Arthur Mensch, co-founder of Mistral AI, is being questioned at the National Assembly - 12/05/2026

I swear it.

Very well, Madam President, Mr. Rapporteur who is not here, so Madam Rapporteur. Ladies and gentlemen, deputies, I want to thank you for your invitation to discuss this key subject, which is Europe's autonomy in artificial intelligence and, more generally, in digital services. I think we shouldn't make a distinction between today's digital services and especially tomorrow's, which are essentially artificial intelligence. You have storage, you have a bit of execution of agents on it, etc. But fundamentally, digital services, the cloud, it's artificial intelligence. There is no distinction to be made on this subject. And so that might answer your question. I noted your two questions, I will answer them later.

Perhaps to give a bit of context. As you know, artificial intelligence is redefining economic and geopolitical balances. The question is not so much whether Europe can compete, but rather how it can do so. Because I think that if it doesn't compete, in reality, it completely gives up any form of presence and voice in the concert of nations. So the question is, can it do so without depending on extra-European actors? The answer is obviously no. The second question is, can it do so by gaining leverage over its suppliers? By gaining leverage as a technology exporter and by rebalancing the economic imbalances that already exist and will increase? I think the answer is yes.

A few elements on Mistral AI. It's a company we founded a little over three years ago, on April 28, 2023, with the intuition in 2023 that generative artificial intelligence was going to significantly reshuffle the cards in the field of digital services, and fundamentally change the way the economy works because it moves intelligence from labor to capital and machines. We saw in 2023, because my co-founders Guillaume and Timothée and I had a certain experience with the big American players, that the world was heading towards a clear oligopoly, and we obviously wanted to counter this oligopoly. And so we created Mistral in 2023 with about fifteen people in France and England at the start, essentially researchers.

What we did was first demonstrate to the world that we knew how to train language models, which are used for generative AI. And then we demonstrated by making an open model, meaning we gave them to the world to show that they could also be used, modified, deployed wherever one wanted, etc. So it was a technique of decentralization and displacement of established players. And then from there, we continued to invest in models. Today, we have invested a billion this year in R&D. So we invest very significantly, we build models, we are one of the leaders in open models that can be modified and deployed wherever we want.

And we have a business model that is both on top, meaning we provide a platform for deploying agents, for delegating tasks to artificial intelligence. We create business applications in contact with our clients, who are generally large companies. And then we have a business model that is at the bottom, meaning we also host the models, we put them on machines and we make them generate what are called tokens, which is the economic unit of artificial intelligence. That is to say, we train models that generate sequences. From these sequence generations, we take actions, we execute things, we think. These sequences are made of what we call tokens, which are numbers, between 0 and 65,000. And so we generate these tokens for our clients by hosting them, we transform electrons into tokens.

Today, a few figures: we are 1,000 employees, a valuation of 12 billion euros, and 1 billion euros in revenue targeted by the end of the year. Our clients are enterprise clients, public sector clients. To name a few in France, we have the DINUM, the Caisse des Dépôts, France Travail. In the private sector, CMA CGM, Stellantis, TotalEnergies, BNP Paribas, etc. We do about 30% of our revenue in France, 70% abroad, and about three-quarters of our revenue in Europe. To give you an idea of where we are, we are fundamentally a technology exporter to both the United States and Asia.

What you need to understand about generative artificial intelligence, and fundamentally about what we call AI today, is that it's a model that consists of transforming energy into intelligence, into the generation of tokens that are then used to delegate tasks, to do mathematical research, to operate logistics chains, to improve tools, to deploy embedded systems in robots, in drones, etc. So we are in a business where we transform electricity into token generation. And it's fundamentally something that should be treated more or less like a natural resource. That is to say, we need to think about intelligence as we think about energy.

So our goal is to provide intelligence that is cheap, affordable, let's say in English, that is secure in terms of supply, meaning we don't need to get it from Americans, for example, and that is also sustainable because it transforms energy that is less carbon-intensive than elsewhere because we use, in particular, French energy. I think we don't think about it enough in this way. Intelligence is like electricity, it's like energy. And besides, we have this tendency, which in my opinion is a rather false one, of separating digital services from artificial intelligence. Today, the cloud is artificial intelligence. The growth of the cloud is artificial intelligence. High value-added services, with high margins that allow for R&D, it's artificial intelligence. So everything else, once we gain in artificial intelligence, we can build all the rest of the cloud services, which are essentially commodities today.

