Google DeepMind CEO Demis Hassabis on AI’s Next Breakthroughs, What Counts As AGI, And Google’s AI Glasses Bet

The leader of Google's AI program weighs in on the cutting edge of AI research, Google's plans to put the technology in its products, and the imperative of publishing AI-generated protein structures.

AI is evolving fast, but AI researchers still have substantive work ahead of them. Figuring out how to get AI to learn continuously, for instance, is a problem that “has not been cracked yet,” Google DeepMind CEO Demis Hassabis told me last week. Tackling that problem, along with building better memory and finding more efficient use of the context window, should keep Hassabis and his team busy for a while.

In a live Big Technology Podcast recording at Davos, Hassabis spoke with me about the frontier of AI research, when it’s time to declare AGI, Google’s product plans — ranging from smart glasses to AI coding tools — and plenty more. I always find Hassabis’s perspective to be a good indicator of where the AI field is headed, and today I’m publishing our conversation in full.

You can read the full Q&A below, edited lightly for length and clarity, or listen to our discussion on Apple Podcasts, Spotify, YouTube, or your podcast app of choice.

Alex Kantrowitz: A year ago, there were questions about whether AI progress was starting to tail off. Those questions seem to have been settled for now. What specifically has helped the AI industry get past the concerns?

Demis Hassabis: For us internally, we were never questioning that. Just to be clear, I think we’ve always been seeing great improvements. So we were a bit puzzled by why there was this question in the air.

Some of it was people worried about data running out. And there is some truth in that — Has all the data had been used? Can we create synthetic data that’s going to be useful to learn from? But actually, it turns out you can wring more juice out of the existing architectures and data. So there’s plenty of room. And we’re still seeing that in both the pre-training, the post-training, and the thinking paradigms, and also the way that they all fit together. So I think there’s still plenty of headroom there, just with the techniques we already know about and tweaking and innovating on top of that.

A skeptic would say there have been a lot of tricks put on top of LLMs. There’s ‘scaffolding’ and ‘orchestration.’ An AI can use a tool to search the web, but it won’t remember what it learns. Is that just a limitation of the large language model paradigm?

I’m definitely a subscriber to the idea that maybe we need one or two more big breakthroughs before we’ll get to AGI. And I think they’re along the lines of things like continual learning, better memory, longer context windows—or perhaps more efficient context windows would be the right way to say it—so, don’t store everything, just store the important things. That would be a lot more efficient. That’s what the brain does. And better long-term reasoning and planning.

Now it remains to be seen whether just scaling up existing ideas and technologies will be enough to do that, or we need one or two more really big, insightful innovations. And probably, if you were to push me, I would be in the latter camp. But I think no matter what camp you’re in, we’re going to need large foundation models as the key component of the final AGI systems. Of that, I’m sure. So I’m not a subscriber to someone like Yann LeCun, who thinks they’re just some kind of dead end. I think the only debate in my mind is, are they a key component or the only component? So I think it’s between those two options.

This is one advantage we have of having such a deep and rich research bench. We can go after both of those things with maximum force—both scaling up the current paradigms and ideas. And when I say scaling up, that also involves innovation, by the way. Pre-training especially I think we’re very strong on. And then really new blue sky ideas for new architectures and things—the kinds of things we’ve invented over the last 10 years as Google and DeepMind, including transformers.

Can an AI model with a lot of hard-coded stuff ever be considered AGI?

No—well, it depends what you mean by a lot. I’m very interested in hybrid systems, is what I would call them. Or neuro-symbolic, sometimes people call them. AlphaFold, AlphaGo are examples of that. So some of our most important work combines neural networks and deep learning with things like Monte Carlo Tree Search. So I think that could be possible.

And there’s some very interesting work we’re doing, building the LLMs with things like evolutionary methods, AlphaEvolve, to actually go and discover new knowledge. You may need something beyond what the existing methods do.

But I think learning is a critical part of AGI. It’s actually almost a defining feature. When we say general, we mean general learning. Can it learn new knowledge, and can it learn across any domain? That’s the general part. So for me, learning is synonymous with intelligence, and always has been.

If learning is synonymous with intelligence, these models still don’t have the ability to continually learn. They have goldfish brain. They can search the internet, figure things out, but the underlying model doesn’t change. How can the continual learning problem be solved?

