Mistral AI Emerges as Europe's Sovereign AI Champion With Ambitious 1-Gigawatt Infrastructure Plan

Mistral Is in the Right Place at the Right Time

Mistral is having a moment. With access to less funding and fewer compute resources than OpenAI and Anthropic, the French AI lab has lagged behind its American rivals in model performance. But recent turmoil stateside has created a window of opportunity. In June, the Trump administration placed restrictions on the distribution of models from Anthropic and OpenAI, giving Europe a glimpse of an unwelcome future in which its access to bleeding-edge AI could be suddenly revoked. A few weeks later, one of OpenAI's models broke loose from a testing sandbox and hacked multiple companies; Anthropic then revealed that its models had engaged in similar behavior. The incidents revived a long-running debate over safety risks tied to proprietary, closed-weight models, whose inner-workings are a closely guarded secret. Mistral frames itself as the antidote: a Europe-based alternative to the American labs, whose models -- most of which are published under an open source license for anybody to use -- cannot escape scrutiny or be switched off unilaterally. "If you don't end up in a situation where most people are building open source, you're giving way too much power to companies that are going to become state-like -- that will behave in a very aggressive way to make sure that nobody can compete," Mistral CEO Arthur Mensch told a packed room at an AI conference in Paris last month. "The alternative to open source winning is actually a pretty dark world." Mensch's argument is self-serving, but effective. Last September, Mistral raised almost $2 billion at a $13.5 billion valuation; it's reportedly teeing up another raise that will bump that figure to $23 billion. The lab's revenue has reportedly increased twenty-fold in the last year, helped along by deals with the French government, Microsoft, HSBC, and others. "The continental strategy of the EU to become more technologically sovereign ... and the increased hostility of the US is a magic formula that all of a sudden puts Mistral -- whose performance has not been spectacular -- in a favorable position," says Andrea Renda, director of research at the Centre for European Policy Studies. Mistral has long believed the AI market would be too large to be controlled by any single country without causing geopolitical instability, Mensch says. "It's comparable to energy -- electricity," he told WIRED in an interview after the conference. "You want to make sure that you have security of supply, diverse ways of sourcing the technology, so that nobody can turn you off." That case has become easier to make since the US government, with the return of Donald Trump to the White House, began to demonstrate a willingness to leverage its domestic capabilities against trading partners. "More and more, AI is understood as a major vector of power," Mensch told WIRED. "The new administration makes everything a little more emotional." The recent surge in the adoption of open-weight models is part of that picture. One of few ways that European businesses can guarantee undisrupted access to AI, Mensch argues, is to run open-weight models on domestic infrastructure. "Everybody outside the US and China should participate in the open source ecosystem, because it takes leverage away," says Nicolas Granatino, founder of startup accelerator StemAI, who holds a stake in Mistral in a personal capacity. Until fairly recently, it was unclear how to monetize open-weight models effectively, according to Granatino. Unlike the leading American labs, locked in a race to superintelligence, Mistral has shifted its focus towards smaller, bespoke models for manufacturing, utilities, and financial services. It has also developed a cloud business through which customers can access its models, and a Palantir-style team of engineers who embed within client organizations. "At the moment, we see the emergence of a product that is making the open source commitment easier," says Granatino. "You can make money running the infrastructure" and help clients to customize models with their own data. Meanwhile, the American labs that charge a premium for access to their proprietary models are finding that their performance advantage is being continually eroded by distillation, the process of training a lesser AI model on the outputs of a more capable model. "That seems like it's always going to be difficult to stop," says Neil Lawrence, a professor of machine learning at the University of Cambridge. For companies whose business is structured around open source, like Mistral, distillation isn't so much of a problem, because anybody can access and build atop their open-weight models to begin with. Whether Mistral has arrived at this juncture through foresight, blind good fortune, or a combination of both, the stranglehold of the American labs is beginning to loosen as more businesses turn to open-weight models. Though gaps in publicly available data confuse the picture, the market share of open-weight models appears to be rising steeply, driven by rapid growth in the adoption of Chinese models, like DeepSeek, in particular. "We revealed to the world that you could actually build AI systems outside the control of US labs," Mensch says. "That is now changing the structure of the market itself."

Mistral AI wants to build 1 gigawatt of European compute by 2030 -- and lock in customers now.

Mistral AI wants to turn European AI sovereignty from a talking point into a product -- one with a service-level agreement attached. The French artificial intelligence company announced Tuesday a three-part expansion of its infrastructure business: regional inference endpoints that let customers choose whether their AI workloads run in Europe or the United States, a new "Priority Tier" backed by an uptime guarantee for mission-critical deployments, and a coalition of European enterprises making multi-year compute commitments that Mistral says will underwrite 200 megawatts of infrastructure across Europe by the end of 2027 -- and a full gigawatt by the end of 2030. In a move that may raise eyebrows among sovereignty purists, the company also said it will begin hosting third-party open models on its platform, starting with GLM-5.2 from Z.ai, the Chinese AI lab formerly known as Zhipu. Taken together, the announcements mark a decisive shift in how Mistral positions itself. The company that built its reputation training open-weight language models is now selling something closer to critical infrastructure: assured capacity, regional control, and contractual reliability for enterprises and governments that want frontier AI without surrendering control over where it runs. "When we spoke in June, the story was around how Mistral was building a full-stack AI offering," Timothée Lacroix, Mistral's co-founder and chief technology officer, told VentureBeat in an exclusive interview ahead of the announcement. "Today, the announcement is about strengthening one part of this infrastructure, which is the inference part." That one part, it turns out, comes with a price tag measured in the tens of billions of dollars. Inside Mistral's plan to build 1 gigawatt of European AI compute by 2030 The headline numbers deserve scrutiny, because they imply staggering capital requirements. Mistral currently operates less than 200 megawatts of capacity, according to the company. Details shared with VentureBeat show the near-term buildout resting on three sites: a 44-megawatt facility near Paris that became operational in the second quarter of this year, a 23-megawatt facility in Sweden built in partnership with EcoDataCenter using renewable energy and advanced cooling, and a 10-megawatt site in Les Ulis, France, that came online in the third quarter. Getting from there to one gigawatt by 2030 is a different order of magnitude. Independent estimates suggest just how different: research firm Epoch AI calculates that a typical one-gigawatt AI data center requires roughly $38 billion in upfront capital expenditure, with servers and GPUs -- not buildings or land -- consuming the majority of the cost. Goldman Sachs Research pegs next-generation AI facilities at $15 million to $20 million per megawatt before accounting for the chips inside them. Lacroix did not dispute the scale of the challenge. The investment required for a gigawatt of capacity "is a large investment that requires also a lot of scaling and revenue behind it," he said. The urgency, in his telling, comes from a supply crunch that is about to get worse. "More and more, and especially around 2027 and 2028, we see that the demand for AI compute is exceeding what the market has to offer, especially in Europe," Lacroix said. McKinsey has estimated that meeting global AI demand could require $5.2 trillion in data-center capital expenditure by 2030 -- and Europe, by most analyses, is starting from behind. A company valued at a fraction of its American rivals cannot close that gap with venture capital alone. Which explains the most consequential -- and most unusual -- piece of Tuesday's announcement. European Compute Units turn AI sovereignty into a five-year contract Mistral is assembling what it calls an anchor group of enterprises whose long-term commitments will collectively finance infrastructure none of them could justify alone. Those commitments convert into "European Compute Units," or ECUs -- a claim on Mistral-built capacity over multiple years that participants can spend on inference, training, model adaptation, or other AI workloads as their needs evolve. If that structure sounds more like a power-purchase agreement than a cloud contract, that appears to be the point. Data-center financing increasingly resembles large infrastructure projects -- gigawatts, substations, energy agreements -- rather than traditional technology spending, and lenders want demand locked in before capital gets deployed. Mistral raised €830 million ($962 million) in debt earlier this year to fund its data center near Paris, TechCrunch reported in March, and pre-committed enterprise demand is exactly what makes that kind of financing repeatable at ten times the scale. Lacroix was unusually direct about the mechanics. "The entire point of compute units is to have commitment," he said. "The goal is to have customers commit for around five years, or at least a long time." Asked what happens if a customer wants out early, he didn't soften the answer: "There is no getting out." What makes a five-year, no-exit commitment palatable, he argued, is flexibility in how the capacity gets consumed. "Typically this can be spent on raw inference that you then feed through any other AI stack. It can be spent on raw compute as managed Kubernetes, and it can be spent at the very top with our full AI offering," he said. "My hope is that they will use it with our full-stack services and will love it." The anchor group already includes some of Europe's industrial heavyweights. Amadeus CEO Luis Maroto said in a statement that "capacity, deployment control, and operating continuity become increasingly important for all enterprises." ASML chief Christophe Fouquet -- whose company led Mistral's $13.4 billion (€11.7 billion) Series C last year -- called building European AI capacity one of the few industrial endeavors that "will matter more to Europe's next generation," while Capgemini's Aiman Ezzat framed it as "a question of who shapes the future of European industry." CMA CGM chairman Rodolphe Saadé said the shipping group's Mistral deployment is "already under way among thousands of employees." Commitments of that duration only make sense, of course, if the sovereignty being purchased is real. On that question, Mistral's announcement contains an asterisk worth reading closely. The fine print on sovereign AI: what data can still leave Europe The centerpiece product is Mistral Regional Endpoints, now generally available, which let customers pin inference and its associated processing to Europe or the U.S. Alongside it, the new Priority Tier -- in public preview -- offers committed service levels, custom rate limits, and an uptime SLA for mission-critical workloads. Mistral claims it is the only European AI lab offering both a choice of processing region and an SLA-backed service tier, and Lacroix said a third option is coming: an endpoint "that stays on Mistral-controlled infrastructure, so on Mistral compute" -- for customers who want their inference not just in Europe, but off hyperscaler hardware entirely. Then comes the fine print. Mistral's own materials note that in-region inference remains subject to "limited, safeguarded transfers" to sub-processors that may sit outside the chosen region. Pressed on what actually leaves Europe, Lacroix pointed to the connective tissue of modern AI applications: tool calls. "There are some tool services, like some tool calls, that might be hosted in places where we don't fully control this," he said, citing web search as an example. "A few of our web-search providers might not all be in Europe, and in that case, we need to potentially gate that capability." His answer to the compliance question -- would this satisfy a European bank or a defense ministry? -- was that gating is the feature, not the bug. Capabilities that cannot be sourced in-region can be switched off entirely, restricted to certain users or workspaces, or, given sufficient demand, rebuilt with European providers. "Any capabilities that we don't find a provider for in Europe -- if it needs to be done in Europe, we'll find some way to implement it or find ways to address it," Lacroix said. For enterprise buyers, that is a more honest framing than most sovereignty marketing offers: full regional control is available, but the moment an AI agent reaches out to the open web, sovereignty becomes a configuration decision rather than a default. The same pragmatism runs through the announcement's most surprising line item. Why Europe's open source AI champion is hosting China's GLM-5.2 A French national champion -- one that has partnered with the French army and positioned itself as Europe's answer to American AI dependence -- hosting a Chinese lab's model invites an obvious question. Lacroix's answer was disarmingly matter-of-fact. "It's a great model. Everyone loves it. It's open weight, so there was no good reason for us not to do it, really," he said, noting that Mistral's own stack is already built on open-source software like Kubernetes. On security vetting, he argued that open weights fundamentally change the risk calculus. "The risks in taking a new model, at the layer of the weights, are -- at least in my opinion -- rather limited," Lacroix said. "We checked basically all of the safety and compliance evals that we have. We'll control that model, its outputs, and what it does the same way we do any of our models. We have the same inputs and