\n ACT 2: France knows how to train talent. Why does it struggle to build power?\n <\/a>\n <\/p>\n <\/div>\n\n<\/div>\n\n\n\nACT 3: After Mistral: who will be sovereign in the new global AI order?<\/h2>\n\n\n\nIntroduction: moving beyond the binary definition of sovereignty<\/h3>\n\n\n\n For a long time, we thought about sovereignty in the categories of classical industry: whoever manufactures is in control, whoever buys is dependent.<\/p>\n\n\n\n
This logic still holds for many critical technologies. Yet it falls short when applied to artificial intelligence. A model can be designed in one country, trained in another, hosted somewhere else, adapted locally, combined with other models, connected to proprietary data and replaced within a few weeks. Its weights may be open while its compute infrastructure remains foreign; its data may be sovereign while its architecture is not; its deployment may be local while most of the economic value flows back to an outside provider. In such a system, asking whether an AI is \u201csovereign\u201d or \u201cnon-sovereign\u201d is almost too simple a question.<\/p>\n\n\n\n
Academic literature on digital sovereignty had already identified this difficulty. Daniel M\u00fcgge shows that AI sovereignty covers different, sometimes contradictory goals, ranging from industrial autonomy to security, political control and the protection of citizens. Other work on European digital sovereignty also stresses that it has no single definition and must be understood through the concrete dependencies between infrastructure, markets, technologies and institutions (M\u00fcgge, 2024; Adler-Nissen & Eggeling, 2024; Blancato, 2024).<\/p>\n\n\n\n
The recent evolution of European doctrine is telling in this respect. In 2026, the Commission\u2019s work on digital sovereignty places less emphasis on reproducing everything in Europe than on the ability to reduce critical dependencies while remaining open and connected. In particular, it highlights interoperability, openness, resilience and the ability to regain control when a dependency becomes problematic (European Commission, 2026).<\/p>\n\n\n\n
The Mistral case shows why this change of framework has become necessary. A Chinese model can be hosted on European infrastructure, used with French data, integrated into a local company\u2019s systems and still remain the product of an intelligence designed and trained elsewhere. Depending on the criterion chosen, this situation can therefore appear both sovereign and dependent. It is precisely this apparent contradiction that we need to move beyond.<\/p>\n\n\n\n
Perhaps AI sovereignty should no longer be thought of as a state one either possesses or loses, but as a set of capabilities one masters to a greater or lesser degree.<\/strong><\/p>\n\n\n\nThe decisive question then becomes far more demanding: no longer \u201cis this AI French, European, American or Chinese?\u201d, but along which dimensions are we truly able to choose, understand, control, replace and create?<\/strong><\/p>\n\n\n\nIt is from this question that we can propose a different map of sovereignty in artificial intelligence.<\/p>\n\n\n\n
I. The six sovereignties of artificial intelligence<\/h3>\n\n\n\n Recent work on digital sovereignty converges on one point: technological autonomy can no longer be reduced to national ownership of an infrastructure or a company. Daniel M\u00fcgge shows that AI sovereignty covers several goals that can come into tension, while Huw Roberts stresses the need to distinguish technological control from the political capacity to steer systems toward collective ends. The European Joint Research Centre also proposes a multidimensional approach linking governance, infrastructure, data, markets and human capabilities (M\u00fcgge, 2024; Roberts, 2024; Di Marco et al., 2025).<\/p>\n\n\n\n
Building on this work, while taking into account the specific features of generative models, I propose distinguishing six sovereignties of artificial intelligence<\/strong>. They are not six independent states, but six dimensions along which a country, a company or an institution can enjoy very different degrees of autonomy.<\/p>\n\n\n\nThe first is cognitive sovereignty<\/strong>. It concerns the ability to design, train and evolve the models themselves. Who has the scientific skills needed to create the intelligence we use? Who understands its architecture and inner workings well enough not to depend entirely on decisions made elsewhere? This is the dimension closest to our traditional conception of technological sovereignty, but it is only one part of it.