Algorithmic efficiency

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed ai research

40% 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 15 + 5 + 5 + 10 + 5 = 40/100, 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: A deployed kernel optimization shows measurable execution and automated evaluation. Humans still define the workload and capability target; general research efficiency and autonomous iteration are not established. 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: Repeat matched training to the same held-out quality; record FLOPs, wall time, joules and all-in cost separately from inference. 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

Compute, energy, and total dollars needed to reach a fixed independently evaluated capability target. Report training and inference separately.

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 narrow optimization. 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

December 9, 2025: Google reports a 23% speedup in a Gemini training kernel, translating to about a 1% reduction in total training time. Google Cloud: AlphaEvolve results.

Interpretation and tracking value

Developer-reported optimization of one workload; it is not a 23% reduction in training cost, energy, or compute needed for a fixed general capability.

Next comparable measurement

Repeat matched training to the same held-out quality; record FLOPs, wall time, joules and all-in cost separately from inference. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.