METR: Independent Assessment of AI R&D Acceleration at Anthropic in October 2026

Zach Stein-Perlman

Executive summary
From August 12 to October 29, 2026, METR staff performed a high-access evaluation at Anthropic to assess the degree to which AI is accelerating AI R&D. If AI systems started to significantly accelerate further AI development, this could lead to a sudden emergence of extreme and risky capabilities.
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.
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.
In our second section we share some of the key empirical findings from our Full Report:
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.
Interviews and surveys are consistent with AI usage causing a doubling in the pace of capabilities progress. Anthropic staff self-report dramatic uplift from use of AI systems in interviews and surveys, although these are challenging to interpret. We were told that R&D is accelerated primarily via AI assistance rather than fully autonomous contributions, but there are some signs that fully autonomous contributions will play a larger role soon.
We did not see evidence for significant bottlenecks which would prevent further capabilities progress due to limits on algorithmic progress, training compute, experiment compute, or data in the next 12 months. However, our understanding of the status of different bottlenecks changed several times as we received more detailed information, so we are not confident in our current assessment.
Some of the key findings that most informed our conclusions about risk were redacted by Anthropic.
In our third section, we describe the assessment process:
The scope, setup, and timeline of this assessment.
Details of the information shared with METR.
Details of what information and evidence we were not able to share given Anthropic’s redactions.
Finally, we describe the biggest limitations of our analysis:
We could be underestimating capability acceleration if we did not assign enough importance to (1) evidence that some growth rates have further increased recently; (2) further automation of AI R&D tasks leading to a further acceleration in progress; (3) evidence that AECI growth has sometimes understated growth in real-world impact.
We could be overestimating capability acceleration if we did not assign enough importance to (1) non-AI factors in Anthropic’s research progress; (2) AECI’s focus on verifiable tasks; (3) reasons why Anthropic’s early growth rate (2023–2025) was unusually low.
The remainder of the report goes into detailed discussion of each of the key components. We include appendices with supplemental material.

Twitter thread.

Ryan Greenblatt:

This report on AI R&D automation (RSI) is a big update towards ASI within 1-2 years. It says AI progress is sped up by RSI right now by around 2x. In AI 2027, this happens around March 2027.
I was seriously wrong about RSI speed up; I previously expected much less in 2026 (around 1.15x-1.3x). It seems as though there was specific privately available evidence that would have changed my mind.
I haven't seen an unredacted version of this report; this is purely about the public report. Because so much of the underlying evidence was redacted from the public report, I think conclusions about things other than the observed 2x faster rate of progress on benchmarks depend significantly on trust for the authors of the report. (E.g., conclusions about whether this speed up is from RSI depend on trust.) I do trust the authors of the report a lot to be careful and rigorous and generally do a good job. (Note that I also work at METR.) Less extensive redactions would greatly reduce this need for trust.

Ajeya Cotra:

Our colleagues found that Anthropic’s capabilities progress has doubled, mainly due to AI. Ryan’s modeling from public info suggests the whole company’s R&D would be going 4-5x faster. Labor uplift would be higher still. This is a major update to very short timelines.
I am sad that so much of the underlying evidence in this report was redacted. I think evidence transparency is very important for the world to figure this out in time. Evidence about risk should be transparent.

Daniel Kokotajlo: says the METR report confirms his worry from two days ago, which was:

We at AIFP are spooked by [Anthropic's S1 update]. IF (big IF) Anthropic really is imminently about to meet the RSP threshold of 2x acceleration over baseline, then it seems timelines to full RSI and superintelligence may be very short indeed. For example if we naively optimize the AIFM parameters to achieve 2x acceleration over baseline progress in Jan 2027, we get superintelligence sooner than depicted in AI 2027.

See also GradientDissenter's take.

