already had and then pushing with relentless focus and pace, acting almost like a startup to get back to the frontier and be ahead in many areas.
You say if anyone's going to do the breakthrough, it could and should be us. When you think about that, is continuous learning the next breakthrough that you're most excited by?
I think there's quite a few things that are missing. There's continual learning. I think there's a lot of mileage in looking at different memory systems; at the moment, we have these long context windows which are kind of a bit brute force—you just put everything in them. I think there's a lot of interesting architectures to be invented there. And then there's stuff like long-term planning, hierarchical planning. These systems are not very good at planning at long time horizons, many years into the future, which we can do with our minds.
There's quite a lot of problems I think that are still left to overcome. Maybe one of the biggest is consistency. I sometimes call these systems "jagged intelligences" because they're really amazing at certain things when you pose the question in a certain way, but if you pose the question in a slightly different way, they can still fail at quite elementary things. A general intelligence shouldn't be that sort of "jagged."
When you reposition files and you set up agents to perform in certain ways and then the files fall over... configure it—it completely falls over. That's a disaster.
Yeah. Well, the general intelligence, if you think about how our minds work, shouldn't have those kinds of holes in it.
We said about a plateauing of scaling laws. Everyone talks about a commoditization of models in terms of capabilities. Do you think we see that, or do you think we see one to two continuously accelerate ahead of the others?
Yeah, I feel like, you know, the three or four leading labs now—of which we're one—I think the gap is starting to pull away because a lot of these tools also, of course, help you build the next generation. Things like coding tools, math tools... it's getting harder and harder, I would say, to eke out the same gains from just the same ideas. So I think those labs that have the capability to invent new algorithmic ideas are going to start having a bigger advantage over the next few years, as the last set of ideas—all the juice is being wrung out of them.
You were very open with a lot of your research for years, and we see many very good quality open models. How do you think about the future of open? I have many portfolio companies that use frontier models to set a benchmark and then use open models to get as close as possible but with more cost effectiveness. What does that future look like?
Yeah, I think it's probably similar to what we're seeing today. We're big supporters of open science and open models. We've done many, many things, obviously, from the original transformers to AlphaFold—these are all things we've given out into the world to help the research community, and we plan to continue to do that, especially in applied scientific domains. Applying AI to science is obviously my passion.
But I think increasingly what you're going to see is the open source models probably one step back from the absolute frontier. It usually takes about 6 months for the open source community to sort of re-implement and figure out what those ideas are. But we are also pushing hard on a suite of open source models called Gemma, which we're determined to make best-in-class for their sizes. Specifically for small developers or academics or the beginnings of a startup, I think they're perfect for that, and also edge computing too. So we're very interested in open source models for certain types of applications.
How do you think about a world post-LLMs? You have different people with different views. You and Yann LeCun with very different views.
For me, I kind of disagree with Yann on a few things. I think there might be a 50/50 chance there's some things missing that we still need to make breakthroughs in—perhaps world models, these kinds of approaches. But my betting is pretty strong: we've seen how successful these foundation models have been. They can do incredibly impressive things. I don't think that's going to go away. We're still seeing returns from the scaling laws. So the only question really is, when you think about a future AGI system, is an LLM foundation model going to be the key component only, or is it the total system? I just think it's a question of "is there anything else needed," not that it's going to get replaced. I think it's going to get built on top of these foundation models, just like the way we do with our world models.
When we think about that future 5 years out, as you said, potentially with AGI—what does that world look like? Many people have different concerns. If we just start generally, what does that world look like to you?
I think on the positive side—and the things, obviously, I've spent my whole career and life building towards AGI for—is I think it will be the ultimate tool for science and medicine. In terms of advancing scientific discovery and finding cures for diseases, I think we need that kind of technology. And so I'm hoping in 5-plus years' time, we'll be entering a new golden age of scientific discovery.
My mother's got multiple sclerosis. So it's something that I'm always most excited about. The thing I worry about is the drug discovery process—getting it through all the trials and knowing that it takes a decade before my mother will get any benefits from it. How do we solve that?
I think we'll get to that point soon. First of all, what we're doing is... after we did the AlphaFold project to do protein folding, we spun out a company called Isomorphic Labs, which is doing extremely well. The idea there is we're focusing on solving the rest of the drug discovery process: a lot of chemistry, designing the compounds, checking it's not toxic, and all the different properties you need for drugs to be safe. I think we'll have that whole drug design engine ready in the next 5 to 10 years.
