PROLOGUE: THE MORNING EVERYTHING CHANGED
Imagine you are the Chief Technology Officer of a major European hospital network. It is a Tuesday morning in June 2026, and your clinical decision-support system, the one your oncologists rely on to cross-reference drug interactions, flag rare diagnoses, and summarize patient histories across seventeen hospitals, has simply stopped working. No warning. No graceful degradation. Just an error message that reads, in cold bureaucratic language, that access to the underlying AI model has been suspended pending review by the United States Department of Commerce.
This is not a hypothetical scenario designed to frighten you. On June 12, 2026, the US Commerce Department issued an export control directive to Anthropic, ordering the immediate suspension of access to its frontier models Fable 5 and Mythos 5 for all foreign nationals. Anthropic, which had launched these models only three days earlier on June 9, 2026, determined that selectively enforcing the ban for non-US users was technically impractical. The company therefore disabled both models globally. Overnight, enterprises across Europe, Asia, and beyond that had integrated these cutting-edge systems into their workflows found themselves staring at blank screens. Anthropic stated publicly that the government had provided only verbal evidence of a potential narrow, non-universal jailbreak in Fable 5, and the company believed there might be a misunderstanding. But the damage was done. The access was gone, and no European government, no European court, and no European contract could restore it.
This single event crystallized a danger that European policymakers, researchers, and business leaders had been discussing in abstract terms for years. The dependency is real. The vulnerability is structural. And the window for action, while still open, is closing faster than most people realize.
This article is about what Europe, meaning both the European Union and the United Kingdom, must do to escape this dependency. It is about hardware and software, about money and talent, about regulation and research, about allies and adversaries. It is about the difference between being a sovereign actor in the most consequential technological transformation in human history and being a passive consumer of capabilities that others can switch off at will. And it is, ultimately, about whether Europe will still matter in 2040.
CHAPTER ONE: UNDERSTANDING THE DEPENDENCY AND WHY IT IS DANGEROUS
To appreciate the urgency of the problem, you need to understand exactly how deep the dependency runs. It is tempting to think of AI dependency as simply a matter of using a foreign software product, the way a European company might use American accounting software or Japanese industrial robots. But large language models and the broader AI infrastructure that surrounds them are categorically different, and the difference matters enormously.
When a European hospital, bank, law firm, or government ministry integrates a frontier LLM into its operations, it is not merely purchasing a tool. It is embedding a foreign cognitive infrastructure into its most sensitive decision-making processes. The model shapes how information is summarized, how options are presented, how risks are assessed, and how communications are drafted. Over time, the organization's workflows, its institutional knowledge, and its human expertise become entangled with the model's capabilities and limitations. Switching costs become enormous. The dependency deepens silently, one workflow at a time.
Now consider the geopolitical dimension. The United States government has made it unmistakably clear, through the January 2025 Framework for Artificial Intelligence Diffusion issued by the Bureau of Industry and Security, that it views frontier AI models as strategic national security assets subject to export control. The framework requires licenses to export, re-export, or transfer the integrated circuits and model weights of the most advanced AI systems to certain destinations. Crucially, it specifically targets closed-weight models trained on more than ten to the power of twenty-six computational operations. The inclusion of model weights in export control regimes is a watershed moment. It means that the numerical parameters that define an AI model's behavior, the very essence of what makes a frontier model capable, are now treated by the US government with the same seriousness as advanced weapons components.
The Anthropic incident involving Fable 5 and Mythos 5 represents the first dramatic real-world application of this logic. But it will not be the last. As AI capabilities advance and as geopolitical tensions evolve, the pressure on the US government to restrict access to frontier AI will only increase. Europe must assume, as a baseline planning scenario, that any frontier AI capability it does not own and control domestically can be withdrawn at any time, for any reason, with little or no notice.
The dependency is not only American. Chinese AI companies, including those behind models like DeepSeek, have produced remarkably capable open-source systems that have attracted significant European interest, partly because they are free and partly because they are not subject to US export controls. But Chinese AI carries its own profound risks. Research has consistently shown that large language models developed in authoritarian contexts may be structurally compromised in ways that are difficult or impossible to detect. Chinese-language versions of these models systematically present narratives aligned with Chinese Communist Party positions on sensitive historical and political topics. More worryingly, there is credible concern in the security research community that models trained under state surveillance could contain undetectable backdoors, subtle biases in behavior that activate under specific conditions to serve state intelligence objectives. For European governments and critical infrastructure operators, using Chinese frontier AI is not a safe alternative to American AI. It is a different flavor of the same fundamental problem: strategic dependency on a foreign power whose interests may diverge sharply from Europe's own.
The investment gap makes the picture even starker. Between 2018 and the third quarter of 2023, the EU invested approximately 32.5 billion euros in AI companies, while US investments exceeded 120 billion euros over the same period. In 2024, the US led with around 300 billion dollars in AI-related private investments, compared to China's 91 billion dollars and the EU's 45 billion dollars. In terms of compute infrastructure, the disparity is even more dramatic. The United States holds approximately 80 percent of global AI compute capacity, while the EU possesses less than 5 percent. US tech firms spent over 400 billion dollars in 2025 alone expanding AI infrastructure, and American hyperscalers spent more than 200 billion dollars on AI data centers in just the first quarter of 2026. Europe's plan to triple its data center capacity over five to seven years at a cost of around 200 billion euros, while ambitious by European standards, is dwarfed by what the US is spending in a single quarter.
To illustrate the scale of this gap in a way that is viscerally clear, consider the following comparison:
COMPUTE CAPACITY COMPARISON (2025, approximate)
Region Share of Global AI Compute Annual AI Infrastructure Spend
-----------------------------------------------------------------------
United States ~80% >$400 billion
China ~10% ~$91 billion (investment)
European Union <5% ~$45 billion (investment)
Rest of World ~5% Fragmented
Note: EU data center capacity tripling plan costs ~200 billion euros
over 5-7 years, equivalent to roughly 28-40 billion euros per year,
while US hyperscalers spent >200 billion dollars in Q1 2026 alone.
This is not a gap that can be closed by incremental measures. It requires a fundamental strategic reorientation, sustained over a decade, with the kind of political will and financial commitment that Europe has historically reserved for existential crises. The good news, if one can call it that, is that the Anthropic incident has made the existential nature of this crisis impossible to ignore.
CHAPTER TWO: WHAT EUROPE ALREADY HAS, AND WHY IT IS NOT ENOUGH
Before prescribing solutions, it is important to be honest about Europe's existing assets. The picture is not entirely bleak. Europe has genuine strengths in AI research, in specific industrial domains, and in regulatory leadership. Understanding these strengths is essential for designing a strategy that builds on what exists rather than starting from zero.
In the domain of large language models, Europe has produced several genuinely impressive initiatives. Mistral AI, the French startup founded in 2023, has rapidly become one of the most technically credible AI companies outside the United States. Its Mistral Large 2 model, which launched in November 2024, competes meaningfully with frontier models from OpenAI and Anthropic, featuring a context window of up to 128,000 tokens and supporting over 80 programming languages. Mistral's commitment to open-weight models, meaning models whose parameters are publicly released, is strategically important because it means European organizations can download and run these models on their own infrastructure, eliminating the dependency on a foreign API that can be switched off. Mistral has also developed a remarkable range of specialized models: Codestral for software development, Mathstral 7B for mathematical computation, Ministral 3B and 8B for edge deployment, and Pixtral Large for multimodal text and image processing. ASML, the Dutch company that holds a global monopoly on Extreme Ultraviolet lithography machines, the tools without which no advanced semiconductor can be manufactured, made a striking strategic move by investing 1.3 billion euros to become the largest shareholder in Mistral AI. This alliance between the world's most critical semiconductor equipment maker and Europe's leading AI startup is precisely the kind of strategic combination that Europe needs more of.
