Understanding AI project insight (6 Oct 2026): Kiran's aim is not just to explain AI but to test whether conclusions drawn from 3-4 years of heavy AI use generalize beyond his own experience. His current hypotheses: AI works best on tightly scoped tasks with explicit goals and non-goals; model switching costs for coding are low because workflows have standardized; model quality changes quickly so attachment to one provider is fragile; vocabulary and concepts materially affect output because the framing steers model behavior; and giving AI broad autonomy has produced worse results than keeping the human responsible for the work. The essay series should pressure-test these experience-derived beliefs rather than assume them.