Then you're right—the next problem is the clinical trials still take many years. But I think AI can help there in terms of maybe simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that's suitable for their genomic makeup. So I think AI can help there, too.
But I think the real revolution will come when a few dozen or so AI drugs get through the whole process. Then the government and the regulatory bodies see that and they have enough data to sort of back-test the predictions of those models. Then maybe in the future—maybe 10 further years—we can really just trust the predictions that the models are making and skip out some steps. Perhaps animal testing is not needed anymore; maybe we can go up the dosage ladder quicker because you can rely on these models. So I think we've got to do it in two steps: solve the drug design problem first, and then look at the regulatory length of time it takes.
Speaking of regulatory, AI safety is a big topic and a big concern. I think it was... again, I watched it last night over dinner, which was a great watch, the documentary. I think it was Stephen Hawking who said we must get it right because we might not get another chance. Do you think that's right?
Yeah, I do think that's right. I think those are the stakes that we have to deal with. There's two things I worry about. One is the misuse of these systems by bad actors; they can be repurposed. These are dual-purpose technologies—they can be used for incredible good in science and health, but they can also be repurposed for harmful ends by a bad actor. So that's one issue.
Second issue is a technical one: making sure these systems, as they get more powerful—not today's systems, but maybe in a year or two's time when they become more agentic, more autonomous, as we get towards AGI—can be kept within the guardrails that we want. And I think regulation, the right kind of regulation, could help here in terms of making sure there's at least minimum standards from all of the leading providers, but it needs to ideally be a kind of international standard.
What is the right kind of regulation? And again, I'm quoting yourself back from this documentary. You're like, "I think we need more global coordination," which worries me because we're getting worse at it.
Yes, for sure. It's sort of crazy the timing that we're in, right? With this most consequential technology the world's ever seen, at the same time as a very fragmented international system. It's not ideal, but I think we're going to have to try and do the best we can to at least come up with a set of minimum standards—some benchmarks that test for undesirable properties. For example, deception. Nobody should be building systems that are capable of deception because then they could be getting around other safeguards.
And then I imagine, if things go well, some kind of certification process—it's almost like a "kitemark" of quality, that this model has certain safeguards and certain guarantees. So therefore consumers and companies can safely sort of build on top of it. I think that is how it should go ideally. But it does have to be international because of course these systems are cross-border and cross-territory.
Who is that ultimate verification system? You obviously started with Theme Park... brilliant. Don't put the burgers down too close to the roller coaster. But obviously as a media company, I go through any media platform saying, "I don't know what's real or fake." I'm always having to ask what's real or fake. Who is that arbiter of verification?
Yeah. Well, I think ultimately it's got to be government, but the kind of technical bodies that would be able to do the technical work would be like the AI Safety Institutes. There's a very good one in the UK that was set up under Prime Minister Sunak, and I think is doing great work. There's one in the US, and maybe some of the leading countries that have the best research should also have an equivalent body that is staffed with high-quality researchers, too, that can evaluate and audit these kinds of systems against certain benchmarks and independently check whether they are meeting the right standards.
If I could give you a magic wand that was only applicable to AI safety implementation, what would be your implementation idea or program that you would put in place?
Yeah, I think we need some kind of international body, maybe similar to the atomic agency, something like that, that the AI Safety Institutes sort of feed into. The research community has to also be involved in deciding: what are the right set of benchmarks to check? What types of traits? What types of capabilities? Maybe there are other safeguards, too. For example, it wouldn't be desirable to have AI systems output tokens that are not human-readable—some kind of machine language that we couldn't understand. I think that would introduce a new vulnerability.
There's quite a few things like that which I think most of the leading labs would agree are probably not best to do. And then these institutions would test against those things. I think that would give the public confidence—and academia could be involved as well, as well as civil society—that these systems, which are going to get incredibly powerful, have been independently checked and audited.
That's it. Your magic wand's done now. That was the one.
Maybe I used it on the wrong thing, but time will tell.
Exactly. You said there about science being one of the most exciting areas in five years' time. I have to ask it because it's one of the biggest concerns: the labor displacement problem. I just had Marc Andreessen on the show and he said that I was a Marxist for bringing it up... Mark's wonderful, so I'm not blaming him, but he was like, "It's completely rubbish. I don't agree with it at all. We've always overcome it." How do you think about the labor displacement problem when you look at how truly capable these systems are, and what that does to labor markets?