Executive summary
From August 12 to October 29, 2026, METR staff performed a high-access evaluation at Anthropic to assess the degree to which AI is accelerating AI R&D. If AI systems started to significantly accelerate further AI development, this could lead to a sudden emergence of extreme and risky capabilities.
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.
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.
In our second section we share some of the key empirical findings from our Full Report:
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.
Interviews and surveys are consistent with AI usage causing a doubling in the pace of capabilities progress. Anthropic staff self-report dramatic uplift from use of AI systems in interviews and surveys, although these are challenging to interpret. We were told that R&D is accelerated primarily via AI assistance rather than fully autonomous contributions, but there are some signs that fully autonomous contributions will play a larger role soon.
We did not see evidence for significant bottlenecks which would prevent further capabilities progress due to limits on algorithmic progress, training compute, experiment compute, or data in the next 12 months. However, our understanding of the status of different bottlenecks changed several times as we received more detailed information, so we are not confident in our current assessment.
Some of the key findings that most informed our conclusions about risk were redacted by Anthropic.
In our third section, we describe the assessment process:
The scope, setup, and timeline of this assessment.
Details of the information shared with METR.
Details of what information and evidence we were not able to share given Anthropic’s redactions.
Finally, we describe the biggest limitations of our analysis:
We could be underestimating capability acceleration if we did not assign enough importance to (1) evidence that some growth rates have further increased recently; (2) further automation of AI R&D tasks leading to a further acceleration in progress; (3) evidence that AECI growth has sometimes understated growth in real-world impact.
We could be overestimating capability acceleration if we did not assign enough importance to (1) non-AI factors in Anthropic’s research progress; (2) AECI’s focus on verifiable tasks; (3) reasons why Anthropic’s early growth rate (2023–2025) was unusually low.
The remainder of the report goes into detailed discussion of each of the key components. We include appendices with supplemental material.

Twitter thread.

Ryan Greenblatt:

This report on AI R&D automation (RSI) is a big update towards ASI within 1-2 years. It says AI progress is sped up by RSI right now by around 2x. In AI 2027, this happens around March 2027.
I was seriously wrong about RSI speed up; I previously expected much less in 2026 (around 1.15x-1.3x). It seems as though there was specific privately available evidence that would have changed my mind.
I haven't seen an unredacted version of this report; this is purely about the public report. Because so much of the underlying evidence was redacted from the public report, I think conclusions about things other than the observed 2x faster rate of progress on benchmarks depend significantly on trust for the authors of the report. (E.g., conclusions about whether this speed up is from RSI depend on trust.) I do trust the authors of the report a lot to be careful and rigorous and generally do a good job. (Note that I also work at METR.) Less extensive redactions would greatly reduce this need for trust.

Ajeya Cotra:

Our colleagues found that Anthropic’s capabilities progress has doubled, mainly due to AI. Ryan’s modeling from public info suggests the whole company’s R&D would be going 4-5x faster. Labor uplift would be higher still. This is a major update to very short timelines.
I am sad that so much of the underlying evidence in this report was redacted. I think evidence transparency is very important for the world to figure this out in time. Evidence about risk should be transparent.

Daniel Kokotajlo: says the METR report confirms his worry from two days ago, which was:

We at AIFP are spooked by [Anthropic's S1 update]. IF (big IF) Anthropic really is imminently about to meet the RSP threshold of 2x acceleration over baseline, then it seems timelines to full RSI and superintelligence may be very short indeed. For example if we naively optimize the AIFM parameters to achieve 2x acceleration over baseline progress in Jan 2027, we get superintelligence sooner than depicted in AI 2027.

See also GradientDissenter's take.

From the Ryan Greenblatt quote in OP:

This report on AI R&D automation (RSI) is a big update towards ASI within 1-2 years. It says AI progress is sped up by RSI right now by around 2x. In AI 2027, this happens around March 2027.
I was seriously wrong about RSI speed up; I previously expected much less in 2026 (around 1.15x-1.3x). It seems as though there was specific privately available evidence that would have changed my mind.

