An American user on Reddit asked a question that touches a raw nerve: if DeepSeek trained a model referred to as 4.1 for about $6 million and published all the details of the work, why doesn't the United States fund a national open LLM? The author of the post argues that the cost would be marginal compared to the federal budget, that a public alternative would reduce security risks and that it would prevent monopolistic practices on token pricing.

The problem, however, is almost never the training cost. A national open LLM would change the incentive structure: today the public sector and regulated enterprises depend largely on proprietary APIs, with per-token fees and access mediated by cloud infrastructure. An open model, deployable on owned hardware, would shift TCO from recurring fees to investments in infrastructure and internal skills. It would not be a silver bullet, but it would remove a positional rent.

The paradox is clear: the U.S. government distrusts Chinese models and urges not to trust them, but has not produced a federal alternative with the same openness. The result is that the demand for digital sovereignty is met by commercial vendors or foreign projects, not by a stable public capability. Those who benefit today are proprietary model providers and platforms that monetize access; those who would pay the consequences of a change are the same intermediaries, while public agencies, SMEs and all contexts where auditability and data residency matter would gain.

The question should also be read in hardware terms. A national open model is not just a weights file: it requires serving pipelines, quantization strategies and inference infrastructure that can run on-premise. The real bottleneck is not the availability of a checkpoint, but the ability to run and maintain it in production without depending on an external cloud. Those who work on local stacks know these trade-offs well: training cost is a one-time item, while day-to-day management of VRAM, latency and updates is what determines the real TCO.

The discussion opened by the post does not ask for an industry subsidy, but for basic public infrastructure. An open national LLM would have second-order implications: it would redefine the bargaining power of administrations, create a benchmark for evaluating commercial models and push vendors to compete on services and compliance rather than on access control. In third order, it would signal that sovereignty is not bought on demand, but built with distributed technical capacity.

For those concretely facing the choice between cloud and self-hosted, AI-RADAR offers analytical frameworks at /llm-onpremise to weigh trade-offs such as TCO, sovereignty and hardware requirements. The American user's question remains open, but the fact that it keeps circulating with this insistence is already a symptom: the gap is not in models, but in public direction.