METR：Anthropic 可能已在 7 月越过自身 AI 加速预警阈值

Nonprofit METR got eleven weeks of access to Anthropic's internal research data. Its finding: the rate at which Anthropic's models are improving has roughly doubled, and about 80 percent of that acceleration traces back to AI being used in the company's own research. Anthropic redacted the most important evidence, though. Whether AI is speeding up the development of better AI is considered a key early warning signal in the safety debate. The concern is that a feedback loop could form where models improve their successors faster and faster, which according to METR could lead to a "sudden emergence of extreme and risky capabilities." Anthropic flags exactly this kind of acceleration as a risk indicator in its Responsible Scaling Policy : if the pace of AI progress doubles because of automated AI research, that should trigger a warning level. That's what METR set out to verify. The nonprofit studies, among other things, whether AI developers could lose control of their systems. From August 12 to October 29, 2026, a team of 2.5 full-time staffers received what they describe as unprecedented access to benchmark results from unreleased models, training metrics, employee surveys, and internal analyses, along with roughly 15 interviews with Anthropic researchers. METR took no money from Anthropic but worked on Anthropic devices for security reasons. Anthropic CEO Dario Amodei had recently committed to bringing external auditors into the company . In October, Anthropic then announced a collaboration with METR involving "employee-like access" . The current audit is a separate project with a narrower scope, according to METR. Since October 2025, Anthropic's model test scores have been climbing about 1.9 times faster than they did between March 2024 and October 2025, according to METR. The measurement is based on the AECI, a composite metric that bundles many benchmarks together. Anthropic achieved in one year what previously took two, roughly comparable to the jump from Claude 2.1 to Sonnet 4.5. The analysis also included three unreleased models that finished training between July and October 2026. Before October 2025, Anthropic's models gained about 15 AECI points per year; since then, the rate has hit 29. METR is confident the acceleration isn't driven by faster growth in training compute. Instead, the auditors estimate that about 80 percent of the current speedup comes from AI being used in Anthropic's own research. One employee said in the interviews: "Now only bottlenecked on ideas & compute, no longer bottlenecked on code." Others reported that experiments that used to take one to two days to set up are now "~free." In informal Slack polls from October 2026, employees said on average that the latest internal model made them roughly twelve times more productive. METR treats these self-assessments as "very loose evidence," calling them unrepresentative and likely to overstate the actual gains. Self-reported productivity gains rose from 1.2x with Claude 3.7 Sonnet to 12x with internal Model 4. | Image: METR[ AI isn't doing fully autonomous research yet. The acceleration comes mainly from AI acting as an assistant, but there are "signs of life" that independent contributions could play a bigger role soon. METR found no bottlenecks that would slow the pace over the next twelve months, which means there's a "significant risk" that the acceleration will keep growing over the coming year. METR also stresses how imprecise the results are. The true acceleration factor could be as low as 1.3 or as high as 3. When models from OpenAI, Google, and xAI are factored in, the industry-wide number drops to just 1.4, possibly because Anthropic lagged behind competitors in 2025 and a catch-up effect is skewing the figures. Views inside Anthropic aren't uniform either. One researcher pegs AI's share of the productivity gains at just 15 to 20 percent and distrusts colleagues' self-reports. Another considers new hires the single most important fa