Thank you very much. Big finish. I'm Mike Allen, co-founder of Axios, on behalf of my co-founders, Roy Schwartz and Jim VandeHei. Thank you to all of you who for coming up nine years now have been fans of Axios, and thank you for turning out here in San Francisco in this historic bank, this very cool setting for this Axios AI Plus SF Summit. Welcome to all of you around the world for our big finish. Demis Hassabis, PhD, co-founder and CEO of Google DeepMind. He's a neuroscientist and entrepreneur and AI pioneer. Demis was a chess prodigy at five, a Nobel laureate at 48. He's a British-born genius. Demis Hassabis, welcome to Axios.
Thank you so much. Thanks for having me. We've been looking forward to this. We appreciate being here.
It was just over 400 days ago that you found out you were a Nobel laureate. And you said in that moment, you said this is surreal. This is the big one.
What has changed since then about your life and work? What has it made possible?
Well, look, it's still pretty surreal actually. Still hasn't fully sunk in, but it has made quite a big difference. The thing it makes a difference to is when you speak to people not in your field, including big government people, things like that, who maybe don't know that much about AI. If you have the Nobel Prize, it's a sort of shortcut to almost anyone to know that you're an expert in your field. So it's going to be useful, I think, in the future.
And you had endless resources at your disposal. Are there new resources that you have or that you think you can tap now?
Not really. I mean, you're right. We're lucky at Google DeepMind. We have a lot of resources. They're not endless. We always need more compute. No matter how much compute you have, but we have a lot of great things, which is why we're able to do such a broad portfolio of things. But it's mostly again this platform it gives you to speak out about things that you care about. And I haven't done a lot of that yet, but I think it will be important. Maybe we're going to talk about AI safety and other things. I think the Nobel and the platform that gives you could be useful for that.
And what's on the short list of, in addition to AI safety, that you think you'll be talking more about using your platform?
Yeah, well, it's not just about safety in the long term. AGI safety obviously we think a lot about that, but it's also about responsible use of AI today. What are the kinds of things we should be using AI to improve and to power up and to accelerate, and maybe what sorts of things we should be careful about even in the near term. So I think that's one thing. I think also just getting society ready for what's to come. AGI is probably the most transformative moment in human history, it's on the horizon, and we need to get prepared as a society and as a species. And I think of course governments and other important leaders are going to be critical in that, and I think having something like the Nobel platform opens pretty much any door.
One of the things that distinguishes you is you're deep in the science and yet you also are on the front line of this fight and this race among companies, hyperscalers, superpowers. And you sort of in the mold of Steve Jobs, you also have a product mind. You want to create delightful things for people, but you always say you're a scientist first.
Yeah, science. I'm a scientist first. The reason I say that is that's the default approach I take to everything. And what I mean by that is the scientific method, that way of thinking. I really love it. I think the scientific method is the most important idea humanity's ever had. It created the Enlightenment and then modern science. Basically, modern civilization depends on this idea of scientific method and experimentation and then updating your hypothesis. And I think it's an incredibly powerful method, but I think it can be applied to more than just science. I think it can be applied to everyday living and indeed business. And that's what I've tried to do, to take that to its limit. And I think that's what gives us an advantage in some ways as a research organization, as an engineering organization. Yes, we're in the middle of this ferocious, probably the most ferocious competitive battle maybe tech has ever seen. But one of the things that I think gives us an edge is the rigor and precision we bring to our work, because we have a scientific method at the heart of it, and we blend world-class research with world-class engineering with world-class infrastructure. And I think you need all three of those things to be at the frontier of something like AI, and I think we're pretty unique in having world-class capabilities in all those areas.
Yeah, so in Axios fashion, we're going to divide our conversation between zoom out and zoom in. So zoom out, getting your priceless mind on the state of AI. So we're going to talk about the blunt state of AI. And what I'm going to ask from you is given the known knowns today, be blunt, clinical, no hype, no soft selling. Can we do that?
All right. What does the next 12 months of progress look like? What do you believe that if we sit here a year from today, what will have changed in the world?
I think the things that we're pressing hard on are the convergence of modalities. So Gemini, which is our main foundation model, has always been multimodal from the beginning. It takes images, video, text, audio, and can now increasingly produce those types of outputs as well. And I think we're getting some really interesting cross-pollination by being multimodal. One of the best examples of that is our latest image model, Imagen 3, which shows some astonishing understanding of visuals, and it can create infographics that are really accurate. So I think over the next year you're going to see that progress a lot. And I think for example in video, when that converges with the language models, you're going to see some very interesting combinations of capabilities there. I think the other things we're going to see over the next year, and I'm personally working on, is world models. So we have this system called Genie, Genie 3, which is like an interactive video model. You can generate a video but then you can start walking around it like you're in a game or simulation, and it stays coherent for a minute. I think that's very exciting. And then maybe the other thing is agent-based systems. The field's been talking a lot about agents, but they're not reliable yet enough to do full tasks. But I think over the next year...