FYI this is part of a longer post. The 1.15x-1.3x speedup number he gives there is not directly comparable to the 1.9x acceleration in AECI capabilities under discussion, unless you assume that AI provided ~1x speedup prior to October 2025.^[1] (That is, no real speedup at all.) Which is a defensible assumption, to be clear!^[2] Oct 2025 is before actually-good coding models like Opus 4.5. In his full post, Greenblatt does an involved conversion between acceleration and speedup:^[3]

A 2x acceleration in overall AI progress from RSI corresponds to a much larger than 2x RSI speed up for AI software R&D (algorithms and data); I did an estimate using my public preexisting modeling of drivers of AI progress and the public report which yielded a very rough estimate of 4.5x for how much RSI is currently accelerating AI software progress (relative to no AI automation). In the AI 2027 scenario, this much acceleration happens around March 2027.

Here are the worked details of this estimate based on my public modeling. There are two main reasons RSI speed up of AI software R&D must be larger:
Not all of AI progress is from better software (some is from scaling up compute in training runs). I've previously speculated that around 2/3 of AI progress is from software improvements which implies that 2x overall AI progress acceleration requires 3x faster software progress.
Diminishing returns: A fixed amount of progress requires more work over time, so to ongoingly maintain 3x faster software progress via AI automation, you need increasingly large speed boosts from AI automation (as in, speed ups relative to how fast software R&D would go without AI automation). Based on this report, it seems that there is a substantial amount of additional AI progress from automation that has occurred.
We can estimate the effects of diminishing returns by first determining how many additional years of software progress we've seen (relative to the preexisting trend) and then estimating how much software R&D would slow down after a year of software progress if you held inputs fixed due to diminishing returns. I've previously estimated software R&D would slow down by around 2x for each year of software progress if you held inputs fixed and based on the report, it seems as though we've perhaps seen roughly 0.35-1.0 years of additional software progress incorporated into trained models so far (geometric mean of ~0.6). This combines to 3 * 2^(~0.6) = ~4.5.

(Relevant public modeling and numbers I'm using: 1, 2)

So Greenblatt's point here is basically that he thinks 2x total acceleration today would mean 4.5x software speedup today (as opposed to compute growth).

However I think there's actually an error in the calculation?^[4]

He says AI progress is roughly 2/3rds software progress, so therefore it's also 1/3rd compute growth.
He then says therefore 2x AI progress acceleration requires 3x acceleration in software progress.
But calculating out the expected total acceleration from 3x software acceleration:
1. Before: 2/3 software * 1 + 1/3 compute * 1 = 1x acceleration
2. After: 2/3 software * 3 + 1/3 compute * 1 = 7/3 ≈ 2.33x acceleration
Rather, if we want to get 2x total acceleration, we need:
1. After: 2/3 software * 2.5 + 1/3 compute * 1 = 6/3 = 2x acceleration

Carrying 2.5x software acceleration forward, we get a total software speedup today of 3.8x.

Of course, that's still rather higher than the AIFM's estimate of ~1.3x today (or Greenblatt's prior "1.15x-1.3x"). The latest AIFM only expects 3.8x speedup after the Automated Coder (Daniel: 12/27, Eli: 7/28, Brendan: 2/28).

Why exactly such speedup might have happened so much earlier than the latest AIFM expected^[5] requires some deep investigation,^[6] but my early, rough understanding is that the difference is plausibly:

AECI is not a perfect reflection of overall AI capabilities progress; notably, Epoch's ECI does not show the same acceleration for Anthropic capabilities,^[7] and if you include non-Anthropic models, that removes acceleration entirely. We can perhaps explain the surprising difference between AECI trendlines and ECI trendlines as:^[8]
1. Anthropic's unreleased models (checkpoints of Fable 5.5?) are on a faster trendline.
2. Anthropic was actually kind of behind the frontier for a bit, prior to Mythos Preview, so their high growth is partly catchup.
3. Anthropic's latest AECI thinks the Claude 5.5 series is better than Epoch's ECI thinks.
4. METR's AECI chart today has an x-axis that dates models by availability internal to Anthropic, rather than when they were publicly released like ECI, which steepens the trendline slightly.
The AIFM expects that prior to the Automated Coder, RSI is mostly driven by progress towards automating coding, and after the Automated Coder (the rapid "takeoff" regime), progress is then driven mostly by research taste. As part of this, AIs prior to the Automated Coder are modeled as having research taste worse than human level. This is part of what METR is pointing to when they caveat that: "we understand that AI is not yet able to make highly significant and fully autonomous contributions to the most important parts of the model training process."

