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

Abstract

In France, the discourse surrounding Artificial Intelligence frequently centers on distinct themes such as investment, computational power, regulatory frameworks, labor market impacts, national sovereignty, and educational initiatives. Typically, these elements are analyzed in isolation. This viewpoint paper challenges that fragmented approach by proposing a unified framework: viewing France as a \emph{national AI learning system}. Grounded in Human-Centered Learning Mechanics (HCLM)—a recently developed dynamical framework for entropy-regulated representation learning—we conceptualize national AI advancement as a managed equilibrium between the injection of information and the dissipation of entropy.

Within this model, "information injection" encompasses compute resources, data availability, skilled personnel, research output, capital investment, industrial implementation, and institutional experimentation. Conversely, "entropy dissipation" refers to organizational complexity, coordination inefficiencies, energy limitations, regulatory ambiguity, pressures on talent retention, and the potential to enhance industrial absorption. The core argument posits that AI sovereignty is not a function of scale alone; rather, it stems from a nation’s ability to govern its own information dynamics.

By integrating HCLM with neural scaling laws, endogenous growth theory, the concept of creative destruction, and game theory, this paper contends that the French AI debate must transcend the dichotomy between unchecked techno-optimism and precautionary, regulation-first stances. Achieving a competitive, human-centric AI strategy demands a controlled environment where information injection outpaces institutional dissipation, while simultaneously preventing expansion that is unstable, inequitable, or overly energy-dependent. The study presents a mathematical model, quantifiable policy metrics, game-theoretic insights, simulations of national AI regimes, and specific policy recommendations for France. Ultimately, this perspective redefines AI policy as the management of an open, strategic, non-equilibrium learning system.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC