AI Sovereignty as National Learning Capacity: A Human-Centered Learning Mechanics Viewpoint on France, the United States, and China

A French research paper reframes AI sovereignty as a country's capacity to manage its own learning dynamics rather than raw computational scale. The study proposes Human-Centered Learning Mechanics as a framework for balancing information injection (compute, talent, capital) with entropy dissipation (friction, complexity, regulation), arguing France should optimize this equilibrium instead of choosing between tech-optimism or regulatory caution.

This arXiv paper introduces a systems-level perspective on national AI competitiveness that diverges from conventional policy debates. Rather than treating investment, regulation, and talent as separate policy levers, the authors model national AI development as a thermodynamic learning system where success depends on managing the ratio between incoming resources and organizational friction. The framework specifically challenges the assumption that AI leadership requires matching China or the US in raw compute capacity.

The research contextualizes European anxieties about AI leadership within broader economic theory, integrating neural scaling laws with endogenous growth models and game theory. This theoretical integration matters because it provides measurable indicators and policy simulations rather than ideological prescriptions. For France specifically, the paper suggests that regulatory friction need not be disadvantageous if paired with strategic talent retention and thoughtful industrial deployment—a nuanced position addressing legitimate EU concerns about lagging AI infrastructure without endorsing a deregulation agenda.

For the tech industry and policymakers, this work implies that smaller economies can compete by optimizing information flow and institutional coordination rather than pursuing scale-matching strategies. The emphasis on "controlled regimes" and avoiding "unstable, unequal expansion" acknowledges energy and sustainability constraints increasingly important to institutional investors. However, the paper's academic framing limits immediate market impact. It does not address specific regulatory changes, funding commitments, or timeline expectations that would influence investment decisions or corporate strategy shifts.

• →AI sovereignty depends on managing information dynamics and institutional efficiency, not raw computational scale alone
• →France can compete by optimizing the balance between resource injection and organizational friction rather than matching larger competitors' infrastructure
• →The framework integrates scaling laws, growth theory, and game theory into a unified model with measurable policy indicators
• →Regulatory frameworks need not hinder competitiveness if paired with strategic talent retention and industrial absorption capacity
• →The approach rejects both unrestricted tech-optimism and regulation-first approaches in favor of controlled, human-centered AI development