While the artificial intelligence sector appears to be racing ahead, those who choose to run inference and training on-premise are beginning to feel the same creaking that has stalled the US electric vehicle market. With federal tax credits expiring, EV registrations have stopped growing, and it's no coincidence that the two main obstacles are price and charging infrastructure. For IT managers, the translation is clear: AI hardware is too expensive, and supporting infrastructure is still insufficient.

The comparison is not forced. Buying a GPU cluster to run large language models locally means facing a capital outlay comparable to that of a high-end electric vehicle, multiplied by tens or hundreds of units. The tax incentives that once supported data center investments have not been renewed in many jurisdictions, and companies must now justify TCO without the cushion of tax breaks. On top of that comes the cost of electricity—the true “fuel” of an on-premise facility: a latest-generation GPU can consume up to 700 watts, and in a cluster of dozens of nodes, the power bill becomes the primary operational expense, often underestimated in business plans.

The “charging network” problem has a direct counterpart in grid capacity and cooling. It's not enough to rack servers in a cabinet; you need dedicated electrical panels, UPS systems, and air or liquid cooling that many corporate buildings cannot support without costly retrofits. This is a structural bottleneck that slows down data sovereignty projects, much as the lack of charging stations deters EV buyers.

Yet those who opt for self-hosted do so for precise reasons: full data control, minimal latency, GDPR compliance, and freedom from cloud vendor lock-in. But the economic calculus without incentives becomes prohibitive for mid-sized companies, leaving the field to large corporations or those who can centralize resources. This mirrors the concentration of early electric car buyers: pioneers willing to pay a premium, while the masses wait for a price drop that, in the GPU world, seems far from imminent due to demand driven by hyperscalers.

For decision-makers, the fork is stark: accept a cloud pay-as-you-go model with variable but predictable operational costs, or invest in an on-premise architecture that guarantees independence but requires robust financial planning. The lessons from the EV market suggest that without public intervention or substantial discounts from semiconductor manufacturers, local deployment will remain a niche for a few, while the bulk of inference will continue to run on remote data centers. Digital sovereignty risks becoming a luxury affordable only to those who can build their own “charging station.”