AI Sovereignty Is a Moving Target-Aim for Strategic Interdependence

AI Sovereignty's Definitional Dilemma: A Practical Guide for Governments

Date: February 17, 2026

Governments are racing to secure their AI futures. But vague definitions of "AI sovereignty" are slowing real progress.

The term gets used to justify everything from domestic model development to new regulators and data rules. Without precision, strategies sprawl, budgets bloat, and outcomes drift. This playbook reframes sovereignty around clear goals, specific stack layers, and explicit trade-offs-so you can act with intent.

Why the term stays fuzzy

• Inherited ambiguity: Old debates on "digital/data/cyber sovereignty" never settled on definitions, enabling broad agendas but weak execution.
• Actor mismatch: States talk autonomy and oversight; companies mean on-prem, data control, and vendor optionality. Same word, different aims.
• Stack confusion: Energy, compute, cloud, data, models, apps, talent-each layer has its own levers and costs.
• Goal conflicts: Security, growth, oversight, and cultural fit often pull in opposite directions.

Start here: define what you want to control-and why

• Hard sovereignty: Domestic capability across the stack. High control, high cost, often unrealistic.
• Soft sovereignty (strategic autonomy): Assured access, vendor leverage, and policy influence. More feasible, still requires discipline.

Pick primary goals (rank them):

• National security: Resilient, crisis-ready supply chains for compute, models, and critical apps.
• Economic competitiveness: Move up the value chain; avoid lock-in; drive domestic productivity.
• Regulatory oversight: Enforceable standards, auditability, and accountability.
• Cultural alignment: Systems that reflect local language, norms, and values.

Map goals to the AI stack

Decide where you need agency. Then choose the level of control: full domestic, assured access, or strategic leverage.

• Energy and connectivity: Grid capacity, clean power, submarine cables, IXPs for data resilience.
• Compute: Chips, accelerators, onshore data centers, cloud regions, workload portability.
• Data: High-quality public datasets, localization vs. access, sharing agreements, stewardship models.
• Models: Open-source participation, domestic fine-tuning, evaluation in local languages/domains.
• Applications: Priority use cases for public services; interoperability and exit clauses.
• Talent: Scholarships, visas, upskilling, and career paths that keep experts in-country.

Recognize the trade-offs upfront

• Cost vs. agility: Domestic build-out can slow adoption and strain budgets.
• Oversight vs. innovation: Strict data localization aids control but can dampen research and cross-border services.
• Security vs. openness: Open-source reduces dependency but increases exposure if not governed.
• Resilience vs. environment: Extra compute capacity adds redundancy and emissions unless paired with clean energy and load management.
• Sovereignty optics vs. reality: "National" cloud with foreign chips may signal control while risk remains elsewhere.

Policy levers that actually move the needle

• Procurement with teeth: Portability, audit rights, SBOMs, SLAs for incident response, and mandatory exit plans.
• Assured compute: Reserve domestic capacity for critical services; test failover quarterly.
• Open-source participation: Fund maintainers, contribute tests and evals, and mandate reproducibility for public AI projects.
• Alliances and reciprocity: Pool demand for chips and cloud credits; mutual recognition of audits and safety tests.
• Standards and assurance: Align with the NIST AI RMF; require risk registers, model cards, and independent red-teaming.
• Data governance: Public-interest data trusts, culturally informed consent models, and cross-border data corridors with enforcement.
• Talent compacts: Train civil service AI product owners, evaluators, and contract managers; create fellowship rotations with academia and industry.

Examples from current debates

• Chile and Taiwan: Investing in open-source and local models to reflect culture and language, while reducing platform dependency.
• France and Brazil: Building regulatory muscle first-oversight, audits, and enforcement-to steer private-sector AI.
• United Kingdom: A Sovereign AI Unit with significant funding to drive growth and security through domestic capability.
• Europe: Political pressure intensified after Davos, pushing sovereignty from slogan to budget line.

A 90-day sovereignty sprint for your team

• Weeks 1-2: Rank your four goals. Publish the order and why. Set red lines (e.g., critical workloads must run on export-compliant, onshore compute).
• Weeks 3-4: Build a dependency map across the stack. Identify single points of failure and switching costs.
• Weeks 5-6: Write procurement guardrails for all new AI buys: portability, auditability, data exit, safety testing, and SLAs.
• Weeks 7-8: Stand up an assured compute pool for priority services. Run a failover drill.
• Weeks 9-10: Launch two open-source contributions: a domain eval suite in your language and a red-team dataset for public-sector use cases.
• Weeks 11-12: Approve a talent plan: hiring ranges, fast-track roles, training for product owners and risk leads, and a fellowship pipeline.

What good looks like: strategic interdependence

The objective is choice. You should be able to switch vendors, re-route workloads, and replace components without breaking critical services-or your budget.

That means selective domestic capacity, smart alliances, open-source where it compounds advantage, and contracts that keep the door open. Control for what matters, rent for what doesn't, and revisit the balance quarterly.

Quarterly metrics to keep you honest

• Share of critical workloads that can switch providers in 30 days or less.
• Domestic share of compute available for essential services during a crisis.
• Percentage of AI contracts with portability, audit, and exit clauses.
• Number of high-risk systems with independent evaluations and red-team reports.
• Coverage of local-language and domain-specific benchmarks in public deployments.
• Open-source contributions adopted by at least two external organizations.
• Mean time to detect and resolve AI incidents in public services.

One last reframing

Stop asking, "How do we control AI end-to-end?" Ask, "Where do we need decisive agency, and what dependencies are we willing to keep?"

The end state is resilience through strategic interdependence-not isolation. Build the capacity to choose your dependencies, and to reconfigure them when priorities change.

For more practical playbooks and training, see AI for Government and the AI Learning Path for Policy Makers.