How three French researchers built Europe’s most valuable AI company and challenged Silicon Valley’s dominance
Introduction: The Global AI Race and Europe’s Wake-Up Call
When OpenAI released ChatGPT on November 30, 2022, the generative AI era did not just begin. It erupted. Within two months, ChatGPT had attracted 100 million monthly active users, triggered a $13 billion investment from Microsoft, forced Google into an emergency product pivot, and sent a shockwave through every technology company on earth. The message was clear: large language models were no longer an academic curiosity. They were the most consequential commercial technology of the decade, and the race to lead that technology had already started.
For Europe, the implications were uncomfortable. The United States, home to OpenAI, Google DeepMind, Anthropic, and Meta’s AI research division, held an enormous structural advantage in AI research talent, computing infrastructure, and venture capital. China, with its own well-funded national AI ecosystem, was advancing rapidly. Europe, despite producing world-class AI researchers and having some of the most sophisticated technology regulation in the world, had no frontier AI lab of its own to point to.
Then, in the spring of 2023, three French researchers walked out of their jobs at Google DeepMind and Meta’s AI research division, flew to Paris, and founded Mistral AI. What followed was one of the most remarkable startup stories in European technology history: a company that grew from three people and no product to a $14 billion valuation in just over two years, all while championing an open-weight AI philosophy that challenged the closed, proprietary approach favored by its much larger American competitors.
1. The AI Race Before Mistral
How American Companies Dominated Early Generative AI
To understand why Mistral AI mattered, it is worth stepping back to examine the competitive landscape it entered. By early 2023, the generative AI market was being shaped almost entirely by three American organizations. OpenAI, backed by $13 billion from Microsoft and riding the viral success of ChatGPT, was the clear consumer leader. Google, which had pioneered transformer architecture and employed many of the researchers whose work underpinned the GPT model family, was scrambling to deploy its own AI products under the newly rebranded Google DeepMind umbrella. Anthropic, founded in 2021 by former OpenAI researchers Dario and Daniela Amodei, was building a reputation as the safety-conscious alternative, raising billions from Amazon and positioning its Claude assistant as a more reliable and less controversial choice for enterprises.
All three organizations operated with a broadly similar philosophy: train large, capable models, deploy them through tightly controlled APIs and consumer products, and keep the weights, training data, and internal architecture proprietary. This approach made commercial sense. The models were expensive to build and represented genuine competitive advantages. But it also meant that access to frontier AI was concentrated in a small number of American companies, with everyone else depending on their APIs and pricing policies.
Key Industry Milestones Before Mistral Launched
| Date | Milestone |
|---|---|
| November 2022 | OpenAI launches ChatGPT; reaches 1 million users in 5 days |
| January 2023 | Microsoft commits $10 billion to OpenAI |
| February 2023 | Google launches Bard, later rebranded as Gemini |
| March 2023 | OpenAI releases GPT-4 with multimodal capabilities |
| March 2023 | Anthropic releases Claude 1.0 |
| April 2023 | Mistral AI founded in Paris |
Europe had capable researchers, strong universities, and a tradition of foundational AI science. What it lacked was a company willing to build and deploy frontier models at the speed and scale the new era demanded. Arthur Mensch, Guillaume Lample, and Timothee Lacroix decided to be that company.
2. The Founding of Mistral AI
Three Researchers, One City, One Mission
Mistral AI was formally established in April 2023 in Paris, France. The three co-founders came from the elite tier of global AI research. Arthur Mensch, born in 1992, had studied at France’s prestigious Ecole Polytechnique and Universite Paris-Saclay before joining Google DeepMind, where he specialized in large-scale AI systems and contributed to research on efficient model architectures. Guillaume Lample and Timothee Lacroix, both 34 at the time of founding, had worked together at Meta’s AI research division, where they were key contributors to large-scale language model development. All three had originally met during their studies in France, and it was to France that they returned to build something new.
The company’s name was not chosen at random. Mistral refers to a powerful, cold wind that blows through southern France, originating from the Occitan language. The name reflects both the founders’ French identity and an implicit sense of purpose: a force that moves fast, cuts through what is in its path, and cannot easily be stopped.
Their mission was stated plainly: to bring frontier AI research out of the closed laboratories of a handful of American giants and make it accessible, open, and efficient. CEO Arthur Mensch would later describe this as a question of technological sovereignty. Europe, he argued, could not afford to depend indefinitely on American infrastructure for something as strategically important as artificial intelligence.