outputs and monitoring capabilities over all of it." The strategic logic is worth unpacking. By hosting third-party open models under European regional controls and the same SLAs as its own, Mistral is repositioning itself from model vendor to sovereign distribution layer -- the trusted intermediary through which any open model, regardless of origin, can be consumed by a regulated European enterprise that could never call a Chinese API directly. It is the "model garden" playbook the hyperscalers run with Bedrock and Vertex, executed on European soil with European guarantees. Customers appear to be reading it that way. "Mistral allows us to run open models under strict regional controls and service commitments, making it easy for us to maintain data residency and compliance requirements," Matan Griberg, CEO of AI software-engineering company Factory, said in a statement. Lacroix stressed the move is not a retreat from frontier training: the model Mistral had in training as of June "is still training, and we're still very excited about it," he said. But openness to rivals' models signals where the company now believes its moat lies -- not in any single model, but in the infrastructure underneath all of them. Which makes its relationship with the world's most powerful infrastructure company all the more interesting. How the multibillion-dollar Microsoft deal funds Mistral's independence Hovering over every sovereignty claim is Mistral's deepening relationship with Microsoft. In July, the two companies announced a multibillion-dollar expansion of their partnership under which Microsoft will rent capacity from Mistral's European data centers to serve its own cloud and AI demand, while adding Mistral Medium 3.5 and OCR 4 to Microsoft Foundry, bringing Medium 3.5 to Copilot Studio, and enabling Mistral models on Azure Local for disconnected, customer-controlled environments. Mistral CEO Arthur Mensch told The Wall Street Journal at the time that two-thirds of Mistral's customers already work with Microsoft. How does a company selling independence from U.S. hyperscalers square taking one on as its largest tenant? Lacroix described Microsoft not as a patron but as an anchor customer that de-risks the buildout. "It allows us to scale different parts of the business differently by building infrastructure with Microsoft as a customer," he said. "We can scale that team, we can scale our infrastructure, and make sure that we can then, on the side of it, also build for ourselves and for our customers." He compared the arrangement to the neocloud playbook -- companies that built businesses supplying capacity to the hyperscalers themselves. "As that part of our business resembles that of neoclouds, we're following the same thing." It is a genuinely clever inversion: rather than renting American infrastructure, Mistral is renting infrastructure to one of America's largest companies, using Microsoft's demand to finance capacity that also serves European sovereignty customers. But the independence has limits no contract can engineer away -- the GPUs filling Mistral's European data centers come overwhelmingly from Nvidia and other American chipmakers, as SiliconANGLE noted in its coverage of the July deal. Asked directly why a customer should choose Mistral over an EU region on AWS or Azure, Lacroix gave two answers. "The simplest possible answer is capacity. There is more demand than supply right now, and so it adds another option," he said. The second cuts closer to the pitch: "We are a European provider, and on the region that would be Mistral compute, we are fully independent. That's a truly differentiated offering than all of the hyperscalers or pure inference companies can provide." The economics of open models: why agentic AI is pushing inference to the cloud There has always been a tension at the heart of Mistral's business: its best-known models are free to download, and open models have historically been difficult to monetize through APIs. Asked how free weights fund a gigawatt buildout, Lacroix offered the clearest articulation yet of the company's thesis -- that the economics of self-hosting are collapsing under the weight of the models themselves. "When the models were smaller, and we were before the explosion of agentic AI, it was doable for enterprises to host their own -- up to, let's say, 100-billion-parameter dense models -- on their premises," he said. "More and more, with models going into the trillion or more parameters, with the current hardware, and with the increasing amount of tokens that need to be processed, it becomes harder." His conclusion was blunt: "I don't see how, with the current trend of model size and growth of agentic tokens, we keep the full inference on-prem. To me, that is why we think we're