I've got a lot of faith in human ingenuity. We're sort of instantly adaptable. We're general intelligences ourselves, don't forget. Look at what we built around us, it's incredible, with our hunter-gatherer brains. Why would we stop here? Even the tools we have today, just a few years ago, that would have been unthinkable. But today we've just totally adapted to it. Oh yeah. You know, we chat with bots that are basically Turing test level, and we make Omni models that can create any video, and we're kind of like, yeah, you know, that's kind of normal. What separates the great scientist from the good scientist is not their technical capability. It's that creative capability, after the technology is ready.
How are you thinking about democratization? Is it available just to the rich? How can everyone have it? How can we make it best for the world?
Demis, thanks so much for being here again. It's always great talking to you.
So Google I/O, obviously tons of announcements. What are you most excited for?
Oh, wow. It's, I mean, I think we announced a lot of really exciting things. You probably saw me pretty excited about Omni. I think this is a really interesting direction for us on World Models. I've always been a big proponent of that. But obviously, you know, I think Flash 3.5 is the big story. And integrating that into Antigravity 2.0, I think people are going to be really pleasantly surprised. And I think it's an amazing kind of like workhorse model. And then of course, you got a lot more to come. You know, with Pro. Yeah, benchmarks are going up, agents are here with Spark. And I think like on the consumer side, we're really starting to see like, productivity gains. Yes. Like kicking in, like even with Nano Banana for example, content creators are genuinely using these tools, new tools in the toolkits to make more content, that sort of stuff.
I'm curious, like looking at today with like Omni, for example, versus three years ago when Gemini was first released. Yeah. How have your AGI timelines kind of shifted? Have they, has it gone according to plan or has it gone faster, or slower?
I think it's going according to plan. So a few years ago you probably saw maybe we even talked about in interviews, I used to say 5 to 10 years, maybe that's 2 or 3 years ago. I think we're sort of exactly on track. And, and I would say my confidence interval is narrowed. So, you know, these days I'm thinking it's 2030 plus or minus a year.
Wow. Okay. So 2030 plus minus a year. That's that's. Soon. Yeah very soon. How are you guys preparing? Like, internally.
We we're, you know, first of all, we've got to we've done a lot of engineering work under the hood in the last year, which you see, you see the surface of when we announced things like this. But actually you can almost think that whole stack had to be rewritten to be sort of, AI first, model first, and now agent first. So that's quite a big change from like normal software. And obviously there's all this huge infrastructure that serves billions of users every day at lightning speed, and that's all got to work. And I think, we've made massive improvements on that over the past year. And that's why you can see all the goodness of the AI models flowing through into our product services, you know, so quickly and in such compelling ways. So I think, I think that's big and, but yeah, in terms of AGI, I think we're, we're sort of on track and, actually we're sort of on track from where we thought back in 2010 when we started DeepMind. You know, people like Shane, my co-founder, was on record saying around 2028, 2030, you know, we thought of it as a 20 year mission, and I think we're on track for that.
What do you think the capabilities that are missing are still before we hit it yet?
So I think there's still a few things missing. I feel like we still have kind of jagged intelligences in the sense that they're amazingly good at certain things. You're seeing that with things like Omni. I think it's kind of mind blowing. We're a little bit desensitized to all of that now, but it's you can think of it sort of like Nano Banana for video, this first version. And I think content creators like yourself should have a load of fun with it. But yeah, in terms of, like its understanding of physics and the way the world works, it's not perfect, but it's pretty mind blowing if you stop and think about it. And, those are all hallmarks of what we imagine an AGI to be able to do. And of course, is going to be amazingly important for things like robotics and glasses. But on the other hand, there's still a lot, the systems fail at still and, and you can see that in the agentic side of things, it's, it's starting to become, I think in the last six months, actually, genuinely really useful to people in their daily lives. But it's still not quite fire and forget for the average, you know, user. I think it's there's still some reliability and some things, these systems all get stuck on. So those things need to be, you know, consistency, continual learning. There's still some breakthroughs that are needed in that direction.
Gotcha. So this is like sort of a research problem or?
Yes. So we have a ton of research on things like that. Also memory still not quite right, I would say in any of the systems it's a little bit brute force, something smarter I think is needed there. So there's a few, you know, long term reasoning and planning. There's a few areas, critical areas, only a handful, that I still think, need to be cracked before we would have something like AGI that we could call AGI.
Yeah. I mean, we're it's really cool to see the personalization across the devices now. I think last year we were talking about this. And this year you guys are releasing it in the summer with the glasses. You got the watches, and it connects to your phone.
Yeah. The the Fitbits and all of these things. It's slowly coming together but the memory isn't like fully there. Yeah exactly. So it's coming together. I mean, I think one of the things that was most exciting we showed is the progress we've made with glasses this year compared to last year, and soon is going to be in everyone's hands. I can't wait to, you know, have that myself. It's one of the dreams I've always had a personal assistant that helps you with you everywhere you go and just helps you in your daily life, you know, get rid of mundane admin tasks. And email would be great. You know, dealing with my emails, but it's so you have more time to do what you know, what you want in the day. And I think we're really close now to how things, really compelling things. And in some ways, I think the this idea of a universal digital assistant, in my view, is, is the killer app that the form factor of glasses was always waiting for. You know, at Google, of course, has a huge history and long history in glasses. But but I think this is the actual killer app. What we've been sort of building towards now and we're on the cusp of and it's very exciting. And then also integrating into all the other devices and your health, data and things like this if you want to. And then Gemini being to help you, with those kinds of things.
The Gemini subscriptions quite compelling in that way, right. Because it really has everything.
Yeah. Bundled into one. And that's one of the, I think beauties and of of the power of the, the full stack we have at Google, right. You know, from the chips to the models to the, to the products.
Yeah. But jumping ahead here, I think a year ago, we spoke and you said that AI could help cure all diseases in our lifetimes. But since then, what are some kind of, like the biggest developments you've seen? In AI drug discovery have your timelines kind of shifted since then?
Again, I would say they've not shifted, but they've hardened. They've. They've tightened. So I'm more sure let's say about those timelines now. Right. I was hoping it would be in the next decade or two we'd make these big advances in medicine. Now I'm, you know, very sure. I wouldn't say I'm certain, but I'm, I'm I'm very, confident that is the case. Seeing the progress we made in the last year on on our Gemini for science side of things at DeepMind, but also especially at Isomorphic Labs, where we're really trying to, you know, push forward, the models, the specialized models that are required to understand chemistry and biochemistry, the kinds of things you'd need to design drugs. And it's going, amazingly well. We just raised a bunch of money to accelerate even further. And, you know, we have excitingly now, some, test compounds in pre-clinical stage, which is, you know, the beginning of them becoming real, real, cures. And I think that actually will be the biggest watershed moment in AI when that happens.
