DeepMind's Demis Hassabis on Why AGI is Bigger than the Industrial Revolution

Yeah. We’ve been very consistent how we define AGI as basically 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 maybe 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.

Yeah. I mean, I think, look, I’ve got a probability distribution around the timings, but I I would say there’s a very good chance of it being within the next five years. So that’s not long at all. Is that closer than you thought? Has that changed over time? Not really. I mean, actually, you when you it’s funny. My cofounder, Shane Legg, who’s chief scientist here, when we started out DeepMind back in 2010, he used to write blog posts sort of predicting about when AGI would happen. And bearing in mind, in 2010, when we started, almost nobody was working in AI, and everyone thought AI And with the greatest of respect,

No. And 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 basically, we predicted around twenty years it would take from when we started out. And I think we’re pretty much on track.

I think compute is the big one, not just for the obvious reason of scaling up your ideas and your systems as the scaling laws, as they’re called, keeping on building bigger and bigger architectures with more and more parameters. And 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 computers, the cloud is our workbench, basically. So if you have a new idea, a new algorithmic idea, but you want to test it, you kind of got to test it at a reasonable scale.

Otherwise, it won’t hold when you actually 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.

Mhmm.

Do you think that’s true? I don’t think so. I think it’s a bit more nuanced than that. So, of course, when the leading companies all started building these large language models, you’re getting enormous jumps with each generation of new system. You know, maybe they’re almost like doubling in performance. At some point, that had to slow down. So it’s not kind of continuing to be exponential, but that doesn’t mean there isn’t great returns still for scaling the existing, you know, systems up further. So we and the other frontier labs are getting a lot of great returns on on that kind of compute expansion.

So I would say the returns are kind of still very substantial, although they’re a bit less than they were, obviously, at the start of all of their scaling.

I think actually 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 five, ten years ago, I would have been pretty amazed. So I think in most domains, we’re 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, after you put them out into the into the world. You know, they’re not very good at learning further things. And I think some critical capabilities are about missing.

Well, people haven’t quite figured out yet, and all the leading labs are working on this, like, how to integrate new learning into the existing systems that, you know, you spent months training. Of course, the brain does this very elegantly. Right? And probably through things like sleep reinforcement learning. So, you know, you just kind of get consolidation, it’s called, in the brain where, you know, your memories during the day are replayed, and then some of that information is elegantly incorporated into your existing knowledge base. And perhaps we I’ve I thought for a while, maybe we need something like that to incorporate new information along with the existing information base.

Yeah. Well, we made some organizational changes. So I think we’ve always had the deepest and broadest research bench at Google and at DeepMind. I mean, if you look at the last decade or plus, you know, fifteen years, I would say about 90% of the breakthroughs that underpin the modern AI industry were done by either by Google Brain or Google Research or DeepMind. So one of our groups, if you think of, like, AlphaGo and reinforcement learning and, of course, transformers, you know, these are all the key breakthroughs.

So I would back us to sort of make those breakthroughs in the future if there are any missing ones. And I think we’ve basically helped put together all the talent from around the company sort of pushing in one direction. And then we talked earlier just 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 the company. So I think a lot of it was assembling together all the ingredients we already had and then kind of pushing with relentless sort of focus and and and pace, acting almost like a start up, really, to get back to the the frontier and and be ahead in in many areas.

I think there’s quite a few things that are missing. There’s 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 there’s there’s a lot of interesting, probably, 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, as with our minds, we can do.

There’s quite a lot of problems, I think, that are still left to overcome. Maybe one of the biggest is consistency. So, you know, the 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 in if you pose a question in a slightly different way, they can actually still fail at quite elementary things. So a general intelligence shouldn’t be that sort of jagged. When you reposition

Yeah. Well, I mean, the general intelligence, you know, if you think about how our minds work, it shouldn’t have those kinds of holes in it.

