Beyond Independence: Charting a Course for AI Sovereignty

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Author: Denis Avetisyan

As artificial intelligence becomes increasingly central to global power, the question isn’t whether nations will pursue autonomy, but how they’ll balance control with the benefits of open collaboration.

This review argues that AI sovereignty is a spectrum of strategic choices concerning data governance, compute infrastructure, and normative alignment, demanding a focus on measurable outcomes and managed interdependence.

Despite growing national ambitions to control artificial intelligence, the foundational elements of AI-data flows, hardware supply chains, and open-source communities-inherently resist enclosure. This paper, ‘Sovereign AI: Rethinking Autonomy in the Age of Global Interdependence’, develops a framework for understanding AI sovereignty not as a binary condition, but as a continuum balanced between autonomy and interdependence. We argue that strategic policy choices regarding data, compute, models, and norms-focused on maximizing returns and managing openness-are critical for achieving viable AI sovereignty. Ultimately, can nations navigate this complex landscape to foster innovation while safeguarding their interests in an increasingly interconnected world?

The Eroding Foundations of National Authority

The conventional understanding of national sovereignty, rooted in defined territorial boundaries and exclusive control, faces unprecedented challenges from the pervasive interconnectedness of modern AI systems. Data, the lifeblood of these algorithms, flows freely across borders, often processed and stored on infrastructure outside national jurisdiction. This creates a situation where a nation’s critical functions – from infrastructure management to defense – can become reliant on technologies and data streams originating elsewhere. Consequently, the ability to exert absolute control within its borders diminishes as AI’s reach extends beyond them, blurring the lines of authority and necessitating a rethinking of how nations secure their interests in an increasingly digital world. The traditional model of sovereignty, built on physical control, is proving inadequate in the face of intangible, globally distributed intelligence.

The proliferation of artificial intelligence is subtly reshaping geopolitical landscapes by fostering a new form of dependence. Nations increasingly rely on a limited number of external providers for crucial AI infrastructure, algorithms, and datasets, creating strategic vulnerabilities akin to those historically associated with resource control. This dependence isn’t simply about access to technology; it extends to the very foundations of decision-making, critical infrastructure operation, and national security. A reliance on foreign-developed AI systems introduces potential risks ranging from algorithmic bias and data security breaches to the possibility of systemic failures imposed through supply chain disruptions or intentional manipulation. Consequently, the ability to independently develop and maintain core AI capabilities is becoming paramount, as nations navigate a complex web of interdependence and seek to mitigate the risks inherent in outsourcing such a powerful and foundational technology.

The conventional understanding of sovereignty, historically rooted in defined territorial boundaries and control, is proving increasingly inadequate in the age of artificial intelligence. Nations now rely heavily on external providers for crucial AI infrastructure, algorithms, and data processing, creating a situation where control doesn’t necessarily equate to having the technology. This dependence shifts the locus of power away from purely geographical considerations; a nation’s capacity to act independently isn’t solely determined by what happens within its borders, but by its access to – and influence over – these globally distributed AI systems. Consequently, a re-evaluation of sovereignty is essential, one that moves beyond the physical control of land and resources to encompass the ability to meaningfully participate in, and govern, the digital ecosystems that now underpin national security and economic prosperity. This isn’t about relinquishing control, but rather redefining it for a world where influence extends far beyond traditional borders.

The future of national power in an age of artificial intelligence demands a shift from traditional concepts of sovereignty to a more nuanced understanding of interconnectedness. Rather than seeking absolute control, nations must navigate a continuum where domestic interests are balanced with the advantages of global AI collaboration. This paradigm acknowledges that resources – data, computing power, and skilled personnel – are not evenly distributed, creating inherent interdependence. Effective sovereignty, therefore, isn’t defined by rigid borders, but by a nation’s capacity to strategically allocate resources, foster beneficial partnerships, and mitigate vulnerabilities within this complex web of AI development and deployment. The ability to attract and retain talent, secure critical data flows, and participate in international standards-setting will become paramount, shaping a nation’s position along this continuum and ensuring its continued relevance in a rapidly evolving technological landscape.

Constructing Digital Boundaries

Data sovereignty, the principle that data is subject to the laws and governance structures of the nation within which it is collected, and compute infrastructure sovereignty – the capacity to process and store that data domestically – represent critical first steps in establishing national control over digital resources. Achieving these sovereignties requires not only legal frameworks defining data handling and access, but also substantial investment in physical infrastructure such as data centers and networking capabilities. This localized control mitigates risks associated with foreign data access, ensures compliance with national regulations regarding data privacy and security, and reduces dependence on external providers for critical computational services. Furthermore, domestic processing capabilities enable faster response times and improved data security compared to relying on geographically distant infrastructure.

