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Chapter 1: What is the most viral chart in artificial intelligence?
Hello, I'm Stephen Carroll. I'm in Brussels, where many of Europe's biggest decisions get made.
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Chapter 2: How does METR measure AI model capabilities?
Like it's hard to say what that means or how that generalizes. But the idea with time horizon is like maybe it's more intuitive. And I think that helps both for safety and for like business understanding.
So let's talk about what this chart, the main chart here, meter.org, right on the front page, it's this time horizon chart, and it shows Cloud Opus 4.6 as of February 2026, able to complete a task length in 11 hours and 59 minutes with a 50% success rate. I have to admit,
The first time I saw this chart or versions of this chart, what I assumed, and I suspect others assume, is that it was able to go off and work on a task for 11 hours and 59 minutes and then come back with an answer. But apparently it's not that. What do you walk us through? What's really being measured here? By the way, the previous high... was GPT-5.3 Codex. That was five hours and 50 minutes.
So I guess part of the reason this chart just blew people's minds, because literally that's basically a double. But why don't you talk to us about what's really being measured here? Yeah. So fundamentally, in simpler terms, we are plotting the difficulty of tasks that AI is able to complete over time. And the particular way that we measure the difficulty of tasks is in how long it takes humans to
to complete those same tasks that we're asking the AIs to do. So in this case, you know, we're talking about Feropus 4.6, something like tasks that take humans 12 hours to do. We predict that it will succeed at those tasks around 50% of the time.
And yeah, you know, it turns out that when you plot using this particular difficulty measure, how performant AIs are relative to how long it takes humans to complete these tasks, we see an exponential increase increase in capabilities for AIs.
And what that ends up meaning is that you keep on having these doublings of capabilities every, let's say, four months, it seems, on recent trends, where the next model is not merely going to have necessarily an hour-longer time horizon, but perhaps be having some multiple of the time horizon of the previous model that's come out. So then explain how that number, that 12 hours is established.
So there is some engineering task and you say, OK, this is a task that would require 12 hours. But humans have all different types of talent capabilities. How do you establish that? OK, this was a 12 hour task. This was a six hour test. This was whatever it is.
Yeah, so the simple answer is literally we get humans to sit down and complete the tasks that we give to AIs and as close to identical conditions as possible. So first we come up with the tasks and that's, you know, that's a whole kettle of fish. We can talk about exactly how we do that.
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Chapter 3: What are time horizon charts and why are they important?
It's the thing that like a lot of optimization pressure is being exerted on. And then I think that it is kind of the like thing that you would expect as an early warning kind of sign of this AI R&D automation. So to some extent, Meter thinks of itself
As trying to build, you know, science that or advanced science that can say, when are we getting to the point that AI systems could improve themselves or speed up the pace of AI development? When will AI research kind of feed on itself? And the kind of core capability for that might be software engineering and machine learning research ability.
There are other skills that could be relevant to taking over the world.
Right.
I think other people have done time horizons on like cybersecurity.
Yeah. Yeah. But I suppose it is true like the basilisk isn't going to paint its way into like power or something like that. Okay.
It might deceive you. It might be very convincing or cunning in some way. Fair. Hand over the keys. I always say for your mental models, you know, we don't have perfect evidence of this whatsoever.
But my rough sense sort of colloquially or, you know, my prior before evidence comes in is that if we did study tasks on these very different distributions, you know, not machine learning, not software engineering, I'm not sure about
painting exactly, but perhaps other kinds of task distributions that we could enumerate, that basically we would see this similarly shaped exponential progress over time, where every, I'm not sure exactly, but let's say four months, six months, something like that, the level of capabilities as measured in time horizon would be doubling at something like that pace, maybe from a much lower level.
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Chapter 4: How does the Claude Opus model compare to human performance?
How fast do you see it going in the near future?
Yeah. So I was a doubling over every seven months person. There was controversy in our team about what to believe here, because when we originally published this work approximately a year ago, you'd see, you know, if you plotted a single straight line, a single exponential, you'd get something like, you know, six or seven months, let's say.
But if you restricted to just the time since, I think, GPT-4.0, since the 2024 models onwards, you'd see something closer to this sort of like four or five month trend. And some people believed in that.
And some people like me had the intuition that, well, we have so few data points, we should really be estimating over this larger number of data points than a large number of data points says every six or seven months. There are a couple of things that have changed my mind and made me realize my colleagues were right since then.
One is that for the models that have come out since, what trend has better predicted how performant those models would be? And it's very clear that the answer to that is the four-month doubling time and not this seven-month doubling time. You know, there's some possibility that could speed up again. We've seen it speed up once. I think there are some reasons in principle why you might...
expect it to speed up again. I think there are some caveats about this. These are maybe some takes that my colleagues would agree with. And so maybe you should discard that, or you should think that they're going to convince me in the way that they did with the four-month versus seven-month doubling times. I have some suspicion that the
the tasks that Mita is measuring performance on are, in some sense, more and more a narrow slice of possible tasks. And in particular, a more and more narrow slice that is perhaps similar to the kinds of tasks that you'd expect these major AI companies to be training on in the first instance.
And so in some sense, we're increasingly, more so than was the case before, measuring progress on the exact types of tasks that they're trying to get better at. You might think, for instance, the kinds of tasks that would make for good reinforcement learning environments, the kinds of tasks that you can score quickly and cheaply and automatically. I think that progress is real.
I think that progress generalizes to some extent to other types of tasks. I think we're seeing remarkable progress in these more messy tasks, for example. I have one last question, which is, like, how big is your team funding? And, like, also, how many people at Mater are basically, like, really rich from AI? And they're like, you know what? I'm good.
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Chapter 5: What challenges do AI benchmarks face in measuring performance?
And most people, I think, look at these charts and they say like, wow, this is like I want to invest in this or this is like really exciting.
No, I know. I know. Yeah. That's why my first question was, like, you're here for AI safety purposes, but everyone seems to get excited about the line go up chart, right?
Like, there's a disconnect here.
They're all connected.
Like, I say when an industry basically says it's worried by itself, you should pay attention. It's really strange. This gets back to, you know, It's very strange where you have the CEOs of these companies who are in many cases the most alarmist. And there is this sort of cynical thing.
And I don't totally discount the cynical interpretations like, oh, they're saying this because they want to get investors and so forth and they need all this money. But look, it is also true that open AI exists.
But OpenAI a little more were like founded with these very exotic corporate structures of like a private company owned by a nonprofit, etc., which they presumably did because they took pretty seriously the fact that this technology and science was like very strange and not just like... It's not just enterprise software.
Right. Like they were self-limiting in a way.
One other interesting thing, too, that this idea is like, OK, like, first of all, what's the difference between seven month and four month time doubling? Not much. You know, it's like these people's like, oh, I think. Yeah, but it's exponential, isn't it? I guess it's exponential, but it's still funny to me. It's like, oh, I think like AI is going to destroy all white collar work in two years.
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