So good to see you. Last time I saw you was in San Francisco, so I know you are all over the place. I'm curious if this year feels different than the last time you were in Davos. Gemini 3 is out. We've heard that OpenAI called a go a code red internally. Do you feel like Google got its mojo back?
Well, I'm not sure if that's for me to say, but I feel like we had a very good year. It's been hard work, really hard work getting our technology and the models sort of back to state-of-the-art. I think we did that with Gemini 3 especially and Nanabun in our imaging software. And then I think we've also sort of adapted really to this new world of shipping very fast, kind of bringing a startup energy to what we do.
Do you think people underestimated Google or got something wrong?
Yes, maybe. I'm not sure. Well, I mean, I think we always had the ingredients, you know, to be at the forefront of this. Obviously, we've got the long history in it. I think, you know, over the last decade, Google and DeepMind between us, we've invented most of the breakthroughs that the modern AI industry relies on now. Obviously, transformers, most famously, but AlphaGo, deep reinforcement learning, these things. And we have these incredible product surfaces, billions of user services that are natural fits for AI actually, from search to email to Chrome. But it's just getting all of that together and organized in the right way, and I think we've done that in the last couple of years. And there's still a lot more work to do, but I think we're starting to see the fruits of that.
If you think you have an advantage, how big do you think your advantage is? How long does it last?
Well, I think everything starts with research in my opinion, and the models especially being state-of-the-art on all the different benchmarks. And that's what we focused on first when we put Google and DeepMind together. And I think with the Gemini series we're very happy with how that's going. There's a lot more work to do there, but I think we're the only organization that has the full stack from the TPUs and the hardware, the data centers, the cloud business, the frontier lab and all of these amazing products that are natural fits for AI. So really structurally, from first principles, we should be doing very well. And I think there's actually a lot more headroom to come from here.
I wonder what a day in the life of a frontier model leading AI CEO is like. Like I, you know, I've read that you do most of your thinking between 1 and 4 a.m.
Is it ever not a code red inside people? Like, do you ever feel comfortable?
No, you never feel comfortable. I mean, we try, you know, code reds are for very special circumstances, but it's always, I mean, for the last, I would say three, four years, it's been unbelievably intense. 100-hour weeks, 50 weeks a year, and that's the norm. And I think that's what you have to do at the forefront of this unbelievably fast-moving technology. It's ferociously competitive out there, maybe the most intense competition there's ever been in technology, and the stakes are incredibly high. AGI and all of that that can mean, so commercially, scientifically. And then if you add all the excitement of what we're doing, and using it as my passion, as you know, is exploring scientific problems with AI, accelerating scientific discovery itself. This is what I've always dreamed about and I've worked my whole life on AI towards this moment. So it's sort of hard to sleep because there's so much work to do, but also there's so many exciting things to look into and to push forward.
I mean I know you're very focused on AI driving scientific progress, discovering new materials. Even we've seen now Gemini being integrated into humanoid robots. Is the AlphaFold moment for the physical world and AI here? What is that and what does that look like?
Yeah, I spent a lot of the last year actually looking very carefully into robotics. I do think we're on the cusp of a kind of breakthrough moment in physical intelligence. I still think we're about 18 months, two years away from doing, we need to do more research, but I think the foundation models like Gemini show the way forward. I mean, from the beginning we made Gemini multimodal so it could understand the physical world for multiple reasons. One was so we could build a universal assistant maybe that exists on your glasses or your phone that understands the world around you. But of course a second use of that would be for robotics.
So what does that moment for the physical world look like?
