And those two hand-in-hand define what the frontier means.

So at the time, like, Gemini starts, like, there's a lot of already machine learning models making it into products, and I think the... what seemed obvious is that, um, if we create a single more powerful than the average of the other models, like, powering everything, that had to be, like, a leap forward.

Whether there was a single product that could be created around a single, you know, model, that maybe wasn't as clear at the time.

But I think it was very clear that putting all the compute and intelligence into a single powerful model, um, was gonna leapfrog many things Google was using already machine learning for, and that was very exciting to be given that amount of compute and responsibility initially.

But, um, I think it's proven to be indeed kind of the, the core engine of, um, Google intelligence.

That's why I think Gemini Omni is a different category that is really transforming what we had with Gemini that is mostly understanding and text output, and Veo that is text input and like doing the video modeling into turning to a really true world model.

By training jointly, there's a hope that of course everything will transfer and making, you know, a better text understanding model will help the world modeling aspect.

But I think we're seeing this every time we try.

[chuckles]

But, um, you know, there's, there's a few ways to think about memory, but I mean, the simpler one that I like is, um, you know, just working memory, right? Things that are very present because of what we're doing or we're talking about.

And then what's called episodic memory, um, that is kind of, kind of a retrieval system that you can access.

Um, and it's probably less precise, but of course, longer context or potentially has all the context that, you know, you or I care to remember, right? Uh, holistically with all our experience and that is accumulated.

Now, the, it-- there's not only two levels of memory, but it's useful to think about these kind of levels of memory.

Um, computers have the same with cache L1, L2, and so on.

So, so when it comes to models, I think working memory, um, because of transformers and, and so on is, is we have a very powerful mechanism to, to kind of use that memory, um, you know, have hundreds, thousands, millions of tokens at our disposal to modify that, that memory and then do amazing things with it, um, proving complex theorems, you know, gold medal level maths and so on.

[laughs]