Mm

... that are trying to scale up to solve particular problems in AI right now, and they need a lot of humans to do that, again, both in the research kind of core roles, but also in all the other roles that make organizations work, HR, comms, et cetera, et cetera, et cetera.

Operation's a huge one too.

And then yeah, I do think there's also gonna be a lot of roles at companies that are kind of like make the AI work.

Sometimes this is called AI engineer.

Obviously, that's, you know, already been popular.

AI ops, you know, AI workflow designer.

Mm.

I'm curious if that's still your your your litmus test or how you think about it.

Well, for one thing, I think that meter paper is worth unpacking a little bit more because this was one of those things that was...

And I'm a big fan of meter, and I have no shade on them because I do think, do science publish your results? Like, that's good.

You don't have to make every experimental result and everything you put out conform to a narrative.

But I do think it was a little bit...

It was a little bit too easy for people who wanted to say that, oh, this is all nonsense to latch onto that.

And, you know, again, there's something there that I would kind of put in the Cal Newport category too, where for me, maybe the most interesting thing was the users thought that they were faster when in fact they seem to be slower.

What do you make of the claim that 80% of AI startups have Chinese open models? What do you make of the claim and the implications?