Europe and AI: Reducing Dependence for Greater Sovereignty

AI: A Sovereignty Issue for Europe

Artificial intelligence (AI) is no longer just a technological tool but a central element of national power. Governments hope it will help them tackle complex challenges, stimulate economic value creation, and modernize public services. However, this promise comes with an increasing risk of dependency on the major powers that dominate AI development, primarily the United States and China. The crucial question is how other nations can regain control over technologies they do not fully master.

The Paradox of Technological Dependency

AI is profoundly redefining corporate competitiveness and state power. Those who master this technology dictate the rules, while others must comply. This is where the paradox lies: the countries that most need AI to modernize their economies are often the ones that depend the most on technologies they do not control.

For governments, the stakes are twofold: they must capture the economic benefits of AI while avoiding having these benefits be captive to foreign suppliers. This is what is referred to as AI sovereignty, or the ability to independently decide how these systems are used, regulated, and developed within their territory.

In the face of this challenge, strategies vary. Some countries aim for total self-sufficiency, while others, more pragmatic, opt for targeted strategic partnerships to fill their gaps without starting from scratch. The European Union has chosen a third path: that of regulation. The AI Act, which came into effect in August 2024, establishes a risk-based legal framework designed to build trust without stifling innovation. This ambitious bet is not without tensions, as the compliance obligations it imposes, accompanied by penalties that can reach millions of euros, fuel a lively debate on the balance between protection and competitiveness.

The Pillars of Technological Autonomy

AI sovereignty rests on a concrete freedom: the freedom to choose. To choose one's tools, to maintain control over one's data, and not to be locked into a single supplier's ecosystem. This autonomy is not decreed; it is built on three essential pillars.

Accessible Infrastructure

Public strategies often focus on high-performance data centers intended for training large models but sometimes overlook the infrastructure necessary for large-scale deployment. Distributed edge networks, located close to users, are essential for deploying AI applications with low latency and high availability. Numerous use cases, whether in healthcare, logistics, or public services, require this distributed computing power to function effectively.

Economic Inclusivity

The transformation related to AI must benefit the entire economy, from SMEs to research institutions. Usage-based business models (pay-per-use) are crucial: they eliminate heavy initial investments and lower barriers to entry. In France, several initiatives illustrate this ambition. The France 2030 plan allocates 500 million euros to AI development, a significant portion of which is aimed at accelerating adoption by SMEs and mid-sized enterprises. In this context, Bpifrance is launching the "Osez l'IA" program with a budget of 25 million euros, combining data-AI diagnostics, training, and on-the-ground support to help these companies identify concrete use cases.

Data Control

Data control involves their localization within national territory and the ability to finely control access, ensure transparency in processing, and integrate compliance requirements. Global and distributed architectures today allow for the implementation of these rules where data is actually processed.

Europe Builds Its Own Ecosystem

To ensure genuine strategic autonomy, open standards across the entire AI technology stack are becoming essential. Open protocols and interfaces promote interoperability, reduce dependencies, and enhance competition. Conversely, a development exclusively based on closed systems creates a risk of technological lock-in that can ultimately cost much more than initial investments. Independent platforms that allow for connecting, monitoring, and controlling different AI models from a unified interface support a truly competitive market.

It is in this logic that the European Union is investing heavily in its own large language models (LLMs), particularly in European languages. For a long time, AI models primarily trained on data in English or Chinese have dominated the market, disadvantaging regions with limited access to these languages. In response, consortiums comprising research institutes and companies are now developing European families of multilingual LLMs, built on corpora that reflect the cultural and linguistic characteristics of different EU regions, often under open licenses.

The AI Act plays an unexpected role as an accelerator here. It imposes strict transparency and risk management requirements on very large foundational models. As a result, smaller, specialized models that comply with the European framework become more attractive, particularly in public administration, healthcare, or research. It is precisely in this space that French initiatives make the most sense. AMI Labs, the startup founded in Paris by Turing Award winner Yann LeCun, is the most recent illustration: with nearly 890 million euros raised in March 2026, it bets on a radically different open architecture from the dominant LLMs. The goal is explicit: to offer an alternative that is neither American nor Chinese, rooted in European fundamental research. Mistral AI, another French champion, embodies this same ambition with its open-source models that have become a global reference. These players provide credible alternatives to proprietary platforms located outside Europe.

AI sovereignty is not a defensive end; it is an offensive lever for innovation and growth. The key challenge will be to establish dynamic, open, and competitive AI markets that guarantee freedom of choice, reduce dependencies, and open significant opportunities for innovation, particularly for European economies. Regulation, funding programs, and a growing ecosystem of open and commercial LLMs constitute the pillars of this ambition. It is not about retreating into oneself but about building structural conditions that allow Europe to participate in the global AI competition on its own terms.