Having GPUs physically located on national territory is already a concrete slice of sovereignty.
With those chips, a country can train and run locally developed models, including advanced ones, without needing to rely on foreign services every time.
This matters even more when the infrastructure is owned and operated domestically or at least under European control, with local governance and locally held security keys. In that setup, the risk that foreign providers can access, manage, or indirectly control sensitive data and services is meaningfully reduced.
And in principle, switching hardware suppliers over time is easier than escaping an entire proprietary cloud ecosystem.
Still, today, major parts of the AI supply chain, software tooling, and technical standards are still largely non-European. Even with thousands of GPUs on the ground, dependence can resurface through export controls, industrial policy shifts, software lock-in, and even future technical mechanisms that could track or restrict usage.
Real AI sovereignty therefore requires more than buying chips: it requires national skills, open standards, portability across vendors, and a legal-institutional architecture that clearly separates infrastructure ownership, data control, and control of software updates.
The real question is how much power you gain by acquiring foreign hardware and how much autonomy remains out of reach without deeper industrial and institutional alternatives.