You see software as the way to implement this disruption of service, at least some portion of the overall services TAM, or do you believe that software and vertical software will capture all the TAM?

I think there's a couple of things embedded in your questions.

One of the embedded questions that you're asking, are these large language model companies going to be a platform or are they going to evolve into specific products? If you look at the evolution of software, Back in the day, there were early software companies that became platforms upon which a whole bunch of other products were built.

And you could say, well, why didn't the platform build all the products? It's because it's hard.

It takes a lot of work.

It takes a lot of understanding of individual verticals, individual customer needs.

And it's just hard to do that at scale, right? So then a bunch of other companies grew up.

Outside of mission criticalness and the stickiness of the customer set, is there anything else that you care about today?

So how do you think about this dynamic? Where does it go from here?

You know, one of the insights that we've gotten from our portfolio is the need to manage inference costs.

So to kind of break it down for non-tech folks, which I mean non-tech folk myself, you know, there's really, when you think about AI, there's like the training compute that everyone hears a lot about.

They're training these models and they're using, you know, NVIDIA GPUs, which are are very powerful chips that use a lot of energy and are advancing the training of these models.

As you do prompting and you ask the models to do things for you, that's inference compute.

And a lot of the inference compute is, frankly, being accomplished on these training chips, which is quite an expensive way to do inference.

And what you're seeing out in corporate America is people are spending a lot of money on these tokens for inference, and they're saying, I'm spending a fortune, and I'm not seeing the ROI.

What does that mean, actually, in practice at the end of the day? Why is it so important?

Mm.

But fundamentally, they wanna make money off effectively the infrastructure, and it's not infrastructure in the physical sense, but in the technology sense, that everything else is gonna, is gonna lever off of.

But they don't understand-- Like, our companies deeply understand industries.

They deeply understand the data of that industry.

They deeply understand the customers of the customers, and they talk about that as, like, the context of the industry.

That's how the workflow has been built, and that's how you then say, "Okay, I've got all this data.

I've got all this understanding.

Greatest threat to AI.

... and just go through it.

No, it's really-- I mean, I think we're in a really exciting time.

Um, you know, we've seen, uh, the software industry has evolved a number of times over the last 26 years.

I mean, you know, b-back in the day, most, uh, software was consumed on-premise, uh, and it was sold through a li- a, a license with, uh, with a recurring maintenance charge.

As the internet got, um, you know, basically built out, it, it enabled a new way to deliver software, which was through a cloud-based hosted application, uh, versus an on-premise application.

And it also evolved the business model where instead of selling it on a licensed and maintenance stream, uh, it moved into what, you know, became called SaaS, software as a service and a subscription model.

And, you know, that transition, which, you know, really happened in, you know, kind of the mid, you know, '14, '15, '16, feels very much like what's going on today from a public market perspective.

What, what tests, what scorecards, w- anything you can bring to life on how the outside world should be addressing it?