Strategic autonomy has become a central organizing concept in debates about the European Union's future geopolitical orientation, yet the concrete policy areas through which it should be pursued remain contested. Frontier technologies, and artificial intelligence in particular, constitute a critical test case because they underpin both economic competitiveness and national security capabilities. Specifically, AI is the transformative technology that is catching the attention of policymakers around the world since the release of ChatGPT in late 2022. The global contest for leadership in artificial intelligence is currently led by the United States and China, with a small number of technologically advanced middle powers occupying niche positions. By contrast, the European Union combines regulatory influence and strengths in specific segments of the AI value chain with a structural lag in frontier model development, fragmented investment, and a predominant focus on governance rather than capability-building. Still, it has some structural strengths, and in some cases even crucial bargaining power. Given these dynamics, Europe must adopt a coherent and well-defined strategic posture to avoid being constrained by external competitors.

Since 2018, the European Commission has launched the EU AI strategy to encompass all future policies concerning developing technologies. However, it wasn't until 2025 that the EU began discussing competitiveness and sovereignty with the publication of the AI Continent Plan, which outlined the bloc's geopolitical goals. Only in October 2025, with the Apply AI strategy, did the EU directly mention “external dependencies of the AI stack.”. ¹ This is at the heart of the current issue regarding the global direction of AI development and its implications for Europe. If Europe relies on external actors for a transformative technology like AI, the consequences would be felt across all political spheres: from the economy to national security. On the security front, for example, Europe would risk becoming dependent on foreign powers for critical communication and data analysis systems. Furthermore, if advanced AI revolutionizes industry and Europe becomes dependent on foreign models, the competitiveness of Europe’s industry would be jeopardized. This is especially true given that the EU is experiencing a productivity crisis relative to other major economies, and manufacturing accounts for roughly 20% of EU GDP and up to 80% of its exports. .²

Europe is often portrayed as a passive actor subject to technological breakthroughs in the US and China regarding AI. However, what is less known, though increasingly acknowledged, is that the EU is an unmatched power, even a monopolist, in some crucial areas of the AI development cycle.³ The most critical factor is ASML’s monopoly on EUV lithography machines. EUV machines are the first and most fundamental step in training cutting-edge AI models. These machines use extreme ultraviolet light to etch ultra-tiny circuits onto silicon wafers, enabling the production of state-of-the-art, super-powerful chips used in AI systems. Without EUV machines, AI labs cannot build cutting-edge AI models, since only the most advanced chips can sustain the enormous computing power needed to train such models. Europe also has a strong educational system that contributes to a steady stream of AI experts. Europe has 30% more AI researchers per capita compared to the US, and its university network is consistently ranked among the best in the world, including for AI education.⁴ Lastly, the EU remains the world's largest trading bloc, granting it significant market power. This makes it an indispensable market for foreign AI companies seeking to monetize their substantial investments in AI models. This bargaining power enables the EU to push through regulations and impose conditions on foreign firms, such as requiring higher degrees of localized data centers and investments. This is known as the famous Brussels effect, which has already proven effective with previous digital policy legislation, like the GDPR, which set a global trend for data privacy management, among other examples. Similar effects could be achieved through the full implementation of the EU AI Act, which, in turn, could indirectly expand Europe's influence on external competitors.

Despite specific areas of strength, the overall structural outlook remains challenging. Capital, computing power, and talent are still fragmented across member states and regions, which in turn increases the cost of any AI investment due to a lack of necessary economies of scale.⁵ If individual countries go it alone in funding their AI programs, they risk duplicating efforts and excluding smaller nations from the race, which in turn will turn to foreign off-the-shelf products to meet their needs. Fragmentation in capital markets is the main reason why European startups often relocate, cease operations, or are acquired by U.S. firms or funds. This, in turn, is directly linked to the second structural weakness: the brain drain. At present, the EU is a net exporter of top-tier AI talent; these professionals are the ones most likely to develop innovative products with direct economic benefits and business creation, but they often require large-scale, risky investments—conditions that are much better met in the US. At the raw resource level, the EU is highly dependent and uncompetitive in terms of computing power availability and energy costs. This computing power is a fundamental metric required to develop advanced AI models. Simply put, the more computational power is available, the more data can be used for training during the development phase. This metric directly depends on the number of advanced AI chips available, and these require massive upfront investments, as well as ongoing ones, since they tend to become obsolete quickly. Current estimates suggest that the European Union accounts for approximately 51% of global advanced computing capacity, while policy targets aim to increase this share to around 16%, roughly in line with the EU’s contribution to global GDP. At the same time, industrial electricity prices in Europe remain significantly higher than those in the United States and China—often up to three times higher—raising the operational costs of data center infrastructures that underpin large-scale AI training. These cost differentials weaken Europe’s attractiveness as a location for energy-intensive AI investment and exacerbate existing scale disadvantages.

European AI sovereignty should not be achieved solely by catching up to competitors. Instead, the EU should target its interventions in strategic sectors and secure critical infrastructures. This entails sectoral policies in domains like defense technology and critical infrastructure, while accepting some level of dependence in less sensitive areas such as consumer applications. The Defense Artificial Intelligence Data Space (DAIDS) exemplifies what such an approach could enable in the military sphere. DAIDS is a proposed EU-wide, federated framework for the trusted and sovereign sharing of defense-related data, aiming to overcome current national fragmentation in command, control, communications, intelligence, surveillance, target acquisition, and reconnaissance (C4ISTAR). It would enhance interoperability among AI-enabled data-sharing platforms used by European armed forces, allowing sensors and communication systems to operate across borders and services. It would establish common technical and governance standards, and align with the EU AI Act and the EU Data Act. Consequently, DAIDS would render the data backbone of European military communications both interoperable and sovereign.⁶ Furthermore, DAIDS aims to connect with EU industrial policy tools such as the European Defence Industry Programme (EDIP) and Security Action for Europe (SAFE) to create financial incentives for Member States to adopt the framework, for example through preferential loan terms or grant conditions for DAIDS-compliant C4ISTAR projects. Such a sector-specific intervention does not require the Europeanization of every layer of defense hardware; instead, it prioritizes the digital infrastructure that structures how information is collected, processed, and disseminated across national armed forces. DAIDS therefore exemplifies an open sovereignty approach, in which the European Union seeks to retain control over a critical node in its security architecture, defense data exchange, while preserving openness to a diversity of technological solutions at other layers of the system.

Europe is right to try and enter the AI race; it is now developing the strategy it wants to choose to position itself among the global players. Nevertheless, it suffers key comparative disadvantages, from fragmentation to high energy costs, which will probably not go away in the short term. It is thus necessary to target policy intervention to secure critical infrastructures while remaining open to the rest of the world’s AI innovation and continuing to nurture homegrown capacities. Europe could even use its monopoly position in key parts of the AI cycle as bargaining chips to ensure its technological independence, fostering European “indispensability”. If a project like DAIDS were to be proven effective, it could work as a blueprint for other targeted actions in different areas, including the economy. Such open sovereignty approaches are flexible tools that adapt well to the current unpredictable times and needs of the EU.