Governing the Ungovernable: Structural Contradictions, Institutional Limitations, and the Constraints of State Authority in AI Regulation in China and the United States

Both China and the United States have developed complex artificial intelligence (AI) governance frameworks; however, neither system is able to fulfill its stated objectives. This review article employs a comparative political-economy approach to explore how each nation's foundational political structure leads to unique, intractable regulatory failures. In China, the Party-state model creates an innovation-control paradox: the imperatives of information sovereignty embedded in its generative AI regulations and cross-border data transfer restrictions ultimately undermine the open research environments essential for advancing frontier AI development. This situation disproportionately impacts smaller technology companies and academic researchers while reinforcing the dominance of established industry leaders. Conversely, the United States grapples with severe regulatory fragmentation, leaving existing governance gaps that enable persistent cross-sectoral AI risks, as industry lobbying effectively reframes necessary safety regulations as economically unpatriotic. By examining specific legislative instruments, including the Cyberspace Administration of China's 2023 Interim Measures for Generative AI, Executive Order 14110, and using the EU AI Act as a comparative benchmark, this paper evaluates critical friction points in compliance cost distribution, institutional coordination failures, and the macroeconomic repercussions these frameworks impose on emerging markets in Southeast Asia, Sub-Saharan Africa, and Latin America. The paper concludes that meaningful governance progress requires separating information control from technical AI oversight in China, establishing a unified federal AI statute in the United States, and creating multilateral regulatory coalitions. These coalitions would enable emerging economies to maintain policy autonomy while striving for interoperability with leading AI ecosystems.