--:-- Harry Stebbings: Sovereign AI is going to be the most important category.
--:-- Arthur Mensch: So the cost of energy at the end of the day is not the one thing that matters in the economics of a project.
--:-- Harry Stebbings: It's pretty clear that there is not enough compute.
--:-- Arthur Mensch: I mean, the inference market behaves very much like a commodity market, which is pretty high margin today.
--:-- Harry Stebbings: He's our last hope. He is the last hope in Europe for us to build a frontier AI lab. Yep, you guessed it. I have Arthur Mensch, co-founder and CEO of Mistral on the show today. It is now a $21 billion company. It is the company that is keeping Europe relevant in the race for frontier models.
--:-- This is Arthur like you've never seen him before. He's unleashed, he's funny, and he says some incredible things that I have to say I was not expecting. This was such a joy to do, and I'm so excited to welcome Arthur to the show.
--:-- [Ad break for Base 44, Laredin, and Shopify]
--:-- Harry Stebbings: You have now arrived at your destination. Arthur, dude, I am so excited for this. I have interviewed some of our favorite counterparts in the US. You are our remaining hope in Europe. No pressure, but thank you so much for joining me today, dude.
--:-- Arthur Mensch: Thank you, Harry, for having me today.
--:-- Harry Stebbings: Now, I was doing some research and I say some quite opinionated things, Arthur, but it seems I'm not the only one because you the other day said that leaving frontier AI to the US and China will end up costing Europe what remains of its place in the world.
--:-- Arthur Mensch: I did say that. And I truly believe that this is such an important technology that it needs to be distributed in the world. We're talking about a technology that is comparable to electricity, a technology that is falling more and more under export control. AI is going to be a big enabler of economic growth. If there's no AI being produced in Europe in particular, Europe will struggle to actually produce the growth it needs to produce.
--:-- Harry Stebbings: I'm going direct. To what extent does the success of Mistral depend on US overreach? Obviously when we have a Trump administration, we see things like Fable being banned and it causes a lot of questions and the need for sovereignty. In a world where there's not US overreach, does that threaten sovereign providers?
--:-- Arthur Mensch: I'd say this is structural. This is not really linked to any way of operating or of showing power. It's more linked to the nature of the technology itself. If you start as an enterprise or as a state to use AI in all of your core processes, it means that you want to make sure that the money you're spending on those processes actually accrues pretty close to you. If you don't, it means you have a trade balance problem. It means you have enormous flows of cash that are going from one place in the world, can be Europe, to another place in the world, which can be the US.
--:-- There is a macroeconomic problem to not being able to produce artificial intelligence, to do R&D in European territory. That is not linked to a certain way of saying that Europe is falling behind or a certain way of using soft power. So it's structural. I won't say that some people are not making it a little bit emotional, but when we started Mistral in 2023, we started with this conviction that this was a sovereign technology. As a company, as a state, wherever you are, whether in the US, in Europe or in Asia, you need to own the technology. You need to be able to customize the systems, to run them very close to your data. You need to be able to control the cost. You need to work in true partnerships with companies that understand how to build AI systems.
--:-- Sovereignty precedes the 2025 election. It turned out to be true, what we said in 2023. It turned out to become quite emotional when we start to speak about Greenland. But it's mostly an economic problem. It's not really a political problem. It's just too important a technology.
--:-- Harry Stebbings: When I hear you speak, I think of Lin at Fireworks, who came on the show and said very similar things. We need companies to have their own models, to train them very close to their own data. How do you feel when you hear me say that? And is that the evolution of the company, allowing companies to own their own models and work with their own data?
--:-- Arthur Mensch: The category we define, and the reason why companies work with us, is that we bring them sovereign AI technology. In sovereign AI technology, you need three things. First, you need to have a source of model that is secured. So you need a company that can train the models itself. We have that model factory. Mistral Large Four is actually leading the open-weight models in agency coding. It's leading in cyber defense and cyber security. That aspect of being able to constantly produce improvements on models and to be able to tell our customers that in two years from now, you'll get better models because we are constantly training them and spending billions on it is super critical to anything sovereign. It's the reason why Mistral has been created, and it's the main part where we're investing money.
