METR报告：Anthropic AI加速研发致能力翻倍，2027或现RSI

METR came out with an extensive report on how much AI is accelerating AI R&D at Anthropic, and thus how close we might be to recursive self-improvement (RSI) and how far that might go.

This is one of the most important questions in the world.

Their answer: Things are already accelerating on the order of a 2x speedup.

This is several months ahead of the AI 2027 timeline. A naive extrapolation says we may be less than a year away from full RSI. There are no clear bottlenecks in the way.

This report updated many towards shorter timelines.

This is consistent with what OpenAI and Anthropic themselves have been saying, and with extrapolation from the progress we are observing. We are running out of time.

That doesn’t have to be the way this plays out. We could stop the labs. The labs could choose to stop. Or the estimates could be wrong, or some bottlenecks could be hit, or the results could be less impactful than we expect. Indeed do many things come to pass. And if it does happen this quickly, one would expect it to go quite poorly, and likely to cause everyone to die, but perhaps it goes well.

It doesn’t look good.

The central conclusions are:

• Capabilities are advancing at roughly twice the rate they did from March 2024 to October 2025 (CI ~1.3x-3x), with wide uncertainty. And that a majority of that acceleration is probably due to AI doing AI R&D. Since October 2025, Anthropic has made ~2x progress on the ECI versus periods prior to October 2025.
• Yes, this seems like what has been obviously happening.
• There are no observed bottlenecks or strong diminishing returns that would prevent advancement from accelerating further.
• Unfortunately, Anthropic redacted the analysis of various bottlenecks.

METR: These conclusions are based on key empirical findings from the full report.

Some of the key findings that most informed our conclusions about risk were redacted. ​

Ajeya Cotra (METR): For the last few months, some of my colleagues at METR have been working on a deep-access investigation to understand how much Anthropic is being accelerated by its AI systems. From my reading of their report and Anthropic’s RSP, it looks like the “dramatic acceleration” threshold has already been crossed.

I do not find either of these results surprising. I made minor updates towards faster outcomes and fewer bottlenecks, but not major ones. I had already made major ones.

The situation looks rather grim. But this is not a surprise.

The information from inside the labs makes you think the situation is grim and timelines are short, but those with that information were already shouting from the rooftops that their secret information meant the situation is grim and timelines were short. Why did you not think this was the standard reaction to their information?

I did find it surprising that so many found this surprising, including METR. Their team did not expect substantial acceleration. Ryan Greenblatt expected only 1.15x-1.3x speedup this year. Six weeks ago in the Opus 5.5 system card the estimate was at least a 1.5x speedup.

If you are making major updates in response to this report, that is probably good and wise, but indicates you had previously made a mistake in your estimations.

If you only look at public models, you can make a case that there is a step change at Mythos but that otherwise the rate of progress has not changed. I was always skeptical of that, but it was a valid claim.

Add in the orange data points from internal models and you cannot tell that story. When we include the recent additional internal releases, this takes the estimate from the ~1.7x in the Haiku system card up to a new estimate of ~1.9x.

Peter Wildeford: It seems important to realize that:

– AI companies have unreleased internal models that are more powerful than the public ones we plebs have access to

– Only when you account for these internal models, you can see that AI progress is actually accelerating faster than before

– AI companies use their AIs with like 10,000x more compute than us plebs, so they can do much more than we can even with the same AIs

You could also have gotten to a similar result by extrapolating the automation graph:

Benjamin Todd: Extrapolating Anthropic’s internal AI R&D data results in Claude leading internal research by mid-2027, and plausibly near full automation by the end of 2027, lining up with other methods. (h/t @luc_feron )

The information METR is relying on most is largely redacted. The public report centers on AECI and other estimates. METR is implying that such arguments are merely the legible case they are allowed to make, and that there is a stronger case.

Kudos to Anthropic for allowing this investigation.

METR: Anthropic offered unprecedented access to internal data on their AI R&D process, including a variety of metrics on pretraining, reinforcement learning (RL), training compute in each domain, benchmark scores for internal models, AI speedup analyses, forecasts of a future model’s metrics, and interviews with relevant staff.

This is all highly sensitive stuff, so there are important redactions involved. I would have liked to see less of this, but if this is what it takes to get the report then so be it.

I would also suggest to Congress that they should want to see the full report along with everything else Anthropic and Dario Amodei know and really think about this, and to do that they should follow Kelsey Piper’s suggestion and subpoena Anthropic.

Table of Contents

• Top Conclusions.
• Data Access and Redactions.
• The Pace of Progress.
• 1, 2, 3, 4.
• Timing Is Everything.
• What Could Possibly Have Gone Wrong?
• AECI.
• The Public Information Alone Could Suggest Slower Speedup.
• Well, That Escalated Quickly.
• This Infinite Series Likely Has a Finite Sum.
• We Cannot Say What We Cannot Say.
• Study of Anthropic’s Compute Allocation Was Postponed.
• Security Mindset.
• It’s a Secret to Everyone.
• Review of the Risk Report from August 2026.
• A Word From Our Cofounder.
• People Update Their Timelines and Expectations.
• You Are Not Ready.
• If (If Anyone Builds It, Everyone Dies) Then Don’t Let Anyone Build It.
• How Can We Legally Define RSI?

Top Conclusions

This is how they describe their subjective conclusions.

METR: In our first section, we share our subjective conclusions about risk. We now believe that:

• As of October 2026, Anthropic is likely improving the capabilities of their models on well-specified tasks at around twice the rate they did between March 2024 and October 2025. This estimate is given with high uncertainty; we would not be surprised if the true acceleration was closer to 1.3x or 3x the historical rate.
• A majority of this acceleration today is likely attributable to Anthropic’s extensive use of AI to do R&D, though there is significant uncertainty and other causes likely played a large role in acceleration in late 2025.
• The lack of observed bottlenecks or strong diminishing returns means there is a significant risk that further capabilities progress could lead to even faster acceleration, though, as we say below, we may be missing important bottlenecks.

Here are their top empirical findings:

• Since October 2025, benchmark scores (measured via AECI, Anthropic’s version of the Epoch Capabilities Index) for Anthropic’s models have been growing at an estimated 1.9x the pre-October 2025 rate [95% confidence interval: 1.5–2.8x], implying that Anthropic has made roughly two years of historical progress over the last year.
• We are confident that the acceleration was not due to an acceleration in the scaling of training compute.