Experiment efficiency

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed ai research

25% 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 5 + 5 + 5 + 5 + 5 = 25/100, 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: Faster workflows suggest early gains, but the missing all-experiment denominator prevents credit for dependable campaign-level efficiency. 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: Compare matched research campaigns; count all runs and independent improvements and divide by total dollars and researcher hours. 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

Validated improvements per 100 experiments and per total dollar; include failed runs and researcher support.

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: Qualitative progress; required rate unknown. 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's assessment includes evidence of faster research workflows, but does not publish a standardized count of validated gains per 100 experiments. METR: Anthropic AI R&D acceleration assessment.

Interpretation and tracking value

Private operational evidence cannot establish the project's all-in experiment-efficiency metric. Failed runs and support work need explicit accounting.

Next comparable measurement

Compare matched research campaigns; count all runs and independent improvements and divide by total dollars and researcher hours. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.