And it essentially consists of knowing how to operate open-source software because it has become the global infrastructure. Our goal is obviously to anchor this value chain. So where we are in the value chain is between the operation of clusters and the operation of servers up to the creation of business applications. And I think it's an atomic unit of the value chain that goes from the construction of semiconductors, so from ASML, for example, this company that does lithography, to deployment in companies. So that means that the cloud and AI are really the same thing.

What I can say is that we need to see sovereignty. We need to stop thinking about sovereignty as isolationism. We need to think about it as business people think about it. That is to say, we need to think about sovereignty as a matter of leverage. In a world where you import all your digital services from the United States, you have no leverage over the United States. In a world where you create part of your service, and possibly export it, you have additional leverage. In a world where you sell goods to the United States, you have leverage over them. We see it in European Union negotiations, we export a certain number of things. It gives us arguments and cards to play when we are at the negotiating table.

If we don't have this capacity to not import all our digital services from the United States, we have a real problem. The problem that arises is that the total market we're talking about, it must be compared to the payroll. Today, at Mistral, our consumption of artificial intelligence for our employees is 10% of our payroll. Now, we make this technology, so we adopt it a little faster than elsewhere. But if you extrapolate, in 3 to 4 years, all of Europe uses artificial intelligence for 10% of its payroll. 10% of Europe's payroll is about a trillion. A trillion, so it's digital services because you have to generate the tokens, etc. It's a trillion if we use non-European technology, it's a trillion in trade deficit to add to our existing trade deficit on digital services. It's a trillion that goes elsewhere to be invested in R&D and is not reinvested in R&D in France or in Europe.

So I think that's a very important point, is that we need to think about this sovereignty issue from a macroeconomic point of view. It's an essential macroeconomic issue that I think we don't realize the extent of. So that's a first issue. There is obviously an issue of economic security, meaning when you know how to deploy artificial intelligence in essential services, you want to ensure that access to the service cannot be cut off, which is actually quite likely. We've seen it in different places. So that means it's important for all sovereign, governmental aspects. It's obviously important for defense because artificial intelligence is now at the heart of military operational centers and becomes the platform that allows for operational decisions on the battlefield, for intelligence, etc. So if we don't have access to this technology, we'll have to import it from elsewhere, and that means we could have this access cut off.

So I think it's particularly critical. Finally, there is an additional issue that is not one we really deal with directly because we don't have a significant consumer-facing activity, which is that artificial intelligence that generates content shapes cultural representations, shapes language, and also shapes ethics. These are models that have a certain policy, they activate certain things, they will write a certain type of code, etc. And so all of this becomes a mediation of information and a mediation of action. And so if we have to import models from elsewhere and if we have no alternative solution in Europe, we will be forced to use the biases and choices made by others, the United States and China in particular.

So a few recommendations to finish. Artificial intelligence offers the hope of reshuffling the cards. That's true, it goes back to your first question. One of the assets of Europe and France in particular is its talent base. It's an asset, it's an entry into the system. That is to say, if you don't have large, innovative companies that can attract these talents and convince them to work for us, they will simply go elsewhere and bring their value elsewhere. That's what we've seen a lot, and I think it's something that is starting to reverse. We need to benefit from it. Beyond talent, I mean Europe doesn't have many assets. We have heavier regulation, a fragmented market. Obviously, a fragmented market means more difficulty in selling, but it also means longer-term partnerships. So that can be considered an asset. There is a very strong asset of cultural diversity in language, etc., which is a real diversity for artificial intelligence.

What I can say is that fundamentally, we insist that if there is a market that exists. And the problem we see is that this market materializes a little too slowly compared to what it should be. That is to say, companies are not yet fully aware of the amount of artificial intelligence they will use in the coming years. And in fact, the consequence of this is that those who can invest before the demand are rather the Americans. And so, for example, there is an energy surplus in France, about 9 GW on average, this energy surplus will be captured rather by the companies that are the most aggressive from a capitalist point of view and who will be able to put money on the table, transform these electrons into tokens before others. And so for us, our issue, we manage to be quite aggressive, but we would like to be more aggressive, and for that, we need visibility on the market.

This market is obviously the private market, so companies that choose to work with us for 5 years, for 7 years, and we have plenty. But it's also public demand, which forms nearly 50% of Europe's GDP. And so being able to ensure that this public demand is channeled across the entire value chain, starting from where we are strong, where we have added value, which is artificial intelligence, and doing it in a concentrated way, meaning not deploying with subsidies, simply making demand at the end of the chain on digital services. It's an essential asset, an asset that our American and Chinese partners use massively. It's an asset we have always been afraid to use in Europe. That absolutely must stop.

And finally, in general, we seek to structure markets, to ensure we have a relatively unified demand. So unified markets are useful. Unified capital markets are useful. We never do enough of it. And then finally, from a purely infrastructural point of view, France has the good fortune to have surplus electricity. We need to continue to do so because artificial intelligence, we can consider it's about 1 kW per person in 5 years. 1 kW per person in 5 years means 40 GW to build in France, which means about 400 GW to build in Europe. That means about 2 trillion in investments to make. So we really need to be aware of it and build electricity, but above all, ensure that the electrons are transformed into tokens by European companies.

And to build 400 GW in Europe, we need to accelerate projects. That means we need permits more quickly. We also need a notion of reserving energy capacities. There. And then on your second question, I think I've addressed it a bit. Beyond AI, digital services. Yes, you can do database management, you can do hosting, you can do virtual machines, etc. Those are services with extremely low margins. You can't build a business at scale starting from a place where the margin is low. If you want to build a business at scale, you start from where the margin is important, and then once you are big enough, you use a part of your R&D, but you don't even need it to be large, to go and create all the commoditized services.

If you start from a place where the margin is low and you have no volume, if you have neither volume nor margin, you have no chance of reaching scale. So either you are in a place with low volume, high margin, or you have low margin but high volume, which was the Chinese strategy, and you move up the value chain. But if you are in a place where you have a high margin and not much volume at the beginning, you can only move down the value chain. So I think that's really important to take into account. You have to start from high-level services, go down to low-level services because those have less margin, and that's how we approach the problem from a strategic point of view.

It's the economic unit of artificial intelligence. It's something that is billed in millions of tokens. 1 million of tokens on one of our models, for example, will be 1 euro. So you send me a million tokens. I generate a million tokens for you. You will pay me 1 million euros for the input. 3... finally 1 euro for the input, 3 euros for the output. So that's it.

It's a few letters in a word. It's a number, essentially. It can also be a few pixels in an image when it's input. So we manage all multimodal data. It can also be a piece of sound. So that's the unit. Our business model is partially a hosted model, so consumption-based. That is to say, you use technology, you host technology with us, and you pay per token, you pay per unit of time in CPU. So when you do something other than artificial intelligence, you pay for storage.

We also have a model that is more of a platform model where we provide our software, deploy it on a client infrastructure, which then pays for its infrastructure, its GPUs, its CPUs, its storage, and on top of that, it's more of a license model on which we have a service layer to build value and transform companies.

The token is the client unit of the digital service. So when you buy a digital service, you buy tokens. The unit for us and the way we finance our projects, etc., is rather the megawatt. The megawatt is the electricity we will transfer into intelligence. 1 MW... so I'll give it to you in gigawatts because I know the figures well in gigawatts. When you invest and create a data center, you put in graphics cards that will generate tokens when you host the model. To create 1 GW, you will invest 50 billion dollars over 5 years. So it costs you about 10 billion per year. With that, I'll give you a bit of the margins. With 10 billion in data center per year, you can make about 20 billion in revenue in tokens. You can sell 20 billion tokens if you do things correctly. If you are about at scale, 1 GW is about at scale. So that's it, meaning you have a 50% gross margin between the digital service and the investments that the digital service provider makes.

So it's a significant margin. It's a margin that allows for investment and R&D. In particular, it allows financing what costs extremely much in artificial intelligence, which is the training of models. Because we use GPUs ourselves, we use today tens of MW to train models. So those are costs only for us that we bear. So that's about what we can keep in mind. 1 GW. I mentioned that if you have just 10% of the European payroll that becomes artificial intelligence, we're talking about 400 MW because we're talking about 10,000 euros per year, and 10,000 euros per year rents you about 1 kW of GPU. A GPU is 2 kW. So you rent half a GPU with that.

So finally, you are in a situation where to build this, you need to make 20 trillion in investment. It's 20 trillion in investment, it will bring back... it's about 8 trillion per year of revenues that can go back to the States. So we are on scales that are quite significant, if you will. And what you need to take into account is that, there, there are these 50% gross margin between the service provider and the one who makes the service. In fact, in the chain that transforms the electron into token, about 10% goes to the electron. So if your only role, if the only role of Europe is to be the energy provider, that means 90% of the value is elsewhere. And so it leaves Europe and will be invested, recycled in R&D, used as power by other actors. So you have to take that into account.

Very well. I will take your questions in order. So on the ethical subject, we build systems that help humans be more efficient, that will automate a certain number of processes while leaving humans in the loop on important decisions. And so we have a whole team of designers, about ten designers with us, who will think about human-machine interactions, ensuring that when artificial intelligence summarizes information to help with decision-making, it provides enough elements so that the human remains informed. Now, I'm not telling you it's an easy subject because humans tend to be a bit lazy, and so if the thing does it for them, they think less and less. So there is this subject of managing to keep them in the loop, you have to stimulate the human when they are facing the machine. And so these are obviously subjects we think about.

Then you have a sub-part of ethics which is the subject of engagement in defense. So we work with the Ministry of the Armed Forces, we also work with certain allies of France, and it turns out that this technology is considered a dual-use technology and so it is regulated in terms of exports, in terms of export controls. And so we have the entire framework of regulations on export controls that applies to us. And so that allows us to have... we don't claim to have an ethical framework, we don't claim to have the legitimacy to have a sufficient ethical framework. We don't have democratic legitimacy as a company. On the other hand, we operate within frameworks that have democratic legitimacy, and that allows us to work correctly and not to do what we are not allowed to do.

Then, when we work with the Ministry of the Armed Forces in France, we don't claim, unlike other actors, to have a right of oversight over the final use. Because we don't claim to have the legitimacy to explain to the French armed forces what they can and cannot do with the technology. We can tell them, and we can do it by project, we can explain the reliability of things, etc. So, but then, who is sovereign in the end in the use of technology? The army has democratic legitimacy, we don't. And so as such, we have a duty to advise, we explain how it works, and I can tell you that today it is used in a perfectly reasonable manner and within the limits of what artificial intelligence can do. We don't have...

You have to be aware that artificial intelligence enables defensive capabilities, makes armies much more operational, and improves operations. It enables defensive capabilities, but it's also absolutely critical for deterrence. Armies like the Russian army use AI massively in their drones. If you're not capable of having counters that are themselves activated by AI, you don't have sufficient deterrence. Therefore, the deployment of AI in autonomous systems, in particular, must absolutely not be cut. It's absolutely indispensable for having conventional deterrence today. This also means we must control AI end-to-end. That's the first point.

Next, on the subject of cyber defense and cyber attacks, one of our American competitors knows very well how to do fear marketing. But the fact that models are excellent programmers and are absolutely capable of orchestrating attacks, discovering vulnerabilities, and proposing exploits—this has been ramping up for six months. It's rising linearly, predictably. It's rising everywhere at the same time. Another American competitor has a model that does exactly the same thing. Chinese actors are doing it too, and our models are capable of discovering all the vulnerabilities reported by MITRE, for example. So, we work with our clients to help them on these cyber issues. It's obviously a sovereign issue, which is another argument for saying we must have control over this technology. You can't have the French army's codebases scanned by MITRE. It creates an irremediable dependency, and we absolutely must find solutions.

Next, on the subject of time. I would say we don't have time. I think this is linked to physical constraints. We're talking about natural resources. Here, we're talking about using natural resources to transform electricity into tokens. There isn't infinite electricity in the world. The United States is saturating its electricity to transform it into AI. They're deploying a trillion dollars next year. When they deploy a trillion dollars, it means they want two trillion dollars in return. So they see a huge market being created, and they see that whoever possesses the resource, whoever supplies the chips, whoever possesses the electrons, whoever possesses the power plants or has access to massive energy, is the one who wins.

So today, you have 9 GW, 90 TWh per year available in France. There are many things we can do with it, and we will do some AI. You have actors with huge balance sheets who have plenty of money to put on the table before seeing the demand to transform electrons into intelligence. But they're going to do it in the next two years. Once they've done it, you have no more electricity. You have to wait for new power plants, create new ones, etc. So there's a monopolization of European energy resources happening, accelerating at a crazy speed, and we're not aware of it. It's really irremediable.

We already have a demand problem. Because the Americans arrived on the cloud first, they provide all the European industry, they provide the French state, and the market share for cloud and AI tends to compress for non-American actors. Not only do they grow with the market, but their share also increases. I think this can be reversed with AI, and we're starting to see it with our clients, our revenue, and our growth. But the problem is that even if you solve the demand problem, you have a supply problem, because once the supply is monopolized by American actors, we suddenly have no more supply and can't transform electrons into tokens. And this is happening in the next two years.

What needs to be put on the table today in terms of investment is tens or hundreds of billions of dollars. And to do that, you need visibility on the market. And that visibility must be indicated by European actors, by European states. Today, it's not indicated massively enough for us to take the risk of deploying 100 billion dollars. So I think we need to become aware of this urgency, and unfortunately, I don't see very well how we'll become aware of it. We aim to build 1 GW by 2029. That's our objective. It's not enough. We should build more, but for that, we need a form of market and demand visibility that we don't have today.

Finally, on the last question you asked about distillation. What is distillation? You take a large model and use it to make a smaller one that will be more efficient. We use it massively because we train large models, which costs us a lot. Then we use our large models to train small models that we serve to our clients because it costs them less to serve. So what this means is that it's absolutely indispensable. If you want to know what's in your models and control them, you must be able to build the largest models. That means you need to know how to train them, which means you need a lot of compute capacity of your own, which is pure R&D. So does it allow catching up? No, it's a technology that essentially reduces costs internally. We take large models and use them internally to make small ones, and we need the large models, which means we have to invest a lot in them.

So I'll answer, I can explain our role in Campus IA, which is very minor. We have a very small participation. We took a participation because in the value chain, we're capable of building clusters. So we buy the servers, we plug them in. It looks like physical racks that will be put in buildings. But we don't do the buildings. We don't do the buildings, we don't do the transformers, we don't do the cooling. We'll rather seek partnerships to do that. So in that capacity, we need data center developers. So we took a very, very minority position in this project, and above all, it's a supplier, meaning it will become a supplier for us for part of the construction. Another part will be used to supply hyperscalers because you start from a situation where 80% of European digital services are imported from hyperscalers. And so naturally, the starting point, if you start building infrastructure and look at infinite demand, you end up with 80% hyperscaler. I think the objective France and Europe must have is to ensure this proportion decreases over time. So that's why Campus IA today will serve us partly and will certainly serve hyperscalers partly.

So that's what I can say. As for the name, I didn't choose it. On a number of technical aspects, I don't know. But what's sure is that part of the revenue is indeed reinvested in public research. Next, on the subject of capital, you mentioned it. That's the real infrastructure. You can build a data center, lease it, it gives you an equity return, and it's essentially a pretty good business. If you have a lot of funds, deploying them in data centers today, knowing that everyone wants data centers, gives you a pretty significant return. So there are plenty of actors who want to do it. When you have a site of this size, you need actors with strong enough backs, meaning they can deploy tens of billions of euros. And so, naturally, because we forgot to create pension funds in France, for example, we turn to foreign financiers. Because there's no capital market. European capital markets are completely devoid of solution providers for this type of investment. So we look elsewhere, and I think we should benefit from it. Foreign investments, if they're under control, if the governance is good—and I think today on this subject, BPI France plays a role and has a seat on the board—these foreign investments are beneficial because they allow us to accelerate. Obviously, we lose some economics because when foreign capital arrives, they take a return, so we lose the return. But on the other hand, we build something, so we can build something. What we propose is to capture the margin from operating servers up to digital services. That's as much margin as we can reinvest in R&D. We're not capable today of a fully integrated model where we operate from the ground up to digital services. Maybe that will be the case one day. But there's a sharing of value. We can create a lot of value with this. We'll be able to reinvest it in R&D. Obviously, there are foreign investors who will take a return somewhere. But on the other hand, without these foreign investors, we couldn't build. So you always have to think of sovereignty as a path. You start from a state of the economy and balances in the value chain, and you say this state is not satisfactory. We need to change this state, it will take time. And how do we change this state? We need to put resources in the right place, and then it will change gradually.

On environmental subjects, obviously all this costs money. There are externalities. If you can go up the value chain to semiconductors, you need a lot of inputs, you need rare land, you need land, though I think that's not the main issue because it's still super dense. So 1 GW fits on about a hundred hectares. So there's really no land issue. Of course, if you look at the city level, if you look at Europe, there's no issue. So it's not the main issue in any case. Next, you have an electron issue. That's a real issue. The interest of building in France for us in particular is that 70% of electrons are nuclear. So the carbon footprint of the electron is largely reduced. And so building in France is fundamentally better than building elsewhere because tokens, people need them. If someone builds 1 GW of capacity in France, they won't build 1 GW of capacity in Texas, and ultimately the atmosphere will warm up a little less. So I think we need to think about it from that perspective.

Next, you have other inputs, you have water in particular. There have been many technological advances to avoid waste heat and ensure that cooling is more efficient and that most electrons are used for computation rather than everything around it. And then you have biodiversity issues, obviously. From what I've observed, everything has been done and implemented to preserve these aspects. So for us, it's important. Obviously, things aren't perfect. Ecologically, it would be better if there were a forest, but economically, it still has a role. So it has an ecological effect. We need to think about AI within ecology. We transform energy into electrons. It's a sort of natural resource of electrons into tokens. You consider the token as a sort of natural resource. For natural resources, you need a sort of mine. The mine is a data center. And I think we need to do it because if we don't, we have additional issues, and I think we need to do it. The more we do it ourselves, the more voice we have in how we think about externalities. If we don't do it ourselves, we'll just import what's offered to us from outside. And I can tell you that outside, they think about biodiversity and climate change quite differently from how we think about it. So if we want to have a voice, we must be capable of internalizing production. And that's absolutely essential. So there are obviously compromises to make. In the same way we accepted to build lithium mines in France, we need to accept to build data centers. But what would be a shame is to build data centers where the bulk of the economic value completely escapes France. That's the issue. So rather than taking these externalities, let's ensure they can be recycled into R&D and beneficial aspects for France.

Just to add, today our new generation of models, we train them in France, and I think we have the largest compute cluster in France. So it's a source of pride for us because from a carbon footprint perspective in particular, it's largely reduced compared to what we could do before when we weren't in France. So that's what I can say. Finally, on the subject of computing carbon footprints, unlike some of our competitors, we think we need a life cycle approach rather than a compensation approach, because obviously it's a bit too easy when you have large balance sheets to do compensation, when in fact you need to think in an integrated way about everything that goes into projects and have transparency per project rather than global transparency balanced via quotas.

Of course. On the subject of heavy regulation and fragmented market, which are somewhat the same thing. One of the regulatory burdens, one of the regulatory problems in Europe, is that fundamentally every time you enter a European country, you must open a new entity, understand a new stock option regime, understand different labor regulations. And so it means you quickly need a team on the ground. I've signed hundreds of documents to open entities in about ten European countries. I've opened dozens of bank accounts because generally you need an associated bank account. You have a whole set of regulations that are national and not unified. You have taxation that is obviously not unified, when it absolutely should be if you want employee incentive systems that make sense. So we try to patch things, do things as fairly as possible, but we end up with fiscal situations that are frankly disparate between countries. So that's relatively catastrophic. You can't have a unified market if you don't have unified taxation, unified social law, etc. That's the first thing, I'd say it's the main burden.

