The AI race is on. Multiple countries are all competing to attain dominance in the AI marketplace, with some countries sprinting at high speed, while some countries are taking baby steps, and others moving at snail’s pace. One of the key measures of a nation’s AI capabilities is AI sovereignty, which describes a nation’s ability to develop and control its own AI capabilities, especially key AI building blocks such as the infrastructure, business networks, data, workforce, and policies that govern the sector, ensuring it aligns with national interests and values.
In practice, sovereign AI could mean the development of both physical and data infrastructures as well as large language models (LLM) created domestically and trained on local datasets that ensure inclusiveness by infusing local dialects, cultures, and practice. Here, the emphasis is on how independent the country is in AI development and deployment, taking into account metrics such as level of reliance on external technological resources and other cost elements, thereby enabling enhanced economic competitiveness’, sustainable development and national security.
A related concept is data sovereignty, which refers to, how data is subjected to the laws and regulations of the country or region where it is created or stored. Sovereign AI though related to data sovereignty is broader. It stresses the overall control of the AI ecosystem within a given nation. There are 7 strategic pillars of sovereign AI namely, control and ownership of theentire AI, aligning AI development with values, national priorities and regulatory frameworks, ensuring data sovereignty, maintaining local AI infrastructure control, fostering AI talent development, safeguarding regulatory compliance and driving economic competitiveness. These pillars form a stronger foundation which can spur a nation’s economic growth, innovation, and global competitiveness through harnessing AI capabilities strategically. Further, sovereign AI can be actualised through national AI strategies, deeper government investments in AI, data localisation and enabling public-private partnerships to accelerate the development of sovereign AI capabilities.
As of 2025, no country has achieved the gold standard of having complete or absolute AI sovereignty, although countries such as the US, China, France, Germany, and Russia have made significant strides towards this by investing heavily in their own AI ecosystems, infrastructure, and data governance.
| Country | Sovereign AI Status | Key Strengths |
| China | Near-sovereign | Infrastructure, local data, closed ecosystem |
| USA | De facto sovereign | Dominates chips, models, and research |
| Russia | Partial National control | Building domestic alternatives to Western tech, |
| France | Emerging sovereignty | Open-source models, EU alignment |
| India | Building sovereignty Public AI | data localization efforts |
| UAE | Accelerating rapidly Sovereign models (Falcon) | R&D investment |
Status of Sovereign AI in key markets.
A notable example of regional and collective efforts will be the European Union (EU), where the EU AI Act promotes a sovereign, ethical AI framework while driving country-level investment in independent infrastructure and open-source Large Language Models (LLMs). In other parts of the world; Brazil, South Africa, Saudi Arabia, for example, there are strategies to progress AI sovereignty but there is still a huge dependance on foreign compute and models, with UAE being an exception, since they are investing heavily in sovereign models known as, Falcon LLM, relevant infrastructure, and AI cities like G42 and MBZUAI.
Emerging economies like Africa are shifting from isolated efforts to a unified continental approach. In this direction, the African Unionadopted a Continental AI Strategy in July 2024, backed by a five‑yearimplementation plan and the creation of an ambitious $60 billion Africa AI Fund. Thus, while momentum is building, execution remains a challenge and realising sovereign AI across Africa will need the strengthening of national institutions, scaling skills and infrastructure, enforcing data governance, and ensuring African voices shape global AI norms.
Why does AI sovereignty matter?
As the pivotal role of AI is now undisputed in our society, the discussion around the need for AI sovereignty is more urgent and relevant than ever. Nations must prepare safeguards against possible disruption of systems which are dependent on AI coupled with new threats and risks linked to AI usage. Worthy of note is that advocates for sovereign AI argue that sovereign AI does not mean digital isolation, rather it is a push for strategic resilience that aligns with global cooperation. Developing sovereign AI requires multifaceted, coordinated, and sustainable efforts across several key areas, driven by investment in digital infrastructure, workforce development, research, development and innovation (RDI), regulatory and ethical frameworks, stimulation of the AI industry, and international cooperation.
Attaining AI sovereignty remains a tall order as it is fraught with many challenges including dependence on external AI providers since there is no evidence to suggest that a country can independently have all the requisite resources available at any given time. Data sovereignty and access challenges, inadequate local compute and infrastructure, talent and skills shortage, regulatory, legal and ethical fragmentation and techno-dependence on foreign AI models are complex challenges bedeviled the goal of attaining AI sovereignty across many countries. There is a growing debate about how realistic the goal is for attaining AI sovereignty. There is a call for theadoption of a betterstrategic approach overall, premised on the notion that nations should focus on how they can harness the power of AI to solve important national problems within their context, through maintaining some control and autonomy over relevant AI capabilities to align with national interests and values.
In conclusion, full AI sovereignty when attained, will mean a country assuming full control of its entire AI chain from chips, cloud, data, and models; however, this is extremely rare today. While some countries, such as China and the U.S., are the closest to achieving AI sovereignty, even these global giants still must depend on global supply chains when it comes to chip manufacturing, among others. Therefore, the realistic path is for countries to work towards strategic AI autonomy rather than full AI sovereignty.
Dr. Kwami Ahiabenu, the writer, is a Technology Innovations Consultant. You can reach him at [email protected]
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