Artificial Intelligence as a General-Purpose Technology: Modeling Long-Term Growth and Economic Transformation

This paper develops a macroeconomic framework for analyzing artificial intelligence (AI) as a general-purpose technology (GPT) with the potential to reshape long-term global growth. Integrating endogenous growth theory with sector-specific diffusion modeling, we treat AI as a recursively improving technology whose productivity effects evolve over time through usage, innovation, and spillovers. Sectoral AI adoption is modeled via logistic diffusion functions, calibrated using global data across five major industries: finance, manufacturing, healthcare, retail, and the public sector. Simulations reveal that AI generates a delayed but significant productivity acceleration—producing a hump-shaped total factor productivity (TFP) growth trajectory consistent with past GPT patterns. We evaluate optimistic, baseline, and lagging diffusion scenarios and identify policy interventions that influence adoption speed and inclusivity. The findings suggest that AI’s macroeconomic potential depends not only on technological advancement but also on coordinated investments in human capital, innovation infrastructure, and equitable governance. This research contributes a dynamic, policy-relevant model for anticipating the economic transformation driven by AI and outlines priorities for future empirical and theoretical work.