Yeah. I feel like maybe, you know, the three or four leading labs now, which we’re one, I think the gap is sort of starting to pull away. Because a lot of these tools also, of course, help you build the next generation, so things like coding tools, math tools. And it’s getting harder and harder, I would say, to kind of eke out the same gains from just the same ideas. So I think those labs that have capability to, you know, invent new algorithmic ideas are gonna start having bigger advantage over the next few years as the the last set of ideas are sort of, you know, all the juices being rung out of them.

with

Yeah. I think it’s probably similar to what we’re seeing today. I mean, we’re we’re big supporters of of open science and and open models, and we’ve done many, many things, obviously, from from the original transformers to AlphaFold. You know, these are all things we’ve sort of given out into the world and to help the research community. And we plan to continue to do that, especially in applied domains, you know, scientific domains, applying AI to science, which is obviously my passion. But increasingly, what you’re going to see is the open source models are probably one step back from the absolute frontier.

It usually takes about six months for the open source community to sort of reimplement and figure out what those ideas are. But we are also pushing hard on a kind of suite of open source models called Gemma, which are we’re determined to kind of make best in class for their sizes. So specifically for small developers or academics or the, you know, 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 have with very different views. For me, I don’t think it’s you know, I kind of disagree with Jan on a few things in terms of I think there might be there’s there’s a fifty fifty chance there’s some things maybe missing that we still need to make breakthroughs in. Perhaps they’re world models, these kinds of approaches. But my betting is pretty strongly is 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 gains from the from returns from the scaling laws. I think the only question really is when you think about a future AGI system is is an LLM foundation model gonna be the key component only, or is it the total system? Right? So I just think it’s it’s a question of, you know, is there anything else needed? Not is it not I don’t think it’s gonna get replaced. I think it’s gonna get built on top of these foundation models, just like the way we do with our world models.

When we think about

I think on the positive side and the things, obviously, I’ve I spent my whole career and life building towards AGI is I think it will be the ultimate tool for science and medicine. So in terms of advancing scientific discovery, finding cures to diseases, I think we need that kind of technology. And so I’m hoping, in five years plus time, we’ll be sort of entering a new golden era, golden age of scientific discovery.

Yeah. So I think we’ll get to that point soon. First of all, what we’re doing is, you know, after we did the AlphaFold project to do protein folding, then we spun out a company called Isomorphic Labs, which is doing extremely well. And that is supposed to you know, the idea there is we’re focusing on solving the rest of the drug discovery process, which is 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, you know, the next five plus five to ten years. Then you’re right. The next problem is the clinical trials still take many, many years. Right? But I think AI can help there in terms of 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. And so I think AI can help there too. But I think the real revolution will come when a few, maybe a dozen or so, AI drugs get through the whole process.

And then the government and the regulatory body see that, and they have enough data to sort of back test the predictions of those models. And then maybe what we can do will be in the future where maybe ten further years where we can really just trust the predictions that the models are making and actually then maybe skip out some steps, perhaps like the 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 gotta do in two steps.

Solve the drug design problem first, and then look at the regulatory length of time it takes.

Yeah. I do think that’s right. I think that is the stakes that, you know, we have to deal with. And, you know, there’s two things I worry about. One is the misuse of these systems by bad actors, and they can be repurposed. These are dual purpose technologies. They can be used for incredible good in science and health, as we just discussed, 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 they be kept on 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 sort of minimum standards from all of the leading providers. But it needs to ideally be a kind of international standards. What is the right kind

For sure. I mean, that’s not it’s sort of crazy, the timing that we’re in, right, with this most consequential maybe technology the world’s ever seen at the same time as a very fragmented sort of international system. And it’s not ideal, but I think we’re gonna have to try and do the best we can to at least come up with a sort of set of maybe minimum standards, some benchmarks that test for undesirable properties, for example, deception. You don’t you know, nobody wants should be building systems that are capable of deception because then they could be getting around other safeguards.

And then I imagine, you know, if things go well, some kind of certification process that basically it’s almost like a kite mark of, you know, quality that this model has certain safeguards and certain guarantees. And so therefore, consumers and companies can safely sort of build on top of it. And 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 they’re cross territory.

