The US government’s abrupt decision to suspend foreign access to Anthropic’s Fable 5 and Mythos 5 models clarifies what “AI sovereignty” is really about. In the emerging AI economy, competitive advantage will come not from owning a single model, but from being able to evaluate, select, and orchestrate many models.
ÉVIAN—The US government’s sudden decision, on June 12, to restrict foreign access to some of Anthropic’s most advanced models is further confirmation that AI is now a geopolitical issue of the highest order. Until recently, countries competed by building services, infrastructure, and applications on top of frontier AI systems. Now, access to the systems themselves is a strategic concern.
The prevailing assumption used to be that AI would follow the logic of globalization. Countries would rely on a handful of frontier models, mostly developed in the United States, while competing in downstream services, semiconductors, data, and applications. Access to the most advanced AI systems was largely taken for granted. But if this assumption no longer holds, the central question is not which model is best, but which can be accessed at all.
With frontier capabilities becoming an issue of national security and diplomacy, governments will be tempted to pursue “AI sovereignty” through the development of national champions or domestic alternatives to the leading US options (ChatGPT and Claude). But as understandable as this instinct is, it risks addressing the wrong problem.
After all, AI is advancing too fast for such a strategy to pay off. Technological advantages that appear decisive can vanish within months. Today’s breakthrough becomes tomorrow’s baseline. Models that dominate headlines for a few months are quickly matched or surpassed by competitors. Even countries willing to invest tens of billions of dollars in developing models face daunting odds when competing directly against the world’s largest technology firms. That is why the question is not whether a country can build a frontier model, but whether it can secure reliable access to frontier intelligence wherever it emerges.
The recent Anthropic case illustrates the point. If access to a leading AI model can be restricted overnight, dependence on any single provider becomes a strategic risk. That does not mean every country should build its own frontier model. But it does mean that no country can assume uninterrupted access to someone else’s system.
For America’s closest allies, the first priority should be to preserve access. Countries such as Japan and other G7 members share not only democratic values with the US, but also deep security interests. Supporting the technological resilience of allies ultimately strengthens America’s own strategic position. Moreover, AI remains an immature and rapidly evolving technology whose future trajectory is uncertain. Continued collaboration between American model developers and allied countries that contribute technology, talent, infrastructure, and markets will be essential for expanding the ecosystem itself. AI should not become a technology that is hoarded; it should be one that is developed together.
In the emerging AI economy, competitive advantage will increasingly come not from owning a single model, but from being able to evaluate, select, and orchestrate many models. Organizations that can seamlessly switch among competing systems will be more resilient than those that depend on a single provider and expose themselves to too many vulnerabilities—from technical failures and commercial disputes to geopolitical pressure. Just as countries seek to diversify energy sources and semiconductor supply chains, they will also diversify their AI providers.
Yet orchestration alone is not enough. Countries must also be able to evaluate AI systems independently. Governments need the ability to assess which models are genuinely useful compared to others, and what risks particular systems may pose. Such judgments cannot be delegated entirely to foreign companies or foreign governments.
This is why recently established AI safety institutes and national cyber-security agencies are becoming increasingly important. Rather than merely trying to regulate AI, their role is to provide the independent technical expertise needed for informed national decision-making. Countries that cannot assess AI systems for themselves will inevitably find their choices shaped by others.
Relying on multiple AI models rather than a single provider is its own form of deterrence, because it makes coercion less effective. If governments and organizations can seamlessly switch between multiple frontier models, restricting access to any one of them loses much of its strategic value. Model orchestration becomes both an economic capability and a geopolitical asset.
But diversification is not enough. To achieve true AI sovereignty, countries must be able to combine national data with multiple frontier AI systems, generate knowledge about what works, and translate that knowledge into decision-making. This last step—sovereign decision-making—becomes difficult when a country depends on others to interpret its most important information. If the analysis of defense intelligence, economic data, public-health risks, or critical infrastructure ultimately relies on external actors, political autonomy becomes constrained.
The implications extend well beyond AI policy. Once access to intelligence becomes a geopolitical issue, the challenge facing middle powers is clear. Rather than trying to replicate US or Chinese capabilities, these countries must ensure that they have room for strategic maneuver while remaining deeply integrated into the global economy. This is a familiar challenge: Throughout modern history, successful middle powers have prospered not by isolating themselves from larger powers, but by maintaining strong alliances while preserving the capacity for independent action.
The emerging geopolitical competition will revolve around who designs, finances, operates, and optimizes the critical infrastructure of the AI age: data centers, power grids, communications networks, logistics systems, ports, and digital public infrastructure. AI capability will become embedded in the systems on which modern societies depend.
In this environment, the meaning of “safe and trustworthy AI” must evolve, too. Today’s debates about AI safety often focus on technical questions about model alignment, transparency, bias, misinformation, or harmful outputs. These are important concerns, but for governments, businesses, and the wider public, trust requires something broader.
A trustworthy AI ecosystem is one in which users can rely on continued access, retain meaningful control over their data, and avoid becoming dependent on political decisions made elsewhere. It is an ecosystem in which access is not conditioned on political alignment, and where participation does not require the surrender of digital sovereignty. Trust lies not in any one model, but in the institutions, governance arrangements, and international relationships surrounding it.
As AI becomes critical infrastructure, reliability, resilience, and political neutrality may matter as much as raw model performance. The most powerful model is not necessarily the most valuable if access to it can be withdrawn abruptly, or if dependence on it constrains strategic choices.
AI sovereignty lies not in building a national version of ChatGPT, but in preserving freedom of action in a world where access to intelligence itself is contested. It is about optionality, not ownership.
