Seems like we always talk where it's very cold or very hot. I know. Exactly. Very hot. So, let's see if we can get through this without sweating.
Demis, everyone right now is freaking out about AI. They're banning AI models in DC. A lot of the concern though is about these text-based models that can create software, find vulnerabilities in computers. I'm wondering if you think, as a lot of people do, that the path to AGI runs through these models like Mythos that may soon be sort of self-improving, or do you think that it still requires a multimodal approach, like what you're working on at Gemini?
Well, there's a lot there to unpack just in that first question. First of all, with the things that we're seeing with cyber and Mythos, I've been pretty vocal for a long while that as we get closer to AGI — and I think we're on the cusp of that now — I sort of said statements like we're in the foothills of the singularity, that we need a bit more systematic approach. Of course, there's amazing opportunities ahead, a lot of the things you talked about in the introduction: solving all disease, finding new energy sources. All these are the reasons I've worked on AI my whole career. But there are also risks, and cyber is one. I'm actually there are going to be even more serious things. That's just a kind of warning shot for humanity, and I hope we take it seriously. But there'll be bio, nuclear, other kinds of risks coming down the line maybe in the next couple of years, and we've got to get ready for that. I think we need a more systematic way to deal with the issues, and maybe a kind of standards body that ideally would be international that would help test the latest frontier systems to make sure they're robust and the guardrails are sufficient. That's on the one hand of what you just mentioned. In terms of the technical approaches to AGI, we've always had a kind of, you know, the broadest (I would say) and deepest research bench. That's why over the last decade, I think maybe 90% or plus of the big breakthroughs in AI that underpin the modern AI industry came from, you know, Google Brain or DeepMind as separate research entities, and now together as Google DeepMind — from Transformers that underpins all large language models to AlphaGo and all the reinforcement learning pioneering that we did back then. So I think our approach has always been to bet on multiple things and push them as hard as possible. So obviously we have our scaling work, our own multimodal foundation models Gemini. We're pushing hard on coding, but also we have our multimodal generative media models like Omni and Veo, and we think those are important to give these models understanding of the world around us, the context around us. I think in the end, to have a full AGI system, you need to be able to also understand the physical world around you, and you definitely need that for things like robotics to become a reality and things like assistant on smart glasses, which I think are two very interesting applications.
I'm going to take that as a no. Thank you. So when you started DeepMind, you were so far on the frontier, and when you joined Google, it just seemed like between DeepMind and Google there was almost all of the major talent in AI under one corporate roof. Now you have at least three major competitors on the frontier who are all vying for the top minds. I'm wondering, do you think that DeepMind today still has the right talent to win the race to AGI?
Yeah, I think there's a lot of talent movement between all the leading labs, and we win our fair share of the top talent. But what I would say is that we have by far the biggest and broadest research bench of any of the leading labs out there. And we continue to put out the absolute frontier work, whether that's on the foundation models or these other models that eventually will feed into the foundation models, like our Omni and Veo models too. So, but it's a ferociously competitive market out there right now. Probably the most ferociously competitive there's ever been in the tech industry. And I think that was inevitable. When I look back on this, we started this back in 2010 where I started DeepMind and nobody was working on AI, definitely not in industry, but even in academia it was basically thought to be career suicide. 'Of course we know AI doesn't work, we tried it in the '90s at places like MIT and it was a dead end.' That was the prevailing view. But a small band of us felt that with the right ideas and using learning systems, reinforcement learning, and betting on neural networks, a lot of fast progress could be made. And we were right in the end. But it also meant that now the whole world in the last few years has woken up to the potential of AI. Every important company in the world is going to get involved in it.
Yeah. So we're here at CAN, we're at an advertising conference. There's a lot of people here who are extremely creative, and I'm sure a lot of them, even here in the audience, are using your video creation tools to create ads, to do other things in the creative arts. What can you do with these tools now that you couldn't do a year ago?
Well, I would say every month these tools and the underlying models are improving massively. And a year ago, the biggest change I would say with our tools, like with our new Omni model and things like Imagen for images, is the ability to kind of live what the output of the generative models is. So I think that's become extremely useful for creators. Part of the creative process obviously is you generate the first idea, the first concept, but you like some of it but not other parts of it. You don't want to have to regenerate the whole thing, which is where we were a year ago. You want to be able to describe in natural language, ideally as you would to a designer, 'Okay, keep that part the same but change this to something else,' and then iterate that maybe hundreds of times till you get to the final polished version that you want. So I think that kind of fine-grained control has been a big change over the last year, as well as just general relentless quality improvements.
