Well, I think what Anthropic's doing with code is very interesting with their Claude Code. There's a lot of excitement around that in the developer market. We're pleased with the performance of Gemini 3, but they've done something special there. When the CEO of Google DeepMind, a Nobel Prize winner running arguably the most resourced AI lab on the planet tells you a competitor has done something special, you pay attention. That was Demis Hassabis last month and he's not wrong. Since he said this, Anthropic's Claude Code has crossed a billion dollars in annual revenue in under 6 months. Spotify's best developers reportedly haven't written a single line of code since December. Even engineers inside Microsoft are using it over their own Copilot. So, when Demis praises the competition and then pivots to what Google is actually betting on, it tells you something about where he thinks the real race is heading.
I'm very excited about the stuff that we're doing on multimodal.
Do you want to explain that?
Yes. Multimodal being Gemini from the beginning as being multimodal, and by that I mean being able to deal with more than just language and text, but actually image, video, audio as a native input and output. And we're bringing that all together. That's always been our strength and the reason we want to do that, and I think that's what I'm excited about this year, is that's what you would need for a kind of an assistant that travels around with you in the real world, maybe on your glasses or your phone. It needs to understand the world, the context around you, the physical world. And of course for robotics that's critical too. And I think I've been spending quite a lot of time on that last year and I think that's going to have some big moments in the next couple of years.
Can you talk a little bit about these big moments? Is it a question of trying to create devices that would sort of, you know, the new iPhone or the glasses?
Yeah. There's actually so many simultaneous things one has to do, which is why it's very exciting but also quite daunting at the moment. At least from our perspective as Google DeepMind, as kind of like, you know, we like to think of ourselves, we describe ourselves internally as the kind of engine room of Google. And we're providing the engine, which is these, you know, these models like Gemini and Veo and Nano, all these kind of state-of-the-art models. And then we've got to figure out, you know, how do we want to incorporate them into features in products that are really useful to the end user. So there's that whole aspect of work which is enhancing what already exists from email to your Chrome browser to search. But then there are also, you know, all these very exciting new greenfield areas of digital assistants like the Gemini app, but what does that become over time? And that including new devices. And we're working on, and we've announced recently, you know, partnerships with Warby Parker and Gentle Monster on new types of smart glasses. Obviously, Google has a long history with smart glasses. But I think maybe we were a bit too ahead of our time when we first started this 10, you know, 10 plus years ago at Google with the devices. But now I think, you know, I think what was missing was a killer app for that. And I think a kind of universal digital assistant that helps you in your everyday life could well be that killer app for things like smart glasses that's connected to your phone.
That pivot matters. While everyone else is fighting over who has the best chatbot, Demis is making a different bet entirely: that the future of AI isn't a text box on your screen. It's something that moves through the world with you. Google just announced smart glasses partnerships with Warby Parker and Gentle Monster. And Demis is basically admitting that when Google tried this 10 years ago with Google Glass, the tech was ready, but there was nothing worth doing with it. Now he thinks a universal AI assistant might finally be the reason people actually want a computer on their face. And for that you need an AI that can see, hear and understand physical space, not just read and write text.
With China, from what I can see in China, there is no AI race. It's very different from what you hear. There's no sort of race to reach AGI. There is a lot more focus on applications and finding efficiencies. Is that perhaps the more realistic approach?
Look, it's perhaps the more, I don't know about realistic, but the less risky approach perhaps. And I think, by the way, I think the Chinese market from what I understand is just as intensely competitive as the Western companies are with each other. It's just that I think you're right. They're more focused on the near-term applications. What can you concretely do right now rather than maybe these more research-heavy frontier capabilities that would get you to AGI. I think that's fine. I started DeepMind and our job is now at Google DeepMind and Alphabet as a whole. We want to build AGI. We think that's the ultimate goal and then that will unlock so many opportunities and possibilities in the world that we've talked about many times. So I think that's really the north star. And on the way we'll create lots of useful technologies, but I think you've got to have that as a north star if you want to progress the research in as innovative a way as possible. And I think that's why in my opinion the Western companies are still in the lead on that.
How many months are you ahead? Is it a matter of months or...?
I think probably it's only a matter of months now would be my guess. Although interestingly, some of the Chinese leaders, entrepreneurs I talked to, they feel like they're further behind than that. I'm not sure that's the case. You know, maybe it's only a matter of 6 months or so now, but I think it's important that because I think what even things like DeepSeek, which I think was a bit of an overreaction in the West to that, actually is a bit overblown. The Chinese sort of labs haven't proven they can innovate beyond the frontier yet. They're getting faster and faster at catching up to the frontier, what the frontier labs are doing, but they haven't sort of innovated beyond that, like the next Transformers or something like that. They haven't proven they have that capability yet.
That six-month number got a lot of attention. Bloomberg ran with it. CNBC ran with it. But what's more interesting is the distinction Demis draws underneath it. He's not saying China can't build good models. They clearly can. Alibaba's Qwen family actually overtook Meta's Llama in total downloads on Hugging Face. DeepSeek proved you could train competitive models for a fraction of the cost. But Demis' argument is that catching up to the frontier and pushing past it are fundamentally different skills. The transformer architecture that all of this runs on was invented at Google in 2017. And so far, no Chinese lab has produced that kind of foundational breakthrough. They're getting faster at closing the gap, but the gap keeps moving. Whether that holds is probably the most important question in AI right now.
