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Skip to ContentMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioOpinionEveryone wants AI sovereignty. No one can truly have it.The world is too interconnected for nations to go it alone. But they can specialize.
By Cathy Liarchive pageJanuary 21, 2026Sarah Rogers/MITTR | Getty Images Governments plan to pour $1.3 trillion into AI infrastructure by 2030 to invest in “sovereign AI,” with the premise being that countries should be in control of their own AI capabilities. The funds include financing for domestic data centers, locally trained models, independent supply chains, and national talent pipelines. This is a response to real shocks: covid-era supply chain breakdowns, rising geopolitical tensions, and the war in Ukraine.   But the pursuit of absolute autonomy is running into reality. AI supply chains are irreducibly global: Chips are designed in the US and manufactured in East Asia; models are trained on data sets drawn from multiple countries; applications are deployed across dozens of jurisdictions.   If sovereignty is to remain meaningful, it must shift from a defensive model of self-reliance to a vision that emphasizes the concept of orchestration, balancing national autonomy with strategic partnership.  Why infrastructure-first strategies hit walls 
A November survey by Accenture found that 62% of European organizations are now seeking sovereign AI solutions, driven primarily by geopolitical anxiety rather than technical necessity. That 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 AI data centers globally. In the United States, AI data centers accounted for roughly one-fifth of GDP growth in the second quarter of 2025. But the obstacle for other nations hoping to follow suit isn’t just money. It’s energy and physics. Global data center capacity is projected to hit 130 gigawatts by 2030, and for every $1 billion spent on these facilities, $125 million is needed for electricity networks. More than $750 billion in planned investment is already facing grid delays. 
And it’s also talent. Researchers and entrepreneurs are mobile, drawn to ecosystems with access to capital, competitive wages, and rapid innovation cycles. Infrastructure alone won’t attract or retain world-class talent.   What works: An orchestrated sovereignty What nations need isn’t sovereignty through isolation but through specialization and orchestration. This means choosing which capabilities you build, which you pursue through partnership, and where you can genuinely lead in shaping the global AI landscape.  The most successful AI strategies don’t try to replicate Silicon Valley; they identify specific advantages and build partnerships around them.  Singapore offers a model. Rather than seeking to duplicate massive infrastructure, it invested in governance frameworks, digital-identity platforms, and applications of AI in logistics and finance, areas where it can realistically compete.  Israel shows a different path. Its strength lies in a dense network of startups and military-adjacent research institutions delivering outsize influence despite the country’s small size.  South Korea is instructive too. While it has national champions like Samsung and Naver, these firms still partner with Microsoft and Nvidia on infrastructure. That’s deliberate collaboration reflecting strategic oversight, not dependence.   Even China, despite its scale and ambition, cannot secure full-stack autonomy. Its reliance on global research networks and on foreign lithography equipment, such as extreme ultraviolet systems needed to manufacture advanced chips and GPU architectures, shows the limits of techno-nationalism. 

