How sovereign is AI if the GPUs aren’t yours?

I’ve been looking more into the hardware side of Sovereign AI, and this FT piece had a point I hadn’t really thought about:

National data centre projects are consolidating America’s AI lead

Countries are pouring money into local AI data centres to reduce dependence on foreign infrastructure.

But there’s a weird contradiction:

Local data centre ≠ local AI stack.

You can have:

local data centre → NVIDIA GPUs → proprietary software → foreign models/tools → foreign expertise

and still be dependent on the same ecosystem you were trying to become independent from.

The UAE example in the article makes this especially clear: building huge amounts of AI infrastructure locally can still come with restrictions around what hardware can be used and which geopolitical ecosystem you have to align with.

There’s also a newer paper looking at the physical side of this problem. It estimates that a 1,024-GPU sovereign cluster in the UAE using evaporative cooling could consume 30M+ litres of water per year. Their argument is basically that sovereignty, cost and resource sustainability can pull in different directions.

So I’m wondering whether “on-prem” has become too easy a synonym for “sovereign AI.”

At the enterprise level, there are already very different approaches emerging — HPE/NVIDIA Private Cloud AI, Google Distributed Cloud, Dell/Palantir, and Lyzr Optimus are all pushing AI closer to infrastructure the customer controls, but with very different assumptions about what should remain vendor-controlled.

Where would you draw the line? Is owning the machines enough, or does a genuinely sovereign deployment need control over the hardware and the software/runtime/model stack above it?