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

arXiv:2606.00729v3 Announce Type: replace Abstract: 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 a

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