--:-- Second, you also need to have control over your data. In general, you don't want that data to flow back to the AI providers. We've made sure at the technological level that our platform to run the agents, our platform for customizing models, actually sits on our customer's tenant. That's super important in many cases. When we work with manufacturing companies, they don't want their IP to flow back to a subprocessor.
--:-- Third, that's not enough in general because AI is consuming a lot of energy. When you move at scale, you actually need a lot of GPUs that as an enterprise, you have difficulty to procure. You also need to have token generation services and you need to have token factories. You need a combination of data that sits in your infrastructure and tokens that are produced in managed infrastructure at a lower cost.
--:-- What we do as a company is those three things. We train the models, we deploy the platform and the data plane with our customers on their tenant, and then we host what is energy intensive. That's the reason why we've been building our own infrastructure. We have these three things, and those are the three ingredients that are necessary to serve sovereign AI workloads.
--:-- Harry Stebbings: Can I ask, on one point you said the model is secured. I have the world's best experts on the show, Arthur. I'm very lucky. But they tell me different things. Some people say, you know, the Chinese open source models, you download them, you run them locally. Hey, they're totally safe. And I'm like, wow, great. And then next week, someone says, hey, but we have no idea whether there's prompt injections that you don't know. There's back doors. Absolutely not. You cannot be sure that they're safe. Help me out here, Arthur.
--:-- Arthur Mensch: Well, at Mistral, we have one of our businesses to really run the inference for open models. So we run ours, but we also run others.
--:-- Harry Stebbings: Why do you run others?
--:-- Arthur Mensch: Because customers want choice. When you have infrastructure, you want to provide as much choice as possible to saturate the infrastructure. That is the reason why we've come to actually serve more and more open models. In different domains, you may want to pick a different model. You may want to pick smaller ones. You may want to pick bigger ones. The more choice you have, the better usage you can make of the infrastructure, the lower your cost can be. In a way, it's a commodity market. So you want the cost to be as low as possible. That's the reason why we sell those models.
--:-- But what you say is true. Models are compression of knowledge. If you don't control the data supply chain, if you don't know what goes into the models, you cannot guarantee anything on the output. When we serve our models, we can give further guarantees to our customers than when we serve third-party models. Precisely because theoretically, you can make sure that in a certain environment with a certain context, the model behaves in a certain way. There's no way of excluding that. There's no way of measuring that. We can monitor that online. That's what we do. But you can't exclude that it's being used as a vector of attack.
--:-- I think it is very theoretical because I don't think it's the case today. But on the other hand, this is part of the threat model. Our customers are asking us to be able to provide them with all of the open-weight models that are available. We're helping them do it in a secured way where we can help them monitor what's going on. But in many cases, they're actually very keen to use our models because we're able to tell them that the data supply chain, everything we've trained them on, is something that we have validated before.
--:-- Harry Stebbings: What percent of token traffic is your own models versus other open source?
--:-- Arthur Mensch: I would say around 80% comes from our own models.
--:-- Harry Stebbings: Wow, that's a lot more than I thought. On the 20%, everyone tells me that American CIOs would never use Chinese open source models. And actually that fear really is there. You have them as customers, you have amazing customers. Are they fearful of using Chinese open source models?
--:-- Arthur Mensch: It depends how critical their industry is in general to governments. If you work with defense, for instance, we work with defense customers in Europe, they would never use Chinese technology. If you work in the finance sector, again, because it's critical infrastructure, they are very reluctant to do it. If you work in retail, if you work in certain domains of manufacturing, the reluctance is different.
--:-- I think there is a common understanding that open-weight models, if they are being deployed on sovereign infrastructure like ours, the only valid threat is that they go rogue and have weird behaviors in certain domains because you may have a supply chain attack on it. There is a common understanding that this is very theoretical and it's certainly not the case today. But indeed, it's part of the conversation.
