Is AI sovereignty possible? Balancing autonomy and interdependence | Brookings
AI Analysis
The article discusses AI sovereignty, emphasizing the need for countries to balance autonomy and interdependence in AI infrastructure. It highlights 'managed interdependence' as a practical approach to mitigate risks through strategic alliances.
Key Takeaways
- AI sovereignty is about enhancing a country's capacity to independently manage AI infrastructure.
- Full-stack AI sovereignty is structurally infeasible due to transnational dependencies.
- Managed interdependence involves strategic alliances to reduce AI stack risks.
- AI sovereignty discussions are driven by national security and economic competitiveness concerns.
- Potential risks include protectionism and fragmented markets.
Why It Matters
AI sovereignty is crucial for national security and economic competitiveness, impacting how countries manage critical AI infrastructure. The concept of managed interdependence offers a strategic approach to navigate global dependencies, which is vital for maintaining technological edge and resilience in the face of concentrated control by a few firms and jurisdictions.
Is AI sovereignty possible? Balancing autonomy and interdependence | Brookings
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Research
Is AI sovereignty possible? Balancing autonomy and interdependence
Brooke Tanner, Cameron F. Kerry, Andrew W. Wyckoff, Nicoleta Kyosovska, Andrea Renda, and Elham Tabassi
February 17, 2026
- One practical alternative is “managed interdependence,” an approach that relies on strategic alliances and partnerships to reduce risks throughout the AI stack.
- While sovereign AI can benefit national security and economic competitiveness, it can also become a vehicle for protectionism, fragmented markets and standards, and duplicative or stranded public investment.
- AI sovereignty has entered policy discussions as governments confront the strategic importance of AI infrastructure, data, and models amid rising dependence on a small number of firms and jurisdictions.
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- 7 min read
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Executive Summary
Executive Summary
The concept of artificial intelligence (AI) sovereignty has entered policy discussions as governments confront the strategic importance of AI infrastructure, data, and models amid rising dependence on a small number of firms and jurisdictions. This report defines AI sovereignty as a spectrum of strategies to enhance a country’s capacity to make independent decisions about critical AI infrastructure deployment, use, and adoption, rather than literal autarky. Motivations vary— from protecting national security and resilience and supporting economic competitiveness, to ensuring cultural and linguistic inclusion in model training and datasets and strengthening influence in global governance. These aims are often legitimate, but “sovereign AI” can also become a vehicle for protectionism, fragmented markets and standards, and duplicative or stranded public investment. The central finding is that full-stack AI sovereignty is structurally infeasible for almost any country because AI is a transnational stack with concentrated choke points across minerals, energy, compute hardware, networks, digital infrastructure, data assets, models, applications, and the crosscutting enablers of talent and governance. The practical alternative is “managed interdependence,” an approach that relies on strategic alliances and partnerships to reduce risks throughout the AI stack. Countries can operationalize managed interdependence by mapping dependencies by layer, prioritizing feasible interventions, diversifying suppliers and partners, and embedding interoperability and portability through technical