Satya Nadella

The best news to be is in a big market that can accommodate a couple of winners and you're one of them.

That's what I meant by the hyperscale.

Got it.

In the model layer, one is models need ultimately to run on some hyperscale compute.

So that's sort of that nexus I feel is sort of going to be there forever, right?

Because again, it's just not the model, but the model needs state.

That means it needs storage and it needs to regular compute for running these agents and the agent environments.

And so that's kind of how I think about why the limit of one person running away with one model and building it all

It may not happen.

It's a good point.

I mean, like, so the way...

We have built out, at least the way we want to build out the fleet is, in some sense, ride Moore's law.

I think that this will just be like what we have done with everything else in the past, which is you kind of every year sort of keep refreshing the fleet.

You depreciate it over whatever the lifetime value of these things are, and then get very, very good at the placement of the fleet such that you can run different jobs at it with high utilization, right?

So sometimes they are very big training jobs that need to have highly concentrated peak flops that are provisioned to it.

that also need to cohere or what have you, that's great.

So we should have enough data center footprint to be able to give that.

But at the end of the day, these are all anyway becoming so big, even in terms of if you say, take pre-training scale and if it needs to keep going, even pre-training scale at some point has to cross data center boundaries.

It's all more or less there.

So great.