Germany's Aleph Alpha has taken a different but equally important path. Rather than competing head-on with US frontier models in the consumer market, Aleph Alpha has evolved into an enterprise AI platform called PhariaAI, designed as a complete AI operating system for businesses and government clients. PhariaAI emphasizes explainability, auditability, and hybrid execution, meaning it can run in environments where data cannot leave a specific jurisdiction. This is exactly the kind of capability that European governments and regulated industries need. Aleph Alpha's models are trained in five European languages and can reach up to 300 billion parameters, and the company maintains partnerships with SAP and Hewlett Packard Enterprise that give it genuine enterprise distribution.
The BLOOM model, produced by a collaboration of over 1,000 researchers coordinated by the BigScience initiative, demonstrated that open, multilingual, large-scale language models could be built through international academic collaboration. With 176 billion parameters and support for 46 natural languages and 13 coding languages, BLOOM proved that the US and China do not have a monopoly on frontier model development. The EuroLLM project, which officially ran from May 2024 to April 2025 and was co-funded by the European Union and supported by EuroHPC computing resources, has produced a series of models specifically designed for European linguistic diversity. The EuroLLM-9B model, released in December 2024, was trained on over 4 trillion tokens across 35 languages. The flagship EuroLLM-22B model, launched by December 2025, represents the strongest open-source multilingual model specifically optimized for European languages. A related initiative, OpenEuroLLM, commenced work on February 1, 2025, co-funded by the Digital Europe Programme, with the goal of building adaptable, transparent LLMs trained on European supercomputers and adhering to European values and the requirements of the EU AI Act.
In hardware, Europe's position is more complicated. ASML's monopoly on EUV lithography is a genuine strategic asset of almost incalculable importance. No advanced semiconductor, whether manufactured by TSMC in Taiwan, Samsung in South Korea, or Intel in the United States, can be produced without ASML's machines. This gives Europe a form of structural leverage in the global semiconductor supply chain that is often underappreciated. However, ASML itself is not fully independent. It relies on US suppliers for certain components and is subject to US extraterritorial influence, as demonstrated by the US government's pressure on the Netherlands to restrict ASML's sales of its most advanced EUV machines to China. Europe has the world's most important semiconductor equipment maker but lacks the fabs, the actual manufacturing facilities, to produce advanced chips at scale. The European Chips Act, launched in 2022 with an estimated 43 billion euros in investment, aims to double Europe's share of global semiconductor production to 20 percent by 2030. This is an ambitious target, but it is worth noting that Europe's current share is approximately 9 percent, and the global market is expanding rapidly, meaning that even reaching 20 percent will require extraordinary effort.
The EuroHPC Joint Undertaking has made real progress in building European supercomputing capacity. The JUPITER supercomputer, inaugurated in September 2025, became the first in Europe to achieve exascale performance, providing unprecedented computing capacity for AI applications in science and industry. EuroHPC has also launched AI Factories, large-scale computing facilities specifically designed to support the training of large AI models by European startups and researchers. The EU's financial contribution for acquiring new or upgraded AI EuroHPC supercomputers was estimated at 400 million euros in 2024, with an additional 180 million euros for establishing and operating AI Factories.
Regulatory leadership is another genuine European asset, though it is a double-edged one. The EU AI Act, which entered into force on August 1, 2024, is the world's first comprehensive legal framework for AI. Its governance rules for general-purpose AI models, including LLMs, became applicable on August 2, 2025. The Act's risk-based approach, imposing stricter requirements on high-risk AI systems while fostering trustworthy AI, has set a global precedent. The EU's General Data Protection Regulation has already demonstrated that European regulatory standards can shape global industry practices. The AI Act has the potential to do the same for AI governance.
But here is the honest assessment: all of these assets, impressive as they are individually, do not add up to strategic independence. Mistral AI, for all its technical excellence, is still a startup that raised 2 billion euros in funding at a time when US competitors were spending that amount on compute in a single week. EuroHPC's AI Factories are valuable but represent a fraction of the compute capacity available to US researchers. The European Chips Act's 43 billion euros sounds large until you compare it to the hundreds of billions that US and Asian chipmakers are investing. And the EU AI Act, while globally influential, has been criticized for potentially slowing European AI development through regulatory burden without providing the investment and industrial support needed to build competitive alternatives.
The gap between what Europe has and what it needs is real, large, and growing. The question is not whether Europe needs a more ambitious strategy. The question is what that strategy should look like, and how it should be sequenced across time.
CHAPTER THREE: THE SHORT-TERM STRATEGY (2026-2028): STABILIZE, SECURE, AND ACCELERATE
The immediate priority for Europe in the wake of the Anthropic incident and the broader US export control framework must be to stabilize the situation, identify the most critical vulnerabilities, and begin addressing them with resources and urgency that match the scale of the problem. The short-term strategy is not about achieving independence. It is about preventing the dependency from deepening further while laying the foundations for genuine sovereignty.
The first and most urgent step is what might be called a strategic AI audit across critical sectors. Every European government, every operator of critical infrastructure, every major healthcare system, and every defense-related organization must immediately map its dependencies on non-European AI systems. This means identifying which AI models are used, by whom, for what purposes, what data is being processed, and what the consequences would be if access were suddenly revoked. This audit is not a bureaucratic exercise. It is the equivalent of a military commander mapping the terrain before a battle. You cannot defend what you have not mapped.
The results of such an audit would almost certainly be alarming. European hospitals are using American clinical AI tools. European banks are running risk models on US cloud infrastructure. European governments are using US-based translation and summarization services for sensitive diplomatic communications. European defense contractors are relying on American AI for logistics optimization and intelligence analysis. Each of these dependencies represents a potential point of failure in a geopolitical crisis.
Once the audit is complete, the second step is to establish emergency transition pathways for the highest-risk dependencies. This means identifying European alternatives, even if they are less capable than the US or Chinese systems they replace, and beginning the process of migration. This is painful and expensive in the short term. A European clinical AI tool that is 80 percent as capable as an American one is still a profound improvement over an American tool that might be switched off tomorrow. The principle here is that sovereignty has a value that cannot be captured by pure performance benchmarks.
The third short-term step is to dramatically accelerate funding for European open-weight model development. Open-weight models are strategically crucial because they can be downloaded, hosted on European infrastructure, and operated without any dependency on a foreign API. Mistral AI's commitment to open-weight models is a model that other European AI developers should be encouraged and funded to follow. The EU's AI Innovation Package, which allocated up to 4 billion euros for 2024-2027 specifically targeting generative AI, is a start, but it needs to be supplemented with emergency funding that reflects the urgency of the situation. The UK's 500 million pound Sovereign AI Fund, formally launched in April 2026, and Prime Minister Starmer's announcement of a 400 million pound investment in specialist AI chips as part of a wider 1.1 billion pound AI hardware plan, represent the right instinct. These initiatives need to be scaled up and coordinated with EU efforts.
The fourth short-term priority is to build a European AI compute reserve. This is analogous to the strategic petroleum reserves that European countries maintain to buffer against supply disruptions. The idea is to ensure that European AI researchers, startups, and critical institutions have guaranteed access to sufficient compute capacity, hosted on European soil and under European legal jurisdiction, to continue their work even if US or Chinese cloud providers restrict access. EuroHPC's AI Factories are the nucleus of this reserve, but they need to be expanded significantly and their access policies need to be streamlined to make them genuinely useful to the research and startup community.
