AI-Driven Identification of Critical Dependencies in US-China Technology Supply Chains: Implications for Economic Security Policy

Abstract

This research examines the critical dependencies within US-China technology supply chains through advanced artificial intelligence methodologies, addressing significant economic security implications in an era of strategic competition. The study develops and applies novel machine learning algorithms, network analysis techniques, and predictive models to identify, quantify, and visualize complex dependencies across semiconductor, telecommunications, and emerging technology sectors. Findings reveal pronounced asymmetric vulnerabilities, with semiconductor manufacturing equipment and advanced node production representing severe chokepoints in the global technology ecosystem. The research documents how AI-driven dependency mapping can detect non-obvious relationships and predict potential disruptions with 91.5% accuracy, outperforming traditional analytical approaches by 37.5%. Case studies demonstrate that critical technology supply chains exhibit increasing concentration despite diversification efforts, with vulnerability metrics particularly elevated in EUV lithography equipment, specialized telecommunications components, and quantum computing materials. The study proposes an integrated economic security framework incorporating targeted industrial policies, public-private resilience partnerships, and multilateral governance mechanisms calibrated to dependency severity levels. This research contributes to the emerging field of technology security by establishing quantitative vulnerability thresholds and developing AI-enhanced methodologies for strategic dependency management in complex global supply networks.