What happened
CSET published a two-pronged framework for assessing sovereign AI, examining both why states pursue it (national interest, technological sovereignty, soft power) and how they implement it (across the layers of the AI technology stack), illustrated with five country case studies — the United States, China, France, India, and Singapore. The central argument: policymakers should move beyond a false binary of 'fully sovereign' versus 'fully open' AI stacks and instead recognize a spectrum of 'strategic partial sovereignty,' since real-world AI stacks are hybrid. The piece notes the U.S. controls 75% of global AI compute versus China's 15%, framing the stakes for countries navigating dependency.
Why it matters
Provides executives and policymakers navigating sovereign AI investment or vendor-dependency decisions a structured vocabulary to move past binary sovereignty debates toward concrete tradeoff analysis on interoperability, regulatory divergence, and economic cost.
Action needed
Use the two-pronged framework to map your organization's or country's AI stack dependencies and identify where partial-sovereignty tradeoffs are being made implicitly.