But, you know, it's clearly, it's part history, it's part, um, here's pattern recognition, it's part here's your lib's story through the whole process, and then looking at where we are.

Y- you're right.

F- so first of all, Mitch, I, I always like to say, I threatened to write a book for 20-something years and just never carried through with it.

You and I threatened to write a book for a long time.

Um, a- and, and I'll be honest with you, I, I wanted to write a book.

But this book, you know, when some things happen, Brad, Mitch, you, you've probably seen this in your life and, and careers as well, sometimes things just happen organically and holistically.

I, I didn't sit here and say, "Hmm, what do I wanna write a book about?" This just, this dropped into my lap.

Yeah.

Not more and more intelligent, please, but more and more capable.

So I'm going to take a different tack to both of you.

I think we could use as much AI as we could eat, right? I think, you know, like in Javan's paradox, the more AI we have to use, the more we're going to use it.

I think the real question here is, do I need a sledgehammer to kill a fly? Because what we're developing is stratification in our AI models.

I don't need the AI that supposedly can cure cancer or fold proteins or nuclear kind of physics to pick out, guys, as you said, where's the best shoe store for me to get a pair of loafers this weekend.

I think as the AI market matures, we're going to see this stratification.

And it's more than just picking the right model for the job.

That's one of the many reasons I think we say that we're just at the beginning of this, because that level of sophistication is getting built rapidly, but it can only be absorbed so fast by the humans, which, by the way, all of this is for, is for humans.