Sonya Huang frames sovereign AI as an own-versus-rent decision across cost, speed, performance, and proprietary data—and argues that teams should begin with evaluations.
Why we selected it
This is a company-level argument for sovereign AI rather than a national-policy definition. Huang treats ownership as a spectrum: keep using hosted frontier models where they are the best tool, while identifying the parts of a product’s intelligence that are important enough to control down to the weights.
The most useful part is the sequence. Decide what is worth owning, build the team, make the work legible, and define evaluations before reaching for fine-tuning or a more elaborate model stack.
Viewing note
This is an investor and ecosystem perspective, not independent proof that an open model will outperform a hosted model on your workload. Use the cost, latency, performance, and proprietary-data questions as a decision frame, then test the answer with your own evaluation.