AI Sovereignty as National Learning Capacity: A Human-Centered Learning Mechanics Viewpoint on France, the United States, and China
For policymakers and researchers in AI strategy, this paper offers a unified framework to analyze national AI competitiveness beyond scale, but it is a conceptual viewpoint without empirical validation.
This viewpoint paper proposes interpreting national AI development as a learning system using Human-Centered Learning Mechanics (HCLM), arguing that AI sovereignty depends on balancing information injection (compute, data, talent) with entropy dissipation (coordination frictions, regulatory uncertainty). It provides a mathematical model, policy indicators, and simulations to reframe AI policy for France as governance of a non-equilibrium learning system.
Artificial Intelligence is often discussed in France in terms of investment, compute capacity, regulation, employment, sovereignty, and education. These dimensions are usually treated separately. This viewpoint paper proposes a unified interpretation: France should be understood as a \emph{national AI learning system}. Building on Human-Centered Learning Mechanics (HCLM), recently formulated as a dynamical framework for entropy-regulated representation learning, we interpret national AI development as a controlled balance between information injection and entropy dissipation. Information injection corresponds to compute, data, talent, research, capital, industrial deployment, and institutional experimentation. Entropy dissipation corresponds to organizational complexity, coordination frictions, energy constraints, regulatory uncertainty, talent mobility pressures, and opportunities to strengthen industrial absorption. The central claim is that AI sovereignty does not emerge from scale alone but from a country's capacity to regulate its own information dynamics. This paper connects HCLM with neural scaling laws, endogenous growth theory, creative destruction, and game theory. It argues that the French AI debate should move beyond the binary opposition between techno-optimism and regulation-first caution. A competitive and human-centered AI strategy requires a controlled regime in which information injection grows faster than institutional dissipation, while avoiding unstable, unequal, or energy-intensive expansion. We provide a mathematical model, measurable policy indicators, game-theoretic propositions, illustrative simulations of national AI regimes, and concrete policy implications for France. The proposed viewpoint reframes AI policy as the governance of an open, strategic, non-equilibrium learning system.