--:-- What is also part of the conversation is not just, "Is it a threat to deploy third party models that I don't fully understand?" What is part of the conversation is, "Am I going to be able to get updates in six months from now?" Because what's going on is models have gotten so strong that they are able to do cyber attacks that you can use for pretty much advanced purposes. The consequence is that they are more and more being treated as defense systems. It's being treated de facto by the US as a defense system, because at some point in May, they started to ban them from outside of the US. If you look at what China is doing today, they're also looking at ways of restricting exports beyond a certain threshold. The reason for that is that effectively, this is a dual-use technology. As any dual-use technology, security of supply matters even more. The companies we work with realize that they need security of supply, not only on the infrastructure side, but also on the model IP side.
--:-- Harry Stebbings: With that in mind, do you think your OpenAI and your Anthropics withholding their best models is good or bad for society? And is that just an inherent feature that will be prevalent within this industry?
--:-- Arthur Mensch: Is it good or bad for society? I don't know. What is clear is that there is immense leverage to centralize the AI providers in a world where there's no provider that is allowing decentralized deployment, that is allowing sovereign deployment. What we are doing is extremely important for companies.
--:-- If we end up in a world where all companies need to rely on two or three centralized AI providers that give them enormous leverage, they will use the data that is being produced by the systems to make better systems and to go after the business of their customers. At the end, this is the end game. This is pretty clearly what is being priced in today.
--:-- The approach we're taking is to make sure that this world in which you have an oligopoly of AI providers that are leveraging all data being produced by enterprises to make better systems that can intermediate with enterprises is not the world we live in. We're very aligned in that with all of our customers that, of course, don't want to live in such a world.
--:-- I would say the fact that there are internal models that are being used to do certain research is the canary in the coal mine of what can happen in a world where you have massive concentration of power in artificial intelligence. Luckily, it's not that complex to build models. It's about deploying capital. It's about having the right team that is training the right thing. We've been able to do it. The open-weight frontier is catching up with the closed frontier. In many domains, it's actually above. I don't think we'll end up in a world where if you want to do mathematics, you need to work at OpenAI. Luckily, there are enough people in the world that don't want this to occur.
--:-- Harry Stebbings: You said the word energy a couple of times. One of the core underlying tenets of being successful is having the energy and the infrastructure to power European AI. We have a very different energy system to the US and we have a very different energy system to China. In China, they'll move 3 million people to put some form of energy infrastructure. And in the UK, we won't move a birdhouse. It's quite concerning, despite the joke. How do you think about the energy supply challenge?
--:-- Arthur Mensch: Well, I think the first thing to realize is that energy is around 10% of the bill in the total cost of ownership of compute. So we're not talking about the place where we can make most of the optimization. The cost of energy at the end of the day is not the one thing that matters in the economics of a project. What matters is the cost of the chips and what matters is the cost of capital. That is dominating the bill of material.
--:-- So it's a problem. It will become more and more of a problem as we deploy more compute in Europe because we'll run into supply constraints. Again, it's not evenly distributed. If you look at France, for instance, there is excess energy, so you can build capacity. If you look at Nordics, it's also the case. In the UK, it's a little less the case. Germany, it's a little less the case.
--:-- But there is a realization that AI is going to drive energy consumption forward. In the next two or three years, the true challenge is to make sure that for Mistral, we build as fast as possible to use the excess of energy that is already there. That is what we are at work doing: raising capital, building more, building the team to build all of this capacity. There is excess of energy. It's not the bottleneck today. It will become the bottleneck in 2029, 2030. But right now, the question is, how can we build on existing energy?
--:-- We need to think ahead of time. Starting in 2029, 2030, as multiple gigawatts of capacity are being deployed every year on AI, we do need to ramp up the production. What we are hopeful for is that in France, for instance, there is an enormous EPR program where nuclear energy is going to be produced much more. You have large efforts on the solar side as well. It's going to be a problem. It's mostly a coordination problem because if you have demand coming from AI on energy, the business case for building new nuclear plants is pretty well made.
