THE FRICTIONS THAT MAKE AI FORECASTING HARD

Summary from the TLDR AI newsletter

AI forecasting fails when capability gains are mistaken for end-to-end impact, because real-world workflows contain social, institutional, physical, and tacit bottlenecks. Better forecasts require practitioner-informed, cross-framework research that measures whether AI actually changes the limiting step, not just one task.