I can give you some clues. We are working very hard on it. We’ve done some work—I think the best work on this in the past—with things like AlphaZero. The learn-from-scratch versions of AlphaGo. AlphaGo Zero also learned on top of the knowledge it already had. So we’ve done it in much narrower domains. Games are obviously a lot easier than the messy real world, so it remains to be seen if those kinds of techniques will really scale and generalize to the real world and actual real-world problems. But at least the methods we know can do some pretty impressive things.

And so now the question is, can we blend that, at least in my mind, with these big foundation models? And so of course, the foundation models are learning during training, but we would love them to learn out in the wild, including things like personalization. I think that’s going to happen, and I feel like that’s a critical part of building a great assistant—that it understands you and it works for you as technology that works for you.

And we’ve released our first versions of that just last week. Personal Intelligence is the first baby steps towards that. But I think to have it, you want to do it more than just having your data in the context window. You want to have something a bit deeper than that, which is, as you say, actually changes the model over time. That’s what ideally you would have. And that technique has not been cracked yet.

Sam Altman, toward the end of last year, told me that AGI is under-defined. And what he wishes everybody could agree to was that we’ve sort of whooshed by AGI and we move towards superintelligence. Do you agree?

I’m sure he does wish that, but absolutely not. I don’t think AGI should be turned into a marketing term for commercial gain. I think there has always been a scientific definition of that.

My definition is a system that can exhibit all the cognitive capabilities humans can, and I mean all. So that means the highest levels of human creativity that we always celebrate, the scientists and the artists that we admire. So it means not just solving a math equation or a conjecture, but coming up with a breakthrough conjecture—that’s much harder. Not solving something in physics or some bit of chemistry, some problem, even like AlphaFold’s protein folding. But actually coming up with a new theory of physics, something like Einstein did with general relativity. Can a system come up with that? Because of course, we can do that. The smartest humans with our human brain architectures have been able to do that in history.

And the same on the art side—not just create a pastiche of what’s known, but actually be Picasso or Mozart and create a completely new genre of art that we’d never seen before. And today’s systems, in my opinion, are nowhere near that. Doesn’t matter how many Erdős problems you solve, which—I mean, it’s good that we’re doing those things, but I think it’s far, far from what a true invention, or someone like Ramanujan would have been able to do.

And you need to have a system that can potentially do that across all these domains. And then on top of that, I’d add in physical intelligence. Because of course, we can play sports and control our bodies to amazing levels—the elite sports people that are walking around here today in Davos. And we’re still way off of that on robotics as another example.

So I think an AGI system would have to be able to do all of those things to really fulfill the original goal of the AI field. And I think we’re five to ten years away from that.

I think the argument would be that if something can do all those things, that would be considered superintelligence.

Of course not, because the individual humans could—we can come up with new theories. Einstein did, Feynman did, all the greats that were my scientific heroes—they were able to do that. It’s rare, but it’s possible with the human brain architecture.

So superintelligence is another concept that’s worth talking about, but that would be things that can really go beyond what human intelligence can do. We can’t think in 14 dimensions or plug in weather satellites into our brains—not yet, anyway. And so those are truly beyond human or superhuman, and that’s a whole other debate to have. But once we get to AGI.

You were asked on the Google DeepMind podcast—which is a great listen—if you have a system today that is close to AGI. I thought it might be Gemini 3. You named Nano Banana. The image generator. What?

Sometimes you have to have these fun names…

How is the image generator close to AGI?

Look, let’s take image generators. But also, let’s talk about our video generator, Veo, which is the state of the art in video generation. I think that’s even more interesting.

From an AGI perspective, you can think of a video model that can generate you 10 seconds, 20 seconds of a realistic scene — it’s sort of a model of the physical world. Intuitive physics, we’d sometimes call it in physics land. And it’s sort of intuitively understood how liquids and objects behave in the world. And obviously one way to exhibit understanding is to be able to generate it, at least to the human eye, being accurate enough to be satisfying to the human eye. Obviously, it’s not completely accurate from a physics point of view, and we’re going to improve that, but it’s steps towards having this idea of a world model—a system that can understand the world and the mechanics and the causality of the world.

And then, of course, that would be, I think, essential for AGI because that would allow these systems to plan long-term in the real world over perhaps very long time horizons, which, of course, we as humans can do. I’ll spend four years getting a degree so that I have more qualifications, so that in 10 years, I’ll have a better job. These are very long-term plans that we all do quite effortlessly. And at the moment, these systems still don’t know how to do. We can do short-term plans over one timescale, but I think you need these kind of world models.