going to monetize our cloud inference." Inference, he noted, is particularly well suited to the cloud because it "does not need to hold any data" and can be encrypted in transit. In other words: open weights get Mistral into the enterprise, and the physics of trillion-parameter agentic workloads brings the inference -- and the revenue -- back to Mistral's data centers. The thesis will get an expensive test. Mistral has raised roughly $4 billion to date, according to PitchBook data -- a fraction of the war chests assembled by OpenAI and Anthropic -- and Bloomberg reported in June that the company is in talks to raise about €3 billion at a roughly €20 billion valuation, nearly double its Series C mark. The revenue behind the buildout will have to come from exactly the enterprises Tuesday's announcement is courting. And Europe, in Mistral's telling, is only the first market for what it is selling. Asked whether the framework could be replicated in the Middle East, Asia, or anywhere else anxious about AI dependence, Lacroix didn't hedge: "It's completely right. We're starting this in Europe because it's also an easier part of the world for us to scale into, especially in the infrastructure. But we definitely want to extend this, depending on customer demand." Every layer of the stack, he said, "can be controlled, changed, replaced depending on where we operate and what the requirements are -- that's pretty much where we excel." That is the wager underneath the SLAs, the compute units, and the Chinese model flying a European flag: in a world where the U.S. and China dominate frontier AI, the durable business is selling everyone else control. To fund it, Mistral is asking Europe's largest enterprises to sign five-year contracts with no exit -- while making a bigger, longer commitment of its own. A gigawatt, after all, is a promise measured in decades. For Mistral, too, there is no getting out.

Europe's AI sovereignty is under threat. Could Mistral be the answer? | Fortune

Just one month later, a snap decision in Washington, D.C., proved Mensch's point for him. In June, the U.S. Commerce Department temporarily cut off foreign access to Mythos, a powerful AI model developed by Mistral's San Francisco-based rival Anthropic. European firms and governments had been scrambling for access to the Anthropic model because of its advanced cyber capabilities: Tests showed the model could not only find software vulnerabilities but also exploit them autonomously, running full attacks end-to-end. Those same capabilities let defenders spot flaws and build patches before adversaries gained access to AI as advanced as Mythos. Given the national security stakes, the prospect of foreign governments being able to suspend access to critical models at a whim started to feel like an unacceptable risk. Suddenly Mistral -- the front-runner among Europe's handful of frontier AI builders -- looked like Europe's most viable alternative. "It's been a validation of what we've been warning our customers about," says Mensch, a tall 34-year-old with slightly ruffled brown hair who is dressed in a simple white T-shirt when he appears via video link. "Make sure that you can actually own that technology ... so you know that you're not going to be turned off from one day to another." The big question is whether Mistral is actually ready to take up the mantle of European AI champion that so many desperately want it to seize. Despite its sovereign AI rhetoric, Mistral remains somewhat dependent on U.S. technology for chips and cloud-computing infrastructure. It recently announced an expanded strategic partnership with Microsoft. Perhaps more critically, Mistral's AI models also aren't currently competitive with the bleeding-edge American AI systems from Anthropic, OpenAI, or Google DeepMind, or even models from some Chinese AI powers. There are even questions over whether Mistral wants to be cast as Europe's sovereign AI savior. Despite raising the alarm about the continent's need for sovereign AI, Mensch is careful not to define Mistral as merely a regional champion, instead positioning the company as a global AI player. Around 40% of Mistral's revenue comes from the U.S. and other non-European clients. Skepticism about Mistral's ability to compete on the global stage has dogged it for years. Now, a new era of politics and a shift in large enterprises' approach to purchasing AI models may have given Mistral the perfect opportunity to prove its doubters wrong. Mensch grew up near Paris and is the son of a teacher and a software engineer. From an early age, it was clear he'd inherited his father's aptitude for math and science. "I guess the apple doesn't fall too far from the tree," Mensch says. He went on to attend the elite French engineering schools École