Simulation efficiency

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed physics

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: Fast atmospheric simulation demonstrates strong bounded execution and testable output. Unmatched error tolerances and excluded full costs limit broader autonomous efficiency claims. 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 at a common physical error tolerance; include training/setup amortization, energy and full cost per accepted solution. 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

Time, energy, and dollars per solution at a fixed independently checked error tolerance, including training and setup costs.

Evidence requirements

Release equations, assumptions, units, code, and data needed to reproduce the result. Verify dimensional consistency and applicable physical constraints. Test outside the fitted range when scientifically appropriate. Use blind analysis and correction for multiple hypothesis searches. Mathematical derivations can use formal proof where practical, but formal correctness does not substitute for empirical validation of physical assumptions.

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 throughput; equal-error comparison incomplete. 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

July 22, 2024: NeuralGCM-1.4° simulated 70,000 days in 24 hours on one TPU; the cited X-SHiELD comparison simulated 19 days using 13,824 CPU cores. Neural general circulation models, Nature.

Interpretation and tracking value

Different resolutions, hardware and model formulations prevent interpreting this as a universal fixed-error speedup. Training, initialization and energy are excluded from that throughput figure.

Next comparable measurement

Compare at a common physical error tolerance; include training/setup amortization, energy and full cost per accepted solution. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.