A new era of discovery: AI and the frontiers of science with Demis Hassabis

Hey, Mike. Good to see you.

Thanks. Yeah, thanks for doing it.

Yeah, I thought people seemed to pick up on that line specifically.

Yes, so it was kind of expected. But it's great to be able to expand on it a little bit. So I'm talking a lot about, obviously, AGI being on the horizon. I think we are only a few years away now from the full version of that. And the singularity is a kind of good word, I think, to describe the era that is precipitated by a technology like AGI arriving on the scene. And partly, it's because it's going to be so transformative, I think the most important invention ever, that it's hard to make many predictions beyond that horizon because it will change so much. And I think, this year, with the agents, agentic systems that we're all seeing and using, I think we can start feeling it now. And that's what I was sort of meaning by that. When we look back, I think, in five, 10 years' time, I think we'll see it as like, oh, yes, 2026, '27 is when it was starting.

Well, I think all of you, I mean, I've been feeling it just by using our own coding systems and coding agents and just vibe coding things, even little game prototypes in my spare one or two hours in the small hours of the morning, which would have taken six months before. And I just think that kind of acceleration is very interesting to see. I think we're seeing it in areas of science, obviously with AlphaFold, which was a few years ago now, but almost every branch of science AI being applied to. And I'm also seeing the speed at which we're making advances in things like Isomorphic Labs for drug discovery. So I think taking that all together and looking at the current progress of the foundation models and the chatbot systems and things like Omni, all of that, taken together, is just astounding progress, I would say.

Well, I feel like it's, I've been saying, recently, around 2030, plus or minus a year, I think is a reasonable estimate, from what I'm seeing now.

Well, I think we're doing pretty well. So our original statement was, step one, solve intelligence. Step two, use it to solve everything else. And I think that was pretty prescient. That's what we felt AGI would be is a general purpose technology that could help. I was thinking more about science and medicine, specifically, in increasing our understanding of how the world works around us. That's always been my passion, as you know. And I think we are on the cusp of that now.

Yes, well, I mean, look, solved, it's just a neat term. What does it actually mean? But what I meant by that was building AGI. And I think we're a few years away. So that would be the first step to me.

Well, no, I think that's not the case. I think it will be, it's going to be a gradual improvement. I think that's what we're seeing. Probably a pretty fast improvement. But I have a particular test in mind, which I call kind of the Einstein test. So imagine you had a knowledge cutoff of one of these leading frontier models of 1901. Would it be able to come up with his incredible sort of insights and discoveries that he did in 1905, including special relativity? Could a system come up with those insights and breakthroughs? And I think today systems clearly can't. But I think I don't see why, in the future, they won't be able to. So that's the creativity or leap of insight that I'm still looking for from these systems for it to be full AGI.

Look, I think it's sort of weird, this sort of idea of, we're back, and then we're winning, and then we're not back. And the thing is that, at least the three frontier labs and actually the whole of the frontier, it's the most ferocious competition there's ever been, I would say, in tech history, maybe in corporate history. So there's that going on. Very capable, brilliant people in all the organizations, each trying to push, as hard as they can, on the research and the breakthroughs. I think we're doing really well for what we need to do, both in terms of the underlying technologies. We have a kind of broader research bench than I think the other labs have. We've always had that. I think that's what gives us our innovative capacity to invent the next breakthroughs. You see, now, new models that are coming out, our 3.5 Flash Pro coming soon. We're very happy with the progress that we're making on the underlying models. We also have all our generative media models, Omni being the latest. So that's going well. Where I think things are going extremely well is on the consumer side. So I think that's what the really big leap has been, for us, in the last year, is streamlining our tech stack, actually almost rewriting it from the ground up, to be AI first and now agent first. That's a big change for a incredible engineering organization, like Google, that serves billions of users every day on multiple product surfaces on our incredible engineering stack. But that has to adapt so that you can ship the model, the goodness of the latest models, directly into all of those products at massive, massive scale. And I think we've got that down really well now, better than any of the other, shall we say, big tech companies. And I think you're starting to see that in the progress we're making with the adoption of Gemini app, 900 million monthly users now, incredible growth there that we're seeing, and also things like AI Mode in Search, an unbelievably popular and incredibly useful, I think, version of AI for information seeking. So I think if you look across our surfaces and what we're doing with our models, especially the small, efficient models that are still very powerful, so that's really important for our internal use cases because we've got to serve billions of users. But it's also very important for some of our big enterprise customers as well. So I think when you take a look at that, at aggregate, and then you also can see the advantage we have being full stack, all the way from the chips, to the data centers, to cloud, to frontier lab, to billion-user products that have AI embedded in them, I think we're the only organization in the world that has that full stack. And we're continually trying to optimize that feedback loop so that they all feed into each other.

