Dan Shipper

Yeah, I totally agree.

And I think that's why some of the stuff we're doing, the big model companies appreciate it and pay attention to it because they do have apps that they work on internally.

But I also think of them a little bit like oven makers.

So they're making an oven.

You can use an oven for a lot of things.

And we make souffles.

And so they make a new oven and they come to us and they're like, tell me about the souffle you can make, you know?

because that helps them figure out, like, how do I make the oven better?

But they're not going to make... How was the temperature?

Exactly.

They're not going to make an oven just for soufflés, but it takes them seeing it being used in a particular context where someone's, like, pushing it as far as it can go for them to even realize, oh, there's an opening here.

There's a vector along which I want to improve it.

And I think people don't quite realize that and quite realize the...

The difficulty and also promise in making tools that are so general that you can't fully predict how they'll be used and how to improve them.

So the dominant startup metaphor for the last 10 or 15 years has been jobs to be done.

You have to narrow in on a specific job that your customer needs your model for or your product for and then make it better.

And if you ask, Okay, what problem does AI solve?

It's like, well, it solves every problem.

Theoretically, it could solve every problem, right?

Better and worse degrees.