Why Paris?
The choice of Paris as headquarters was significant. France has a long tradition of state-supported scientific research and has invested heavily in AI education through institutions like the Ecole Polytechnique, INRIA, and ENS. The French government, under President Emmanuel Macron’s “AI for Humanity” initiative launched in 2018, had already committed to building Paris into a global AI hub. The presence of a strong local talent pool, favorable research tax credits, and proximity to European regulators in Brussels made Paris the natural home for a company that intended to be both a technical leader and a policy voice for European AI development. In 2024, CEO Mensch was appointed to a generative AI expert committee by the French Prime Minister’s office, and in May 2025, he was made a Knight of the National Order of Merit by the French government.
3. Early Growth and Funding
The Largest Seed Round in European History
Two months after founding, Mistral AI had no product and no revenue. It did have an elite founding team, a clear thesis, and an ability to execute. That combination proved more than enough for investors. In June 2023, just six weeks after incorporating, Mistral AI closed a seed round of approximately $113 million (105 million euros), led by Lightspeed Venture Partners. Other investors in the seed round included former Google CEO Eric Schmidt and French billionaire and technology investor Xavier Niel. The round valued Mistral at $260 million before it had shipped a single model. It was the largest seed round in European startup history.
A Valuation Sprint Without Parallel in Europe
The funding trajectory from that seed round to the present tells the story of how rapidly investor conviction in Mistral’s thesis grew. By December 2023, just six months after the seed round, Mistral had closed a Series A of approximately $415 million (385 million euros) at a post-money valuation of roughly $2 billion (1.8 billion euros). Investors in the Series A included Andreessen Horowitz, BNP Paribas, and Salesforce Ventures. In February 2024, Microsoft made a 15 million euro strategic investment alongside a deal to distribute Mistral’s models through the Azure cloud platform, a partnership that gave Mistral enterprise-grade distribution without ceding the independence that its open-weight strategy depended on.
June 2024 brought the Series B: $645 million (600 million euros) led by General Catalyst, taking the valuation to $6.2 billion (5.8 billion euros). At that point, Mistral was the fourth most valuable AI company in the world and the most valuable AI company headquartered outside the San Francisco Bay Area. The Series C followed in September 2025, with Dutch chip equipment maker ASML leading a 1.7 billion euro round that valued Mistral at 11.7 billion euros, equivalent to approximately $13.8 billion. ASML took an 11 percent equity stake, a move that was as much strategic as financial: the company wants to apply AI directly to semiconductor manufacturing, one of the most technically demanding industrial applications in the world.
| Round | Date | Amount | Valuation | Lead Investor |
|---|---|---|---|---|
| Seed | June 2023 | $113 million | $260 million | Lightspeed Venture Partners |
| Series A | December 2023 | $415 million | $2 billion | Andreessen Horowitz |
| Series A extension | February 2024 | $16 million | About $2 billion | Microsoft |
| Series B | June 2024 | $645 million | $6.2 billion | General Catalyst |
| Series C | September 2025 | $2 billion | $13.8 billion | ASML |
By the close of the Series C, Mistral had raised over $3 billion in total, its three founders had each accumulated a net worth of approximately $1.1 billion according to Bloomberg’s Billionaires Index, and the company had grown from three people to between 400 and 700 employees. Revenue hit $100 million in November 2025, up from $10 million in its first year of commercial operations in 2023, representing a 10x increase in approximately 24 months.
4. The Technology Behind Mistral AI
Efficiency as a Design Philosophy
From the beginning, Mistral’s technical strategy was built around a counterintuitive premise: bigger is not always better. While OpenAI and Google were competing on the scale of their largest models, Mistral focused on building models that delivered competitive performance at a fraction of the size and cost. This efficiency-first approach was not just an engineering preference. It was a commercial and philosophical statement about what AI development should look like.
The flagship demonstration of this philosophy was Mistral 7B, released in September 2023, just five months after the company was founded. Despite having only seven billion parameters, a small number by the standards of frontier AI, Mistral claimed and largely demonstrated through independent benchmarking that Mistral 7B outperformed Meta’s LLaMA 2 13B model on all tested benchmarks and matched LLaMA 34B on many, despite having less than a quarter of its parameters. The model was released under an Apache 2.0 license, meaning anyone could download, use, modify, and build on it for free, including for commercial purposes. The response from the developer community was immediate and enthusiastic.
