Over $200 billion in sovereign AI commitments have been announced across the Global South between 2024 and 2026. The data centers behind them land in cities, and the water, energy, and carbon costs fall hardest on the places least equipped to absorb them. This talk presents findings from "The Environmental Cost of Digital Sovereignty" (arXiv:2607.13443, accepted at IEEE GHTC 2026), a comparative environmental stress analysis of sovereign AI deployments in the UAE, Bangladesh, India, and Kenya, reframed for the people who site, permit, and power urban AI infrastructure.

We model hypothetical GPU clusters at 256 and 1,024 GPUs under evaporative, hybrid, and immersion cooling regimes. The gaps are staggering. The same 1,024-GPU cluster emits 14,330 tons of CO2 per year on Bangladesh's 97.9% fossil grid but only 412 tons outsourced to Sweden's clean grid, a 35x difference driven entirely by siting. In the UAE, classified "Extremely High" water stress (4.82/5.0), evaporative cooling consumes 34.3 million liters of water per year, the daily needs of 940 people, while immersion cooling cuts that to 0.57 million, a 98.3% reduction.

We identify a sovereignty-sustainability trilemma: no country can simultaneously maximize AI sovereignty, minimize environmental impact, and keep resource access affordable for citizens. From it we derive seven design principles for environmentally responsible sovereign AI, including immersion-first cooling for hot climates, siting compute on clean grids, and desalination-loop accounting for coastal cities. City planners and infrastructure engineers will leave with a quantitative framework for evaluating where and how to deploy AI infrastructure.