Research Updates

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When AI Infrastructure Is Optional but Governance Lock-In Is Not: 

An AI-SNI Local Governance Diagnostic of the Temple (GA) Data Center Proposal

This working paper applies the AI-Strategic Node Index (AI-SNI) to assess whether a proposed large-scale data center constitutes a structurally necessary node within AI-mediated systems. Using the Temple, Georgia “Project Bus” case, the analysis finds that the facility does not meet criteria for structural necessity or non-substitutability under current evidence, and primarily introduces governance burden and long-term path-dependency risk. 

Governing Structural Centrality: 

Greenland as an AI-Strategic Node under the AI-SNI Framework

This policy brief applies the AI-Strategic Node Index (AI-SNI) to examine Greenland as a structurally significant node in Arctic great-power interaction. AI-SNI diagnostics place Greenland in a Tier 3 exposure regime, reflecting high sensing and decision-loop centrality and latent optionality, constrained primarily by infrastructure–governance asymmetry. Designed for Track-2 dialogue, the brief is diagnostic and non-prescriptive. 

Governing Structural Centrality: 

Greenland as an AI-Strategic Node under the AI-SNI Framework

This policy brief applies the AI-Strategic Node Index (AI-SNI) to Greenland as a node-level diagnostic case, assessing how structural centrality in AI-mediated systems interacts with governance capacity under Arctic conditions. An AI-SNI score of 0.52 (Tier 3) indicates relevant structural exposure rather than ordinal risk. Diagnostics show high sensing and decision-loop centrality, moderate modelling leverage, and latent resource optionality, constrained primarily by infrastructure–governance asymmetry. 

From AI Capabilities to Structural Governance: 

Operationalizing the AI-Strategic Node Index (AI-SNI) for Practical AI Governance

This policy brief introduces the AI-Strategic Node Index (AI-SNI), a governance-oriented diagnostic instrument for identifying structural exposure, leverage concentration, and governance fragility in AI-enabled systems. It operationalizes the AI-Strategic Node Framework Conceptual and Methodological White Book (v0.1) to support non-prescriptive AI governance, informing national strategy, cross-border coordination, and institutional risk assessment in contexts of AI-enabled warfare, sustainability, and global security governance. 

AI-Strategic Node Framework (AI-SNF): 

Conceptual and Methodological White Book

This white book introduces the AI-Strategic Node Framework (AI-SNF), a conceptual and methodological framework for identifying and interpreting structural AI-mediated strategic nodes within global technological, infrastructural, and governance systems.

AI-SNF adopts a non-actor-centric and non-ranking approach, conceptualizing strategic relevance as an emergent property of structural positioning, cross-dimensional coupling, and system-level dependence in AI-enabled architectures. It is designed for governance diagnostics, risk interpretation, and policy analysis, rather than prediction, prescription, or competitive ranking.

Version v0.1 represents a foundational methodological release supporting future empirical applications and derivative instruments, including the AI-Strategic Node Index (AI-SNI).

Greenland as a Structural AI Strategic Node: 

Perception Integrity, Temporal Dominance, and the Arctic Reconfiguration of Algorithmic Power

This working paper reframes Greenland as a structural AI strategic node within global systems of sensing, decision-making, infrastructure, and governance, arguing that its integration shapes long-term strategic option spaces rather than immediate tactical outcomes in the AI era. 

When decapitation no longer matters: 

AI-delegated execution and the potential failure of preemptive strike logic 

This working paper analyzes preemptive strike doctrine under conditions of AI-enabled delegated execution. It argues that preemption’s risk-reduction logic depends on a disruptable human decision bottleneck, historically embodied in leadership decapitation. As retaliatory execution becomes pre-authorized and institutionally insulated from real-time human intervention, leadership removal no longer alters the probability, scale, or certainty of response—a condition termed decapitation irrelevance. Under such conditions, preemptive strike collapses from a rational risk-management strategy into destruction without leverage, relocating deterrence stability from crisis-time discretion to pre-crisis institutional design. 

After a year of work, the Global Artificial Intelligence Development, Governance, and Competitiveness Assessment Framework (Ver. 0.9) is now fully developed and documented. It comprises 7 analytical layers, 25 indicator clusters, 148 core indicators, and 665 extended metrics, and forms an integrated assessment architecture for AI development, governance capacity, and systemic competitiveness. Formal publication and subsequent applications are currently under preparation.  

Nonlinear Uncertainty in Drone Warfare: 

Why Indeterminacy Outperforms Precision in Contested ISR Environments 

This policy report examines why uncertainty in drone warfare is a structural condition of contested ISR environments and argues that robustness-oriented, indeterminacy-preserving postures can outperform precision-centric approaches in sustaining strategic stability.