The pattern is clear: Nations that specialize and partner strategically can outperform those trying to do everything alone.  Three ways to align ambition with reality  1.  Measure added value, not inputs.   Sovereignty isn’t how many petaflops you own. It’s how many lives you improve and how fast the economy grows. Real sovereignty is the ability to innovate in support of national priorities such as productivity, resilience, and sustainability while maintaining freedom to shape governance and standards.   Nations should track the use of AI in health care and monitor how the technology’s adoption correlates with manufacturing productivity, patent citations, and international research collaborations. The goal is to ensure that AI ecosystems generate inclusive and lasting economic and social value.   2. Cultivate a strong AI innovation ecosystem.  Build infrastructure, but also build the ecosystem around it: research institutions, technical education, entrepreneurship support, and public-private talent development. Infrastructure without skilled talent and vibrant networks cannot deliver a lasting competitive advantage.    3. Build global partnerships.  
Strategic partnerships enable nations to pool resources, lower infrastructure costs, and access complementary expertise. Singapore’s work with global cloud providers and the EU’s collaborative research programs show how nations advance capabilities faster through partnership than through isolation. Rather than competing to set dominant standards, nations should collaborate on interoperable frameworks for transparency, safety, and accountability.   What’s at stake 
Overinvesting in independence fragments markets and slows cross-border innovation, which is the foundation of AI progress. When strategies focus too narrowly on control, they sacrifice the agility needed to compete.  The cost of getting this wrong isn’t just wasted capital—it’s a decade of falling behind. Nations that double down on infrastructure-first strategies risk ending up with expensive data centers running yesterday’s models, while competitors that choose strategic partnerships iterate faster, attract better talent, and shape the standards that matter.  The winners will be those who define sovereignty not as separation, but as participation plus leadership—choosing who they depend on, where they build, and which global rules they shape. Strategic interdependence may feel less satisfying than independence, but it’s real, it is achievable, and it will separate the leaders from the followers over the next decade.  The age of intelligent systems demands intelligent strategies—ones that measure success not by infrastructure owned, but by problems solved. Nations that embrace this shift won’t just participate in the AI economy; they’ll shape it. That’s sovereignty worth pursuing.  Cathy Li is head of the Centre for AI Excellence at the World Economic Forum. by Cathy LiShareShare story on linkedinShare story on facebookShare story on emailPopular10 Breakthrough Technologies 2026Amy NordrumThe great AI hype correction of 2025Will Douglas HeavenChina figured out how to sell EVs. Now it has to deal with their aging batteries.Caiwei ChenThe 8 worst technology flops of 2025Antonio RegaladoDeep DiveArtificial intelligenceThe great AI hype correction of 2025Four ways to think about this year's reckoning.
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The pursuit of AI sovereignty is fundamentally unattainable in today’s interconnected world, according to a recent analysis by the World Economic Forum, spearheaded by Cathy Li. The article highlights a shift away from a defensive strategy of self-reliance towards a more nuanced approach centered on orchestration, strategic partnerships, and a measured focus on added value rather than simply accumulating infrastructure assets.

The global push for sovereign AI, driven by factors like supply chain disruptions, geopolitical tensions, and the war in Ukraine, has generated significant investment – a projected $1.3 trillion by 2030 – focused on domestic data centers, locally trained models, independent supply chains, and national talent pipelines. However, this approach encounters significant obstacles. The global nature of AI supply chains, involving chips designed in the US and manufactured in East Asia, models trained on data from multiple countries, and applications deployed across numerous jurisdictions, demonstrates the inherent limitations of complete autonomy.

The article emphasizes that nations should move beyond a simplistic “infrastructure-first” model. The Accenture survey reveals that 62% of European organizations are seeking sovereign AI solutions, primarily due to geopolitical anxiety, and this sentiment extends globally. This investment, totaling $475 billion globally, faces hurdles beyond just capital. Energy and physics constraints present significant challenges, with projections of 130 gigawatts of data center capacity by 2030 requiring substantial investment in electricity networks – estimated at $125 million per $1 billion in infrastructure. Adding to this complexity is the mobile nature of talent, drawn to ecosystems offering capital, competitive wages, and rapid innovation.

Ultimately, the article posits that a more effective strategy involves specialization and orchestration. Successful AI strategies, according to Li, don’t attempt to replicate global hubs like Silicon Valley but instead identify and develop specific advantages, forging partnerships around them. Singapore’s model, focusing on governance frameworks, digital identity platforms, and AI applications in logistics and finance, offers a relevant example. Similarly, Israel’s strength in startups and military-adjacent research exemplifies outsized influence despite its small size. South Korea’s collaboration with Microsoft and Nvidia, while utilizing national champions like Samsung and Naver, illustrates deliberate strategic oversight rather than dependence. Even China, despite its scale and ambitions recognizes the limits of techno-nationalism, relying on global research networks and foreign lithography equipment.

The key takeaway is that specialization and strategic partnerships enable nations to outperform those pursuing complete isolation. Li argues that nations should measure success not by the amount of infrastructure they own, but by the added value generated—linking AI adoption to improvements in economic productivity, patent citations, and international research collaborations. Cultivating strong AI innovation ecosystems—including research institutions, technical education, entrepreneurship support, and public-private talent development—is equally crucial. Finally, building global partnerships, exemplified by Singapore’s collaboration with cloud providers and the EU’s research programs, promotes faster advancements than isolation.

Overinvesting in independent capabilities, the article cautions, fragments markets and impedes cross-border innovation. This risks nations accumulating expensive, dated infrastructure while competitors, through partnerships, attract talent and shape global standards. The winners, according to Li, will be those who redefine sovereignty as active participation coupled with leadership—determining their dependencies, choosing where to build, and actively shaping global rules. This intelligent strategy – based on measuring success by the problems solved—represents a crucial shift in mindset and will determine nations’ ability to participate and shape the future of the AI economy.