Competition and Collaboration in the AI Race: Country-LevelDirectional Evidence for Risk Monitoring and Policy

Artificial intelligence (AI) is reshaping national economies, yet country-level AI-macro relationships remain poorly understood. Using annual data for the United States and China, 1980-2020, we develop a four-layer triangulation framework-Pattern Causality, Granger causality, VECM-based cointegration, and lead-lag correlation-to map directional associations between AI activity indicators and macro aggregates through weighted networks and heatmaps. Three patterns recur. First, nonlinear AI-macro dependence is moderate and mostly positive, making AI indicators useful monitoring signals rather than stand-alone decision triggers. Second, publications and patents carry short-run predictive content in Granger tests, making them candidate early-warning indicators for macro surveillance. Third, cointegration places AI indicators mainly on the adjustment margin: Macro fundamentals condition long-run AI-macro co-movement more than AI indicators lead it. The international layer adds an important qualification. United States-China collaboration variables raise AI node centrality, especially for China, but a mechanical-expansion null benchmark shows that most of this increase is expected from enlarging the network; beyond-mechanical collaboration evidence concentrates in the cointegration layer. Overall, the US pathway is patent-oriented and selective, whereas China's is denser and more collaboration-intensive. The framework supports AI-macro risk monitoring and hypothesis generation, not structurally identified causal claims.