Jay Jacobs

And essentially, as much money as the major large language model providers are plowing into capital expenditures, they can't keep up with AI demand.

So even just in the last several months, I think the narrative has shifted in the market from that of

Are we worried companies are over-investing in CapEx to what if companies are actually under-investing in CapEx?

Could we start to see bottlenecks in artificial intelligence?

Or some of the most powerful models, frankly, have to be throttled because there's so much demand to use them versus the compute that's actually available across the economy.

So, yes, the CapEx is accelerating.

The numbers are quite staggering of what we see being invested each year.

However, the demand is backing it up and the revenue from demand is immediately backing it up.

So this is not the same as speculatively building telecom infrastructure.

And then, you know, if we build it, they will come kind of scenario.

This is meeting real demand in real time.

Well, it looks across the entire artificial intelligence tech stack.

I mean, it starts with some of the lowest levels, which is really in the infrastructure.

So think about the power that's supplying data centers, the data centers themselves, the real estate, the hardware going into those data centers.

Think about all the semiconductors, whether it's memory, whether it's GPUs, whether it's CPUs that are powering those data centers.

On top of that, there's the data layer.

Think about the proprietary data that's training a lot of large language models.

There's the large language models themselves that are being more and more powerful.

We're seeing that software improve significantly year over year.

And then, of course, you have the applications and products that are using those large language models to utilize agents, whether that's