AI watchdog report indicates we may have less than 6 months before AI super intelligence arrives
The estimate covers well-specified tasks; METR's judgment that AI drives most of the acceleration rests partly on redacted evidence. Read the full story on CryptoSlateUnderstand the crypto stories that matter and what to watch next. Get The Catalyst in your inbox
Anthropic's AI models were likely improving on tasks with clear success criteria roughly twice as fast in October 2026 as they did between March 2024 and October 2025, according to a Nov. 5 assessment by METR, an independent model-evaluation group.
METR judges that AI assistance probably accounts for most of the recent acceleration. But Anthropic redacted much of the evidence behind that judgment, leaving outsiders with a clearer view of the faster benchmark trend than of what caused it.
The central published measurement uses AECI, Anthropic's version of the Epoch Capabilities Index. METR estimated that AECI's fitted growth rate after October 2025 was 1.9 times its earlier rate, with a 95% confidence interval of 1.5 to 2.8 times. The analysis includes snapshots of unreleased models, so it covers more than the products customers could access.
METR interprets that trend as roughly two years of historical benchmark progress achieved in the past year. The 1.9-times fit is distinct from its broader judgment of roughly twice the historical pace on well-specified tasks. That judgment carries substantial uncertainty, including whether the same acceleration extends to broader work.
Those limits matter because success on clean, easily graded tasks may translate unevenly into real-world usefulness. METR also cautions that an unusually slow historical baseline could make the acceleration appear larger. In the other direction, model results arrive after training, potentially concealing a more recent change in research speed.
Related ReadingMETR says it confidently ruled out an acceleration in the growth of training compute as the explanation for the faster progress. The detailed evidence supporting that finding was redacted.
Beyond that exclusion, separating causes was less decisive. METR says non-AI factors likely played a significant role in the acceleration in late 2025, while judging that AI assistance now accounts for a majority of it.
The distinction also leaves people at the center of the most important training work. METR says AI was not yet making highly significant, fully autonomous contributions to those parts of Anthropic's model-training process. The reported speedup primarily involved researchers using AI assistance.