Mixtral and the Mixture-of-Experts Architecture
In December 2023, Mistral released Mixtral 8x7B, a model built on a fundamentally different architecture from most consumer AI systems: the Mixture-of-Experts, or MoE, framework. Rather than activating all of a model’s parameters for every input, a Mixture-of-Experts model routes each token through only a subset of specialized sub-networks, called “experts.” Mixtral 8x7B contained 46.7 billion total parameters but activated only about 12.9 billion during any given inference pass. This made it substantially faster and cheaper to run than comparably capable dense models, while maintaining strong performance across reasoning, coding, and language tasks. Mistral claimed Mixtral 8x7B outperformed or matched Meta’s LLaMA 70B and OpenAI’s GPT-3.5 on most benchmarks, at a fraction of the computational cost.
The Mixtral 8x22B model followed in April 2024, scaling the MoE approach to a larger parameter pool and extending context window support to 64,000 tokens. Subsequent releases extended the model family further: Mistral Large 2, released in July 2024 with a 128,000-token context window and strong multilingual capabilities; Pixtral, the company’s first multimodal model supporting both text and image inputs; Codestral, a code-specialized model for developers; Mathstral, optimized for mathematical reasoning; and Magistral Small and Magistral Medium, the company’s first reasoning models with chain-of-thought capabilities, released in June 2025.
The December 2025 release of Mistral Large 3 represented the company’s most ambitious model to date: a sparse Mixture-of-Experts architecture with 41 billion active parameters and 675 billion total parameters, positioned as a direct competitor to GPT-4o and Gemini Ultra on enterprise benchmarks.
Le Chat: Mistral’s Consumer Assistant
On the consumer side, Mistral launched Le Chat, a French phrase meaning “the cat,” as its public-facing AI assistant. The product launched publicly in February 2025 on iOS and Android, with a Pro subscription tier at $14.99 per month providing access to advanced models, unlimited messaging, and web browsing. Le Chat integrates live news sources and enterprise connectors, offering something closer to a professional research and productivity assistant than a simple chatbot. By early 2025, Le Chat had attracted millions of users and was growing steadily as awareness of Mistral’s models expanded beyond the developer community.
5. The Open-Weight AI Strategy
What Open-Weight Means and Why It Matters
The term “open-weight” refers to AI models whose trained parameters are publicly released, allowing developers to download, inspect, modify, and deploy the model on their own hardware without going through the original company’s API. This is distinct from “open source” in the strictest sense, which would imply the training code and data are also released, but it represents a fundamentally more transparent and accessible approach than the fully proprietary systems used by OpenAI, Google, and Anthropic.
Mistral has released several of its models under open licenses. Mistral 7B was released under Apache 2.0, and Grok-1 model weights were released on Hugging Face for research and commercial use. The practical implications are significant. Developers and organizations that use open-weight models can run them on their own infrastructure, avoiding the API fees, data privacy concerns, and vendor lock-in associated with closed models. For European enterprises particularly sensitive to where their data flows, this is a material advantage.
CEO Arthur Mensch has argued publicly that openness in AI is not just a commercial strategy but a safety and accountability measure. He contends that transparent models can be audited, studied, and improved by a broader community of researchers, making safety failures more visible and correctable than in closed systems where problems may be identified only internally. He has also been a consistent critic of regulatory approaches that restrict open-weight models, arguing that such rules would concentrate power in the hands of the few large companies that can afford fully proprietary development.
The Trade-off Mistral Navigates
The open-weight strategy is not without commercial tension. Mistral’s most capable frontier models, including Mistral Large 2 and Mistral Large 3, are proprietary and accessible only through the API and enterprise licensing. The company has settled into a two-tier structure: open-weight models for the developer community, research sector, and smaller enterprises; and commercial, proprietary models for organizations requiring frontier capability with enterprise support. This mirrors the structure Meta uses with LLaMA, though Mistral’s commercial models are more central to its revenue generation than Meta’s, for which AI is a supporting capability rather than a primary business.