I think it's having robots reliably do useful tasks out in the world. And I think there's a few things holding that back still. Part of it is the algorithms are still not quite there. They need a little bit more robustness. They have to work with less data than you get for the LLMs or the models that work on just the digital realm. You can sort of create synthetic data. It's a lot harder to make that kind of data. And there's still sort of some problems in the hardware that are not solved, specifically things like the arm and the hand. Actually, when you look into robotics very carefully, you get a newfound appreciation, at least I did, for the human hand and how exquisite evolution has designed that. It's incredible. And it's very hard to match the reliability, the strength and the dexterity that the human hand has. So, there's still quite a lot in my opinion pieces to put together. But there's very exciting things. I mean, we just announced a new deep collaboration with Boston Dynamics. They've got some very exciting robots, and Hyundai, and actually applying it to automotive manufacturing. And we'll see over the next year how that goes in sort of prototype form and then maybe in a year or two we'll have some really impressive demonstrations that we can scale up.
A year ago DeepSeek seemed cataclysmic for the West. Now a year later it's quiet. China seems to have been quieter. Has your opinion on competition from China changed?
Not really. I mean I didn't think it was cataclysmic in the first place. I think it was a massive overreaction in the West. It was impressive and I think it shows that the Chinese are very capable. The leading companies, I think companies like ByteDance actually I would say are the most capable, and they're maybe only six months behind, not one or two years behind the frontier. So I think that's what DeepSeek showed. Some of the claims were overexaggerated about the amount of compute they used and being so minimal and so on because they relied on some Western models and also fine-tuning on the outputs of some of the leading Western models. So it wasn't sort of de novo. And the other thing I think so far is not yet to be seen is can China actually, the Chinese companies innovate beyond the frontier themselves. So they're gaining, they're very good at kind of catching up to where the frontier is and increasingly capable at that, but I think they've yet to show they could innovate beyond the frontier.
You helped define AGI. You have said we have a 50% chance of getting there by 2030. Is that still your timeline?
And is AGI still a useful target for you?
I think so. I think that is still my timeline. And it's a little bit longer than some others that you, some of my peers that you're here, but my bar is quite high. It's the ability to, you know, a system that exhibits all the cognitive capabilities humans have. And I think we're still clearly quite far from that. That means, you know, in things like scientific creativity, not just solving a conjecture or solving a problem in science, but actually coming up with the hypothesis or the problem in the first place. And as any scientist knows, finding the right question is actually often way harder than finding the answer. So, I don't, you know, it's not clear that these systems have that capability yet. In fact, they definitely don't right now. I think they will eventually, but it's not clear what's needed still. And there's things like continual learning, you know, online learning going beyond what they've been sort of trained for and then they're static out in the world. They need to learn on the fly. So there's quite a few in my view missing capabilities that are quite critical to what I would regard as an AGI system.
Google's a major investor in Anthropic. Dario was here earlier today. Do you agree or disagree with his prediction that AI will wipe away 50% of entry-level white collar jobs in 5 years?
I think that's also my timeline, and my view on that would be a lot longer. I mean, I think we're starting to see maybe the beginnings of that this year in terms of maybe entry-level jobs or internships, those types of things. But I think we would have to solve a lot more of this consistency that AI doesn't have right now. I call it jagged intelligences. We're very good at certain things and very poor at other things. And if you want to offload or delegate an entire task to say an agent, rather than having what we have today which are more like assisted programs, you're going to need a lot more consistency across the board. It's no good for it to be good at 95% of that task. You need it to be good at the whole task for you to be able to actually just sort of fire and forget on it. So I think there's still quite a lot more to be done before we'll see that kind of disruption.
But that kind of disruption will happen?
I think eventually, sure. I mean, in the limit with AGI, if you have systems like that I think that changes the whole economy actually. It's way beyond the question of jobs. I think potentially if we build it right we're in a post-scarcity world where we've solved some of the kind of fundamental root nodes of the world like energy sources, new clean renewable basically free energy sources if we solve fusion, something like that, with the help of AI, new materials. I think we'll be, you know, 5, 10 years past AGI we'll be in a radically abundant world. And so what does that mean to the economy and how society works?
Before we get to a post-scarcity world though, if we get there, there is so much anxiety about what happens in between. You know, I'm a mom. I know you have kids. What scares you most for them? What do you talk to them about? What do you tell them that's coming? I mean, we, you know, I've just heard so many, oh my gosh, for college graduates are going to have such a hard time.