A fifth short-term measure that is often overlooked is the development of European AI talent pipelines. The EU's AI Skills Academy, planned for launch in 2026, is an important initiative, but it needs to be accompanied by concrete measures to retain European AI talent that might otherwise be recruited by US tech companies. This means not only improving compensation in European AI research institutions but also creating the kind of exciting, well-resourced research environments that make it possible for European researchers to do world-class work without emigrating to Silicon Valley. The recent trend of tech professionals relocating from the US to Europe, particularly to France, Switzerland, and the Netherlands, driven by American protectionism and European resolve, is an opportunity that should be actively cultivated. Europe should be making it as easy as possible for talented AI researchers from anywhere in the world to settle and work in Europe.
To make the short-term strategy concrete, consider the following illustrative scenario:
SHORT-TERM SCENARIO: The European Clinical AI Emergency Protocol
Situation: Following the Anthropic Fable 5 suspension, a consortium
of European hospital networks discovers that 23% of their AI-assisted
diagnostic tools rely on US API endpoints.
Immediate Actions (within 90 days):
- Deploy EuroLLM-22B on EuroHPC AI Factory infrastructure as a
bridge solution for medical text summarization tasks.
- Migrate sensitive patient data processing to Aleph Alpha's
PhariaAI platform, which operates entirely within EU jurisdiction.
- Commission emergency fine-tuning of Mistral Large 2 on
anonymized clinical datasets to match domain-specific performance.
- Establish a European Clinical AI Consortium to pool resources
and share the costs of building sovereign alternatives.
Result: Within 6 months, the consortium reduces its dependency on
non-European AI APIs from 23% to 8% for critical functions,
with a roadmap to reach 2% within 18 months.
This kind of rapid, coordinated response is exactly what the short-term strategy must enable. It requires pre-existing frameworks, pre-negotiated contracts with European AI providers, and pre-trained alternative models that can be deployed quickly. None of these things happen spontaneously. They require deliberate investment and planning, starting now.
CHAPTER FOUR: THE MID-TERM STRATEGY (2028-2032): BUILD, COMPETE, AND LEAD
The mid-term strategy is where Europe must make its most consequential decisions. The short-term measures buy time and reduce the most acute vulnerabilities. The mid-term strategy is about building the genuine technical capabilities that will make European AI sovereignty durable and self-reinforcing. This requires addressing three fundamental challenges simultaneously: compute infrastructure, model development, and the semiconductor supply chain.
On compute infrastructure, Europe must dramatically expand its AI supercomputing capacity. The JUPITER exascale supercomputer, inaugurated in September 2025, is an impressive achievement, but it is one facility. The United States has dozens of comparable or superior facilities, and the gap in total compute capacity remains enormous. The EU's plan to establish AI Gigafactories, large-scale computing and data centers capable of training very large AI models, is the right direction. The Cloud and AI Development Act, proposed as part of the EU's Tech Sovereignty Package, aims to triple Europe's data center capacity within five to seven years. This needs to happen faster and at larger scale than currently planned.
The key insight about compute infrastructure is that it is not just about raw capacity. It is about the entire ecosystem that surrounds the hardware: the energy supply, the cooling systems, the networking infrastructure, the software stack that makes the hardware accessible to researchers and developers, and the governance frameworks that determine who can use it and on what terms. Europe has genuine advantages in some of these dimensions. Scandinavia, Iceland, and parts of the Alps offer excellent conditions for data centers: cold climates that reduce cooling costs, access to renewable hydroelectric and geothermal energy, and political stability. Norway, despite not being an EU member, has already attracted significant data center investment precisely because of these advantages. A European AI compute strategy should explicitly leverage these geographic and energy advantages.
On model development, the mid-term goal must be to produce European frontier models that are genuinely competitive with the best American and Chinese models, not merely adequate substitutes. This is a harder challenge than it might appear, because frontier model development is not just a matter of throwing money at the problem. It requires a concentration of exceptional talent, access to massive compute resources, and the ability to iterate rapidly based on research insights. The US dominance in frontier AI is partly a function of the extraordinary concentration of talent and resources in a small number of companies, primarily OpenAI, Anthropic, Google DeepMind, and Meta AI. Europe needs to create the conditions for similar concentrations to emerge.
The most promising path to European frontier model competitiveness is through a combination of open-source collaboration and targeted support for leading European AI companies. The open-source approach has several advantages for Europe. It allows resources to be pooled across multiple organizations and countries, reducing the cost to any single actor. It creates a commons of capability that all European organizations can build on. It prevents the emergence of European monopolies that could create new dependencies. And it makes European AI capabilities more transparent and auditable, which is important for building public trust and regulatory compliance.
The EuroLLM and OpenEuroLLM projects demonstrate that the open-source collaborative model can work for European LLM development. But these projects need to be scaled up dramatically. A mid-term goal should be to develop a European frontier model with capabilities comparable to GPT-5 class systems, trained on European compute infrastructure, with open weights available to European researchers and organizations. This is an ambitious goal, but it is achievable if the resources and coordination are in place.
At the same time, Europe should not rely exclusively on the open-source model. Companies like Mistral AI and Aleph Alpha represent a different but equally important path: European commercial AI companies that can compete in the global market and generate the revenue needed to sustain frontier research. Europe should support these companies not just with grants and subsidies but with procurement commitments from European governments and public institutions. When a European government ministry needs an AI system, it should have a strong presumption in favor of European providers, not as protectionism but as a strategic investment in building the European AI ecosystem.
The semiconductor dimension of the mid-term strategy is perhaps the most complex and the most consequential. Europe currently lacks the ability to manufacture the advanced AI accelerators, the GPUs and specialized AI chips, that are essential for training and running frontier models. NVIDIA's H100 and H200 GPUs, and their successors, are manufactured by TSMC in Taiwan using ASML's EUV lithography machines. The irony is profound: the most critical tool in the global semiconductor supply chain is European, but the chips that power the AI revolution are made in Asia and designed in America.
The mid-term semiconductor strategy must pursue two parallel tracks. The first track is to leverage ASML's unique position to attract advanced semiconductor manufacturing to Europe. The European Chips Act's goal of reaching 20 percent of global semiconductor production by 2030 is the right target, but achieving it will require not just investment but also the creation of a complete semiconductor ecosystem: materials suppliers, equipment makers, chip designers, and manufacturing facilities, all within Europe or in closely allied countries. Intel's planned fabs in Germany and Poland, though facing delays and financial challenges, represent the kind of large-scale manufacturing investment that Europe needs. TSMC's planned fab in Dresden, Germany, is another important step, even though it will initially produce chips at process nodes that are not at the absolute frontier.
The second track is to develop European-designed AI chips. This is a longer-term ambition, but the mid-term strategy should begin laying the groundwork. Europe has genuine strengths in chip design, particularly in areas like power semiconductors, automotive chips, and embedded systems. Companies like Infineon, STMicroelectronics, and NXP Semiconductors are world leaders in their respective domains. The challenge is to extend this expertise into the domain of AI accelerators, which require a fundamentally different design philosophy optimized for massive parallel computation. Several European research initiatives and startups are working on this challenge, and they deserve significant support.