Yet it's possible that recent models have actually been quickly improving in research taste, and that this is driving capabilities progress as much or more so than pure AI coding automation is. In particular there is evidence from P-Zero's TasteVal to suggest this is the case,^[9] though the AIFP already incorporated an unreleased version of that evidence into the latest AIFM. They didn't update all the way to the implied TasteVal numbers, but I don't tend to think that one should, as there's counterevidence from Epoch's research into AI R&D automation (1, 2).^[10]

^{^}
Also you have to assume 100% of acceleration is from AI and not other inputs. Today's METR report estimates it's 80% at Anthropic… not that far off, to be fair.
^{^}
The latest AI Futures Model estimates a 1.08x-1.09x speedup vs. human-only work with same compute/resources as of 10/7/25 (the trendline breakpoint in METR's post today), based on a few different sources of evidence. (Daniel's presets 1.09x, Eli/Brendan 1.08x)
For today (11/5/26), the AIFM estimates 1.26x-1.37x speedup (Daniel 1.37x, Eli 1.26x, Brendan 1.30x), which is quite similar to Ryan Greenblatt's 1.15x-1.3x, which makes sense as they've worked together on ex. AI-2040, and the AIFP's uplift calculations used Greenblatt's work, etc.
Also ex. an Anthropic researcher interviewed by METR for today's report said there was "~0 uplift pre Opus 4.6/Opus-4.5" on AI R&D.
^{^}
I've done a bit of formatting cleanup on this.
^{^}
EDIT: Greenblatt confirms this was an error. Now corrected on his end.
^{^}
To be clear, the latest AIFM is more conservative than AI 2027 was.
^{^}
Which Daniel Kokotajlo says the AIFP is urgently working on.
^{^}
Yes METR's data today includes unreleased models, but even without those, the Haiku 5.5 system card shows a 1.7x acceleration in AECI, which is higher than Anthropic-models-only Epoch ECI acceleration of ~1.45x after a breakpoint at ~October 2025 (using the same rough date range for choosing the pre-10/25 trendline).
^{^}
METR's report also provides possible explanations here.
^{^}
Specifically, TasteVal shows a breakpoint in the trendlines on AI research taste around December 2025 (suspiciously close to the capabilities trendline, but admittedly after it, though METR's AECI analysis today uses "the date at which the model could be used by most Anthropic employees instead of public release dates" which accounts for some of the gap). (Anthropic's model cards use the public release dates.) Models just prior to the breakpoint were at ~0.1x Human expert, and now Opus 5.5 is at 2.30x (95% CI 1.15-4.37x) Human expert.
^{^}
Personally I think the difference between TasteVal's and Epoch's research might partly result from the fact that the former specified particular research problems tractable to known methods, while the latter involved work that was either open-ended or required novel ideas.
TasteVal disclaimed that "we do not measure significant aspects of taste, such as picking problems worth solving" and "the strongest submissions to our tasks did not use novel ideas or perform non-obvious cross-domain adaptation". Epoch AI notes in its InnovationEval that "AI did not discover anything comparable to the original innovation" and in its "Can AI automate Epoch?" that "Models generate promising research directions, but struggle to design experiments that are actually informative".
I suspect this relates to AI weakness in "relevance realization", aka the ability to identify the information most useful for improving your understanding of the world. Less of that is required for TasteVal's tasks.

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