Sovereign AI and the Power Distance Representation of Bregman Divergences: Algorithmic Accessibility, Transparency, and Technical Autonomy

This companion article examines the sovereign AI implications of Frank Nielsen's 2026 representation of Bregman divergences as equivalent power distances (arXiv:2607.24197). Building on the anatomical review of the mixed-parameter lifting and its reduction of Bregman proximity, Voronoi and minimum-enclosing-ball problems to Euclidean and Laguerre operations, the paper situates the result within the requirements of technological autonomy, algorithmic transparency and reduced dependence on proprietary geometric toolchains. It analyses three principal contributions to sovereign AI: (i) the conversion of specialised Bregman queries into calls on mature open-source Euclidean nearest-neighbour and power-diagram libraries; (ii) the consequent lowering of barriers to information-geometric methods on locally controlled data; and (iii) the provision of Euclidean incidence certificates that enhance auditability of geometric decisions inside national AI systems. Limitations are acknowledged: the representation addresses neither data localisation, compute sovereignty nor model ownership. The paper concludes that the power-distance identity supplies a concrete, limited but non-trivial increment to the technical-autonomy strand of sovereign AI, particularly for Global South institutions seeking inspectable statistical geometry without specialised commercial infrastructure.