Investment in AI supercomputer infrastructure and sovereign cloud solutions enables nations to maintain domestic control over data processing and AI model deployment. These systems are designed to minimize data latency and enhance security by keeping operations within national borders. Operational efficiency is a key performance indicator, with a target GPU utilization rate exceeding 75% considered crucial for maximizing return on investment and ensuring cost-effective AI capabilities. Achieving this target necessitates optimized hardware configurations, efficient model parallelization, and robust resource management strategies within the sovereign cloud environment.

The development of Large Language Models (LLMs) prioritizing Arabic and other regionally specific languages directly addresses the potential for cultural homogenization and technological dependence. Currently, many prevalent LLMs are trained primarily on Western datasets, resulting in outputs that may not accurately reflect or resonate with non-Western cultural nuances, historical contexts, or linguistic structures. Creating LLMs specifically trained on Arabic text and cultural data ensures more accurate and relevant responses for Arabic-speaking users, fostering digital content creation and consumption in the native language. This localized approach reduces reliance on foreign-developed LLMs, supports national linguistic identity, and enables the development of AI applications tailored to specific regional needs, such as culturally appropriate educational tools, automated translation services, and content moderation systems.

The development of domestic digital control capabilities, including sovereign cloud infrastructure and localized large language models, is strategically focused on enhancing national resilience within the existing globalized framework. This approach prioritizes maintaining agency and reducing systemic risk, rather than pursuing technological isolation. Investment in these capabilities should be evaluated using a ‘Shadow Price of Funds’ metric, with a target range of 1.54 to 2.17. This range represents a threshold for acceptable investment returns, accounting for the strategic value of domestic control alongside purely financial considerations and ensuring projects deliver quantifiable benefits beyond conventional ROI calculations.

Governing Intelligence: Frameworks for a New Era

Approaches to artificial intelligence governance are significantly influenced by historical philosophical thought. The concept of indivisible authority, stemming from the work of Thomas Hobbes, suggests centralized control as necessary for maintaining order and security in the deployment of AI systems. Conversely, Lockean principles of limited government and individual rights advocate for accountable, transparent AI governance structures with checks and balances. This duality manifests in current debates surrounding AI regulation, with some advocating for strong central oversight to mitigate risks, while others prioritize decentralized models that protect individual liberties and promote innovation. The tension between these perspectives-absolute control versus distributed responsibility-continues to shape the development of AI governance frameworks globally.

Data Trusts represent a governance model designed to facilitate responsible data sharing by establishing a legal structure where an independent trustee manages data on behalf of a defined group, ensuring its use aligns with pre-defined ethical guidelines and national regulations. This approach addresses concerns regarding data sovereignty and control by enabling data to be shared for beneficial purposes – such as AI model training or public health research – without relinquishing ownership or oversight. The trustee is legally obligated to uphold the interests of the data subjects and adhere to national laws concerning data privacy, security, and usage, effectively acting as an intermediary between data providers and data users while preserving national control over strategically important information assets. This model is particularly relevant for cross-border data flows, allowing nations to participate in collaborative AI development while maintaining jurisdictional authority.

The establishment of standardized AI incident reporting procedures is critical for proactive risk mitigation. These procedures should mandate the reporting of malfunctions, unintended behaviors, and security breaches across all stages of AI system development and deployment. Data collected through incident reports allows for pattern identification, facilitates root cause analysis, and informs the development of preventative measures. Organizations such as the AI Safety Institute play a key role by providing technical expertise in incident investigation, vulnerability assessment, and the dissemination of best practices. Leveraging their capabilities enhances the accuracy and efficiency of incident response, contributing to a more robust and secure AI ecosystem. Consistent reporting and analysis are essential for building a comprehensive understanding of AI risks and iteratively improving safety protocols.

Effective AI governance necessitates a calibrated approach balancing national interests, international collaboration, and ethical considerations. Policy-making in this domain is significantly influenced by a ‘Sovereignty Weight’ of 0.7, indicating a prioritization of national autonomy while still allowing for a degree of openness to international standards and data exchange. This weighting suggests that while nations will likely prioritize control over AI development and deployment within their borders, a substantial portion – approximately 30% – of policy will be shaped by collaborative efforts and adherence to globally recognized ethical principles. The implementation of this weighting affects decisions regarding data localization, cross-border data flows, and the adoption of common AI safety standards, creating a framework that balances national security and economic competitiveness with international responsibility.

