Artificial Intelligence, Smart City Infrastructure, and Urban Productivity: Evidence from a Dynamic Panel of Leading Digital Economies, 2010–2026

This paper investigates the causal relationship between artificial intelligence (AI) investment, smart city governance infrastructure, and urban total factor productivity (TFP) across ten leading digital economies over the period 2010--2026. Drawing on a novel panel dataset that integrates ICT capital expenditure, digital infrastructure indices, Global Innovation Index scores, and the United Nations E-Government Development Index, we estimate dynamic System Generalized Method of Moments (GMM) models combined with Spatial Durbin specifications and machine-learning-based regime clustering. Our results indicate a statistically and economically significant positive association between AI investment and urban TFP: a ten percent increase in AI investment (as a share of GDP) is associated with approximately 1.5 percent higher TFP, conditional on digital infrastructure endowment and innovation capacity. We further document an inverted-U (EKC-type) relationship between AI intensity and employment polarization, suggesting that economies surpassing a threshold AI investment level of approximately 5.2 percent of GDP begin to experience convergence in skill demand. Spatial spillover effects are quantitatively important, with indirect TFP effects accounting for roughly one-third of total impacts. These findings are robust across alternative specifications, sub-period analyses, and a jackknife leave-one-out procedure. Our study contributes to the emerging literature on AI-driven urban transformation by providing causal panel evidence and a tractable theoretical framework, and offers policy implications for economies at different stages of digital transition.