Yeah. I mean, there's also, you know, even controversies within the ad industry where people try to figure out: 'Are they using AI at all? Is this 100% human created and you should disclose this?' etc. Do you think that's a conversation that's sort of temporary because we haven't adjusted yet to how AI is going to change creativity, or do you think that that's here to stay, that there will always be that conversation?
Well, there's two different parts. For certain, we need to deal with misinformation and deepfakes. That is something we were cognizant of years ago when we first started building these generative models, 3-4 years ago. We foresaw that we would be in a world where these systems would be really good — obviously that's what we were planning to do — and they would eventually be, as you know, photorealistic. And so we would need a digital watermarking system, which we created called SynthID, that was robust, sort of unhackable, and would be embedded imperceptibly in the image so that anyone — a citizen, a journalist, or a government — could detect whether that image was generated by an AI or not. All of our models that generate anything from music to images to videos come with SynthID embedded in it, and we've also open-sourced it and given it to the rest of the industry to use. A lot of our industry colleagues have now adopted that standard: OpenAI, Nvidia, and many other big ones. So I hope eventually that should become almost a regulation: if you're creating generative media, it should come with provenance detection. Obviously that will also help with things like right holders and IP rights too, so that can all be sort of connected together. As to whether it should be disclosed if you use AI for a piece of the work that you're doing, I'm not sure. I think that might be just an era we're in where, okay, we were using Photoshop or some other tool before, and now this is a more advanced tool, but it's just a tool for your own creativity. I'm just not sure that needs to be disclosed in the sense that you're talking about, other than you should know that the output was synthetically generated.
If you look at your career, creativity is this through line. I mean, you started out creating video games. You studied this as a neuroscientist, the nature of creativity in the brain. You're, you know, even AlphaFold, I think you could think of as a very creative approach to science, right? Now we have all these tools. There are people who would say, 'This is going to make us less creative. We're now just asking a model to do what you slaved for years doing.' So how do you think about it? How's it going to change creativity?
It's definitely going to change it. But I think what I'm seeing is a twofold change. One is it's democratizing some of the creative tools so more people can try this and try out their ideas relatively quickly and relatively easily. That's double-edged though, because it also produces a lot more things that maybe are not very creatively valuable. But it also means a lot more people can break into those industries. I think there's a lower bar to entry, less gatekeeping. So that will probably mean new creators, professional creators, find a path wherever they are in the world through using these tools. And then the other thing I'm seeing is on the professional side. We work with many professional directors and amazing collaborators, and we try to talk to them to design our tools to help enhance and empower their creative process. I think it's going to be incredible. They can sort of do 10x more things than they used to be able to do. Try out more stream ideas, iterate faster. They all have way more ideas than they can ever produce in their lifetimes, and these tools allow them to try out things in relatively inexpensive and quick ways. So for the professional, they're going to be able to iterate their way to much cooler things way more quickly. But just like with any new tool — the internet's the same, computers are the same — if you use it in a lazy way, it kind of takes away from the creative process. But if you use it in an innovative way, it should add to the creative process. I think it's going to take a while for the creative industries to figure out the best way to use these things. I talked to my game designer friends in the games industry. They're very excited about these tools, but I would say at least the games industry, which is the creative industry I know best, we're still yet to figure out the deeper ways of using this. But it's very early. The game industry is using it for obvious things like to create some assets and graphics, but can it change the nature of games and introduce whole new genres of games? That's what I think could be possible, like it was in the '90s when I started out in the games industry when graphics and AI first came on the scene for computer games, and it allowed us to make whole new types of game genres. That's what I hope to see these new tools spark.
Do you think, you know, there's a criticism that these were trained on the outputs of humans, right? Should there be some sort of auditability that lets people see, 'Oh, this output used partially one of my creations, and I should get compensated for that'? Do you think that should happen?
Well, maybe a new economic model is needed. I think that the tech industry and the creative industry together need to work together. That's what happened with streaming, which changed music, and things like YouTube with Content ID. YouTube specifically and Spotify and other companies like that came up with new really robust business models. So I think that will probably be needed. But it's very difficult, as everyone in the creative industries knows, to specifically attribute 'this is 1% this and 5% this and 10% that.' It's going to be difficult to kind of agree on objectively what that is. And even as human creators, the things we create are the output of all of the experiences we've had and the things we've learned and exposed ourselves to, other art forms and other creators, and then we remix all of that with our own creativity to generate new things. So in some sense, that's always been the creative process. But we'll have to see, probably new business models will eventually be needed.
