Ryan Kidd

So these, these teams are scaling fast.

Okay.

And many more are getting founded.

Open fill has, sorry, coefficient giving has huge amounts of, of, you know, grant money to spend on this stuff, right?

There are

like a dozen AI safety focused VC firms out there to fund your profit.

Like there are incubators like Catalyze Impact, Selden Labs.

I believe Constellation has one now.

There's tons of programs like Matt's.

I think the problem is that the level, once you have built an organization, especially if you're scaling very fast, right?

There's hits a certain size.

The main constraint becomes like, is this person good enough to warrant the extra management overhead?

Can they take on some management responsibilities?

So you have this situation where like at OpenAI, like where people are managing like 10 to 20 individuals, maybe up to like, I believe one person, Anthropic Alignment Science had 18 reports.

So they're really flat.

So you have this real problem where you need to hire people who can quickly ascend the ranks and be research leads, be managers and so on, even PIs of new teams.

And that is like the limiting constraint.

And that's the reason why a lot of people do some moderate reskilling and then can't get hired because I think these jobs, there are many, many opportunities, but what we find is when we talk to these hiring managers, they say, we find it extremely hard to hire.

We have the money, we have the clear need, but people are not at our bar.

And that's what that's what math is trying to do is to get people up to that bar.