Beyond Control: The Cultural Imprint of Artificial Intelligence

The concept of cultural hegemony, as theorized by Antonio Gramsci, offers a crucial lens through which to examine the development and deployment of artificial intelligence. AI systems are not neutral tools; they are constructed by individuals and institutions carrying specific worldviews, and these are inevitably encoded within the algorithms and datasets that power them. This means AI can subtly reinforce existing power structures and propagate dominant ideologies, potentially marginalizing alternative perspectives or even perpetuating biases. Controlling the narratives embedded within AI – from the language models it uses to the data it learns from – is therefore paramount. A failure to address this could result in a future where AI, rather than reflecting a diversity of human experience, inadvertently solidifies a single, hegemonic worldview, shaping not just what people think, but how they perceive reality itself.

The development of genuinely autonomous AI models necessitates a departure from universally applied algorithms and a prioritization of national cultural contexts. Without this focus, AI risks perpetuating biases inherent in its training data, potentially marginalizing unique societal values and knowledge systems. Nations are increasingly recognizing the importance of fostering domestic AI capabilities – not to isolate themselves, but to ensure these powerful technologies reflect their specific histories, languages, and ethical frameworks. This ‘model autonomy’ allows for the creation of AI systems that are not simply technologically advanced, but also culturally relevant and responsive to the needs of their citizenry, ultimately promoting inclusivity and preserving diverse perspectives in an increasingly interconnected world.

International collaborations, such as the Global Partnership on Artificial Intelligence (GPAI), are increasingly recognized as pivotal for navigating the complexities of AI development. These partnerships aren’t intended to diminish national sovereignty, but rather to establish frameworks where countries can collectively address shared challenges – from ethical considerations and data governance to technical standardization – while simultaneously protecting their unique strategic interests. Through cooperative research, data sharing, and the development of common principles, GPAI and similar initiatives aim to promote responsible AI that is not only innovative but also aligned with diverse societal values and legal frameworks. This collaborative approach is considered essential for preventing the concentration of power in a few nations and ensuring that the benefits of artificial intelligence are distributed more equitably across the globe, fostering a more inclusive and trustworthy AI ecosystem.

The concept of Sovereign AI moves beyond simple protectionism, instead focusing on proactively influencing the developmental trajectory of artificial intelligence to align with a nation’s specific cultural identity and long-term goals. This isn’t about building walls, but about carefully curating the data that fuels these systems, with a significant emphasis on trustworthiness and quality; a benchmark of over 80% of publicly available AI datasets sourced from verifiable origins is considered crucial. By prioritizing datasets with established provenance, nations aim to mitigate biases, ensure accuracy, and foster AI applications that genuinely reflect their values and societal priorities, ultimately shaping a future where artificial intelligence serves as a powerful tool for national progress and cultural preservation.

The pursuit of ‘Sovereign AI’ detailed in the paper isn’t about isolation, but informed participation. It demands a rigorous assessment of what truly constitutes control within increasingly interconnected systems. This aligns perfectly with Tim Bern-Lee’s sentiment: “The Web is more a social creation than a technical one.” The paper highlights the critical need to balance strategic autonomy with the realities of global interdependence, acknowledging that complete self-sufficiency is neither desirable nor achievable. Instead, it proposes a nuanced approach centered on measurable returns and managed openness, mirroring the collaborative spirit inherent in the Web’s original design. The focus isn’t on building walls, but on establishing clear guidelines for participation and ensuring normative alignment within a shared digital space.

What’s Next?

The notion of ‘sovereignty’ itself proves remarkably resistant to precise definition, and its application to artificial intelligence is no exception. This work proposes a continuum, a gradient of control rather than a discrete state. The immediate challenge lies not in achieving absolute autonomy – a likely chimera – but in quantifying the returns on investments in each layer of the stack: data, compute, models, and, crucially, normative alignment. The field must move beyond aspirational pronouncements and embrace metrics that reveal the true cost of independence versus interdependence.

A persistent tension remains. Strategic autonomy, as framed here, requires a deliberate balancing act. A fully ‘open’ system sacrifices control; a completely ‘closed’ one risks stagnation and brittleness. The next phase of research should focus on developing architectures that facilitate managed openness-systems capable of selectively sharing, adapting, and evolving without surrendering core principles. This is not merely a technical problem; it is a question of designing for graceful degradation, accepting that perfect control is an illusion.

Ultimately, the pursuit of ‘sovereign AI’ may prove less about achieving technological dominion and more about accepting a fundamental truth: complexity is not inherent virtue. The most elegant solutions will likely be those that achieve the desired outcome with the fewest necessary components, discarding the superfluous in favor of clarity. The art, then, is not building higher walls, but identifying what can be safely removed.

Original article: https://arxiv.org/pdf/2511.15734.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-22 15:19