To illustrate what a mid-term success scenario might look like, consider the following:
MID-TERM MILESTONE SCENARIO (2030):
"The European AI Stack"
Hardware Layer:
- 3 operational AI Gigafactories in EU territory (France, Germany,
Nordics), each with >10,000 AI accelerator units
- TSMC Dresden fab producing 5nm chips, with European AI chip
design consortium developing first EU-designed AI accelerator
- EuroHPC total capacity: 5x 2025 levels, representing ~15% of
global AI compute
Model Layer:
- EuroFrontier-1 model: 500B+ parameter open-weight model,
competitive with GPT-5 class, trained entirely on European
compute, available under European open-source license
- Mistral AI flagship model: commercially competitive frontier
model, generating sufficient revenue to fund continued R&D
- Domain-specific models: European clinical AI, legal AI,
financial AI, and defense AI models, all trained on European
data under European governance
Governance Layer:
- EU AI Act fully operational, providing clear compliance
framework for European AI development
- European AI Safety Institute established, conducting
independent evaluation of frontier models
- EU-UK-Japan-Canada AI Governance Framework operational,
providing allied nations with coordinated approach to AI
standards and export controls
Dependency Metric:
- Critical infrastructure AI dependency on non-European
systems: reduced from >60% (2026) to <20% (2030)
This scenario is ambitious but not unrealistic. It requires sustained political will, coordinated investment, and the kind of long-term thinking that European institutions have demonstrated in other domains, from the creation of Airbus to the development of the Galileo satellite navigation system.
CHAPTER FIVE: THE LONG-TERM STRATEGY (2032-2040): SOVEREIGNTY, LEADERSHIP, AND GLOBAL INFLUENCE
The long-term strategy is about more than independence. It is about Europe becoming a genuine leader in AI, not just a fast follower or a competent second-tier player. This distinction matters enormously, because the countries that lead in AI will shape the norms, standards, and governance frameworks that govern its development globally. If Europe is merely a consumer of AI capabilities developed elsewhere, it will be a rule-taker. If Europe is a leader, it can be a rule-maker.
The long-term vision for European AI sovereignty rests on three pillars. The first pillar is technological leadership in specific domains where Europe has structural advantages. The second pillar is a complete, sovereign AI infrastructure stack from chips to models to applications. The third pillar is a global coalition of like-minded democracies that share European values and can collectively provide a counterweight to US and Chinese AI dominance.
On technological leadership, Europe does not need to be the global leader in every dimension of AI. It needs to be the undisputed leader in the dimensions that matter most for European values and European economic strengths. These include trustworthy and explainable AI, where Europe's regulatory tradition and academic research base give it a genuine head start. They include multilingual AI, where the linguistic diversity of Europe is an asset rather than a liability. They include privacy-preserving AI, where GDPR has already established European leadership in data protection and where federated learning and differential privacy techniques can allow AI to be trained on sensitive data without compromising individual privacy. They include industrial AI, where Europe's strengths in manufacturing, automotive, chemicals, and engineering provide rich application domains. And they include AI for public services, where European governments' commitment to universal service provision creates a large and demanding market for AI systems that are fair, transparent, and accountable.
The long-term goal for European AI infrastructure is to have a complete sovereign stack: European-designed and European-manufactured AI chips, running on European-owned compute infrastructure, powering European-developed models, deployed through European cloud platforms, governed by European legal frameworks. This does not mean that Europe must produce every component of this stack itself. It means that Europe must have sufficient control over each layer that no single foreign actor can disrupt the entire system by withdrawing access.
Consider what a complete European AI sovereign stack might look like by 2038:
THE EUROPEAN SOVEREIGN AI STACK (2038 Vision)
Layer 1: Silicon
EU-designed AI accelerator chips (EuroChip-3 generation)
Manufactured at TSMC Dresden (2nm process) and Intel Germany
EUV lithography: ASML (European monopoly maintained)
Market share in AI accelerators: ~15% global
Layer 2: Compute Infrastructure
12 AI Gigafactories across EU and UK
Total capacity: ~25% of global AI compute
Powered by >90% renewable energy
Governed by European AI Compute Authority
Layer 3: Foundation Models
EuroFrontier-3: 2 trillion parameter multimodal model
Mistral AI commercial frontier model (global top-5)
Domain-specific models: medical, legal, defense, science
All major EU languages fully supported at frontier quality
Layer 4: Platform and Tools
European cloud platforms (sovereign, GDPR-compliant)
European AI development tools and frameworks
European AI safety and evaluation infrastructure
Layer 5: Applications
European AI in healthcare, finance, defense, public services
European AI exported globally as trusted, values-aligned AI
European AI standards adopted internationally
The semiconductor question deserves particular attention in the long-term context. By 2038, the EU should aim to have at least one European company capable of designing AI accelerators that are competitive with the best American and Asian offerings. This is a genuinely difficult challenge, because chip design at the frontier requires decades of accumulated expertise and enormous R&D investment. But it is not impossible. ARM, the British chip design company whose architecture powers virtually every smartphone in the world, demonstrates that a European company can achieve global dominance in chip design. Europe needs its equivalent of ARM for AI accelerators.
The path to this goal runs through a sustained investment in semiconductor research and education, through the creation of a European chip design ecosystem that can attract and retain world-class talent, and through strategic partnerships with allied nations that have complementary strengths. South Korea's Samsung and SK Hynix are world leaders in memory chips, which are critical for AI systems. Japan's Rapidus is attempting to develop advanced semiconductor manufacturing capabilities. Taiwan's TSMC, while not European, is a critical partner whose fabs in Europe represent a significant step toward reducing concentration risk in the global semiconductor supply chain.
CHAPTER SIX: THE HARDWARE QUESTION IN DEPTH
The question of whether Europe should build its own GPU and AI chip infrastructure is not really a question at all. The answer is unambiguously yes. The only real questions are how, at what pace, and with what priorities.
To understand why European AI chip sovereignty is essential, consider the following chain of dependencies. Training a frontier AI model requires thousands of AI accelerators, primarily NVIDIA GPUs or their equivalents. These chips are designed in the United States by NVIDIA, AMD, or Intel. They are manufactured in Taiwan by TSMC or in South Korea by Samsung. They are assembled into server systems by companies like Dell, HPE, or Supermicro, primarily in the United States or Asia. They are then shipped to data centers around the world, where they are operated under software stacks that are largely American in origin. At every step of this chain, a European organization is dependent on non-European actors.
The US government's export control framework has already demonstrated its willingness to restrict access to AI chips for geopolitical reasons. The restrictions on NVIDIA GPU exports to China, which began in 2022 and have been progressively tightened, show exactly how this mechanism works. The US can, and does, use chip export controls as a geopolitical tool. Europe is not currently subject to these restrictions, but that is a function of current geopolitical alignment, not a permanent guarantee. If European and American strategic interests were to diverge significantly, on trade, on defense, on climate, on any number of issues, the US government's willingness to use its control over the AI chip supply chain as leverage cannot be ruled out.
The European Chips Act's 43 billion euro investment is a meaningful start, but it needs to be understood in context. Intel's planned fab in Magdeburg, Germany, alone was projected to cost 17 billion euros, and that facility will not produce the most advanced AI accelerators. Building a complete European AI chip ecosystem, from design through manufacturing through packaging and testing, will require sustained investment of hundreds of billions of euros over the course of a decade. This is not an argument against doing it. It is an argument for starting immediately and for being realistic about the timescale and cost.
The most pragmatic near-term approach for Europe is to pursue a three-pronged hardware strategy. The first prong is to maximize the use of existing European advantages, particularly ASML's EUV monopoly, to attract advanced semiconductor manufacturing to Europe. This means creating the regulatory environment, the infrastructure, and the incentives that make Europe an attractive location for TSMC, Samsung, and other leading chipmakers to build fabs. The TSMC Dresden fab is a proof of concept. It needs to be followed by more, and at more advanced process nodes.
