Brandon McBee

And there was a point you guys hit on in the intro that I think is really worth acknowledging, and it was this concept of model routing.

The idea that not everyone needs just the latest model, that it's different types of models, I can hit different use cases.

This is something we've been talking about for a while as it relates to the infrastructure side of things as well.

Because you don't need that latest model for everything.

Accordingly, you don't need the latest piece of infrastructure to support every single inference or training query that's out there.

You can kind of conceptualize this matrix of different sizes of workloads relative to different sizes of GPUs.

And all of a sudden, that tells you, my God, H100s could last six, seven, eight years.

A100s are going to last longer.

And it totally changes the entire conversation around depreciable life of infrastructure.

as that was a really popular topic during 2025.

People were saying like, oh, this stuff will last two years.

It's worth zero afterwards.

And like, we've never seen any semblance of that because of the point you guys are accurately making, which is users are going to need to find a way

to use the appropriate model for their prompts.

And that'll be solved by model routing, to your point.

But that just further enables this concept that infrastructure is going to be used longer.

And we see that every day in our portfolio, extending all the way back to A100s.

It's a great question.

The scenario you presented, Anthropic would be our client.

Okay, got it.