Azeem Azhar

Now, some of this, I think, is just expediency.

It's just, let's take advantage of the changes.

But some, I think, is real.

We know, for example, that Google has done a deal with Commonwealth Fusion Systems for a 400 megawatt power tranche when their fusion reactor goes live in a few years.

And

Helion Energy, which is another fusion company, has ties with Microsoft to power data centers.

So this is a really, really significant problem.

It's a big issue in the US, much less of an issue in China, where they've mastered the ability to deliver clean electrons at scale.

There's also this squeeze coming in between inference, which is the bit of the AI activity that makes money, and training, which is when you're doing your product development for your next model.

Model companies will be battling between where do they put their resources into training the next model or into serving customers for revenues today.

They have lots of resources, but even those resources are not infinite.

And earlier this week, Brookfield

which is an asset manager lined up with our estimates that in a few years, about 70 to 75% of compute cycles will be used on inference.

So that's going to be a tension, right?

Do we pay bills today or do we build the next big thing in some different way?

And you see the labs.

I mean, I think the contrast between Anthropic and OpenAI is most marked in how they approach that, right?

Anthropic appears to be rather more focused in thinking through the economics of that particular trade-off between training and inference.

There are levers to address that.

Efficiency gains being one that is an obvious approach.