Brandon McBee

And it's important to keep in mind

We can run any type of silicon on our platform.

We are entirely customer-led in what we build.

We don't go commit to CapEx and speculatively hope people come and use infrastructure.

We wait until a client says, we want you to go do this specific build.

Here's what we want it to look like.

And then we go commit to that CapEx.

It's more like a success-based CapEx approach.

And the client isn't asking for anything but NVIDIA infrastructure.

And I think a large contributor to that is they built this incredible ecosystem around their chipset.

They have been dedicated to that for, I think, over 15 years at this point through the CUDA architecture.

And NVIDIA, from what we hear from our clients,

that platform just remains the most efficient the most scalable the most reliable uh set of infrastructure that is in the market right so i i think others there's always been i mean if you think over the past few years right there's always been talk like what is it but yeah this other silicon and these other chips and at the end of the day like people are still using

NVIDIA infrastructure, they're committing to NVIDIA infrastructure for five plus year contracts in these billion, multi-billion dollar commitments because they know that that is going to be a critical part of how they scale their business.

We really don't see demands on a material basis for anything but that NVIDIA compute.

And that's what we are building today.

So I believe in our last quarterly report, our CEO, Mike, qualified that.

Inference workloads represent well in excess of 50% of infrastructure utilization on our platform.

It's the exact same infrastructure that you use for training as well.

Going back to my comment of it's very fungible between those different types of workloads.