What does AI sovereignty mean for a country? 3 pillars to dominate the AI arms race

AI is becoming a cornerstone of national power. Nations are locked in an escalating AI arms race, as witnessed even this week at the Paris AI summit. But what does it take for a country to secure its future in AI? Is it solely about foundational models, or is that merely scratching the surface? Here are three essential pillars for anyone shaping AI policy.

I. Hardware and compute: The engine of AI

Owning the chips that drive AI is a critical capability that every country must develop indigenously. Today, most nations depend on Taiwan’s TSMC for their chips, enabled by the Dutch company ASML. The secret weapon is a $400 million Extreme UltraViolet Lithography machine that enables chip fabrication at a 2nm process. In simple terms, this means chip components are packed so tightly that billions more transistors can fit on a single die, vastly increasing compute power. China’s Huawei, for example, has launched the Mateo60 — a 7nm chip, a couple of generations behind TSMC — while India currently produces chips at 28nm, six generations behind.

Equally important is a robust compute infrastructure. With cloud services dominated by Microsoft, Google, and AWS, countries must hedge against this by establishing national data centers (as seen in India’s AI mission), funding local companies, securing tech transfer deals, or importing GPUs, as Jio is doing in India. Open, decentralized infrastructure is also crucial to prevent monopolies from simply relocating to non-US territories.

II. Cultivating talent and community: The heart of homegrown innovation

AI is built by people — through collecting, cleaning, and labeling data; developing hardware and software; and designing user interfaces. A country’s competitive edge lies in fostering a vibrant ecosystem of researchers, engineers, and visionaries. This requires investing not only in short- and medium-term initiatives like hackathons and competitions but also in long-term funding for research institutions and dynamic public-private collaborations.

Nurturing an AI-native generation will be vital for driving sustainable, homegrown progress.

III. Data sovereignty and electricity: Securing the AI foundation

Data is the raw material of AI, but its true value emerges only when it is controlled locally. Establishing governance and technological frameworks that ethically harness culturally relevant data is key to empowering a nation to tailor AI systems for its unique needs. This autonomy not only shields against external influences that have long plagued AI development but also sparks breakthrough innovations aligned with national priorities.

Lastly, there is electricity. While training AI models is crucial, it is equally important to ensure that there is enough power to run these models at scale during inference. Investing in clean energy and achieving energy independence — rather than relying on foreign sources — will be essential.

The era of endless debate is over — it’s time for decisive action and building a future that relies less on politics, and more about enabling the next generation of builders to do their thing.