Anthropic's AI acceleration is probably real

METR researchers spent three months inside Anthropic to investigate whether AI is accelerating Anthropic's research, and how much. Their answers are yes, and by a lot. They estimate that between October 2025 and October 2026, Anthropic's model capabilities improved at a 2x faster growth rate than between March 2024 and October 2025. They attribute about 80% of that acceleration to AI, and 20% to non-AI factors. If true, this is the strongest evidence yet that AI is measurably speeding up frontier AI development itself – the feedback loop behind worries about recursive self-improvement leading to runaway AI progress. Despite its urgency, METR’s report is hard to assess, because Anthropic redacted the majority of METR’s analysis. As a result, I’ve seen plenty of criticism that we can’t really learn anything from this report. This is wrong. There is plenty of public information we can use to triangulate METR’s claims, and figure out what we should actually learn from it. Specifically, I use public data to answer three questions: • Is the acceleration real? (yes.)• Is the acceleration because of AI? (probably.)• Is the acceleration going to continue? (who the fuck knows?)Let’s take each of them in turn. Is the acceleration real?The majority of METR's unredacted analysis focuses on AECI, Anthropic's internal version of Epoch's Capabilities Index (ECI). Both indices do the same thing: they take a model's scores on many benchmarks and boil them down to a single capability number, after estimating how hard and how discriminating each benchmark is. The difference is that AECI is fit to mostly internal benchmarks. On AECI, Anthropic's models were improving at about 15 points a year before October 2025, and about 29 points a year after, a difference of 1.9x. At first, this seems wrong. The frontier of public ECI shows no break at all in October 2025. But that is the frontier across all labs (not Anthropic's specific trend), and using Epoch's latest methodology. Once you make t