And I think if you imagine robotics, that’s exactly what you want for robotics—robots planning in the real world, being able to imagine many trajectories from the current situation they’re in in order to complete some task. That’s exactly what you’d want.

And then finally, from our point of view, this is why we worked with Gemini as being multimodal from the beginning: Able to deal with video, image, and eventually converge that all into one model. That’s our plan. It will be very useful for a universal assistant as well.

Let’s talk product a little bit. I watched the documentary The Thinking Game, along with 300 million other people. There was something kind of interesting that happened there. Throughout the documentary, yourself and some colleagues kept pointing your phone at things and asking an assistant what was going on, and I was yelling at the computer, as I usually do, and said, “This guy needs glasses!” He needs smart glasses to be able to do it. The phone is the wrong form factor. What is your vision for AI glasses, and when is the rollout happening?

I think you’re exactly right. And that was our conclusion. It’s very obvious when you dog food these things internally that, as you saw from the film, we were holding up our phones to get it to tell us about the real world. And it’s amazing that it works. But it’s clearly not the right form factor for a lot of things you want to do—cooking, or roaming around the city and asking for directions or recommendations, or even helping the partially sighted. There’s a huge use case there to help with those types of situations.

And for that, I think you need something that’s hands-free. And the obvious thing is, for those of us anyway that wear glasses like me, is to put it on glasses, but there may well be other devices too. I’m not sure that glasses is the final form factor, but it’s definitely, it’s obviously a clear next form factor.

And of course, at Google and Alphabet, we have a long history with glasses, and maybe we were a bit too early in the past. But I think my analysis of it and talking to the people working on that project—a couple of things: the form factor was a bit too chunky and clunky, and the battery life and these kinds of things, which are now more or less solved. But I think the thing it was missing was a killer app.

And I think the killer app is a universal digital assistant that’s with you, helping you in your everyday life, and available to you on any surface—on your computer, on your browser, on your phone, but also on devices like glasses when you’re walking around the city. And I think it needs to be seamless, and knows each of those contexts and understands each of those contexts around you.

And I think we’re close now, especially with Gemini 3. I feel we finally got AI that is maybe powerful enough to make that a reality. And it’s one of the most exciting projects we’re working on, I would say. And it’s one of the things I’m personally working on—making smart glasses really work. And we hope to—we’ve done some great partnerships with Warby Parker and Gentle Monster and Samsung to build these next-generation glasses, and you should start seeing that maybe by the summer.

Warby Parker did have a filing that said that these glasses are coming out pretty soon…

And the prototype design—it depends how quickly that advances—but I think it’s going to happen very soon. And I think it will be a category, a new category-defining technology.

Given your personal involvement, is it safe to say that this is a pretty important initiative?

I like spending my own time on important things, but I like to be at the most cutting-edge thing. And that’s often the hardest thing—picking interim goals and giving confidence to the team, and also just understanding if the timing is right.

And over the years I’ve been doing this, many decades now, I’ve gotten quite good at doing that. So I try to be at the most cutting-edge parts. I feel I can make the most difference there. So things like glasses, robotics—I’m spending time on, and world models.

Let’s talk about ads. There’s been some news that Gemini might include ads. There’s been some news that some of your competitors might include ads. The funniest thing I saw about that on social media was someone who said, these people are nowhere close to AGI if the business model is advertising.

Well, it’s interesting. I think those are tells. I think actions speak louder than words. Going back to the original conversation we were having with Sam and others claiming AGI is around the corner—why would you bother with ads then? So that is, I think, a reasonable question to ask.

From our point of view, we have no plans at the moment to do ads, if you’re talking about the Gemini app specifically. I think we are going to obviously watch very carefully the outcome of what ChatGPT is saying they’re going to do. I think it has to be handled very carefully.

Because the dichotomy I see is that if you want an assistant that works for you, what is the most important thing? Trust. So trust and security and privacy, because you want to potentially share your life with that assistant. Then you want to be confident that it’s working on your behalf and with your best interests. And so you’ve got to be careful that the advertising model doesn’t bleed into that and confuse the user as to what this assistant is recommending. And I think that’s going to be an interesting challenge in that space.

That’s what not to do. And Google CEO Sundar Pichai, in a recent earnings call, said there are some ideas within Google of the right way to approach this. How do you approach advertising?