AI Sovereignty Requires Global Cooperation, Not Isolation

As artificial intelligence becomes central to economic and national security, governments worldwide are pushing for sovereign control over this transformative technology. However, a new analysis reveals that complete AI independence is an illusion in today's interconnected world. The very foundations of AI—global data pipelines, semiconductor supply chains, open-source ecosystems, and international standards—resist national enclosure, creating a fundamental dilemma for policymakers. This research develops a practical framework showing that sovereignty must be balanced with strategic interdependence, offering lessons for countries from India to Saudi Arabia as they navigate this complex landscape.

The researchers found that sovereign AI isn't a simple yes-or-no condition but exists along a continuum defined by four interdependent pillars: data ownership and governance, compute infrastructure including chips and servers, model autonomy over foundation models, and normative alignment with local languages and cultural values. They formalized this understanding through a planning model that treats sovereignty as a resource allocation problem, where governments must decide how to distribute limited budgets across these four areas while determining their appropriate level of international engagement. The model yields two crucial policy heuristics: equalize marginal returns across all sovereignty pillars so each additional dollar of spending produces similar gains, and set openness to international collaboration where the benefits of global cooperation equal the risks of dependency exposure.

Ology builds on classical political theories from Hobbes to Gramsci and historical analogies with technologies like electricity and the internet, which similarly evolved from national control to international networks. The researchers developed a formal mathematical model where AI sovereignty (S) is expressed as a weighted function of the four pillars: S = f(D, C, M, N). They introduced a planner's welfare maximization problem that incorporates budget constraints, complementarity between data and compute investments, and trade-offs between sovereignty benefits and openness risks. This framework allows policymakers to calculate optimal resource allocations and international engagement levels based on their specific national circumstances, priorities, and constraints.

Applying this model to India reveals both strengths and s in the country's approach. India has established sovereign footholds in data through initiatives like Bhashini's multilingual resources and in compute through the IndiaAI Mission's GPU procurement and AIRAWAT supercomputer. However, model autonomy remains weaker, with no fully homegrown foundational model yet released, and normative alignment institutions like the IndiaAI Safety Institute are still developing. The analysis shows India's current openness level is neither fully autarkic nor completely globalized, reflecting its participation in international bodies like the Global Partnership on Artificial Intelligence while maintaining data residency requirements. The model suggests India should focus on pairing data and compute investments to avoid stranded resources, harden ModelOps for continuous governance, prioritize domain-specific models for public services, and procure openness with safeguards like data residency and exit clauses.

For the Middle East, particularly Saudi Arabia and the UAE, the framework reveals a different pathway characterized by state-led investment in Arabic-first models and sovereign cloud infrastructure. These countries demonstrate high sovereignty weights, lower fiscal constraints than India, and strong complementarities between data and compute investments. Their approach involves managed interdependence rather than isolation, as seen in the UAE's G42-Microsoft deal with inter-governmental assurance agreements and Saudi Arabia's deployment of Arabic LLMs on DEEM Cloud with global tooling access. The research identifies measurable targets for these nations, including achieving over 75% sovereign GPU utilization, linking 40% of compute hours to Arabic datasets, and conducting annual audits for high-risk AI systems to maintain normative alignment and prevent post-deployment erosion of control.

Extend beyond these case studies to any nation seeking AI sovereignty. The research demonstrates that attempting complete autarky would undermine the innovation and interoperability that make advanced AI systems viable, while unmanaged openness creates unacceptable dependency risks. Instead, countries must develop the institutional capacity to choose, adapt, and influence within globally networked AI ecosystems. This requires building multi-stakeholder institutions involving government, private sector, academia, and civil society to engage in norm entrepreneurship and shape international standards. The framework suggests creating quarterly dashboards to track marginal sovereignty returns and openness checklists to evaluate partnerships, making sovereignty decisions transparent and empirically testable.

Limitations of the approach include of precisely measuring sovereignty returns and exposure risks in practice, as noted in the paper's discussion of India's patchy metrics on dataset quality and utilization rates. The model relies on parameters like the marginal cost of public funds and policy weights that require careful estimation and may change over time. Additionally, the framework assumes rational planning by centralized authorities, which may not fully capture the political complexities and federal dynamics in countries like India where normative values involve multiple governance levels. The researchers acknowledge that further operationalization is needed, including developing standardized sovereignty metrics and institutionalizing the proposed guardrails through practical policy mechanisms.