The AI Productivity Transmission Gap in Small Open Economies: A Dynamic Institutional Absorptive Capacity Model

Artificial intelligence is widely expected to raise productivity, yet its macroeconomic gains remain uncertain, uneven, and institutionally mediated. This paper develops an original Dynamic Institutional Absorptive Capacity (DIAC) model to explain why the same AI shock can produce divergent outcomes in small open economies. The central argument is that AI does not translate directly from firm-level task efficiency into national productivity. Its effect is filtered through complementary intangible investment, skills formation, data governance, competition policy, labor-market mobility, and social insurance. The paper formalises an AI productivity transmission gap between technical adoption and inclusive productivity realisation. Using analytical theory-building, it identifies three regimes: adoption without absorption, constrained complementarity, and adaptive complementarity. It also derives propositions on threshold effects, productivity J-curve dynamics, distributional stress, and policy sequencing. The model implies that small open economies should not maximise AI adoption as an isolated target, but should build institutional absorptive capacity that converts AI exposure into productivity, worker mobility, and shared prosperity.