Yeah. Well, I think there are I mean, ultimately, it’s gotta be government, I think. But, you know, the types of technical bodies that would be able to do the technical work would be like maybe the AI safety institutes. You know, there’s a very good one in The UK that, you know, was set up under prime minister, I think he’s doing great work. And then 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 actually evaluate and audit these kinds of systems against certain benchmarks.

And I kind of, like, independently check whether they are, meeting the right standards. If I could give you, like, a magic

Yeah. I think we need some kind of international body, maybe similar to the Atomic Agency, something like that, that perhaps the AI safety institutes sort of feed into. And the research community has to also do this and be involved in, like, what are the right set of benchmarks to check what types of traits, what types of capabilities. Maybe there are other safeguards too, like, you know, it’s it wouldn’t be desirable to have AI systems output tokens that are not human readable. So, you know, in some kind of machine language that we couldn’t understand, I think that would introduce a new vulnerability.

So there’s quite a few sort of things like that, which I think most of the leading labs would agree probably not best to do. And then these bodies would, you know, these institutions would test against those things. And I think that would give the public confidence, and and, you know, academia could be involved as well, as well as civil society, that these systems, which are gonna get incredibly powerful, have been independently checked and audited. That’s it. Your magic wand’s done now. Yeah. Then that was the one Maybe maybe I used to done the wrong thing.

But

Well, certainly, you know, in the past, with every new revolutionary technology, there’s been a lot of jobs disruption. So that’s for sure. And I think that’s definitely gonna happen. So a lot of old jobs, you know, go away or not viable anymore. But then, actually, 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, have to be very careful to say this time is different.

And I guess that’s what people like Mark are claiming is like, you know, it’s the same as as as the last sort of, you know, 10 massive breakthroughs like the Internet, mobile, and so on. I do think this is gonna be bigger than all of those previous breakthroughs, technological breakthroughs. I mean, I sometimes quantify, like, AGI the coming of AGI is, like, 10 times the industrial revolution at 10 times the speed, so unfolding over a decade instead of a century. You know, 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. I mean, we wouldn’t have modern medicine today. Child mortality was at forty percent back in back pre industrial revolution. So things thing you you wouldn’t want it 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.

thought? No. I I think that’s still the truth. I mean, maybe the both timescales of short term and long term are are nearer than other technologies. But I do think, like, literally today, as of today and in the next year, things are a bit overhyped in AI. I mean, there couldn’t be any more hyped in some ways. But on the other hand, interestingly, I still think it’s still very underappreciated how revolutionary this is gonna be in the sort of time scale of about ten years. We could call that long term.

So there’s still that dichotomy even today with AI.

Well, I think there’s different ways that could play out. So, you know, 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 everyone every country should have a sovereign wealth fund that does that. That would be the investment way of doing it. I think also there needs to be thought about if there is this massive productivity gain, but it’s sort of narrow where that accrues, you know, how do we redistribute, and and how do we distribute that so that everyone benefits from these huge gains?

And I can see all sorts of ways that could be done, including providing infrastructure and other things with that additional productivity gain. I mean, there could be unbelievable things happening in the five to ten 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. Maybe we have 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

Well, I think, actually, AI will, in in the medium to long run, more than pay for itself, I think, in terms of energy costs. So we work on all these projects of optimizing existing infrastructure, like 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, and we have all sorts of the best kind of 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 maybe is these new breakthrough technologies, like fusion, like new batteries, superconductors that I think AI will be essential for helping us reach. And then I think we’ll be in a completely new energy situation than we’ve ever been as humanity where and then that will, of course, help with things like the climate and environment and eventually also help us get into space much more cheaply. Because if you have a, you know, an incredible energy source like fusion, then you have effectively unlimited rocket fuel because you can just distill, catalyze seawater.

Don’t worry, then.