About the Author:
Ren Ito is a former Japanese diplomat. He is Co-Founder of Sakana AI.
ÉVIAN—The US government’s sudden decision, on June 12, to restrict foreign access to some of Anthropic’s most advanced models is further confirmation that AI is now a geopolitical issue of the highest order. Until recently, countries competed by building services, infrastructure, and applications on top of frontier AI systems. Now, access to the systems themselves is a strategic concern.
The prevailing assumption used to be that AI would follow the logic of globalization. Countries would rely on a handful of frontier models, mostly developed in the United States, while competing in downstream services, semiconductors, data, and applications. Access to the most advanced AI systems was largely taken for granted. But if this assumption no longer holds, the central question is not which model is best, but which can be accessed at all.
With frontier capabilities becoming an issue of national security and diplomacy, governments will be tempted to pursue “AI sovereignty” through the development of national champions or domestic alternatives to the leading US options (ChatGPT and Claude). But as understandable as this instinct is, it risks addressing the wrong problem.
After all, AI is advancing too fast for such a strategy to pay off. Technological advantages that appear decisive can vanish within months. Today’s breakthrough becomes tomorrow’s baseline. Models that dominate headlines for a few months are quickly matched or surpassed by competitors. Even countries willing to invest tens of billions of dollars in developing models face daunting odds when competing directly against the world’s largest technology firms. That is why the question is not whether a country can build a frontier model, but whether it can secure reliable access to frontier intelligence wherever it emerges.
The recent Anthropic case illustrates the point. If access to a leading AI model can be restricted overnight, dependence on any single provider becomes a strategic risk. That does not mean every country should build its own frontier model. But it does mean that no country can assume uninterrupted access to someone else’s system.
For America’s closest allies, the first priority should be to preserve access. Countries such as Japan and other G7 members share not only democratic values with the US, but also deep security interests. Supporting the technological resilience of allies ultimately strengthens America’s own strategic position. Moreover, AI remains an immature and rapidly evolving technology whose future trajectory is uncertain. Continued collaboration between American model developers and allied countries that contribute technology, talent, infrastructure, and markets will be essential for expanding the ecosystem itself. AI should not become a technology that is hoarded; it should be one that is developed together.
In the emerging AI economy, competitive advantage will increasingly come not from owning a single model, but from being able to evaluate, select, and orchestrate many models. Organizations that can seamlessly switch among competing systems will be more resilient than those that depend on a single provider and expose themselves to too many vulnerabilities—from technical failures and commercial disputes to geopolitical pressure. Just as countries seek to diversify energy sources and semiconductor supply chains, they will also diversify their AI providers.
Yet orchestration alone is not enough. Countries must also be able to evaluate AI systems independently. Governments need the ability to assess which models are genuinely useful compared to others, and what risks particular systems may pose. Such judgments cannot be delegated entirely to foreign companies or foreign governments.
This is why recently established AI safety institutes and national cyber-security agencies are becoming increasingly important. Rather than merely trying to regulate AI, their role is to provide the independent technical expertise needed for informed national decision-making. Countries that cannot assess AI systems for themselves will inevitably find their choices shaped by others.
Relying on multiple AI models rather than a single provider is its own form of deterrence, because it makes coercion less effective. If governments and organizations can seamlessly switch between multiple frontier models, restricting access to any one of them loses much of its strategic value. Model orchestration becomes both an economic capability and a geopolitical asset.
But diversification is not enough. To achieve true AI sovereignty, countries must be able to combine national data with multiple frontier AI systems, generate knowledge about what works, and translate that knowledge into decision-making. This last step—sovereign decision-making—becomes difficult when a country depends on others to interpret its most important information. If the analysis of defense intelligence, economic data, public-health risks, or critical infrastructure ultimately relies on external actors, political autonomy becomes constrained.
The implications extend well beyond AI policy. Once access to intelligence becomes a geopolitical issue, the challenge facing middle powers is clear. Rather than trying to replicate US or Chinese capabilities, these countries must ensure that they have room for strategic maneuver while remaining deeply integrated into the global economy. This is a familiar challenge: Throughout modern history, successful middle powers have prospered not by isolating themselves from larger powers, but by maintaining strong alliances while preserving the capacity for independent action.
The emerging geopolitical competition will revolve around who designs, finances, operates, and optimizes the critical infrastructure of the AI age: data centers, power grids, communications networks, logistics systems, ports, and digital public infrastructure. AI capability will become embedded in the systems on which modern societies depend.
In this environment, the meaning of “safe and trustworthy AI” must evolve, too. Today’s debates about AI safety often focus on technical questions about model alignment, transparency, bias, misinformation, or harmful outputs. These are important concerns, but for governments, businesses, and the wider public, trust requires something broader.
A trustworthy AI ecosystem is one in which users can rely on continued access, retain meaningful control over their data, and avoid becoming dependent on political decisions made elsewhere. It is an ecosystem in which access is not conditioned on political alignment, and where participation does not require the surrender of digital sovereignty. Trust lies not in any one model, but in the institutions, governance arrangements, and international relationships surrounding it.
As AI becomes critical infrastructure, reliability, resilience, and political neutrality may matter as much as raw model performance. The most powerful model is not necessarily the most valuable if access to it can be withdrawn abruptly, or if dependence on it constrains strategic choices.
AI sovereignty lies not in building a national version of ChatGPT, but in preserving freedom of action in a world where access to intelligence itself is contested. It is about optionality, not ownership.
About the Author:
Ren Ito is a former Japanese diplomat. He is Co-Founder of Sakana AI.