Grant Harvey

And actually computer vision models are now much more advanced that we can start to pull out way more relevant data from these games.

And we can start to pull out patterns and we can start to pull out sort of recognitions about the movements people make or the shapes people form.

And suddenly you've effectively got a scout in every game.

Exactly.

Because basically, historically, and this is why sabermetrics was such an interesting thing was because

Baseball is a very statistics-heavy game because lots of these things are measurable.

But now, things that were historically viewed as unmeasurable because they were unstructured data, you can actually analyze more effectively.

I mean, that's what language models and other models do.

They take unstructured data and do interesting potential stuff with it.

Not guaranteed.

It could be complete dross.

But they give you the ability to interrogate it more effectively.

Just think conceptually, a large language model

reading Tolstoy is the same as a video model watching the Charlotte Hornets.

It's able to take that unstructured information and bring some sense from it in a way that was never previously possible.

These two things, you asked your question Grant about the modules, like we're not coming in there and pitching modules.

We're not saying buy our tools, buy our widgets.

These are our tools in our tool belt.

But our job is to come in there and be like, do you need a screwdriver?

Do you need a hammer?