Sarah Wang

I want to pose this thought to you that I'm sure you've heard before, but given the fact that model intelligence is getting better, it can almost make up for or do some of the heavy lifting in this user signal that Google has collected over 20 years for PageRank, etc.

It can actually help get over that hump and do a pretty good job.

And so my question to you, I guess, in that is, how do you think about this trade-off of compute, latency, cost, right?

There's all these trade-offs that have to happen in terms of what you're actually using to, and, you know, to your point, you said it's like a big optimization exercise.

Like, how do you think about what to optimize?

And I know you have different products, right?

And so maybe your answer is like, well, depends on the product offering we're doing, but bring that into it as well.

Oh, interesting.

Yeah, please.

Yeah, you know, it's interesting.

When we were doing our diligence in the space, which, you know, has taken place over the last few years now, some people came to us with the opinion that search is just getting commoditized.

And I think we look at that and, you know, if you go out there

and search for information we know is out there public, et cetera, you can't find the answers to everything still.

So the fact that it's commoditized, you know, you'd have to kind of divide up, oh, what type of search is commoditized?

So maybe I'll ask you that question.

And then what is the type of search that's still really hard?

And what is the key to unlocking that?

Is it...

data partnerships?

Is it a technical breakthrough?