Cost

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed software

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 10 + 10 + 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 measured API-cost-per-solve proxy supports economical bounded execution. Human review, infrastructure and maintenance remain outside the cost denominator. 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: Track total campaign labor and compute, including failures; divide by accepted changes and report confidence intervals and pricing date. 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

Total campaign dollars / accepted changes; separately report labor and compute. Do not use generated lines of code as useful output.

Evidence requirements

Archive specifications, initial repository states, patches, independent tests, run logs, reviewer decisions, and follow-up defects. Reject test deletion, weakened acceptance criteria, hard-coded hidden answers, and fixes that pass narrow tests while violating the specification. Evaluate the tests themselves. Separate public benchmark results from private prospective evaluations.

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 API-cost 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

Snapshot accessed November 7, 2026: The same ProgramBench configuration averages $36.36 per task with 18.5% fully resolved. Dividing gives approximately $196.54 in API spending per full solve (project calculation). ProgramBench live results and protocol.

Interpretation and tracking value

This excludes developer labor, infrastructure, review, maintenance and deployment. The ratio uses rounded table values and is not a quoted production cost.

Next comparable measurement

Track total campaign labor and compute, including failures; divide by accepted changes and report confidence intervals and pricing date. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.