Why AI Sovereignty Doesn’t Mean Every Nation Must Build the Full Tech Stack

Rethinking AI Sovereignty: Beyond the Self-Sufficiency Trap

The global race for artificial intelligence dominance is often framed as a contest of production capacity. The dominant assumption, embedded in policy papers and political rhetoric, is that a nation’s sovereignty over AI is directly proportional to its ability to build the entire technology stack at home. A provocative new working paper from the Brussels-based think tank Bruegel challenges this orthodoxy, warning that chasing full-stack self-sufficiency is not only unrealistic for most countries but strategically counterproductive.

The paper introduces the concept of “programmable sovereignty,” arguing that true strategic autonomy does not come from isolationist duplication of capabilities. Instead, it is achieved through coordinated specialization: a deliberate division of responsibilities across the AI value chain among allied states, firms, and institutions.

The N-2 Problem: A Structural Reality

Central to the argument is what the authors call the “N-2 problem.” The modern AI stack is vast, spanning raw compute and semiconductor fabrication, data infrastructure, foundation model training, fine-tuning, application deployment, governance frameworks, and security protocols. The gap—represented by N-minus-2—is the structural shortfall between the total number of critical AI capabilities required for genuine autonomy and the limited number that any single medium-sized or even large economy can realistically master and produce domestically.

“The N-2 problem is not a temporary industrial lag; it is a permanent feature of a complex, layered, and capital-intensive technology system,” the paper suggests. For the European Union, which has made “digital sovereignty” a flagship political objective, this poses an acute dilemma. The investment required to replicate the full stack—from cutting-edge GPU clusters to hyperscale cloud platforms and frontier models—would be staggering, and likely duplicative of efforts already underway among allies.

Programmable Sovereignty: Control Through Coordination

Programmable sovereignty redefines control not as owning every link in the chain, but as the ability to set the rules, standards, and interoperability protocols that govern how those links connect. Under this model, different actors focus on the AI functions where they hold comparative advantage. One nation or bloc might specialize in secure cloud infrastructure and regulatory sandboxes, while another leads in application-layer innovation for public services. The critical enabler is a web of trusted cross-border arrangements that ensure access, security, and influence without requiring total ownership.

“Sovereignty can be exercised through coordination, standards, and shared specialization rather than full national duplication of AI capabilities.”

This framing has profound implications for EU industrial policy. It suggests that Brussels should prioritize standard-setting power and interoperability regimes over subsidizing a domestic large language model that competes directly with American or Chinese counterparts. The focus shifts from building an everything-stack to architecting the legal, technical, and commercial interfaces that make the stack governable.

Policy Trade-Offs and Strategic Leverage

The proposal does not ignore the tensions inherent in relying on coordinated specialization. A reporter must examine the fragile balance between resilience and dependence, autonomy and efficiency. If a small group of countries controls the compute layer, can coordination genuinely deliver leverage for smaller or medium-sized economies? Or does it merely codify a hierarchal dependency under a more polite name?

The paper’s logic implies that leverage comes from being an indispensable node in the network, not from replicating the network. A country that provides highly specialized, difficult-to-replace AI functions—such as auditable training data repositories for public-sector models or certified governance middleware—gains structural influence regardless of whether it produces its own chips. The EU’s regulatory heft, exemplified by the AI Act, is itself a form of programmable sovereignty: it shapes global product design without owning a single data center.

Still, the approach demands immense diplomatic trust and technical compatibility. Coordinated specialization would require formalized agreements on data sharing, model evaluation standards, incident response protocols, and mutual recognition of certifications. In an era of geopolitical fragmentation, building this architecture is a diplomatic challenge as complex as the technical one.

Who Is Involved

• Bruegel — The Brussels-based economic think tank publishing the working paper.
• European Union policymakers — Drafting and implementing digital sovereignty strategies, including the AI Act and related industrial plans.
• Governments and regulators — Grappling with AI dependence, including member states within the EU and allied governments globally.
• AI infrastructure and model providers — The handful of large tech firms and cloud providers that currently dominate compute and frontier models.
• Researchers and policy analysts — Engaged in the digital sovereignty debate across institutions like the OECD and Brookings.

The concept arrives at a moment when the European Commission’s digital strategy is under intense pressure to reconcile open markets with strategic security, and when the OECD AI Policy Observatory is tracking widening capability asymmetries between nations. It offers a vocabulary for a third way beyond protectionism and laissez-faire techno-globalism.

The takeaway for policymakers is stark: sovereignty is not a stack to be built, but a posture to be programmed. The challenge is no longer simply how to produce more AI artifacts at home, but how to write the rules of interoperability that determine who controls the connections between them.