A 304KB, ~294-citation research compendium covering AI's economic impacts across five areas: labor market transitions (displacement/creation flows, human-AI complementarity, the productivity paradox), policy responses (robot tax, UBI, retraining, SMB adoption), international impacts (developing economies, AI as public good, governance frameworks), empirical evidence (all major projections vs. measured outcomes through early 2026), and research gaps with 20 fully specified proposals including budgets, timelines, and priority rankings. Key finding: micro-level productivity gains of 14-40% are well-documented, yet ~90% of CEOs report zero measurable business impact — the gap between what AI does and what we can measure is the central policy challenge.
Michael Mull (Fri,) studied this question.