<\/p>\n\n\n\nThe second is data sovereignty<\/strong>. Models only have applied value when they meet data. We therefore need to know who owns that data, where it is stored, under which law it is processed, who can reuse it and whether it is used to improve an external system. A company can perfectly well use an American or Chinese model while retaining very strong control over its data; conversely, it can use a European model while giving up a significant share of that control through its cloud architecture or contractual terms.<\/p>\n\n\n\nThe third is compute sovereignty<\/strong>. It is the most physical and, paradoxically, one of the least visible to the end user. It depends on access to GPUs, data centers, the cloud, networks, semiconductors and now the energy needed to power this infrastructure. The European Commission places compute, data and infrastructure among the central pillars of its current technological sovereignty policy, thereby acknowledging that digital autonomy also rests on industrial and physical resources (European Commission, 2026).<\/p>\n\n\n\nThe fourth is operational sovereignty<\/strong>. It raises a very concrete question: what can I actually do without asking my provider for permission? Can I install the model on my own infrastructure, modify it, audit it, change its rules, stop using it or replace it with another one without rebuilding my entire system? This dimension introduces an essential distinction between ownership and control. One may not have created a technology and still retain considerable autonomy in how it is used; conversely, one can use a European technology while becoming heavily captive to its provider.<\/p>\n\n\n\nThe fifth is normative and cultural sovereignty<\/strong>. It concerns the languages, values, categories, representations of the world, prohibitions and trade-offs built into models. Work on digital sovereignty still gives this dimension relatively limited attention, even though it becomes central once AI systems no longer merely process data but take part in producing texts, knowledge, advice and decisions. Being sovereign then also means being able to understand the norms carried by a model, to debate them and, if necessary, to change them.<\/p>\n\n\n\nFinally, the sixth is economic and industrial sovereignty<\/strong>. It perhaps asks the simplest question: where does the value stay? Where are the jobs, intellectual property, revenues, infrastructure, investments and industrial capabilities created by AI? A country can be legally very protective of its data while transferring most of the economic value to a handful of foreign platforms. Conversely, it can integrate technologies from elsewhere while building around them companies, skills and markets capable of retaining a significant share of that value.<\/p>\n\n\n\nThis framework profoundly changes how we read the Mistral case.<\/p>\n\n\n\n
By offering GLM-5.2 on European infrastructure, Mistral can strengthen its customers\u2019 data sovereignty, their operational sovereignty and, if its infrastructure genuinely develops in Europe, part of their compute sovereignty. It can also contribute to a form of economic sovereignty by keeping integration, hosting and part of the value created around the model in Europe.<\/p>\n\n\n\n
At the same time, using a model designed by Z.ai obviously does not strengthen European cognitive sovereignty over that particular model. Nor does it guarantee normative and cultural sovereignty, since the fundamental choices made during its training and initial alignment were made elsewhere.<\/p>\n\n\n\n
The same technology can therefore increase some sovereignties while reducing others.<\/p>\n\n\n\n
This is what makes binary judgments largely ineffective today. Saying that a system is \u201csovereign\u201d because it is hosted in Europe is not enough. Saying that it is not because its model was developed in China or the United States is equally insufficient.<\/p>\n\n\n\n
The real question becomes: along which dimensions do we want to be sovereign, to what degree, and for which uses?<\/strong><\/p>\n\n\n\nA hospital, a bank, a defense agency, an industrial SME or a university will not need the same level of autonomy on each of these six dimensions. Sovereignty then ceases to be a national slogan and becomes a strategic choice that can be assessed, weighed and, above all, measured.<\/p>\n\n\n\n
This approach also makes it possible to escape a false dilemma that often poisons the European debate. We do not necessarily have to choose between producing everything ourselves and buying everything from others. We must determine what we cannot afford to stop mastering<\/strong>.<\/p>\n\n\n\n
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