Ryan Kidd

And they typically have AI safety organizations they founded and lead.

Then there's iterators, right?

And this isn't just, it's not just engineering, right?

Iterators are active researchers who have strong research tastes, who are pushing the frontier, but they typically aren't creating like novel paradigms based on like theoretical models of things.

They're typically advancing empirical AI safety.

And you can even imagine iterators and technical governance agendas as well.

So this is the majority of people that are working in AI safety today and also the majority of hiring needs in the future.

And then there's amplifiers who I think like the closest example is like TPM archetypes.

I'll say this for iterators, like prominent examples include like Ethan Perez, Neil Nanda, Dan Hendricks.

Actually, I thought I think Dan Hendricks maybe crosses some...

crosses some boundaries there.

But yeah, amplifiers, to distinguish them, they have more focus on amplifying people.

And typically, you'll find them on large research teams, and they're scaling the number of people that can be effectively managed and contribute to organizations.

So a lot of math research managers would fit this category, or TPMs at the various labs.

And interestingly, they're actually quite in demand as well, particularly for labs in like the 10 to 30 FTE range.

They're the most in demand archetype because it's very hard to hire great people managers who also have the requisite research experience.

You're trying to hit two bullseyes.

And there are ways, of course, like Google has this sort of model where you have your research managers and your people managers, your project people managers, and they're somewhat distinct.

And MATS does try to do this for our mentors and our RMs.

But yeah, I think the need for amplifiers is only going to grow because as you've said, things like cloud code and other AI systems are going to erode away the minimum technical skills required to contribute.