We've heard a lot about that today here on the Axios stage. What would you say a year from now? How will agents have progressed? What's an example of how it will work in everyday life a year from now?
Well, look, we have this concept of a universal assistant that we want Gemini eventually to become. I think you're going to see from us over the next year, this will be on more devices as well. By universal, we mean it's not just on your computer or your laptop or your phone, but maybe comes around with you on glasses or other devices. And I think we want to create something that is useful to you in your everyday life, that you consult many times a day. It becomes a part of the fabric of your life and it just improves your productivity but also your personal life, recommendations for books and films and other activities that you'd like. But agents at the moment, you can't delegate to them a whole task and be sure they're going to complete that entire task completely reliably. But a year from now, I think we'll start having agents that are close to doing that.
Best case, worst case. What is the best case for what AI can do for the world and what do you fear most?
Well, look, the best case scenario that I've always dreamed about and why I've worked my whole life on AI, getting closer to this moment we've been working towards for decades now, is a kind of radical abundance. This idea that we've solved a lot of the biggest issues confronting society and humanity today. Whether that's free renewable clean energy, maybe we solve fusion or better batteries and solar materials, semiconductors, material science. We've solved a lot of diseases. So then we're in a situation where we're in this new era, a post-scarcity era, and humanity is flourishing and traveling to the stars and spreading consciousness to the galaxy.
And what do you fear most?
Well, even that utopian view has some questions around it about what will be our purpose as humans if there are these technologies that are solving all these problems. What will be left to solve? I worry about that as a scientist, even the scientific method. But there's also the well-known challenges and risks with AI. Twofold: one is bad actors using AI for harmful ends, or the AI itself as it gets closer to AGI and becomes more agentic, it goes off the rails in some way that harms humanity.
So you mentioned going off the rails. How worried are you about these catastrophic outcomes? Your level of concern? I'm just going to rattle them off. One, pathogens created by an evil actor using AI.
I think that's definitely one of the bad use case scenarios that we have to guard against for sure.
Energy or water cyber terror using AI by a foreign actor.
Yeah, that's probably almost already happening now, I would say. Maybe not with very sophisticated AI yet, but I think that's the most obvious vulnerable vector. And which is why we focus quite a lot as Google and as DeepMind on AI for cybersecurity, to power up the defensive side of that equation.
AI operating outside human control on its own.
Well, this goes back to the agentic stuff. I think as that becomes more sophisticated, and it's clear why the industry will build those things because they'll be more useful as assistants, they're definitely going to happen. But the more agentic and autonomous they are, the more room there is for these things to deviate from what you maybe had intended when you gave the initial instruction or the initial goal. So this is a very active area of research, how to make sure that systems that maybe are capable of continual learning or online learning stay within the guardrails that you set. I think the good news is because AI has become so big commercially and for enterprises, if you think about renting or selling one of your agents as a model provider to another big business, those businesses will want guarantees around the agent's behavior, what it does with their data, what it does with their customers. And if those things go wrong, they're not going to be existential in any way, but you'll lose the business for sure. So because why would that business enterprise go with that provider? They would choose a different provider that was more responsible and had better guarantees. So I think what's great about that is that capitalism will reward naturally more responsible actors.
But it's possible that the AI could jump the moat, jump the guardrail potentially if done wrong.
I mean, it's always a possibility. Nobody really knows what that is. That's one of the big unknowns. I think it's non-zero that potential. So it's worth very seriously considering and mitigating against. But I hear people give very precise percentages about what the chances of p-doom are, which I think is kind of nonsense because no one knows what it is. What I know is it's non-zero.
So you don't quantify it but you say it's non-zero. So clearly if your p-doom is non-zero, then you must put significant resources and attention on that.
Where is the US winning the AI race against China and where are we losing?
I think that we're still in the US and in the West in the lead, if you look at the latest benchmarks and the latest systems. But China is not far behind. If you look at the latest DeepSeek or the latest models, they're very good and there are some very capable teams there. So maybe the lead is only a matter of months as opposed to years at this point.
Because when you put chips aside, AI, China probably is winning.
