METR’s Joel Becker on exponential Time Horizon Evals, Threat Models, and the Limits of AI Productivity
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What is METR and how do Model Evaluation and Threat Research fit together?
So meter stands for M E T R. First two letters model evaluation, that is, we think about what the capabilities of AI models might look like today and tomorrow, as well as their propensities for what they'll actually do in the wild, given that they have some level of capability. And then threat research is the final two letters. We try to connect those capabilities and propensities to particular threat models that we have in order to determine whether AI models pose enormous or catastrophic risk. risks to society. So the secret, if you read this article about how I became the number one most profitable trader on manifold, mostly comes down to this one market where
Hey everyone, welcome to the Laden Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swix, editor.
Hello, hello. We're back in the studio with Joe Becker from Meter. Welcome. Thank you very much, guys. It's a great pleasure to be here. So Joe, your work has impacted the AI field a lot, especially over the last year. I invited you for the AIE summit, which thank you for speaking as well and doing the extra workshop. And there you have a lot of papers that have been very impactful. But I guess upfront, a lot of people, like meter just burst onto the scene. Could you explain Yeah.
So meter stands for M-E-T-R. First two letters model evaluation, that is, we think about what the capabilities of AI models might look like, might look like today and tomorrow, as well as their propensities, what they'll actually do in the wild, given that they have some level of capability. And then threat research. It's the final two letters we try to connect those capabilities and propensities to particular threat models that we have in order to determine whether AI models pose enormous or catastrophic risks to society.
Yeah. Would you say that you've done a lot more M E and T R is like the next phase, or is there T R side of work that I missed? I think there's some T. I think there's some
TRs. Some of the most publicized work does look more like VME. It looks like this time horizon stuff and the developer productivity RCTs, stuff like that. But there's this one full report on our website, GPT-5 report, an analogous one for GPT 5.1 as well. Trying to make this more sort of structured case that it doesn't pose these really large-scale risks, eventually coming to the conclusion that it doesn't. But it's worth thinking: like, why why exactly is that the case? If you and I work with GPT-5, it does seem very capable, that matches up to benchmark scores. Why is it not able to do really s something really enormously wrong? We go through the evidence, we find we we think it's not capable enough on the on the basis of some of this capabilities evidence that you've alluded to to commit these.
catastrophic arms and it's not going to be able to do this. But perhaps in future we'll think it's capable of doing pretty extraordinary things, kinds of things that would be necessary to provide really serious threats. And then maybe you'd lean more on the propensities part, are the protections that we have against these dangerous capabilities sufficient for it not to pose an existential threat. That sort of thing. So I think it's I think threat research very much is there, very much is something that we're aspiring towards in some ways you might sorry, see the capabilities evidence as a kind of input to that.
Yeah. Have the thread models been updated a lot or do you feel like you're still using the same thread models as G V D two of Paperclip Factory, blah blah blah? But like how much are you increasing the bar?
Yeah, so I'm not an expert in the threat modeling piece, a lot more in the capabilities piece. I do think they've been changing to some extent. So something like the autonomous replication threat model, that is being able to set yourself up and control resources, s something like that, has been deprioritized relative to ARD acceleration. That is the possibility there could be some capabilities explosion inside of a lab, and that could be destabilizing for all sorts of reasons that we could talk about.
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Chapters
8 chapters
1
What is METR and how do Model Evaluation and Threat Research fit together?
0:00–7:30
2
How did the Time‑Horizon chart originate and what does it aim to show?
7:30–13:17
3
What criteria and biases are used when picking evaluation tasks?
13:17–19:51
4
What are common misconceptions about the Time‑Horizon metric?
19:51–26:09
5
How did Opus 4.5 change the trend line and what does that mean for future models?
26:09–32:53
6
What do developer‑productivity studies reveal about AI‑driven explosions?
32:53–39:20
7
How does slowing compute growth affect AI progress and capability forecasts?
39:20–45:08
8
What are METR’s plans for 2026 and beyond, and how can listeners get involved?
45:08–56:11
Speakers
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