Sovereign AI Rethinks Autonomy, Balancing Interdependence across Four Sovereignty Pillars for Global Benefits

Artificial intelligence presents a fundamental challenge to traditional notions of national sovereignty, as the technologies underpinning it inherently rely on global networks and shared resources. Shalabh Kumar Singh from Accenture Research and Shubhashis Sengupta from Accenture Innovation, along with their colleagues, address this complex issue by proposing a new framework that views sovereign AI not as a simple ‘yes’ or ‘no’ condition, but as a spectrum balancing national autonomy with international interdependence. Their work moves beyond simple calls for enclosure, instead offering a practical model for policymakers to navigate the trade-offs between controlling vital AI components, data, computing power, and established norms, and benefiting from the global exchange of knowledge and innovation. By applying this model to India and the Middle East, the researchers demonstrate that a managed approach to interdependence, rather than isolation, offers the most viable path towards responsible and effective AI governance in an increasingly interconnected world.

Balancing Autonomy and Interdependence in AI

This research provides a comprehensive analysis of Sovereign AI, presenting a framework for understanding how nations can navigate the complexities of artificial intelligence. The work argues against complete self-reliance, instead proposing that AI sovereignty exists as a continuum, a balance between national autonomy and global collaboration. Complete isolation is both unrealistic and undesirable, and a nuanced approach is essential. The researchers identify four key areas where nations must exert control to achieve AI sovereignty: data, encompassing its generation, access, and usage; compute, providing the necessary hardware and cloud resources; models, including the development and ownership of AI algorithms; and norms, influencing the ethical, legal, and technical standards governing AI development and deployment.

Success requires ensuring that investments across these four areas yield comparable returns, avoiding a situation where one area is heavily prioritized at the expense of others. Drawing on political theory, network science, and international relations, the researchers establish a theoretical foundation for their arguments. They emphasize the importance of networks and interoperability, drawing parallels to previous technological revolutions like electrification and the internet. The work also touches on the idea that control over AI standards and norms can significantly influence global power dynamics.

The researchers propose a practical framework centered around the concept of equalizing marginal returns across the four pillars of sovereignty. This means allocating resources to maximize overall benefit, not simply focusing on one area. Combining data control with computing infrastructure is particularly important, as data without processing capabilities is ineffective, and vice versa. Robust systems for managing the entire lifecycle of AI models, from development to deployment, are also crucial. Instead of pursuing general-purpose AI, the researchers recommend focusing on developing models tailored to specific national priorities and needs.

Actively participating in global standard-setting bodies and shaping the ethical and legal frameworks for AI is also essential. To track progress and measure the return on investment, the researchers propose a quarterly dashboard. They also develop an openness checklist to evaluate potential partnerships and assess the risks and benefits of collaboration. Applying this framework to India and the Middle East, the researchers demonstrate its practical utility. In India, they highlight strengths in data and compute, but identify a need for greater investment in model development and domain-specific applications.

In Saudi Arabia and the UAE, they observe a state-led approach focused on Arabic-first models and sovereign cloud infrastructure, reflecting a high emphasis on sovereignty and strong data-compute complementarities. Ultimately, the research demonstrates that AI sovereignty is not an all-or-nothing proposition. It’s about finding the right balance between autonomy and collaboration, integrating efforts across all four pillars of sovereignty, and proactively governing the development and deployment of AI to align with national values and priorities. Collaboration is essential, but it must be based on clear rules, safeguards, and the ability to negotiate favorable terms. This work provides a nuanced and practical framework for nations to harness the benefits of AI while protecting their interests.

AI Sovereignty as Dynamic Policy Balance

This research presents a formal model for understanding and achieving AI sovereignty, framing it not as a static state but as a dynamic process of balancing autonomy with international collaboration. The work defines AI sovereignty as a function of four key pillars: data ownership and governance, compute infrastructure including chips and cloud services, control over large language models, and alignment with local norms and values. Policymakers can actively manage these pillars to achieve desired levels of sovereignty. The team developed a planner’s model to identify optimal policy strategies, demonstrating that maximizing welfare requires careful allocation of public funds across these four pillars.

Specifically, the model reveals that investments should initially prioritize pillars with the lowest existing capacity, as these offer the greatest sovereign returns per unit of budget. The research mathematically defines AI Sovereignty (S) as a function of these pillars: S = f(D, C, M, N), where D represents data, C represents compute, M represents model autonomy, and N represents normative alignment. Experiments with the model demonstrate that a balanced approach, investing in all four pillars simultaneously, yields the highest levels of sovereignty. The team formalized this through a mathematical equation, revealing that equalizing marginal returns across the four pillars is crucial for efficient resource allocation.

The model also highlights the importance of complementarity between data and compute infrastructure, showing that control over models is significantly enhanced when both data and compute capacities are strong. Furthermore, the research introduces an “openness” index to quantify international participation, demonstrating that complete isolation is rarely optimal. The team’s calculations show that an equilibrium level of openness, balancing the benefits of international collaboration with the risks of dependency, is the most effective strategy. The model predicts that the optimal openness level is determined by a complex interplay of factors, including the benefits of spillovers, scale, and talent acquisition, weighed against the costs of exposure and dependency.

Sovereign AI Policy Balancing Autonomy and Risk

This research pioneers a new model for understanding sovereign AI, framing it not as a simple binary of control versus dependence, but as a continuum balancing autonomy with international collaboration. The researchers developed a framework that identifies key policy guidelines: equalizing marginal returns across data, compute, models, and normative standards, and setting openness where the benefits of global collaboration outweigh the risks of dependency. To operationalize this model, the study proposes a quarterly dashboard to measure policy effectiveness through transparent scoring methods. The researchers detail a method for converting openness into a checklist, enabling a tunable policy variable, and measuring incremental benefit as gains from increased collaboration, such as faster deployment and reduced costs, while quantifying incremental exposure through factors like data sensitivity and compliance risks.

Partnerships and cloud decisions should only be approved if the benefits exceed the risks for each increment of openness. The study also proposes two safeguards: a joint Data x Compute Objective and Key Result (OKR) requiring high GPU utilization and dataset-backed bookings, and ModelOps gates linking model deployment to pre-deployment cards, change logs, and bias/safety audits. Applying this model to India and the Middle East, the researchers demonstrate its practical utility. In Saudi Arabia and the UAE, they highlight large public investment in Arabic-first models and sovereign cloud infrastructure, resulting in high sovereignty weights and strong complementarities between data and compute.

The team proposes measurable targets for these nations, including achieving more than 75% sovereign GPU utilization, allocating 40% of booked compute hours to Arabic fine-tuning and evaluation, and ensuring more than 70% of public AI datasets originate from verifiable sources. Furthermore, the study advocates for co-locating AI infrastructure with renewable energy sources and scheduling training during off-peak hours to promote sustainability and reduce carbon emissions, aiming for at least 50% of compute power derived from low-carbon energy sources. These detailed metrics and policy recommendations demonstrate a rigorous methodology for achieving sovereign AI through managed interdependence.