Lennart Heim

I think to some extent.

I mean, like, if you need to justify millions being spent on something, that's probably easier than justifying billions, right?

So the Oak May I and others announced a fire front of billions for Stargate.

I hope they did some math, right?

Again, but the math here is beyond just training bigger models.

Even if you just stay at current capabilities,

We need to deploy them.

More and more people will be using them.

AI agents might be working.

They got a need to be running somewhere.

And that's to some degree the nice thing about compute.

You're not making a specific bet saying, oh, large language models are the thing.

You're basically saying like, look, having more of something is useful.

It's hard to say because, of course, they publicly don't tell us.

There have actually been some papers coming out of Google where they talked about the environmental footprint.

And in video, I think they asked in video on AWS, the question is like, what is an AI workload?

Are your recommender systems you use for Instagram also AI workloads?

So if you look at it this way, then AI is like computers predominantly being used for deploying systems, right?

This might look different if you look at a frontier lab.

But I think over time, you see the share of deployment getting higher.