Polytechnique and Télécom Paris before he was hired by Google DeepMind in his late twenties. Not for the last time in his career, Mensch's timing was impeccable. At 30, he quit his lucrative job to start Mistral with old college friend Timothée Lacroix and Meta alum Guillaume Lample. Mistral was formally founded in April 2023, just five months after ChatGPT's launch sparked the AI frenzy. At the time, Mistral was filling what many in the sector saw as a gap in the European AI ecosystem: the absence of a homegrown research lab capable of competing with the U.S. front-runners racing to build large language models. The three founders describe their motivations for founding the company similarly: a desire to "open up" and democratize the same closed-source technology they had been building for American labs. Mistral appeared off to a promising start. In 2023, a month after it was founded, it closed a €105 million ($120 million) seed round with backers including U.S. VC firm Lightspeed Venture Partners and former Google chief Eric Schmidt. Three months later, it released its debut model, Mistral 7B -- a freely downloadable model that beat Meta's Llama 2 13B on benchmark tests despite being about half the size. In December, Mistral capitalized on its early successes, closing a €385 million ($440 million) round that pushed its valuation past €2 billion ($2.3 billion). To date, Mistral has raised roughly $4 billion, according to PitchBook data, and is now worth roughly $23 billion. Anthropic, which is currently the world's most valuable AI startup, was recently valued at $965 billion, more than 40 times Mistral's size, while OpenAI sits at $852 billion, roughly 37 times. That funding chasm may partly explain why, by late 2025, Mistral seemed to have hit a slump. Notably, it didn't release a reasoning model -- a type of AI that works through a problem step by step before answering, and the year's biggest differentiator -- until June of that year. Even then, the model trailed one from China's DeepSeek. In December, Mistral pushed back with its Mistral 3 range of models. These include a flagship model, Large 3, and a set of smaller Ministral 3 models that are efficient enough to run on a single GPU. Mistral hopes that this efficiency can be a key differentiator with its bigger, costlier rivals. However, on some benchmarks, Large 3 performs close to levels OpenAI's models achieved a year and a half ago. Mistral argues that most real-world enterprise work doesn't require bleeding-edge capabilities and that raw benchmark scores undersell its models -- but on the hardest tasks, the gap with U.S. frontier models remains. Recently, Mistral has been sharpening its pitch to business customers, investing in specialized models that excel at practical enterprise tasks, such as processing audio and reading text from scanned documents and images. This better reflects how AI is used inside real companies, argues CTO Lacroix. Dressed in cargo shorts and a black T-shirt, he speaks from Mistral's offices in Paris's trendy Canal St.-Martin neighborhood during a record heat wave. The company's meeting rooms, named after video game characters, are buzzing with employees eager to enjoy the air-conditioning -- a rare luxury in Paris. "Everyone's dream is to build the best models... but we have a bit of a more breadth-first approach than depth first, which is typically what you see with the Chinese [companies]." Mistral is aiming to provide a service where enterprise workloads can run on smaller, specialized systems, while larger, more general models step in when tasks demand heavier reasoning or broader capabilities. That approach can reduce AI bills, making it easier for companies to predict and cap their AI spending rather than watching usage spiral. Over the past year, Mistral has grown its number of large enterprise customers to more than 100. It's certainly timely. Horror stories -- including one Claude enterprise client that racked up a $500 million bill -- have made executives skittish about allowing unlimited employee access to frontier AI. Meanwhile, the Anthropic saga has accelerated a shift toward model-agnostic workflows -- where companies can swap different models in and out as the "brain" of the system. Building vital workflows on a single model feels riskier when that model can be pulled offline. Analysts are generally optimistic about Mistral's prospects, but say catching the leading American and Chinese labs on model quality alone will be hard. "The leading AI labs have been accumulating these tricks -- even if the hardware was staying constant, they're doubling or tripling the speed at which they can train models just by algorithmic tweaks," says Stuart Russell, professor of computer science at UC Berkeley. While labs like Mistral