A Fortune article reports that 90% of executives surveyed by the National Bureau of Economic Research say AI has had zero impact on employment or productivity over the past three years, despite companies claiming positive AI adoption in earnings calls and billions invested in the technology. The disconnect mirrors Solow's 1987 productivity paradox, when computers were everywhere but didn't show up in productivity data—and current academic research shows wildly varied results, from MIT's modest 0.5% predicted productivity gain over a decade to the Federal Reserve's 1.9% observation, leaving economists uncertain when or if AI will justify corporate spending. Workforce data adds another wrinkle: while worker AI usage jumped 13% in 2025, confidence in the technology dropped 18% simultaneously, and using more than four AI tools at once actually reduces productivity rather than enhancing it. What's missing is clarity on why the adoption-reality gap exists—whether it's poor implementation, misaligned job design, overhyped vendor claims, or simply that AI works better for some sectors and roles than others.