Well, certainly in the past with every new revolutionary technology, there's been a lot of job disruption. So that's for sure, and I think that's definitely going to happen. A lot of old jobs go away or are not viable anymore. But then the history of it is that a whole set of new jobs arrive that maybe one can't even imagine before, and those are high quality, higher paying. So that's the normal course.
Of course, you have to be very careful to say "this time is different," and I guess that's what people like Mark are claiming—it's the same as the last 10 massive breakthroughs like the internet, mobile, and so on. I do think this is going to be bigger than all of those previous technological breakthroughs. I sometimes quantify the coming of AGI as like 10 times the Industrial Revolution at 10 times the speed—unfolding over a decade instead of a century.
I've been reading a lot about the Industrial Revolution; there's a lot of great books about it. That caused a huge amount of upheaval as well as a lot of advances. We wouldn't have modern medicine today—child mortality was at 40% pre-Industrial Revolution. So things you wouldn't want not to have happened. But ideally this time around, we mitigate some of the downsides a bit better than we did during the Industrial Revolution.
I often listen to amazing voices like yours and I get very excited by how fast it's coming. And then I try and stop myself from being too useful and think, "Ah, I should be more wise." I'm told that we always overestimate what can be done in a year and underestimate what can be done in 10. Is that the truth here, or is it coming faster than we think?
No, I think that's still the truth. I mean, maybe both time scales of short-term and long-term are nearer than for other technologies. But I do think, literally today and in the next year, things are a bit overhyped in AI—they couldn't be any more hyped in some ways. But on the other hand, interestingly, I still think it's very underappreciated how revolutionary this is going to be in the time scale of about 10 years. We could call that long-term. So there's still that dichotomy even today with AI.
With the concern around labor markets, there's also a concern around income inequality and the concentration of wealth to few players. How do you see that shaping out with your comment on the Industrial Revolution?
Well, I think there's different ways that could play out. Maybe pension funds should be buying into all the big AI companies and making sure that everyone has a piece of that. Or sovereign funds—maybe every country should have a sovereign wealth fund that does that. That would be the sort of "investment way" of doing it.
I think there also needs to be thought about: if there is this massive productivity gain but it's sort of narrow where that occurs, how do we redistribute that so that everyone benefits from these huge gains? I can see all sorts of ways that could be done, including providing infrastructure and other things. With that additional productivity gain, there could be unbelievable things happening in the 5 to 10 year time scale, including a breakthrough in some kind of renewable free energy. Maybe we solve fusion—we're working on that with our partners at Commonwealth Fusion. I think AI is going to usher in amazing new superconductors, better batteries, material science... there's all sorts of ways I could see that completely changing the nature of the economy.
How do we solve the energy crisis that comes with an AI revolution? What it means in terms of energy requirements is unprecedented. I know it's an incredibly hard question... but how do we solve that unprecedented need for new energy?
Well, I think AI will, in the medium to long run, more than pay for itself in terms of energy costs. We work on projects like optimizing existing infrastructure—optimizing the grid. I think we could probably get 30-40% more efficiency out of our national grids. And then there's modeling the climate and weather; we have some of the best weather modeling systems in the world, so that helps us work out where the effects are really happening to mitigate that.
And then finally, the most exciting part maybe is these new breakthrough technologies like fusion, new batteries, superconductors... I think AI will be essential for helping us reach those. And then I think we'll be in a completely new energy situation than we've ever been in as humanity. That will, of course, help with things like the climate and environment, and eventually also help us get into space much more cheaply. If you have an incredible energy source like fusion, then you have effectively unlimited rocket fuel because you can just distill/catalyze seawater.
I'm not going to ask you to solve space, don't worry. My question was on being in the UK. You're in London. I'm in London. I'm very proud to be in the UK. You have been, I'm sure, pushed or prodded at every turn to move to the US. Why have you stayed?
Well, I should ask you that question, too! But I think I saw London, when we started DeepMind, as a place—and the UK in general, and Europe to some degree—that has incredible talent. We've always had three or four of the top 10 universities in the world, with Cambridge, Oxford, Imperial, or UCL. We're producing amazing graduates and PhD students who are the envy of the world.