are capable of catching up, he says, the real frontier advantage lies in the ability to move fast and test new ideas repeatedly, which requires access to a lot of computing power. Mistral also appears to be following Palantir's playbook -- winning through deep, hands-on delivery with key customers rather than the mass adoption Anthropic and OpenAI have pursued. London-based hiring tracker Zeki Data found a third of Mistral's 168 open roles are in enterprise go-to-market, a sign, it says, of Mistral leaning toward deeper integration with technical staff embedded in customer systems. Most customers are already on "very large strategic" multiyear deals, says Marjorie Janiewicz, chief revenue officer at Mistral. She argues that these contracts, which pair efficient models with long-term transformation projects, give the company healthier unit economics than labs chasing short-lived pilots. Mistral's revenue growth shows that strategy may be paying off. Its annualized revenue run rate exceeded $400 million in 2025, and, as of early 2026, was on track to surpass more than $1 billion in revenue by the end of the year. (Despite that traction, Mistral's sales pale in comparison to the $47 billion that is estimated to be Anthropic's current annual revenue run rate.) The company is also pushing into manufacturing. In May, it acquired Emmi AI, an Austrian startup specializing in physics-based AI for industrial engineering, which was previously valued at a reported €330 million ($377 million). The acquisition underpins deals with Airbus and BMW. Internally, the founders talk about a big bet on industrial engineering -- using AI agents and physics-aware models to redesign and simulate physical systems, from factory floors to turbines and aircraft wings. Mistral's expanding verticals also include defense, a contentious territory as critics, including the pope, warn that the use of AI in battlefield decisions presents unacceptable risks and blurs accountability. "Inherently, this is a dual-use technology, because it's a technology that allows us to process information, and warfare is all about processing information and taking the right decisions," Mensch says in response to the controversy. "We would love for the world to follow the pope's advice and be fully at peace, but it seems to not be the case." After Washington's two-week shutdown of foreign access to Anthropic's Mythos model, global governments have never been more receptive to Mistral's arguments for AI independence. Jordan Bardella, president of France's National Rally and a member of the European Parliament, called the Mythos shutdown a reminder that AI is "a major issue of national sovereignty" and pushed for France to fast-track support for Mistral. Other European politicians fretted about national infrastructure and defense, with one comparing the loss of Mythos to Iran's blockade of the Strait of Hormuz. To bolster this pitch, Mistral has spent hundreds of millions building physical infrastructure. A data center south of Paris and a second site in Sweden are currently in the works. The company also plans to rent out that capacity as a cloud provider in its own right. In June 2025, it unveiled Mistral Compute, an Nvidia-powered European cloud platform -- a deal French President Emmanuel Macron called "historic" when he announced it alongside Mensch and Nvidia CEO Jensen Huang at Paris-based tech conference VivaTech. "We wanted to diversify our supply chain -- we didn't want all our compute to come from one provider," Lacroix says. All of the new compute he describes still runs on Nvidia hardware, and building its own silicon isn't on the table anytime soon. "Today, Nvidia is a great option," he says. "But we're looking for other chipmakers that know the work." For now, Mistral is in the same position shared by nearly every AI lab outside of China -- controlling its models and data centers, but still forced to rent the underlying hardware from an American company. This is perhaps why Mistral is wary of the term "sovereignty," which, in its current form, Mensch says, is based on a "misunderstanding." "Sovereignty is not about being isolated, it's about having some decent weight on the value chain, and it's about being able to have compounding effects -- reinvesting [in] R&D, making sure that we grow on the technological level," he says. One odd side effect of the buzz is that Mistral has spent the past few weeks plagued by rumors about a giant cat. In mid-June, a fake Mistral model called "Le Chaton Fat" -- French for "fat kitten" and a joking reference to Mistral's former chatbot, Le Chat -- began circulating on social media, complete with an invented benchmark chart claiming it beat Anthropic's Fable 5. While the model itself was fake, the memes pointed to a wider