report · 2026-10-14
How an assessor could investigate evidence of acceleration from the automation of AI R&D
On this page
Why it matters here
METR proposes assessing internal capability trends, attributing any acceleration to AI rather than other inputs, identifying bottlenecks, and testing whether the assessment could detect substantially faster progress. It calls for dated internal capability and R&D-input histories plus researcher interviews. This is a proposed measurement approach, not a completed acceleration assessment.
AI-R&D capabilityEvaluation integrity
What to keep in mind
- No new model results, measured acceleration, calibrated proxy or demonstrated recursive improvement is reported by this proposal.
- Attribution needs a difficult no-AI counterfactual and multiple evidence sources; compute, labor and data changes can confound growth.
- The chart is explicitly illustrative, with no real data or numeric scale. Forecasts and researcher impressions are uncertain; benchmark success need not establish practical usefulness.
- Internal data and interviews are proposed access requirements, not available evidence independently audited here. No underlying cited empirical results are newly adopted.