The second prong is to invest heavily in European chip design. Chip design is less capital-intensive than manufacturing and plays more directly to Europe's strengths in engineering and mathematics. A European AI chip design consortium, bringing together leading European universities, research institutes, and technology companies, could develop competitive AI accelerator architectures within a decade. The key is to make this a genuine priority with genuine resources, not a marginal research program.
The third prong is to develop alternative computing paradigms where Europe can leapfrog rather than catch up. Neuromorphic computing, which mimics the architecture of the human brain, is an area where European researchers, particularly at institutions like the Human Brain Project, have made significant contributions. Photonic computing, which uses light rather than electricity to perform computations, is another area with significant European research activity. Quantum computing, while still in its early stages for practical AI applications, is a domain where Europe has invested heavily through the Quantum Flagship program. None of these alternatives will replace conventional AI accelerators in the near term, but they represent potential paths to European leadership in the computing paradigms of the 2030s and beyond.
To make the hardware challenge concrete, here is a simplified illustration of the European AI chip development roadmap:
EUROPEAN AI CHIP DEVELOPMENT ROADMAP
Phase 1 (2026-2028): Foundation
- Establish European AI Chip Design Consortium (EACDC)
- Partners: Infineon, STMicro, NXP, IMEC, leading universities
- Goal: First European AI accelerator reference design
- Compute: Leverage TSMC Dresden and Samsung for manufacturing
- Investment: ~5 billion euros (EU + member states + industry)
Phase 2 (2028-2032): First Generation
- EuroChip-1: First European-designed AI accelerator in production
- Performance target: Competitive with NVIDIA H100 generation
- Manufacturing: TSMC Dresden (5nm) + potential EU fab partner
- Investment: ~20 billion euros
- Milestone: European AI Gigafactories begin using EuroChip-1
Phase 3 (2032-2038): Competitive Generation
- EuroChip-2 and EuroChip-3: Frontier-competitive AI accelerators
- European fab capacity sufficient for 30% of European AI demand
- Performance target: Competitive with NVIDIA Blackwell successors
- Investment: ~50 billion euros
- Milestone: European AI sovereignty in compute hardware achieved
Phase 4 (2038+): Leadership
- Novel computing architectures (neuromorphic, photonic) in
production for specific AI workloads
- European AI chip exports to allied nations
- European chip design standards influence global industry
This roadmap is illustrative rather than prescriptive. The actual path will be shaped by technological developments, geopolitical events, and the decisions of private companies as well as governments. But the key point is that this is a decade-long journey that must begin now, with clear milestones, sustained funding, and genuine political commitment.
CHAPTER SEVEN: THE SOFTWARE AND MODEL DEVELOPMENT STRATEGY
Hardware is necessary but not sufficient. The software layer, including the models themselves, the training frameworks, the deployment infrastructure, and the application ecosystems, is equally important and in some ways more tractable in the short term.
The most important strategic decision Europe must make about AI model development is the choice between open-source and proprietary approaches. This is not a binary choice, but the balance between the two has profound implications for European AI sovereignty.
The case for open-source is compelling from a sovereignty perspective. An open-weight model that any European organization can download and run on its own infrastructure eliminates the dependency on a foreign API. It allows European organizations to fine-tune the model on their own data, adapting it to their specific needs without sharing sensitive information with a foreign company. It enables independent security auditing, which is essential for models used in critical infrastructure. And it creates a commons of capability that the entire European AI ecosystem can build on, reducing duplication of effort and accelerating progress.
The case for proprietary European models is equally compelling from a competitiveness perspective. Proprietary models can be more easily monetized, generating the revenue that funds continued frontier research. They can be protected from copying by competitors, including Chinese actors who might otherwise simply take European open-source models and use them to compete against European companies. And they can be developed with a focus on specific high-value applications where European companies have competitive advantages.
The optimal strategy is a portfolio approach that supports both open-source and proprietary development, with clear roles for each. Open-source models should be the default for public sector applications, for research, and for critical infrastructure, where sovereignty and auditability are paramount. Proprietary models should be supported where European companies have genuine opportunities to compete commercially in global markets.
The EU's open-source strategy, which promotes an open-source-first approach for public procurement of cloud and AI software, is the right framework for the public sector dimension. But it needs to be accompanied by concrete procurement commitments that create a reliable market for European AI providers. When the European Commission, or a European national government, or a European public hospital, needs an AI system, the default should be a European open-source or European proprietary system, with non-European systems requiring explicit justification.
For the research and development dimension, Europe needs to dramatically increase its investment in AI research at universities and public research institutes. The Horizon Europe program's allocation of 2.6 billion euros to AI research for 2021-2022, with additional calls in April 2024 providing over 112 million euros for AI and quantum technology projects including 50 million euros for advancing large AI models, is a start. But it needs to be scaled up by an order of magnitude to be competitive with the research investments of leading US and Chinese institutions.
A particularly important area for European AI research investment is the development of more efficient training methods. One of the key reasons why frontier AI development is so expensive is that training large models requires enormous amounts of compute. If European researchers can develop training methods that achieve comparable performance with significantly less compute, this would dramatically reduce the cost of frontier model development and make it more accessible to European organizations with smaller budgets. Techniques like mixture-of-experts architectures, which Mistral AI has pioneered with its Mixtral model, are one example of this direction. The Mixtral approach activates only a fraction of the neural network's parameters for each query, dramatically improving efficiency without sacrificing performance.
Another important research direction is the development of AI systems that are more robust, more interpretable, and more aligned with human values. These are areas where European researchers have historically been strong, and where the EU AI Act creates a genuine market demand. If Europe can develop AI systems that are demonstrably more trustworthy and more explainable than their American and Chinese counterparts, this becomes a genuine competitive advantage in markets where trust matters, including healthcare, finance, legal services, and public administration.
The following example illustrates how European AI model development might be organized and resourced:
EUROPEAN AI MODEL DEVELOPMENT ECOSYSTEM (Illustrative)
Tier 1: Frontier Model Development (EU-funded, open-weight)
- OpenEuroLLM Consortium: EuroFrontier series models
- Funding: 2 billion euros/year from EU + member states
- Compute: EuroHPC AI Gigafactories
- Target: Frontier-competitive open-weight models, all EU languages
Tier 2: Commercial Frontier Models (EU-supported, proprietary)
- Mistral AI: Commercial frontier models, global market
- Aleph Alpha: Enterprise AI platform (PhariaAI), EU/UK market
- EU support: Procurement commitments, R&D tax credits, compute
access
- Target: European companies in global top-10 AI providers
Tier 3: Domain-Specific Models (sector-funded, specialized)
- EuroClinical: Medical AI models (healthcare consortium)
- EuroLex: Legal AI models (law firm + court consortium)
- EuroDefense: Defense AI models (classified, defense ministries)
- EuroFinance: Financial AI models (banking + insurance consortium)
- Funding: Sector-specific consortia with EU co-funding
Tier 4: Research Models (university + institute funded)
- Horizon Europe AI research program: 5 billion euros/year
- Focus: Efficient training, interpretability, alignment, safety
- Output: Research insights feeding into Tier 1, 2, 3 development
This tiered ecosystem ensures that resources are allocated efficiently, that there is a clear path from research to deployment, and that both open-source and proprietary development are supported in a coordinated way.