Then you have a whole stack of regulations that are more or less consistent with each other and force us to document many things. You have the GDPR, you have all the laws on copyright and text and data mining. You now have the AI Act, which isn't yet implemented but will be from August. All these things say more or less similar things. They touch on data, personal data, what's on the internet, and they don't say exactly the same thing. They require different documentation. They're implemented by 27 different implementation bodies in Europe that are more or less zealous depending on the country. And so I end up with a compliance team of five people, which is manageable today because we're big enough. But if you're a new entrepreneur in France and you look at all this, and you look at the atmosphere and the fact that we're celebrating having put regulation back on the AI Act in early 2024, you say I'm going to go to the United States, and that's what happens. They go to the United States.

So we've overcome this aspect because we're big enough. It goes into overhead costs, it remains very painful, but above all you lose many entrepreneurs who go elsewhere because they're afraid. You have investors who say Europe has lost anyway, too much regulation. And you have a whole narrative widely used by the United States saying Europe loses because it's bureaucrats in Brussels who are regulating because they don't know how to do anything, they don't know how to innovate. And this narrative is destructive because it's also internalized by Europeans. It's a form of colonialism, if you will. But the point is we need to reverse this narrative. To reverse it, we also need to think about having simpler, more unified regulations that allow us to go faster. That's on that subject.

I spoke of market fragmentation as an asset. It's not an asset, it's rather a hindrance. It means you need different teams selling differently everywhere. It means you have about 60 telecom operators in Europe. You have three in the United States. That means they have 20 times more budget overall. So when they make an AI investment, they can put a lot of money on the table. And that's how you end up with American technology. Startups, the first thing they do is sell to other startups because that recycles venture capital money, circulates a bit to see who will win, and then they sell to huge American companies that buy very quickly, and that's how they scale. Once they've scaled, they export to Europe where no one emerged in time, so they take the whole market, buy the few companies that know how to do things, and they end up consolidated with the whole market. That's how it works. In Europe, we don't have that because you have much greater viscosity because companies are smaller, and on top of that, you have to speak 20 different languages, manage 27 different regulations. So it's rather not an opportunity.

However, once you've done it, European companies are more partnership-oriented, they have more constructive approaches. Sometimes they're champions, we're lucky to have many industrial champions in particular, and those are good partners for us. And since these are partnership approaches, it's difficult for Americans to replicate them. And so in a way, it's relatively defensible revenue. So the viscosity works against you at the beginning, but then it works in your favor when someone attacks you, when a competitor tries to take the market. But it's rather a disadvantage. You'd prefer the market to be much less fragmented.

Finally, last point on regulation as defense and as a way to counter the Americans. I think often the intention is good, saying we'll ensure it favors our European startups. But I think if we look at the entire history of what we've done, it's never been the case. And a very simple reason is that regulation fundamentally puts an overhead that to overcome, you need to be big enough. And so if you don't have companies big enough with such a large regulatory burden, you end up with American actors carrying it. So regulation favors the big ones, and that's even more true in technology. And so any discourse saying regulation can defend us, I think it's a bit like the road to hell is paved with good intentions.

I'd say there's one area where we should be much better, which is ensuring public demand is recycled on European territory. Meaning used to do R&D on European territory. This applies to defense, it applies to the entire public sector. I think there's an awareness that's not necessarily regulatory but political that this is the thing to do, that it's what the United States has been doing since the 1940s, and it worked very well for them. So should we regulate this? Maybe we can manage this aspect a bit. We need to do it while maintaining very high competition in Europe. We shouldn't be planners on state spending but not planners on how things are used. And that way we'll get there. But regulating to defend generally doesn't work.

Justement, cet endroit-là. Oui, mais en revanche, mettre des règles supplémentaires à respecter en se disant que les Européens vont s'en sortir mieux, c'est faux. C'est que vous avez des règles, vous avez forcément du lobby à mettre en œuvre pour que l'implémentation des règles soit jouable. Et en général, bah vous avez les acteurs américains qui sont énormes mais ils ont beaucoup plus de lobbyistes à Bruxelles que nous. Donc c'est eux qui finissent par les rendre les plus favorables pour moi.