Google puts frontier AI compute ahead of other uses

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 15:11

   allocating compute, which is your most precious resource, between cloud and AI research. This is a big topic about Google. I've had people suggest to me that Google should get out of the AI research game and, you know, take all of its cloud computing resources and its chips and just sell that. And you look at the world of business right now and NVIDIA, which is like, which is doing that effectively, a $5 trillion company. There's a push and pull between cloud that wants to, you know, of sell this computing resource at a high markup, and AI research, that needs a very big computer to make the best AI.

2. 15:51

   Now, you've said a big part of your job is figuring out how to allocate compute. How do you do this, and how do you balance these two? Look, I think we are... The ground zero for us is making sure we are investing in the compute that's needed to be at the AI frontier. So that is, you know, in 2015, I set the company to be AI first. So AI is that key horizontal ingredient across everything we do, all the way from search.

3. 16:27

   to cloud, to Waymo, to everything we do. So I think that is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, look, we have these independent businesses which each have their own long-range plans. And so they're using those long-range plans to project forward and we plan for compute for all of them. And so the cloud team, they have a business and they have a projection and they have demand signals, they've committed contracts.

4. 17:01

   So with that help, we can plan on a long-term compute trajectory for them. I think we're fortunate to have this wide set of opportunities. I think there's no company in the world which isn't making trade-offs at any given moment in time. But the first principles we operate on is to making sure we have enough compute to invest in the frontier. And that's non-negotiable and everything else works on top of that.

5. 17:27

   I think what you're seeing in these conversations is if you sit now, remember, we are building our own compute, our own data centers. So there's long lead times. So the compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year, the agent recording takeoff hadn't happened. So today, obviously, when you sit and look at anything, everyone is short, not just us, other companies, and everyone wishes you had 2x of what you had planned for. I think that comes with the territory, right? And at any given moment in time, we are planning for what we would need two to three years out in the future.

6. 18:10

   So I think you're going to find some decisions you make may look suboptimal two years out, right? Depending on where the state of the art is. So that, you know, I don't spend too much time thinking about it. The way I think about computer allocation is making sure Google Deep Mind, both for research and frontier model development, is cutting edge in terms of compute access, right? And after that, we plan for all our businesses. I think it's great we can provide our compute to other people too, like we have done, it allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack.