I asked for a quality response on an article decrying AI's productivity gain. The article didn't resonate with me and my use of AI, so I asked "Scout" (my AI OS) about it. Here is the output: Hard sentence: Yesāyouāre getting a real productivity boost. The āAI (artificial intelligence) wonāt boost productivityā line is usually a macro-average measurement claim, not a statement that no one can get leverage; outcomes depend heavily on workflow design, verification cost, and where the value shows up (measured vs real). Tell it like it is: itās both āhow you use itā and āthe overarching claimā 1) You are getting a productivity boost because youāre using AI in a high-leverage lane. Your pattern looks like this: - Compression of blank-page time (first draft in minutes instead of hours). - Rapid critique + iteration (you run multiple branches, compare, and converge). - Cognitive offload (you keep momentum while holding many parallel threads). - Decision engineering (checklists, risk framing, deal structures, scenario trees). That is not a party trick; itās cycle-time reduction on knowledge work. In practical terms: youāre turning āthinking latencyā into āexecution throughput.ā 2) The āno productivity boostā narrative is usually about why macro numbers donāt move Even if you personally get 2ā5Ć speedups on some tasks, macro productivity can look flat because: - Adoption is lumpy and uneven: Most people donāt use it like you do; they dabble, then stop. - Integration costs are real: The hard part is plugging AI into workflows, approvals, and systemsānot prompting. - Verification overhead eats the gains: In high-stakes work, every claim needs checking. That time can erase speedups. - Value is mis-measured: A lot of benefit shows up as fewer meetings, faster drafts, better decisionsāthings GDP (gross domestic product) and āoutput per hourā often donāt capture cleanly. - Rework risk is non-trivial: Hallucinations (confidently wrong outputs) create hidden cost if people donāt police it.