6. Competition in the AI Industry
How Mistral Stacks Up Against OpenAI, Google, and Anthropic
| Dimension | Mistral AI | OpenAI | Google Gemini | Anthropic Claude |
|---|---|---|---|---|
| Headquarters | Paris, France | San Francisco, USA | Mountain View, USA | San Francisco, USA |
| Model approach | Open-weight + proprietary | Proprietary (closed) | Proprietary (closed) | Proprietary (closed) |
| Architecture strength | MoE efficiency focus | GPT dense transformers | Multimodal at scale | Constitutional AI safety |
| Revenue (2025) | $100 million | $20 billion (annualized) | Not disclosed | Not disclosed |
| Valuation | $13.8 billion | $730 billion | Part of Alphabet | $61.5 billion |
| Total funding | $3+ billion | $174+ billion | Alphabet-funded | $69+ billion |
| EU regulatory standing | Native compliance | Active compliance effort | Active compliance effort | Active compliance effort |
| Enterprise partnerships | Microsoft Azure, BNP Paribas, ASML | Microsoft (full suite) | Google Workspace | Amazon Web Services |
On raw capability metrics, Mistral’s frontier models have consistently closed the gap with OpenAI and Google since 2023. Mistral Large 3 and the Magistral reasoning models perform competitively on standard benchmarks including MMLU, GPQA, and HumanEval. Where Mistral maintains a structural advantage is in efficiency: its MoE architecture delivers competitive performance at significantly lower inference cost, which is a meaningful differentiator for enterprises running AI at scale. Where it remains behind is in the breadth of its product ecosystem, consumer brand recognition, and the depth of its API tooling and developer documentation compared to the established American platforms.
The most important competitive dimension for Mistral, however, is geographic. In July 2024, both Meta and Apple withdrew new AI products from the European Union market due to regulatory compliance concerns, effectively removing themselves from a 450 million-person consumer market. For Mistral, a Paris-based company that has invested in regulatory relationships and built its models with EU compliance in mind from the start, this represented a significant opening. Enterprises and public sector organizations across Europe that need AI solutions with data sovereignty guarantees increasingly look to Mistral as the natural choice.
7. Europe’s AI Strategy and the Regulatory Dimension
The EU AI Act and Its Implications
The European Union’s AI Act, which entered into force in 2024, is the world’s most comprehensive AI regulation. It classifies AI systems by risk level, imposes transparency and safety requirements on high-risk applications, and establishes compliance obligations that apply to any company offering AI products in the EU market, regardless of where the company is headquartered. For American AI companies accustomed to a relatively permissive domestic regulatory environment, navigating the AI Act’s requirements has been a significant operational challenge.
Mistral, built in Europe by European founders who were engaged with policy debates from the company’s earliest days, was designed with this environment in mind. Arthur Mensch has been an active participant in French and EU AI policy discussions, testifying before the French Senate in May 2024, serving on a government AI advisory committee, and advocating publicly for a regulatory framework that distinguishes between base models and applications, restricts the former less heavily than the latter, and supports open-weight development as a transparency mechanism. His argument is that open models are inherently more regulatable because they can be inspected, tested, and audited by independent parties in a way that closed models cannot.
AI Sovereignty as a European Strategic Priority
The French government’s enthusiasm for Mistral AI goes beyond one company’s success. It reflects a broader strategic concern about technological dependency. French President Macron has spoken repeatedly about the need for European “digital sovereignty,” the idea that Europe should not be entirely reliant on American or Chinese technology infrastructure for critical systems. AI, as it becomes embedded in healthcare, finance, education, defense, and public administration, is precisely the kind of critical infrastructure that sovereignty arguments apply to most forcefully.
The announcement in June 2025 of Mistral Compute, a plan to build AI infrastructure in Europe powered by 18,000 Nvidia Grace Blackwell chips, running on Europe’s low-carbon electricity grid and operating under EU data jurisdiction, is the most concrete expression of this sovereignty thesis to date. Planned for launch in 2026, Mistral Compute would transform the company from a model provider into a full-stack AI infrastructure provider, reducing European dependence on AWS, Azure, and Google Cloud for AI workloads while keeping data under EU legal frameworks.
8. Impact on the Global AI Ecosystem
Developer Adoption and the Open-Weight Effect
One of the most significant contributions Mistral has made to the global AI ecosystem is not commercial but architectural. The release of Mistral 7B under Apache 2.0 in September 2023 triggered a wave of adoption among developers, researchers, and startups that would not have been able to access frontier-quality models through paid APIs. Within weeks of release, Mistral 7B had been downloaded hundreds of thousands of times, fine-tuned for dozens of specialized applications, and integrated into open-source projects across healthcare, education, legal research, and software development.
