Greenland as a Structural AI Strategic Node: Perception Integrity, Temporal Dominance, and the Arctic Reconfiguration of Algorithmic Power

This working paper reframes Greenland’s strategic relevance through an artificial intelligence (AI) governance and systems perspective. Moving beyond conventional geographic, military, or resource-based accounts, it conceptualizes Greenland as a structural AI strategic node embedded within global architectures of sensing, early warning, algorithmic decision-making, infrastructure optimization, and governance experimentation. The paper develops a five-dimensional analytical framework—perception integrity, temporal dominance, compute–energy coupling, material security, and institutional governance—to explain why Greenland’s strategic significance is rising despite its minimal population and limited political autonomy. It argues that Greenland functions as an S-class (structural) AI strategic node whose integration shapes long-term strategic option spaces rather than producing immediate tactical payoffs. Rather than offering policy prescriptions, the analysis focuses on structural mechanisms through which AI reconfigures power, risk, and control in international systems. Greenland is presented as an early and unusually clear case of how AI-mediated infrastructures transform the geography of strategy in the twenty-first century.