METR: Anthropic's AI R&D is accelerating at ~2x, recursive self-improvement possibly under a year away
Don't Worry About the Vase (Zvi) · rss · 2026-11-09
METR published a deep-access investigation into how much Anthropic is accelerated by its own AI systems, and the answer is grim: capabilities are now improving at roughly 2x the historical rate (95% CI 1.3–3x), with internal benchmark (AECI) growth at 1.9x pre-October-2025 pace — about two years of progress packed into one.
Key points:
- A majority of the acceleration is likely attributable to AI doing AI R&D, and it was not driven by faster scaling of training compute.
- No observed bottlenecks or strong diminishing returns stand in the way of further acceleration; Anthropic redacted the analysis of several key bottlenecks.
- Naive extrapolation puts full recursive self-improvement possibly less than a year away, months ahead of the AI 2027 timeline.
- Anthropic granted unprecedented access to internal data (pretraining, RL, compute allocation, internal benchmarks, staff interviews). Ajeya Cotra says the "dramatic acceleration" threshold appears crossed; earlier estimates (Greenblatt's 1.15–1.3x, Opus 5.5 system card's 1.5x) were far too low.
- Peter Wildeford notes labs run unreleased internal models with 10,000x more compute than public users; Benjamin Todd's extrapolation suggests Claude could lead internal research by mid-2027 and near-full automation by end of 2027. Zvi suggests Congress subpoena Anthropic for the full unredacted report.
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