The Mixtral 8x7B release, and its MoE architecture, had a particular impact on the research community. Several subsequent open-source models adopted Mixture-of-Experts designs, in part inspired by Mixtral’s demonstration that MoE could work at scale with commercially available hardware. This ripple effect, where a single architectural choice by one company influences the design decisions of hundreds of downstream projects, is a form of ecosystem impact that revenue figures and user counts do not easily capture.
Enterprise Partnerships and Commercial Traction
On the enterprise side, Mistral has built a diverse and strategically important partnership base. The Microsoft Azure distribution agreement, signed in February 2024, made Mistral’s models available to the millions of enterprises already using Azure without requiring them to manage API relationships with a new vendor. The BNP Paribas partnership, announced in July 2024, applied Mistral’s models to customer support, sales, and IT operations at one of Europe’s largest banks, providing both revenue and a high-profile proof of concept for enterprise deployment. The ASML Series C investment in September 2025 opened a pathway into semiconductor manufacturing AI, one of the highest-value and most technically demanding industrial applications in the world. CEO Mensch reported in 2025 that revenue had grown 25 times over the prior year, and that the company had secured hundreds of millions of dollars in signed enterprise contracts.
9. Future Outlook
What Comes Next for Mistral AI
Several developments on Mistral’s near-term roadmap suggest the company is preparing to compete at an increasingly broad level in the global AI market. The launch of Mistral Compute in 2026 would represent the most significant expansion of the company’s scope since its founding, moving it from pure model development into infrastructure. The introduction of Magistral reasoning models in June 2025 placed Mistral in the competitive field for chain-of-thought and extended reasoning applications that have become one of the most commercially important AI product categories. The Devstral coding models, which by December 2025 were outperforming several larger competitors on coding benchmarks, address one of the highest-value developer use cases in the market.
The company’s trajectory on revenue is also encouraging. From $10 million in its first year to $100 million by November 2025 represents a 10x revenue increase in 24 months. The company’s internal projections and external analyst estimates suggest it is building toward a revenue run rate that would support continued independent operation, a meaningful consideration given the much larger capital bases of its primary competitors.
The most important strategic question for Mistral’s future is whether its open-weight philosophy will remain a competitive advantage or become a vulnerability as frontier model capabilities advance. Training the very largest models at the frontier of AI capability requires computing infrastructure and investment levels that increasingly favor a small number of hyperscalers. The Mistral Compute initiative, the ASML partnership, and the company’s ongoing fundraising suggest the founders are acutely aware of this and are building the infrastructure base needed to remain competitive at scale. Whether they succeed will depend partly on execution and partly on how quickly the gap between open and closed frontier models widens or narrows in the next generation of AI development.
10. Conclusion: Why Mistral AI Marks a Turning Point
The rise of Mistral AI matters for several reasons that extend well beyond its own commercial success. It demonstrated that Europe could produce a globally competitive frontier AI company. It proved that the efficiency-focused, open-weight approach to AI development was technically credible and commercially viable, not just philosophically appealing. It gave European enterprises, governments, and developers an alternative to total dependence on American AI infrastructure. And it gave the global AI ecosystem something it badly needed: genuine competition from a player with a fundamentally different set of incentives and values.
Mistral is not, by most measures, yet in the same tier as OpenAI, Google, or Anthropic in terms of revenue, user base, or raw model capability at the frontier. Its $100 million in annual revenue is a fraction of OpenAI’s $20 billion annualized run rate. Its 400 to 700 employees are a small team compared to the thousands at its primary American competitors. These gaps are real.
But the arc of the company’s development in 29 months, from three researchers to $3 billion raised, from a seed-stage idea to a $14 billion company with partnerships spanning Microsoft, BNP Paribas, and ASML, and from no models to a family spanning efficient open-weight assistants, multimodal systems, reasoning models, and specialized coding tools, is a legitimate record of achievement. In an industry where the narrative had become one of inevitable American dominance, Mistral AI arrived as a compelling argument that the story was not yet finished.
Europe has its AI champion. The question now is how far it will go.
