Acceleration

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed ai research

30% complete — judgment-based estimate as of November 7, 2026. Confidence: low. The scale starts at 0% before ChatGPT's public release in 2022 and ends at 100% when AI manages the field's continuing advancement without human participation. This rates the outcome's contribution to that transition, not the percentage of tasks solved, a measured fraction of the field, or time remaining.

The shared scoring rubric uses five equally weighted dimensions: execution, breadth, agenda-setting, validation, and sustained autonomous improvement. Points for this outcome are 10 + 5 + 5 + 5 + 5 = 30/100, in that order; each dimension has a maximum of 20. These component judgments make the estimate inspectable, but are not independently measured scores.

Reason for the estimate: Organizational acceleration supports useful AI-assisted research, but causal attribution, human management and continuation across generations remain unresolved. The dated baseline below supplies the findings and source links used for this assessment. Unmeasured components receive no demonstrated-progress credit; missing evidence does not prove a missing capability.

What would move the score: Freeze a capability target and measure elapsed days, compute and dollars for each increment; retain model generations and independent ablations. Higher autonomy scores additionally require AI to select worthwhile work, validate results and improve the process over repeated cycles without human research management, repair or adjudication. Lower the score if replication fails, quality regresses or previously hidden human assistance is found. Preserve this dated estimate when adding future assessments.

Measurement and acceptance rule

Elapsed time and resources per comparable validated capability increment across generations; report whether research gains persist, flatten, or regress.

Evidence requirements

Freeze private evaluations outside the research agent's control. Publish ablations comparing the same development pipeline with and without the AI-produced change. Control for additional compute, new data, expert interventions, and evaluator changes. Distinguish improving a narrow kernel from improving general research ability. Do not allow an agent to redefine its own success metric.

Related milestones

See the area's proposed milestones. Proposed targets are distinct from the dated evidence below.

Baseline as of November 7 2026

Status: Measured organizational proxy. Sources accessed November 7, 2026. This is a documentary baseline, not an independent replication or an assertion of the worldwide best result. An unknown measurement is not zero.

Dated findings

November 5, 2026: METR estimated Anthropic's capability-index growth rate since October 2025 at 1.9 times its previous rate (95% CI 1.5–2.8). METR: Anthropic AI R&D acceleration assessment.

Interpretation and tracking value

This is an outside assessment of one organization, not a controlled causal estimate of recursive self-improvement. Compute scaling was examined, but other causes and redacted evidence limit attribution.

Next comparable measurement

Freeze a capability target and measure elapsed days, compute and dollars for each increment; retain model generations and independent ablations. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.