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Ryan Kidd: evaluation

4 Jan 2026 The Cognitive Revolution Building & Scaling the AI Safety Research Community, with Ryan Kidd of MATS

“I think that math selection doesn't currently emphasize breadth of knowledge very much, mostly because mentors don't necessarily want that.”

— Ryan Kidd

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Speaker
Ryan Kidd
Attribution
Verified speaker
Claim type
evaluation
Recorded
4 Jan 2026
Publisher
The Cognitive Revolution

Transcript context

…Yeah, I think it boils down to tangible product is, is like king, right? I mean, and I, I say that always in the AI engineering world as well, if any, you know, and I'm far from the world's leading expert on how to break into that space. But what I always tell people if they ask me is a working demo is kind of the coin of the realm. You know, like it's all. People might be interested in what you have to say, but they really want to see that you can make something work. They want to see it online. It could be a replet or it could even be a collab notebook or something, but you've got to make something that can work. And it sounds like this is a pretty similar worldview. You've got to show that you can get in there, make something happen, as you put it with Neil's track in particular, find something interesting. If you can do that, we might have something to talk about. One thing that jumps out is like maybe not as emphasized as I would have thought is being in command of current research. That's something I think at this point, like really nobody can keep up with all the current research because, you know, that exponential has gotten away from all feeble human minds, I would say, maybe with a few hyperlexics that can still keep up. But I have found like Keeping up with research is a pretty-- feels important to me. It feels like an important part of how I stay conversant with people across a lot of different areas. But obviously, what I'm doing in trying to be conversant with people across a lot of different areas is not the same thing as research. How much emphasis do you think mentors in general put on being on top of the literature, so to speak? It varies. So there are some, some of the mentors will ask for questions like, I don't know, what do you think about X concept? Others won't be as interested. Obviously, as you say, right, these costly signals are the most important thing. Like, have you done good research? Do you have a deliverable, like a product? Do you have a strong reference for an important person? That's also key. Do you have, like, have you done your homework in terms of the Blue dot course and other things, right? I think that math selection doesn't currently emphasize breadth of knowledge very much, mostly because mentors don't necessarily want that. And I think that this is maybe a weakness in our process to some extent, if we don't then help people build that breadth. But we do, we have seminar programs. We have tons of opportunities for intermingling between different research streams, which really rapidly builds a breadth of knowledge. And we have like, we used to have like discussion groups and these, these still occur occasionally with like workshops and so on. So I would say like, I really do encourage everyone to do like a basic blue dot course or, or equivalent like AI safety Atlas and case of good courses. as well. But I think that this is not as required for selection, and it's more just to prevent you from entering MATS and then starting to do a research project and realizing, Oh crap, I have no idea where the gaps are. I don't understand how my work fits into anything. How do I get funding after MATS? How do I get a job? Blah, blah, blah. How do I choose a good original research direction? So it's more for your ability to actually deliver within the program, tracking research, and less to do with your ability to get in at the moment. Which is pretty important because maths is just a stepping stone. If you do maths and then you don't produce a great deliverable by the end of maths, sure, it's a great thing on your resume, but it's not going to be enough in many cases because it's such a competitive environment. So yeah, I think that it's really good for people to build a shallow but broad understanding of the literature. So I would recommend don't be checking X constantly for new papers, blah, blah, blah, unless they're in your field. Maybe set up some Google Scholar alerts for Interp, if that's your thing. But more every so often do a periodic deep dive into What are all the cool updates across different fields? You know what I mean? And you can do this by like every year looking at the new Blue Dog course or every month looking at some research roundups or highlights like Z as a newsletter or transformer. And there are other people you can follow on X. That's my main recommendation. So your admissions rate, it's super low, right? I don't want to, we want to encourage people to apply, but it is a very selective program. What does the kind of funnel look like in terms of, I don't know if there's intermediate steps that, you know, would make sense to talk about selected. And then I think the good news, though, is if you do get into the program, your success rate on the other end of like getting into the field in a professional W-2 employee status sort of way is really high. You want to run us through those numbers?…

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