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: Yes—you’re getting a real productivity boost. The “AI (artificial intelligence) won’t boost productivity” 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: it’s both “how you use it” and “the overarching claim” 1) You are getting a productivity boost because you’re 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; it’s cycle-time reduction on knowledge work. In practical terms: you’re turning “thinking latency” into “execution throughput.” 2) The “no productivity boost” narrative is usually about why macro numbers don’t move Even if you personally get 2–5× speedups on some tasks, macro productivity can look flat because: - Adoption is lumpy and uneven: Most people don’t use it like you do; they dabble, then stop. - Integration costs are real: The hard part is plugging AI into workflows, approvals, and systems—not 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 decisions—things GDP (gross domestic product) and “output per hour” often don’t capture cleanly. - Rework risk is non-trivial: Hallucinations (confidently wrong outputs) create hidden cost if people don’t police it.