Logan Kilpatrick: belief

Transcript context

…No, I think you know you're right that I think. there is a slice of users who are definitely willing to pay for a certain level of quality. And I think we really do believe that the pro model has been really pushing that quality. But I also think for us, we've seen so much value from the flash and the flashlight dimensions because we also see an extremely large number of users, especially if you're thinking about building, for example, consumer applications. If you think about the Gemini app, if you think about search, when you're serving to that kind of scale, latency really matters. cost matters, right? Because actually you find that users aren't willing to wait, right? So we find that even when we tweak the model and hurt latency, we actually see that play out in our live experiments on search and the app, even if the model is hugely better from a quality perspective. Because what you're asking users to do is wait. And so I think for us, like part of the reason why we ended up introducing this flashlight skew that wasn't necessarily part of the, you know, the original 2.0 series was because we really felt like there's actually a large scale demand for this, depending on the types of use cases, especially when you're talking at that scale. And so I think for us, it's really important that we're pushing the full range of what kinds of customers we can serve, both internally and externally. Like for our products, the flash and flashlight skew matter a lot for our ability to actually serve to the Google populace. And so we also imagine that that's true for external enterprise and developers. And I think that's played out to be true as we've been but actually seeing this in action. I think the two things that I'll add is, and there's like probably a more nuanced technical story on sort of like the ultra thread, but it's not like, it's also not like the pro models haven't scaled up over time. So like I think there is like, there's, you know, there's a story that you can spend at the end of the day, the naming of these things is like marketing. Like they definitely are getting like extremely capable, they're getting larger, you know, they're getting more powerful. There's you know the test time scale, test time compute scaling with deep think, et cetera, et cetera, and all types of stuff in that dimension. So I think it is possible you could sort of like put the ultra brand on some of these things. I think we've decided, I think the decision so far has been not to do that. But it hasn't been that like we haven't kept scaling up. So I think it definitely has. Yeah, there's almost been a conversation every time we scale up of like, should we call it ultra? And what does that brand mean? Because we could. But there's sort of also a question of how do we keep consistency for users also kind of series to series?…

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