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The Geopolitics of AI Governance: AI Governance as a Geopolitical Infrastructure Artificial intelligence is rapidly evolving from a technological innovation into a foundational infrastructure shaping economic systems, geopolitical competition, and global governance. As AI technologies become increasingly embedded in critical infrastructures, institutional decision-making, and global markets, questions of governance have moved to the center of international policy debates. Existing discussions on AI governance remain fragmented across ethical principles, regulatory frameworks, technical standards, and institutional oversight mechanisms. While these instruments play important roles, they often operate in isolation and therefore fail to capture the systemic structure through which artificial intelligence is governed in practice. This paper proposes a systemic interpretation of AI governance as a multi-layered institutional architecture that connects leadership strategy, normative governance principles, regulatory regimes, international standards, technological infrastructure, national security considerations, and societal accountability. Through this perspective, AI governance is conceptualized not merely as regulatory oversight but as an emerging geopolitical infrastructure shaping technological sovereignty and global power relations. Building on literature synthesis and conceptual analysis informed by expert perspectives, the study introduces a seven-layer governance architecture that explains how different governance instruments interact across jurisdictions and institutions. The paper advances the Operating Infrastructure Thesis, arguing that in the age of artificial intelligence governance can no longer function as an external compliance mechanism applied after technological deployment. Instead, governance must operate as an embedded institutional infrastructure coordinating regulatory compliance, risk management, accountability mechanisms, and technological oversight across the lifecycle of AI systems. In addition, the paper introduces the Interoperability Principle of AI Governance, which suggests that effective governance in a fragmented global regulatory landscape does not depend on full legal harmonization but on governance architectures capable of operating across multiple regulatory regimes simultaneously. By conceptualizing AI governance as a systemic infrastructure rather than a collection of isolated governance tools, this study contributes to the emerging field of Systemic AI Governance. It provides a conceptual framework for understanding how governance architectures shape the development, deployment, and control of artificial intelligence within the evolving global AI order. The findings suggest that the ability to design effective governance architectures may become as strategically important as the ability to develop advanced AI technologies themselves. Jurisdictions and organizations capable of building integrated governance infrastructures will be better positioned to guide the development of artificial intelligence in ways that support accountability, technological innovation, and societal stability.