Well, I should ask you that question too. But I think I think I saw in London when we started DeepMind as a place that and and The UK in general and Europe to some degree, there’s incredible talent here. You know, we’ve always had, I don’t know what it is, three or four of the top 10 universities in the world with Cambridge and Oxford, Imperial, or UCL, these kind of universities. So we’re producing the envy of the world, really, these amazing graduates and PhD students. We have incredible scientists here.

We’ve got rich heritage of that all the way from, you know, Turing and and Hawking and Darwin, Newton. So, you know, we have this incredible history of of scientific breakthroughs and having great thinkers. So I felt we had all the ingredients and the talent and great engineers here, but it just hadn’t been galvanized into an ambitious startup idea, a deep tech startup idea. And but I I felt it was possible. And I felt that there was actually less competition here for that sort of talent, and 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 and all these things. We’re a little bit out of it here. But I think it’s very conducive to to thinking deeply about things, being more original about how you think. And I think that’s great for things like deep tech where, you know, you don’t wanna be distracted by the latest fad.

You know it’s gonna be a 20 emission, which is what we knew at the beginning of DeepMind. So I think being a little bit away from that maelstrom is quite good. I mean, Palmer Lucky

yet. I mean, Daniel might well get there with one of his companies, you know, Spotify, Helsink. I think those are two good options. I think there’s no reason why we can’t have that. I’m I’m gonna try and do that with Isomorphic, which is headquartered here and I think has a potential to be that. That’s one of the disadvantages of Europe is, obviously, we’re a combination of smaller markets. So that’s one thing we have to kind of overcome. Maybe this EU ink thing could be a good innovation.

I’m pulling out the magic wand again. Yes. You can change one. Yeah. You’ve the magic,

I think in The UK, I mean, this may apply to other European countries too. I think unlocking what pension funds can invest in or just for the kind of growth stage. I think we’re brilliant at doing the startup idea and getting it to a certain level like we did with DeepMind. But then if you really wanna cross that sort of chasm into the trillion dollar global, you know, player, then where are the billion dollar rounds gonna come from where you can really take on those the the, you know, the existing incumbents?

And I think that certainly was missing ten years ago when I was doing fundraising for DeepMind, and I think it’s still kinda missing today. Just that kind of level of ambition and and the amount the capital markets can can support. I read about some of your early rounds raising in the Sub

Oh, yeah. It was amazing. It was at a it was at a Founders Fund because we were both SpaceX and DeepMind were part of a same portfolio, kind of amazing portfolio that Peter Thiel had at Founders Fund. I think we were both invited. I think I was invited to my first portfolio kind of conference. I think it must be back in 2011 or 2012, very early days. So we were the small little upcoming thing, and I had a small speaking slot. And then and then Elon was the, you know, big thing in that portfolio.

So he had the keynote. But then we met afterwards. Think it was in Elon says it was like we were passing each other in the bathroom or something. We said hi, and we both hit off, you know, immediately, like, as sort of, you know, people that were almost too ambitious in their thinking perhaps and love sci fi. And and and I really wanted to visit his rocket factory. So I was sort of trying to get an angular invite to to SpaceX in in LA, and I think I got there a couple you know, he invited me at the end of that meeting, and and and that was our second meeting in in the space effect factory.

Yeah. Well, look, I want to literally cure cancer. I know people say that’s the cliche, but I actually what we’re building at Isomorphic is general purpose. So we’re trying to build a platform, a drug design platform, that will be applicable to any therapeutic area. So, ideally, it will help with everything from neurodegeneration, cardiovascular, immunology, cancer. Those are the ones we’re we’re focusing 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?

So I think a lot of people worrying about the economic questions around AGI. I worry a lot about the philosophical questions around it. Like, when it comes let’s say, assume we get the technical right. Let’s assume we get the economics part of it right. Both of those are hard. Then there’s a philosophical question of what is meaning? What is purpose? We’ll find out won’t be 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 to help us, navigate that.

Hard final question.

I would like, my legacy to sort of be remembered for, like, advancing science, building technologies that bring incredible benefits into the world, like curing terrible diseases. Demis, thank you so much