As global leaders gather at India’s AI Impact Summit, the concept of “AI sovereignty” has emerged as a pressing issue uniting both advanced and emerging economies. The overarching question is no longer whether nations should maintain oversight over artificial intelligence systems that influence their societies, but rather how that oversight can be effectively exercised within an increasingly interconnected technological landscape. This shift underscores a fundamental change in how sovereignty is perceived in the era of AI.
AI technologies are becoming integral to public services, critical infrastructure, and national security, making the governance protocols that dictate system interactions increasingly vital. These standards influence where authority resides, who assumes risk, and who can intervene or withdraw should circumstances or values shift. In this context, sovereignty is increasingly expressed through infrastructure design rather than traditional territorial control.
Political power now flows through various layers of the AI stack, including compute resources, data management, models, interfaces, orchestration layers, and application programming interfaces (APIs). When these layers are tightly coupled with proprietary platforms, national sovereignty may be compromised. Conversely, if they are designed to be open and interoperable, it allows for a preservation of choice and control.
This distinction is particularly crucial for middle-power nations—those that possess advanced public sectors and regulatory ambitions yet lack the scale to dominate the global AI market. For such countries, sovereignty hinges not on replicating cutting-edge model development but on ensuring that AI systems can be integrated, governed, audited, and replaced according to national standards.
Without interoperable standards, governments may find themselves reliant on pre-configured intelligence, including models trained abroad that embody foreign assumptions regarding risk, accountability, and social values. This reliance can lead to vendor lock-in, leaving public administrations vulnerable if they cannot transition their health or welfare systems across different providers without incurring excessive costs, thereby compromising their sovereignty.
Standards play a critical role in either fostering or alleviating this dependency. They outline the terms for system interaction, decision-making transparency, and accountability when harm occurs. While proprietary systems may appear to offer control, they frequently serve to reinforce vendor dominance, whereas open standards provide flexibility, enabling governments to adapt, switch providers, and impose domestic priorities on shared technological frameworks.
The importance of this discussion transcends theoretical considerations. As AI systems evolve into autonomous agents capable of invoking tools, accessing databases, and acting independently, the governance of these interactions becomes a significant strategic focal point. Control over agent orchestration increasingly translates to control over the entire ecosystem, and encouragingly, there are emerging open and interoperable protocols being developed under neutral governance rather than being monopolized by a single vendor.
The lessons from the internet’s evolution are instructive: its success was rooted in an open, interoperable architecture that fostered diversity, competition, and decentralized governance. Where sovereignty faltered, it often stemmed from political disengagement—not from the principles of openness itself.
What governments should do now
For governments striving for AI sovereignty without slipping into isolation, the key focus should not be on owning every layer of the AI stack but rather on controlling how these layers interconnect. To this end, governments must regard AI standards as a strategic priority rather than a mere technical detail. Participation in international and regional standards organizations—especially those governing interfaces, auditability, documentation, and agent orchestration—should be coordinated across government departments to align with regulatory goals.
Moreover, governments can leverage public procurement to shape the market by mandating open interfaces, modular architectures, and portability in public-sector AI contracts. This approach directly influences market dynamics and helps prevent vendor lock-in in critical services.
Regulatory focus should center on layers where sovereignty can be most effectively realized today: integration, oversight, and orchestration. While the development of frontier models may remain concentrated, governments can establish enforceable standards for logging, evaluation, model-tool interaction, and human oversight—preserving their authority as technologies evolve.
Data accessibility is also crucial; if the data used to fine-tune models is locked into vendor-specific formats, interoperability suffers. Governments should work toward creating standardized, secure data-exchange environments, known as “refineries,” which prepare domestic data in model-agnostic formats. This ensures that if a government decides to switch providers, its data remains usable and transportable.
Finally, middle-power countries can enhance their sovereignty by forming interoperability blocs. By aligning technical standards with neighboring or like-minded nations, they can create collective markets that compel global AI providers to comply with their standards. In the realm of AI, sovereignty is increasingly a collaborative endeavor; while individual states may be overlooked, coordinated blocs can set the global benchmark.
Ultimately, the challenge for policymakers is to define the type of sovereignty they wish to pursue. One route may lead to dependency disguised as control, while another offers the genuine ability to adapt, choose, and exit. In a world driven by interoperability, the power of standards becomes paramount—governments that overlook this reality may find their sovereignty determined by external forces.
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