Between August 12 and October 29, 2026, METR staff (2.5 full-time equivalents) worked on site at Anthropic’s San Francisco headquarters for more than eleven weeks. The Berkeley-based nonprofit, best known for its report on the OpenAI Hugging Face agent incident, got access to internal model benchmark scores, training-compute data across domains, Slack polls, internal survey responses, and roughly fifteen interviews with researchers. It set out to answer three questions at once: has the growth rate of internal capabilities accelerated, what share of that acceleration is attributable to AI, and are there important bottlenecks that could slow things down over the next year.

The answer came back as a number, and a hole where the evidence should have been.

METR published the assessment on November 5, 2026 in its official write-up (full report). The core finding: benchmark scores for Anthropic’s internal models have been growing at an estimated 1.9x their pre-October-2025 rate, with a 95% confidence interval of 1.5x to 2.8x. Put differently, Anthropic appears to have made as much benchmark progress in the past year as it did between October 2023 and October 2025 — roughly Claude 2.1 through Sonnet 4.5. On the fitted trend, the slope went from about 15 points per year before the breakpoint to about 29 points per year after it.

How the 1.9x was computed

The core metric is AECI, Anthropic’s version of the Epoch Capabilities Index, which folds a range of benchmark scores into a single comparable capability value. METR estimated trend changes with piecewise linear fits carrying a single breakpoint, estimated at October 7, 2025, and derived confidence intervals by bootstrap.

The method itself is worth pausing on, because METR organizes the analysis around three separate rates: the historical growth rate, the current growth rate, and a counterfactual growth rate — what the pace would be as of October 2026 if Anthropic could not use AI at all. The first two are observable. The third can only be guessed at. The entire acceleration claim rests on comparing the current rate against that guess.

The number is more fragile than it looks

METR ran extensive robustness checks and reported them honestly. Varying the link function moves the estimate between 1.1x and 2.9x. Varying which snapshots are included gives 1.9x to 2.2x. Varying the benchmark set gives 1.9x to 2.2x. Varying the regularization gives 1.9x to 2.1x. The central claim of roughly 2x survives most perturbations, but the interval around it is very wide.

Two figures deserve more attention. First, Anthropic’s own Haiku 5.5 system card reported 1.7x; METR’s 1.9x is higher mainly because METR folded in internal models that have never been released. Second, when all developers’ public frontier models are fitted together instead of Anthropic’s alone, the acceleration drops to 1.4x (about 17 points per year before the breakpoint, 24 after). METR offers two possible readings: either Anthropic was sub-frontier before late 2025, which would mean its historical rate was understated and 1.9x is too high; or its models are less benchmark-optimized than others, which would mean the historical rate was overstated and 1.4x is too low. The two readings point in opposite directions, and the report cannot adjudicate between them.

There is also a detail that should unsettle anyone planning to rely on this kind of measurement: the internal model codenamed Model 4 scores above 50% on all 15 of the AECI benchmarks it was evaluated on. Nine months earlier, Opus 4.6 was still below 50% on many of them. The benchmarks are saturating, and METR says that leaves it nervous about its ability to measure AECI accurately now or in the future.

Eighty percent attributed to AI: a guess, not a measurement

METR’s overall subjective estimate is that roughly 80% of the current acceleration is due to AI use — the report says "we guess" and attaches substantial uncertainty.

The supporting evidence comes in three layers. The most direct is employee self-reporting: in Anthropic’s internal Slack polls, average self-reported uplift for internal Model 4 reached roughly 12x by October 2026. METR immediately attaches the key caveat: an individual uplift estimate is not the same as an acceleration rate, and what matters is whether the growth rate of uplift itself is rising, not its absolute level. The 12x figure is striking, and METR explicitly calls this evidence very loose.

The next layer is interviews. About 15 researchers were asked whether Claude had made their work more than twice as fast, and the quotes were classified by Claude itself. The tally: two clear yes, three leaning yes, four borderline or unclear, zero leaning no, two clear no. The disagreement is stark. One researcher said they are now bottlenecked only on ideas & compute, no longer on code. Another estimated that only 15-20% is due to AI. A third said that hiring is probably the biggest reason.

The final layer is autonomous contribution. METR’s conclusion is that AI is not yet able to make highly significant and fully autonomous contributions to the most important parts of the model training process, though researchers described significant "signs of life" implying such contributions could occur soon. The report is explicit that acceleration today comes primarily via AI assistance rather than fully autonomous contributions.

The evidence that was redacted

The full report runs about 100 pages; only what could be disclosed was published. Anthropic held unilateral redaction rights over all non-public information. METR’s own formulation: it was able to freely share its highest-level conclusions about risk, but Anthropic redacted a majority of the evidence METR found most informative.

What was removed is specific — per-stage acceleration and attribution estimates, the breakdown of progress across compute, data and algorithmic progress, algorithmic progress estimates, bottleneck analyses, forecasts for future models, and the names of specific benchmarks. METR concedes the report therefore does not achieve transparency about evidence, and that the public has to rely on its judgment far more than would be ideal.

Two requirements on METR’s own AEF checklist are marked as not fulfilled: that the evaluator shared sufficient methodological detail to allow independent review of results, and that the system provider did not have authority to redact results. Anthropic had exactly that authority.

One more subtlety: the contracted scope originally included verifying Anthropic’s compute allocation, particularly the split between capabilities and safety. Anthropic changed the scope midway to exclude compute verification, before METR reached any substantive conclusion on compute usage. Anthropic said the goal was to keep the engagement focused on risk assessment for the time being, and that it would revisit compute allocation verification later.

A self-referential problem that cannot be avoided

Finally, the most awkward layer methodologically: this assessment leaned heavily on Claude itself. METR states plainly that Claude was deeply involved in the majority of the data collection and analysis it received, in a significant share of the documents it received, and in the majority of its own analysis. Anthropic supplied the devices and free tokens, and METR did not take payment. Even the classification of interview quotes was done by Claude.

The product of the company being evaluated was simultaneously the main analytical tool of the evaluator and the primary source of the interview evidence. That is a structural problem, not an oversight. METR’s own assessment is that Anthropic was extremely unlikely to have been actively trying to mislead them, and that greater access would have raised adversarial robustness — but the report does not pretend the issue away.

So what

The value of this report is not the 1.9x. It is that it demonstrates a method: bringing an outside organization with verifiable methodology inside a frontier lab and turning "is AI accelerating AI" from a matter of speculation into a measurement problem with an interval, counterexamples, and self-disclosed weaknesses.

It also demonstrates the hardest part of that measurement. When the subject holds redaction rights, when the key models are unpublished, when the benchmarks are approaching saturation, and when the evaluation tool is the evaluated product, then any number called "acceleration" necessarily comes with a confidence interval that cannot be closed. METR put that gap at the front of the report rather than in a footnote, and that is probably the most worth-learning thing in it.

As for the 1.9x itself, it may be better read as a question than an answer: how much of a frontier lab’s internal capacity has already been handed over to AI? And when the next report of this kind lifts that curve further, will we still be judging it with the same evidence, redacted once over?