Well, I think we're going to be one of the big winners of the era.

Well, I don't know. I don't really see it like that, in terms of win, win to what, exactly? So I think the AI era is going to be immense. It's going to be unbelievable amount of new opportunities created, new economic progress made. And we've got to make sure that we're one of the key players in that new post-AGI world. And I think we're incredibly well positioned for that. And as to how many players there'll be, my guess is a handful, given how powerful the technology is. But actually, stepping back from that, what I always say is, although we're ferociously competing in this kind of race, all the leaders of the frontier labs need to think about, and they are thinking about, the bigger picture of the responsibility to the world. As we bring this unbelievable technology into the world, we want it to be for the benefit of everyone and the benefit of humanity. And it's something bigger than all of us and the competition is remembering we've got to take these final steps in a careful, thoughtful way and make sure that we're building it with safety and security in mind, as well as enabling all of the amazing, good use cases that we hope to see.

Well, I think a lot of the ways that some of, let's say, my peers are talking about this I don't really agree with. I don't really agree with the style of communication but also some of the substance too. So what I would say, the way I'm looking at it is, there's going to be no doubt huge amount of change. So something this transformative, I've been on record saying, if you want to try and quantify it, it's going to be 10 times the impact of the Industrial Revolution, maybe 10 times faster, so over a decade, instead of a century. And that might be an underestimate. Probably is.

Yeah, so you can think of it as 100x the Industrial Revolution. Maybe an underestimate. So that's huge. But what I say about that, the future is not written. It's very uncertain. Are there more jobs going to be created? Are there different ones? We don't know. Nobody knows. None of these experts, so-called experts, know. And the future is to be written, actually, by the next generation. I'm actually very excited when I talk to students and when I go to places, like, we just came back earlier this year from the India AI Summit. And the youth of India are so excited about what they can do, what these tools will enable them to do, build companies, build applications, compete at the global scale. So this technology is actually democratizing opportunities for what you can build. What would have taken a team of 10, 50 experts to build, maybe a smart kid in their garage can build it themselves. So when I think back to the era I grew up in, with computers and home computers, and then there was the internet and the amount of opportunities that then created for kids of the day, students of the day, who grew up native with that technology, to actually go on and build the future that we see today. And I think that will happen again with the next generation that's going to grow up AI native. And I think, for the next 10 years at least, and it's hard to see beyond that, they're going to be super powered. As long as they're technically trained enough and then they bring creativity and taste and judgment, these kinds of skills, to the table, I think that's going to be the next era. So I'm actually very optimistic about human ingenuity to find amazing ways to use, and we're seeing that all the time, to use these new tools.

I think there will be changes to the job market, as always there is, with any new revolution. But I don't see why there wouldn't be more opportunities. Maybe it will be more like creating startups, one- or two-person startups. More entrepreneurial skills will be needed because you have these incredible creative tools. So there's going to be changes. But I think, overall, it will be better. And then the other thing I would say on the backlash is that I think it's incumbent on the field, our field, the AI field and industry, to show the unequivocal benefits more clearly and more concretely. And that's what we try to do with our AI for Science work and applying AI to advancing science and medicine. So that's why I've spent my whole career working on using these AI tools for things like AlphaFold, as you mentioned, and drug discovery. And I think there's so many areas of science that AI can be applied to. And we're trying to do that, material science, mathematics, energy sources. So that's what I think we need to show more of. And I hope our other peer organizations do more of as well.

Well, I'm in the middle. So I call myself a cautious optimist.

So I'm optimistic. Obviously, I'm optimistic, otherwise, I wouldn't have been doing this for 30 years, that the benefits are going to far outweigh the negatives with something like AI, as long as we take our time, we give ourselves the time, as society, to get this next few years right. We're talking about the next hundreds, maybe thousand years of human flourishing, if we get this right.

But there's a lot of challenges to get that right. So we've got to give ourselves the time to do it, rather than just racing to just get the next thing out. And I think that's where we've got to put our attention to and come together to discuss the bigger impacts that all of this work is going to produce.

Now we had a tool that can be used practically by scientists. These people asking us, I've got this protein involved in malaria or some infectious disease. We don't know the structure. Can we use AlphaFold to solve it? We can easily predict all known sequences in a month. All known sequences in a month? Yeah, easily. A billion, 2 billion. And they are, so why don't we just do that? Yeah, we should just do the lot. Well, I mean, now you can really, that's way better. Why don't we just do that? Well, so that's one of the options. Right. There's this, we should just run, that's a great idea. We should just run every protein in existence and then release that. Why didn't someone suggests this before? Of course, that's what we should do. Why are we thinking about making a service and then people submit their protein? We just fold everything and then give it to everyone in the world. Who knows how many discoveries will be made from that?