AI Policy Effects on Innovation Ecosystem Resilience: A Difference-in-Differences Quasi-Experimental Analysis

Background: Artificial intelligence integration into regional innovation systems presents fundamental questions about policy-driven technological transitions. While AI’s general-purpose capabilities offer substantial potential, real-world impacts depend critically on local digital infrastructure, institutional capacity, and network connectivity. China’s National AI Innovation Pilot Zone program provides a large-scale natural experiment to examine how AI interventions reshape regional innovation architectures, yet causal evidence on systemic effects and underlying mechanisms remains limited. Methods: Using longitudinal data from 264 Chinese cities (2010-2023) and staggered difference-in-differences with two-way fixed effects, we estimate causal effects of AI policy on regional innovation ecosystem resilience. Regional innovation ecosystem resilience was quantified across five computational dimensions via entropy-weighted aggregation of 23 indicators. Mechanism analysis decomposes policy effects through human capital upgrading and financial resource reallocation pathways. Heterogeneity analysis examines moderating roles of internal mobility, external connectivity, and digital infrastructure. Results: The AI policy significantly increases ecosystem resilience. Contrary to direct technology diffusion models, effects operate through systematic restructuring of human capital and financial flows, transitioning regional economies from factor-driven to knowledge-intensive growth modes. Policy effectiveness exhibits strong network dependency: internal mobility determines reconfiguration speed, while external connectivity shapes performance ceilings. Digital infrastructure depth emerges as a critical conditioning variable. Implications: AI policy functions as network-mediated system architecture rather than isolated technological intervention. Effectiveness depends fundamentally on pre-existing connectivity topology across physical, digital, and institutional dimensions, challenging technology-push models that emphasize intervention intensity over structural context. Findings establish a computational framework for modeling policy-driven socio-technical transitions and demonstrate the necessity of network-aware AI governance. Results suggest that future research should integrate real-time digital trace data with network science methodologies to capture dynamic ecosystem evolution.