Sovereign AI is a capability. Commercial independence is a business model.

The development.

In its Phase 2 evaluation announcement, Korea’s Ministry of Science and ICT emphasises real-world adoption as well as foundation-model performance. It outlines expanded B200 GPU support for advancing teams, from approximately 768 units in the first half of 2026 to approximately 1,000 in the second half. This is a stated programme support plan, not a measure of private-company profitability.

Our perspective: sovereignty has several layers.

A locally developed model may still depend on external chips, cloud services, licensed datasets or distribution platforms. None of those dependencies makes a product inherently unviable. But they change what the company controls, what it owes and how much of its revenue remains after delivery costs.

The useful investment question is therefore narrower than national capability: which part of the stack is owned, and which part is rented? That map should include training permissions, model licences, deployment terms, customer data and the portability of the application.

Inference is an operating cost, not an afterthought.

An AI product can grow usage faster than it grows contribution margin. Serving customers entails compute, integration, support and sometimes repeated customisation. Revenue quality depends on the way those costs are recovered, not solely on a performance ranking.

For credit analysis, contracted enterprise use may be more legible than unpriced consumer adoption. Yet an enterprise contract still requires scrutiny of cancellation, service levels, acceptance criteria and liability. A headline contract value is not the same thing as an unconditional receivable.

Capital should match the commercial milestone.

Model development, production deployment and international expansion have different capital needs and different evidence of progress. Financing should identify which stage it supports and which contracts or commercial assets are expected to generate collections.

Our IP-focused private credit lens treats AI as a rights-and-economics question as well as a technology question. Policy support can strengthen an ecosystem; it does not replace customer validation or establish repayment capacity.