Who Can Change Course? An Integrative Review of AI Sovereignty and Collective Learning Capacity

This paper examines AI sovereignty as national learning capacity, using France as an application. Human-Centered Learning Mechanics (HCLM), proposed for entropy-regulated representation learning (Tran, 2026), provides a conceptual lens for relating information inputs, absorptive capacity and avoidable institutional losses. Inputs include compute, data, talent, research, capital, deployment and institutional experimentation. Potential losses include coordination failures, administrative duplication, energy constraints, regulatory uncertainty and limited organizational uptake. Regulation also supports learning through evaluation, trusted data access, interoperability and safety standards. The argument is that sovereignty depends on converting distributed resources and knowledge into usable, coordinated and responsibly evaluated capability. The analysis connects this account with neural scaling laws, endogenous growth, creative destruction and standard game-theoretic mechanisms. It develops conditional scaling relationships, policy indicators and illustrative scenarios, alongside descriptive OECD, IMF and World Bank evidence. Country observations are kept separate from assumed scenario values, and a fully specified public-goods example shows how decentralized incentives can limit shared provision. The framework offers a research heuristic rather than a literal transfer of neural-network equations to countries or an established measure of sovereignty. Its empirical value requires testing with longitudinal, organizational and cross-country data.