Key Statistics: Mistral AI at a Glance
| Metric | Value | Source / Date |
|---|---|---|
| Founded | April 2023 | Paris, France |
| Founders | Arthur Mensch, Guillaume Lample, Timothee Lacroix | Mistral AI |
| Seed funding (largest in European history) | $113 million | June 2023, Lightspeed |
| Total funding raised | $3+ billion (7 rounds) | Tracxn / PitchBook, 2025 |
| Latest valuation | $13.8 billion | Series C, September 2025 |
| Revenue (2023) | $10 million | Getlatka / Electroiq |
| Revenue (2025) | $100 million | November 2025 |
| Valuation growth since seed | 53x in 2 years | Calculated |
| Employees | 400 to 700 | PitchBook / aifundingtracker |
| Global AI company ranking (by valuation) | 4th (as of June 2024) | Various reports |
| Flagship open model | Mistral 7B (Apache 2.0 license) | September 2023 |
| Largest model (2025) | Mistral Large 3 (675B total parameters) | December 2025 |
| Key investor partners | ASML, Microsoft, Andreessen Horowitz, General Catalyst | Tracxn |
| Planned infrastructure | Mistral Compute (18,000 Nvidia Grace Blackwell chips) | Launch 2026 |
Frequently Asked Questions
What is Mistral AI?
Mistral AI is a French artificial intelligence company founded in April 2023 in Paris. It develops large language models with a focus on efficiency, open-weight accessibility, and enterprise deployment. As of late 2025, it is Europe’s most valuable AI company with a valuation of approximately $13.8 billion.
Who founded Mistral AI?
Mistral AI was founded by three French AI researchers: Arthur Mensch, former researcher at Google DeepMind and now CEO; Guillaume Lample, former researcher at Meta AI and now Chief Science Officer; and Timothee Lacroix, also a former Meta AI researcher and now Chief Technology Officer. All three studied at France’s Ecole Polytechnique.
What makes Mistral AI different from OpenAI?
The most fundamental difference is Mistral’s open-weight model philosophy. While OpenAI keeps its model weights proprietary, Mistral has released several key models under open licenses, allowing developers and organizations to download and run them independently. Mistral also places a strong emphasis on computational efficiency through its Mixture-of-Experts architecture, delivering competitive performance at lower cost than many comparably capable dense models.
What is the Mixture-of-Experts architecture?
Mixture-of-Experts is a neural network design where the model is divided into multiple specialized sub-networks called experts. During inference, each input token is routed to only a subset of those experts rather than through the entire model. This means the model can have a large total parameter count for expressive capacity while only activating a fraction of those parameters for any given request, making it much faster and cheaper to operate than a dense model of equivalent total size.
How is Mistral AI funded?
Mistral AI has raised over $3 billion across seven funding rounds since June 2023. Key investors include Lightspeed Venture Partners (seed), Andreessen Horowitz (Series A), General Catalyst (Series B), ASML (Series C), Microsoft, BNP Paribas, Salesforce Ventures, and French investors including Xavier Niel and BPI France, the French public investment bank.
Is Mistral AI available in English?
Yes. Despite being a French company, Mistral’s models support many languages including English, French, Spanish, German, Italian, Portuguese, Arabic, and others. Mistral Large 2 in particular was designed with strong multilingual capability as a core feature, reflecting the company’s positioning as a global AI provider serving an international enterprise market.
What is Le Chat?
Le Chat, which means “the cat” in French, is Mistral AI’s consumer-facing AI assistant. It launched on iOS and Android in February 2025 and is available at a free tier and a Pro tier at $14.99 per month. Le Chat provides access to Mistral’s models through a conversational interface, with web browsing, news integration, and document analysis capabilities, positioning it as a productivity-focused alternative to ChatGPT and Google Gemini.
How does Mistral AI navigate EU AI regulation?
Mistral AI has been proactive in EU regulatory engagement from its founding. CEO Arthur Mensch has served on French government AI advisory committees, testified before the French Senate, and advocated publicly for regulatory frameworks that support open-weight model development. The company’s European headquarters, focus on data sovereignty, and compliance-oriented enterprise offerings give it a structural advantage over American competitors in EU-regulated markets.
So, this was the BigStory of Mistral AI, the Paris-born startup that set out to prove Europe could compete at the frontier of artificial intelligence and built a $14 billion company in just over two years to make that case. At BigStories, our goal is to bring you the journeys behind the companies and ideas that are shaping the modern world. If you found this story valuable, consider sharing it with others who follow the AI revolution, and explore more BigStories that reveal how today’s world is truly being built.
