Beyond Digital Authoritarianism: State Capacity and the Functional Convergence of AI Surveillance in China and the United States

This article investigates whether political regime type determines the global diffusion of AI-based digital surveillance systems. Utilizing a Most-Different Systems Design (MDSD) comparing the United States and China, supplemented by a multivariate logistic regression analysis of the Carnegie AI Global Surveillance (AIGS) Index (N = 176) and several other indexes, the study develops a supply-side theory of surveillance adoption. The quantitative and qualitative analyses reveal that while the adoption of surveillance programs correlate significantly with state capacity (measured by military expenditure and digital surveillance infrastructure) and the presence of security shocks, political regime type is not a statistically significant predictor of adoption probability (p = 0.544). Instead, regime type acts as a mediating variable that shapes the intensity, integration, and legal framing of the surveillance grid. While both nations have converged on functionally equivalent architectures featuring biometric fusion and predictive policing, their institutional paths diverge: the U.S. operates via a “decentralized mesh” of public–private partnerships, whereas China employs an “integrated vertical grid” for preemptive social governance. This study challenges the “digital authoritarianism” narrative by demonstrating that pervasive surveillance is not a regime-specific pathology but a structural outcome of state capacity in high-resource modern regimes.