Governments plan to invest $1.3 trillion in AI infrastructure by 2030 to invest in “sovereign AI,” with the premise that countries should remain in control of their own AI capabilities. Funding includes funding for domestic data centers, locally trained models, independent supply chains, and national talent pipelines. It’s a response to real shocks: Covid-era supply chain disruptions, rising geopolitical tensions, and the war in Ukraine.
But the pursuit of absolute sovereignty is actually underway. AI supply chains are irreducibly global: chips are designed in the US and manufactured in East Asia. Models are trained on datasets drawn from multiple countries. Applications are deployed in dozens of jurisdictions.
If sovereignty is to remain meaningful, it must move from a defensive model of self-reliance to a vision that emphasizes the concept of orchestration, and balances national sovereignty with strategic partnerships.
Why infrastructure-first strategies target walls
a A November survey by Accenture found that 62% of European organizations are now looking for autonomous AI solutions.driven primarily by geopolitical anxiety rather than technological necessity. This figure rises to 80% in Denmark and 72% in Germany. The European Union has appointed its first Commissioner for Tech Sovereignty.
This year, $475 billion is flowing into data centers globally. In the United States, AI data centers account for nearly a fifth of GDP growth in the second quarter of 2025. But money is not the only obstacle to hopes for other countries to follow suit. This is energy and physics. Global data center capacity is expected to hit 130 gigawatts by 2030, and for every $1 billion spent on these facilities, $125 million is needed for power networks. More than $750 billion in planned investment is already facing grid delays.
And that’s talent too. Researchers and entrepreneurs are mobile, attracted to ecosystems with access to capital, competitive wages, and rapid innovation cycles. Infrastructure alone will not attract or retain world-class talent.
What works: An orchestrated autonomy
What nations need is not sovereignty through isolation but through specialization and orchestration. That means what capabilities you build, what you pursue through partnerships, and where you can truly lead in shaping the global AI landscape.
The most successful AI strategies don’t try to copy Silicon Valley. They identify specific advantages and build partnerships around them.
Singapore offers a model. Instead of trying to replicate large-scale infrastructure, it invested in governance frameworks, digital identity platforms, and AI applications in logistics and finance, where it could realistically compete.
Israel shows a different path. Its strength lies in a dense network of startups and military-aligned research institutions that provide influence despite the country’s small size.
South Korea is also educated. Although it has national champions like Samsung and Naver, these firms still partner on infrastructure with Microsoft and NVIDIA. It is deliberate cooperation that reflects strategic oversight, not dependence.
Even China, despite its scale and ambitions, cannot secure full stack sovereignty. Its reliance on global research networks and foreign lithography equipment, such as the ultra-ultraviolet systems required to produce advanced chips and GPU architectures, demonstrates the limits of technonationalism.
The pattern is clear: Nations that master strategy and partner can outperform those that try to do everything alone.
Three ways to align ambition with reality
1. Measure value added, not input.
Autonomy is not how many petaphiles you have. That’s how many lives you improve and how fast the economy grows. True sovereignty is the ability to innovate in support of national priorities such as productivity, resilience and sustainability while maintaining freedom to govern and set standards.
Nations should track the use of AI in health care and monitor how readiness to adopt the technology is linked to productivity, patent citations, and international research collaboration. It aims to ensure that AI ecosystems create inclusive and lasting economic and social value.
2. Cultivate a robust AI innovation ecosystem.
Build the infrastructure, but also the ecosystem around it: research institutions, technical education, business support, and public-private capacity development. Infrastructure without skilled talent and a dynamic network cannot deliver a lasting competitive advantage.
3. Build global partnerships.
Strategic partnerships give nations access to resource resources, lower infrastructure costs, and complementary expertise. Singapore’s work with global cloud providers and the European Union’s collaborative research programs show how the capabilities of nations to advance rapidly in partnership rather than in isolation. Instead of competing over dominant standards, countries should collaborate on interoperable frameworks for transparency, security and accountability.
What is at stake?
Freedom slows down fragmented markets and cross-border innovation, which is the foundation of AI development. When strategies focus too little on control, they sacrifice the agility needed to compete.
The cost of getting this wrong isn’t just wasted capital — it’s decades of falling behind. Countries that double down on infrastructure-first strategies risk ending up with expensive data centers running tomorrow’s models, while competitors that choose strategic partnerships iterate faster, attract better talent, and create standards that matter.
The winners will be those who define autonomy not as separation, but as participation plus leadership. Strategic interdependence may feel less satisfying than independence, but it is real, it is achievable, and it will separate leaders from followers in the next decade.
The age of intelligent systems calls for intelligent strategies—ones that measure success not in infrastructure ownership, but in problems solved. Nations that embrace this shift will not only participate in the AI ​​economy. They will shape it. It is worth the autonomy.
Kathy Lee is the head of the Center for AI Excellence at the World Economic Forum.