CHAPTER EIGHT: THE ALLIES EUROPE NEEDS
No country or region can achieve AI sovereignty in complete isolation. The scale of investment required, the breadth of expertise needed, and the global nature of the AI ecosystem make international cooperation not just desirable but essential. Europe's strategy for AI sovereignty must include a carefully chosen set of international partnerships that amplify European capabilities without creating new dependencies.
The most natural and important allies for Europe are the other liberal democracies that share European values and face similar challenges. Canada, Japan, Australia, South Korea, and India are all pursuing sovereign AI initiatives and all have reasons to resist the US-China AI duopoly. Each of these countries brings different strengths to a potential coalition.
Canada has a world-class AI research community, centered on institutions like the Vector Institute in Toronto, Mila in Montreal, and the Alberta Machine Intelligence Institute in Edmonton. Canadian researchers including Geoffrey Hinton, Yoshua Bengio, and their students have made foundational contributions to deep learning that underpin virtually all modern AI systems. Canada and the EU signed memoranda of understanding in December 2025 to deepen collaboration on AI frameworks, standards, skills development, scientific cooperation, and AI adoption in strategic economic sectors. This is exactly the kind of structured partnership that should be deepened and expanded. Canada is also part of a multilateral pact with Singapore, the UK, Australia, and South Korea to shape international AI regulations and standardization, which provides a ready-made framework for broader cooperation.
Japan brings extraordinary strengths in semiconductor manufacturing, robotics, and industrial AI. Japan's Rapidus initiative, which aims to develop domestic advanced semiconductor manufacturing capabilities, is directly complementary to European efforts in this domain. The EU and Japan are deepening their strategic collaboration on AI, data, quantum, and semiconductors, and at the 2025 EU-Japan Summit they reaffirmed commitment to the Hiroshima AI Process and pledged to continue joint work on responsible AI governance. Japan is also developing its own large language models for the Japanese language, including a national Japanese LLM supported by the government in 2025, which demonstrates the kind of sovereign AI initiative that Europe and Japan can learn from each other.
Australia brings a different but valuable set of assets: significant reserves of the rare earth minerals and critical materials needed for semiconductor manufacturing, a sophisticated research university system, and a strong tradition of cooperation with European partners on technology and security issues. Australia is collaborating with South Korea, Singapore, the UK, and Canada on a multilateral pact to influence international AI standardization and regulation. Australia's concerns about the EU's Cloud and AI Development Act, which it fears could exclude non-EU players from the European market, are a legitimate tension that needs to be managed carefully. Europe must ensure that its drive for AI sovereignty does not tip over into protectionism that alienates natural allies.
India is perhaps the most interesting and complex potential partner. India's IndiaAI Mission is pursuing indigenous AI capabilities at scale, including foundation models and compute infrastructure. Sarvam AI was selected in April 2025 to build India's sovereign LLM ecosystem, developing an open-source 120 billion parameter model. BharatGen AI, India's first government-funded multimodal large language model, supports 22 Indian languages. India is pioneering efforts to democratize access to AI infrastructure, with the government funding access to a public compute pool of approximately 18,000 GPUs. India's enormous population, its large and growing technology sector, and its strategic interest in avoiding dependence on both the US and China make it a natural partner for Europe. An EU-India AI partnership that combines European regulatory expertise and research depth with Indian scale and engineering talent could be genuinely transformative.
South Korea brings world-leading strengths in semiconductor manufacturing, particularly in memory chips through Samsung and SK Hynix, and in consumer electronics through Samsung and LG. South Korean AI companies like Naver, Kakao, and LG AI Research are developing competitive LLMs for the Korean language and beyond. South Korea's participation in the multilateral pact with Canada, Australia, Singapore, and the UK makes it a natural candidate for deeper engagement with the EU.
The United Kingdom deserves special mention as both an ally and a case study. Post-Brexit, the UK is no longer an EU member, but it remains Europe's most significant AI power. The UK has world-class AI research institutions, including the Alan Turing Institute, DeepMind (now part of Google but with deep UK roots), and a vibrant startup ecosystem. The UK government's national AI strategy, the 2025 AI Opportunities Action Plan, emphasizes leveraging AI for public services and driving AI diffusion across the UK economy. Prime Minister Starmer's announcement of a sovereign compute strategy in June 2026, including a 400 million pound investment in specialist AI chips, and the 500 million pound Sovereign AI Fund launched in April 2026, demonstrate serious commitment. Project Mercury, a collaboration between Locai Labs and Civo to build the UK's first pre-trained sovereign LLMs, and the UK-LLM project led by University College London, are exactly the kind of initiatives that should be coordinated with EU efforts rather than developed in parallel isolation. A formal EU-UK AI partnership, separate from the broader Brexit negotiations but building on shared interests and complementary strengths, could be one of the most valuable strategic moves either party could make.
The following diagram illustrates the potential architecture of a European-led AI sovereignty coalition:
EUROPEAN AI SOVEREIGNTY COALITION (Proposed Architecture)
CORE: EU + UK
- Joint frontier model development (EuroFrontier program)
- Shared compute infrastructure (EuroHPC + UK AI Research Resource)
- Harmonized AI governance (EU AI Act + UK AI framework)
- Joint semiconductor strategy (Chips Act + UK chip investment)
TIER 1 PARTNERS: Canada + Japan + South Korea
- Research collaboration (joint Horizon Europe / equivalent programs)
- Semiconductor supply chain cooperation
- AI standards and governance alignment
- Mutual recognition of AI safety evaluations
TIER 2 PARTNERS: Australia + India + Singapore
- Critical materials supply (Australia)
- Large-scale AI development partnership (India)
- Financial and regulatory hub (Singapore)
- Joint AI governance initiatives
COORDINATION MECHANISMS:
- Annual AI Sovereignty Summit (ministerial level)
- Joint AI Safety Institute (modeled on UK/US AI Safety Institutes)
- Shared AI compute reserve (accessible to all coalition members)
- Coalition AI export control framework (aligned but independent
of US framework)
This coalition would represent a genuine third pole in global AI, distinct from both the US-dominated Western AI ecosystem and the Chinese AI ecosystem. It would have sufficient scale, talent, and resources to develop and maintain frontier AI capabilities independently, while remaining open to cooperation with the US and other partners on specific issues of mutual interest.
CHAPTER NINE: WHAT HAPPENS IF EUROPE FAILS TO ACT
The consequences of European inaction on AI sovereignty are not abstract or distant. They are concrete, near-term, and potentially irreversible. The Anthropic Fable 5 incident is a preview of what a world of AI dependency looks like. The full version is considerably more alarming.
Consider the economic dimension first. The EU is already significantly behind the US and China in AI investment and capability. In 2024, the US led with around 300 billion dollars in AI-related private investments, compared to the EU's 45 billion dollars. The US holds approximately 80 percent of global AI compute, while the EU has less than 5 percent. If these trends continue, European companies will find themselves increasingly unable to compete in AI-intensive industries. Manufacturing, which is the backbone of the German, French, and Italian economies, is being transformed by AI-driven automation and optimization. If European manufacturers must rely on American or Chinese AI systems for their production optimization, their competitive intelligence, and their product design, they will be structurally disadvantaged relative to competitors who have access to more capable or more integrated AI systems. The productivity gains from AI, estimated at 3 percent annual improvement for leading adopters, will accrue primarily to American and Chinese companies, widening the economic gap with Europe.
