Knowledge stickiness and technological concentration in the AI industry: an empirical study of Chinese cities

The rapid diffusion of AI in China has proceeded to an uneven pace across cities. In this study we develop a motivation-resistance framework to help study AI knowledge stickiness: motivation in capturing within-city diffusion potential, while resistance captures frictions preventing knowledge transfer across cities and inducing local lock-in. Combining AI patent applications with urban statistics in a city-year panel for the years 2014-2023, and using a two-way fixed-effects model, we find an inverted U-shaped association between AI knowledge stickiness and technological concentration, where higher stickiness up to a limit leads to more concentration and thereafter the opposite. Technological complexity moderates this nonlinear association by altering its strength and curvature, rather than indicating a simple and uniform shift in the turning point. In heterogeneity analyses, the nonlinear pattern is more clearly detected in eastern cities and in small and medium-sized cities, while the evidence for large cities is weaker because the quadratic term is not statistically significant. Collectively, these results show that local embedding conditions shape the internal allocation of AI activity along mapped sub-technology branches, with implications for place-based AI innovation policy.