We have incredible scientists here. We've got a rich heritage from Turing and Hawking to Darwin and Newton. We have this incredible history of scientific breakthroughs and great thinkers. So I felt we had all the ingredients—the talent and great engineers—but it just hadn't been galvanized into an ambitious deep-tech startup idea. I felt it was possible, and I felt that there was less competition here for that sort of talent. We could even draw in the best talent from the top European universities, and that's what it was like in the early days of DeepMind. So I think it was a huge structural advantage for us.
And then the final thing is maybe being a bit away from the Valley. There is some disadvantage in that you're not plugged into the network and the gossip and the latest trends and vibes. We're a little bit out of it here, but I think it's very conducive to thinking deeply about things and being more original about how you think. That's great for deep tech, where you don't want to be distracted by the latest fad. You knew it was going to be a 20-year mission from the beginning of DeepMind. So I think being a little bit away from that maelstrom is quite good. Palmer Luckey often talks about being 400 miles away from the Valley as core to his innovative thinking.
Yes, we're a few thousand miles away. Terrible question: will Europe have a trillion-dollar company? You see the Americans always bash us for our lack of large companies. I ping Daniel Ek and be like, "Come on, dude... but we don't have a trillion-dollar company."
Not yet. Daniel may well get there with one of his companies—Spotify, Helskinki—I think those are two good options. I think there's no reason why we can't have that. I'm going to try and do that with Isomorphic, which is headquartered here and I think has the potential to be that. But I think that's one of the disadvantages of Europe: obviously we're a combination of smaller markets. So that's one thing we have to kind of overcome. Maybe this "EU Inc." thing could be a good innovation.
I'm pulling out the magic wand again. This time applied to European technology: what would you do to implement a growth mindset, an ability to build that trillion-dollar company that we don't have today?
I think in the UK—this may apply to other European countries, too—it's about unlocking what pension funds can invest in for the growth stage. I think we're brilliant at the startup idea and getting it to a certain level, like we did with DeepMind. But then if you really want to cross that chasm into the trillion-dollar global player, where are the billion-dollar rounds going to come from? Where you can really take on the existing incumbents? I think that certainly was missing 10 years ago when I was doing fundraising for DeepMind, and I think it's still kind of missing today—just that level of ambition and the amount the capital markets can support.
I read about some of your early rounds raising in the US... Malibu, families, kids... exactly. Okay, we're going to do a quick-fire round. Meeting Elon for the first time—how was that?
Oh yeah, it was amazing. It was at a Founders Fund conference because both SpaceX and DeepMind were part of the same portfolio—an amazing portfolio that Peter Thiel had. I think we were both invited to my first portfolio conference back in 2011 or 2012, very early days. We were the small little upcoming thing and I had a small speaking slot, and Elon was the big thing in that portfolio. He had the keynote, but we met afterwards. Elon says it was like we were passing each other in the bathroom or something. We said hi and we both hit it off immediately as people who were almost too ambitious in their thinking, perhaps, and loved sci-fi. I really wanted to visit his rocket factory, so I was trying to get an invite to SpaceX in LA. He invited me at the end of that meeting, and that was our second meeting, at the SpaceX factory.
I love it. Not even speaking slots as big as his.
I don't know about that!
Healthcare revolution, disease eradication that you're most excited about? Again, for me it's specifically multiple sclerosis.
Yeah. Well, look, I want to literally cure cancer. I know people say that's the cliché, but what we're building at Isomorphic is general purpose. We're trying to build a drug design platform that will be applicable to any therapeutic area. So ideally it will help with everything from neurodegeneration and cardiovascular to immunology and cancer. Those are the ones we're focusing on first, but eventually it should be applicable to every disease area.
What are you thinking about that you're not reading about or seeing anyone talk about?
I think a lot of people are worrying about the economic questions around AGI that we talked about earlier, but I worry a lot about the philosophical questions. Let's assume we get the technical part right, let's assume we get the economics part right—both of those are hard—then there's a philosophical question: what is meaning? What is purpose? We'll find out what consciousness is. What does it mean to be human? I think that's what's coming down the road, and I think we need some great new philosophers to help us navigate that.
Hard final question. There are many different ways you could describe what you do. What would you most like your legacy to be?
I would like my legacy to be remembered for advancing science and building technologies that bring incredible benefits into the world, like curing terrible diseases.
Demis, thank you so much for putting up with my meandering conversation. You've been fantastic. I really appreciate it.
Thank you very much.