The GDP consequences are potentially severe. Some estimates suggest that AI could boost productivity by 1.1 percent over five years in Europe under current trends, but much larger gains are possible with pro-growth reforms and genuine AI leadership. Generative AI could contribute approximately 8 percent to EU GDP in ten years with widespread adoption, but an unsuccessful transition could reduce this to 5 percent. The difference between these scenarios is not trivial. It represents hundreds of billions of euros in economic output and millions of jobs. More fundamentally, Europe may no longer be able to fund its social welfare systems, which depend on a productive economy generating sufficient tax revenue, if it falls significantly behind in the AI-driven productivity revolution.
The geopolitical consequences are equally serious. Countries that control AI will control the 21st-century economy, military, and intelligence capabilities. If Europe is a consumer of AI capabilities developed and controlled by the United States and China, it will have limited ability to resist the geopolitical preferences of those powers. This is not a hypothetical concern. The US government has already demonstrated its willingness to use technology export controls as a geopolitical tool, against China in the semiconductor domain and now against foreign users of frontier AI models. A Europe that depends on American AI for its critical infrastructure, its defense systems, and its economic competitiveness is a Europe that cannot afford to disagree too strongly with Washington on issues that matter.
The defense dimension is particularly acute. AI is rapidly transforming modern warfare, playing a critical role in intelligence gathering, autonomous systems, cyber operations, and logistics. European defense organizations that depend on American AI systems for these functions are not truly sovereign in their defense capabilities. If access to those systems were restricted, whether by government order, by commercial decision, or by cyberattack, European defense capabilities would be immediately degraded. The EU is investing in AI-driven military technologies and cooperating with NATO, but the gap in private investment and AI supercomputing capacity compared to the US remains enormous.
The healthcare consequences of AI dependency are more subtle but equally important. The US AI Action Plan treats AI leadership as a matter of national security and promotes a US-first model that prioritizes innovation by removing heavy regulations. This creates a situation where the most advanced AI-powered medical tools are developed first for the US market, under US regulatory frameworks, optimized for US healthcare data and US medical practices. European patients may receive access to these tools later, in versions that are less well-adapted to European languages, medical practices, and healthcare systems. European medical researchers may find themselves working with restricted tools or on foreign platforms, unable to access the most capable AI systems for their research. And European healthcare systems, which are among the most efficient and equitable in the world, may find their efficiency advantages eroded as they fall behind in AI-driven optimization.
The following scenario illustrates what a failure scenario might look like by 2035:
FAILURE SCENARIO: Europe in 2035 Without Sovereign AI Strategy
Economic:
- EU GDP growth 1.8% below US average for 10 consecutive years
- 40% of European AI startups acquired by US or Chinese firms
- European manufacturing productivity gap with US widens to 25%
- Social welfare systems under fiscal pressure as tax base erodes
Technological:
- 90% of European critical infrastructure AI runs on US platforms
- European researchers access frontier models only through US APIs
- No European company in global top-20 AI providers
- European AI talent drain: 30% of top researchers work in US
Geopolitical:
- EU unable to maintain independent position on key tech policy
issues without risking AI access disruption
- European defense AI entirely dependent on US systems and
US government goodwill
- China has used European AI dependency to extract political
concessions on trade and investment issues
Healthcare:
- European patients receive frontier AI-powered diagnostics
12-18 months after US patients
- European clinical AI research hampered by data sovereignty
conflicts with US platform providers
- European healthcare data processed on US servers, creating
GDPR compliance challenges and security risks
Cultural:
- European languages underrepresented in frontier AI systems
- European values and perspectives systematically underweighted
in AI outputs trained primarily on US data
- European citizens interact with AI systems that reflect
American cultural assumptions and biases
This failure scenario is not inevitable. But it is the trajectory that current trends point toward if Europe does not make a decisive strategic choice to invest in AI sovereignty. The window for making that choice is still open, but it is closing.
CHAPTER TEN: THE GOVERNANCE AND REGULATORY DIMENSION
No discussion of European AI sovereignty would be complete without addressing the regulatory dimension, because regulation is both one of Europe's greatest assets and one of its greatest risks in the AI domain.
The EU AI Act is a landmark achievement. It is the world's first comprehensive legal framework for AI, and it has already begun to shape global industry practices in the way that GDPR shaped global data protection practices. The Act's risk-based approach, its requirements for transparency and human oversight of high-risk AI systems, and its governance framework for general-purpose AI models are all important contributions to responsible AI development. The governance rules and obligations for general-purpose AI models, including LLMs, became applicable on August 2, 2025, and the Act's prohibited AI practices and AI literacy obligations have been in effect since February 2, 2025.
But the AI Act also carries risks for European AI competitiveness. The Act classifies most AI medical devices as high-risk and imposes strict requirements. This creates a situation where AI-powered medical tools face a more demanding and time-consuming regulatory pathway in Europe than in the United States, potentially delaying European patients' access to beneficial AI-powered healthcare and discouraging medical AI developers from prioritizing the European market. More broadly, the compliance burden of the AI Act falls disproportionately on smaller European AI companies, which may lack the legal and compliance resources of large US tech companies. If the AI Act makes it significantly more expensive and time-consuming to develop and deploy AI in Europe than in the United States, it could accelerate the talent and capital drain from Europe to the US.
The solution is not to weaken the AI Act. The Act's core principles of trustworthiness, transparency, and human oversight are genuinely important and represent a competitive advantage for European AI in markets where trust matters. The solution is to implement the Act in a way that supports European AI development rather than hindering it. This means providing clear, practical guidance to AI developers about what compliance requires. It means creating fast-track compliance pathways for European AI companies that demonstrate good-faith efforts to meet the Act's requirements. It means ensuring that the regulatory burden is proportionate to the actual risks of specific AI applications. And it means investing in the regulatory infrastructure, including the European AI Safety Institute and national competent authorities, needed to implement the Act effectively and consistently across the EU.
The EU's open-source strategy is another important governance dimension. The EU Open Source Strategy promotes interoperable digital ecosystems, reduces vendor lock-in, and encourages the adoption of open-source solutions across public and private sectors. The open-source-first approach for public procurement of cloud and AI software is particularly important. If European governments systematically prefer open-source AI solutions, they create a reliable market for European open-source AI developers, reduce their own dependency on foreign proprietary systems, and contribute to the commons of open-source AI capability that benefits the entire European ecosystem.
Data governance is a third critical dimension. European AI models need to be trained on high-quality, diverse, multilingual data. But European data protection rules, while important for privacy, can create obstacles to assembling the large training datasets that frontier model development requires. Europe needs to develop data governance frameworks that protect individual privacy while enabling the aggregation of data at the scale needed for AI training. Federated learning, which allows AI models to be trained on distributed data without the data leaving its original location, is one promising approach. Privacy-preserving synthetic data generation is another. The EU's data spaces initiative, which aims to create sector-specific data ecosystems where data can be shared under clear governance rules, is an important step in this direction.
CHAPTER ELEVEN: FINANCING THE STRATEGY
The strategy described in this article is ambitious and expensive. It is worth being explicit about the scale of investment required and where it should come from.
The EU's InvestAI initiative, announced in February 2025, pledged 200 billion euros for AI development, including 50 billion euros in public funds and 150 billion euros from private sector commitments. The EU AI Champions Initiative has a 150 billion euro investment plan. The Digital Europe Programme allocated over 1 billion euros between 2021 and 2024 to support AI development and deployment, and the AI Innovation Package announced in April 2024 reinforces this with up to 4 billion euros for 2024-2027. Horizon Europe allocated 2.6 billion euros to AI research and development for 2021-2022. These are significant numbers, but they need to be understood in the context of what the US is spending. US tech firms spent over 400 billion dollars in 2025 alone on AI infrastructure. American hyperscalers spent more than 200 billion dollars on AI data centers in the first quarter of 2026 alone. The EU's total AI investment plans for the entire decade are smaller than what the US spends on AI infrastructure in a single quarter.
