Nathan Labenz: belief

Transcript context

…Yeah, and I think actually also to re-articulate a point that Tulsi made, like Google and specifically Google DeepMind's mission is to like build AI responsibly and make sure it benefits like all of humanity. And I think like that is like so deeply tied to the like Google product surfaces in which like we're serving, what is it like 8, 2 plus billion user products or whatever it is. And so at the same time, obviously the frontier matters, obviously having great models that are really expensive and really, really intelligent matter, and there's tons of use cases for that internally and for our customers. You also need to do the scaling up to billions of users for us to actually do the thing that Google needs to do to achieve the mission. And I feel like we've done a good job hopefully of trying to walk the fine line of actually continuing to push the frontier and build great flash models. And I actually think those two things are like more tied together. You know this better, more than I do, but like more tied together technically. Like it's, you know, it's hard to make great flash models if you actually don't have a great pro model and vice versa. So we'll definitely keep pushing the frontier on both of those things. I think the perception from outside is by analogy too, it's hard to make a good flash model if you don't have a good pro model. People sort of think that there's like an ultra model internally that's the mega training run that's then being used to like help train pro, which maybe in turn is being used to help train flash. Is that true? Is there like a bigger thing inside that is only for these sort of distillation to the mid-size? I mean, we definitely use distillation as a way of kind of bringing down bringing down our sizes. So you will see that like pro influences flash, influences flashlight. We also do the reverse where we scale up, right? So you take the pro, you take the flash recipe and scale up to the pro recipe, for example. And we do have, I think what's been really fun, especially over like seeing as we've used even anti-gravity into this point of Logan made with the harness, I think we've been seeing a lot of examples actually of leveraging pretty awesome models to drive progress internally. Actually, like one thing Varun demos on stage on Tuesday is basically like being able to leverage a bunch of sub-agents to go and complete a bunch of tasks and come back. And you can actually try that as like an early preview in anti-gravity today if you go to slash teamwork. And that's an example, I think, of something we've been using internally, which is an extremely smart model. And it leverages both the combinations of the best of Gemini 3.5 as well as inference techniques. And you're able to actually accomplish so much. And I think that's the kind of direction I'm excited for us to go into more. So I think we're kind of pursuing all of these fronts. We're scaling up from the pre-training and kind of frontier perspective. And I think that's been really continuing to show gains. There's a bunch we're doing on the post-training side. And then there's also just a bunch we're pushing on the inference side. And then that plus trying to make sure we're working with the harnesses. I think we're gonna keep getting things that we're using internally that we even start to push out externally through previews and this.…

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