public hope that Mistral could provide a real alternative to the American labs. "It tells their expectations," Mensch says of the fictional bot, "and we're working to meet those expectations very soon." Chief science officer Lample adds that Mistral has "a couple of new models" slated for release this summer. However, other Mistral executives are trying to tamp down Le Chaton Fat-level expectations. "There cannot be truth to the claim, because they are as fat as the cat," Lacroix says. "But we are building better large models, and we're very excited about what we're building." Over the past few years, every major AI lab's chief executive has had to become something of a geopolitical actor -- summoned to parliaments, G7 summits, and political debates, far removed from the product road maps that are the typical preoccupations of startup CEOs. If Mensch has joined their ranks, he's done so reluctantly, often pulled along by President Macron. In June, at the G7 summit in Évian-les-Bains, Mensch appeared seated next to then-U.K. Prime Minister Keir Starmer. Also in attendance were U.S. President Donald Trump and the CEOs of OpenAI, Anthropic, and Google DeepMind. When asked if he's political, Mensch says, "I see myself as a businessman." However, he understands the influence AI holds. "AI is really about power: Your power as a country, your power as a company, depends on your AI strategy, so we get drawn into discussions that involve power, and in that respect we do a little bit of politics." Mensch has a certain Gallic candor that is uncommon among many tech CEOs. Beyond the debate over AI sovereignty, he has waded into other politically charged arenas. He has lobbied to loosen EU AI Act compliance thresholds, which, he says, fall disproportionately on smaller companies and nonprofits; warned lawmakers that AI-driven job losses "could not be dismissed"; pointed out how excessive dependence on AI could erode human expertise; and, perhaps most controversially, proposed a revenue levy on AI companies to compensate Europe's creative industries. Some have criticized this plan as a way for AI companies to buy their way out of copyright liability for a fraction of what licensing would cost. Mistral itself has been accused by French publisher Nouveau Monde Éditions of training its models on pirated copies of its books. Mistral denies the allegations. "There's some tension between the AI space and the cultural space," Mensch says. "We're proposing a way to give some revenue to the cultural world, because inherently training models are about compressing knowledge, and they contribute to the world's knowledge." Mistral has also faced political pressure from EU lawmakers who criticized the company over its strategic partnership with Microsoft in 2024. The deal drew an EU antitrust review over fears a foreign tech giant had gained outsized sway over Europe's homegrown AI champion. In the company's early years, an advisory cofounder, Cédric O, a former Secretary of State in Macron's government, also handled much of Mistral's public affairs. His role drew scrutiny after he switched from supporting stricter tech regulation to lobbying against tougher EU AI Act provisions on Mistral's behalf. French transparency authority, HATVP, ultimately found no issue with O's transition from the public to private sphere. O has since stepped away from his formal advisory role, according to Mensch. "I see [Cédric] regularly. He's a good friend," he says. For Mensch, much of the anxiety around AI comes down to a single question: Who benefits? He sees the growing backlash against AI as less of a moral panic than a warning about the concentration of power. If only a handful of U.S. giants capture the spoils, he argues, resentment and instability will follow. "You want people to become prosperous on top of artificial intelligence," he says. "One of the contributions we're trying to make is to diffuse the technology so that it becomes more egalitarian." That mission is likely to appeal to European politicians now scrambling to chart an independent path through the AI boom. Mensch, for his part, doesn't seem especially rattled by the geopolitical weight suddenly resting on his shoulders. When asked what the chaotic week after the Mythos shutdown was like for him personally, he shrugs it off as "more hectic for our customers than for us." Whether Mensch will embrace his role as a European champion or successfully chase his American rivals to AI's bleeding edge is still an open question. But Mistral, which takes its name from a strong wind that blows from southern France, certainly has the wind at its back. This article appears in the August/September 2026: Europe issue of Fortune with the headline "Meet Europe's AI upstart taking on the U.S. tech titans."