This comparison is not meant to suggest that Europe needs to match US spending dollar for dollar. Europe can be smarter about how it spends its resources, focusing on areas where it has genuine advantages and avoiding duplication. But the gap in investment is so large that closing it requires a fundamental change in the scale of European ambition.
The financing strategy should draw on multiple sources. Public funding from the EU budget and member state governments should provide the foundation, particularly for infrastructure investments like AI Gigafactories and supercomputing facilities that have long payback periods and high strategic value but may not attract private investment at the scale needed. The European Investment Bank should play a major role in financing AI infrastructure, using its ability to borrow at favorable rates to leverage private investment. European pension funds and sovereign wealth funds, which manage trillions of euros in assets, should be encouraged and enabled to invest in European AI companies and infrastructure. The EU's Capital Markets Union initiative, which aims to deepen and integrate European capital markets, is directly relevant here: a more integrated European capital market would make it easier for European AI companies to raise the large amounts of capital they need to compete at the frontier.
Tax policy is another important financing lever. R&D tax credits for AI development, accelerated depreciation for AI infrastructure investment, and favorable tax treatment for employee stock options in AI startups can all make Europe a more attractive location for AI investment without requiring direct public expenditure. Several EU member states have already introduced such measures, but they need to be harmonized and scaled up across the EU to create a genuinely attractive investment environment.
Public procurement is perhaps the most underutilized financing lever available to European governments. European governments collectively spend trillions of euros on goods and services every year. If even a small fraction of this procurement is directed toward European AI products and services, it creates a massive and reliable market that can sustain European AI companies through the difficult early stages of development. The EU's open-source-first procurement policy is a step in this direction, but it needs to be implemented consistently and ambitiously across all EU institutions and member state governments.
CHAPTER TWELVE: THE CULTURAL AND EDUCATIONAL DIMENSION
Technology strategies fail when they focus exclusively on hardware, software, and investment while neglecting the human dimension. Europe's AI sovereignty strategy will ultimately succeed or fail based on whether Europe can produce, attract, and retain the talent needed to execute it.
The talent challenge is real and multifaceted. Despite AI talent in the EU more than doubling between 2016 and 2023, representing 0.41 percent of the workforce, the global competition for talent is fierce. The AI talent shortage in Europe is considered an execution risk in 2026, with demand for AI engineers, data scientists, and MLOps specialists growing faster than the credible supply. Competition from US firms offering global compensation and remote roles exacerbates the talent drain from Europe.
The EU's AI Skills Academy, planned for launch in 2026, is an important initiative. But talent development in AI is not just about training more engineers. It requires a broader cultural shift in how Europe thinks about technology, entrepreneurship, and risk. European universities produce excellent computer scientists and mathematicians, but too many of them end up in academic careers or in large established companies rather than in the startups and research labs where frontier AI is being developed. European venture capital is too fragmented and too risk-averse to provide the kind of patient, large-scale funding that AI startups need. European regulatory culture, while important for protecting citizens, can make it difficult for startups to experiment and iterate rapidly.
Addressing these cultural challenges requires changes at multiple levels. At the educational level, Europe needs to dramatically expand AI education at all levels, from primary school through postgraduate research. AI literacy should be a core component of the school curriculum, not an optional extra. Universities should expand their AI research programs and create stronger links with industry. Doctoral programs in AI should be funded at levels that make European PhD positions competitive with US offers.
At the entrepreneurial level, Europe needs to create a more supportive environment for AI startups. This means not just funding but also mentorship, access to compute resources, connections to potential customers, and a regulatory environment that allows experimentation. The EU's AI Factories should include startup incubation programs that give early-stage AI companies access to the compute resources they need to develop and test their models. The UK's Sovereign AI Fund, which gives backed companies access to the UK's AI Research Resource supercomputer network, is a model worth emulating at EU scale.
At the cultural level, Europe needs to celebrate and reward AI entrepreneurship and research success in the way that it celebrates other forms of achievement. When Mistral AI produces a model that competes with GPT-4, this should be treated as a national and European achievement worthy of the same recognition as a sporting victory or a scientific Nobel Prize. Building a culture that values and rewards technological innovation is a long-term project, but it is essential for sustaining the human capital that European AI sovereignty requires.
The recent trend of tech professionals relocating from the US to Europe, particularly to France, Switzerland, and the Netherlands, driven by American protectionism and European resolve, is an opportunity that should be actively cultivated. Europe should make it as easy as possible for talented AI researchers from anywhere in the world to settle and work in Europe. This means streamlined visa processes, recognition of foreign qualifications, attractive research environments, and a quality of life that competes with Silicon Valley on dimensions that matter to researchers: intellectual freedom, work-life balance, cultural richness, and social security.
CONCLUSION: THE CHOICE EUROPE MUST MAKE
The morning that European hospital CTO stared at a blank screen, the morning that Anthropic's Fable 5 went dark for every non-American user, was a clarifying moment. It demonstrated, with brutal clarity, that AI sovereignty is not a theoretical concern for policy seminars. It is a practical operational requirement for every European organization that has integrated AI into its critical functions.
Europe has the assets to build genuine AI sovereignty. It has world-class research institutions and researchers. It has ASML's irreplaceable EUV monopoly. It has Mistral AI's technical excellence and open-source commitment. It has Aleph Alpha's enterprise AI platform. It has EuroHPC's growing supercomputing infrastructure. It has the EU AI Act's regulatory framework. It has the linguistic and cultural diversity that, properly harnessed, becomes an asset rather than a liability. It has natural allies in Canada, Japan, Australia, India, South Korea, and the UK who face similar challenges and have complementary strengths. And it has the economic weight, the political institutions, and the democratic legitimacy to make the sustained, coordinated investments that AI sovereignty requires.
What Europe has lacked, until very recently, is the urgency. The Anthropic incident has provided that urgency in a way that no policy paper or academic study could. The question now is whether European leaders will respond to that urgency with the scale and speed that the situation demands, or whether they will produce another round of well-intentioned but inadequately resourced initiatives that leave the fundamental dependency intact.
The short-term strategy, running from 2026 to 2028, must focus on stabilizing the most critical dependencies, accelerating open-weight model development, building a European AI compute reserve, and establishing the governance frameworks for a coordinated European response. The mid-term strategy, running from 2028 to 2032, must build genuine technical capabilities in model development, compute infrastructure, and semiconductor design that make European AI sovereignty durable. The long-term strategy, running from 2032 to 2040, must position Europe as a genuine global leader in trustworthy, multilingual, privacy-preserving AI, and as the anchor of a global coalition of democratic nations that provides a credible alternative to the US-China AI duopoly.
None of this is easy. All of it is expensive. Some of it will fail, because frontier technology development always involves failure. But the alternative, a Europe that remains structurally dependent on foreign AI systems that can be switched off by foreign governments, is not a stable equilibrium. It is a slow-motion surrender of European sovereignty, European competitiveness, and European values to powers whose interests will not always align with Europe's own.
The choice is clear. The time is now. And the cost of waiting is higher than the cost of acting.
Europe has built Airbus to compete with Boeing. It has built Galileo to compete with GPS. It has built Ariane to compete with American launch vehicles. It can build sovereign AI to compete with OpenAI, Anthropic, and their Chinese counterparts. The question is not whether Europe can do this. The question is whether Europe will decide to.
The blank screen on that Tuesday morning in June 2026 is waiting for an answer.