--:-- Harry Stebbings: It totally is. And I agree with you. I am a big believer in Adam Smith's invisible hand, but the lead time is very real. 2029, 2030 is a challenge.
--:-- Arthur Mensch: Yeah, nuclear plants take longer than that to build. Solar plants can go faster. I do believe in that invisible hand, but the states have to play a role in facilitating the build out, but also in planning ahead of time. They have a longer term vision than enterprises in general. We are spending some time, I'm spending some time explaining that the energy demand is going to grow and that it needs to be addressed somehow. It needs to be part of the business plan of any energy supplier.
--:-- Harry Stebbings: You said about the cost of chips being important. What do you know now about compute buying, forecasting demand of compute chips that you didn't know?
--:-- Arthur Mensch: It's quite a process. We've learned to run that process to match supply and demand. It's a complex industrial process. It has nothing to do with software. You need to understand the latency in the supply chain. You need to understand that the supply chain comes from... it's fairly complex because you have GPUs, but you also have CPUs, you have storage, you have electrical parts. You need to make sure that your own suppliers have no problem with their own suppliers.
--:-- That aspect is something we actually learned, but it's a fairly complex one where you need to manage resources. You need to have multiple forecasts. You need to be able to forecast your demand from multiple buckets: enterprises, which are our main ICP, but also AI natives that can take larger pieces of GPUs but want them for a shorter period of time. You have companies like Microsoft that are off-taking capacity from us that we can use to finance more capacity.
--:-- At the end, the problem of building a cloud is about matching supply and demand. It's about managing the supply chain ahead of time, giving visibility to your suppliers. And it's about matching all of this with the finances. You need project financing. You need people that are strong at understanding credit. It's actually quite interesting to build such a business.
--:-- What we've learned is that at the end, if you're able to front capacity ahead of demand, you get something that is pretty easy to sell. And it's going to be the case for the next few years. We're very grateful that we anticipated the fact that we would run out of supply fairly quickly, that our customers would have supply chain problems for their own GPUs. Because now that we have those GPUs, we can solve their needs.
--:-- Harry Stebbings: That was one, which is cost of chips. Cost of capital was another one. If you were resource unconstrained completely, what would you do today that you're not doing?
--:-- Arthur Mensch: We would build much more capacity. I think the big bottleneck in building capacity is, of course, capital. We see the demand and we know that our customers are running out of compute. As we take some of their use cases and we help them scale it, suddenly they realize they don't need eight GPUs, but they need a thousand GPUs. And that's for one use case. We see that demand ahead of our customers. So we have pretty high conviction. That's why we put equity at work to build capacity.
--:-- What we are working on, what we have always been working on, is to make sure we have more capacity under management, even by building ahead of demand. Today, if we just look at what Europe needs, it's pretty clear that there is not enough compute. You may argue that there is maybe a little too much compute being built in the US. But what is clear is that there is a lot of slack to build capacity in Europe in a way where you are 100% confident that it's going to be allocated. So that's what we're working on.
--:-- Harry Stebbings: You said there's a role to be played by the state as well. What would you like to see the state, be it France, be it the UK, do that they're not doing today?
--:-- Arthur Mensch: We've been quite vocal about the kind of ways in which they can intervene in a way which is market neutral and that would facilitate investment. The first thing is, of course, to facilitate permitting and to make sure that there are dedicated sites where things are accelerated, where all stakeholders, whether regional or state level, are aligned. I would say France and Sweden are making a great job at that, for instance. So that's mostly on facilitation of aligning stakeholders because these are physical assets. They are built within regional communities. It's super important to make sure that everybody is benefiting from it. The help of states happens there.
--:-- What they can do at the financial level, and we've said that publicly, is that state guarantees on leasing data centers, for instance, on reserving space for 10 or 15 years, are super useful for companies like us that have relatively small balance sheets and that know how to build and who to build it for. That's a role they can play by putting some guarantee on the table to build further and ahead of time.
--:-- And of course, public offtake. We see the public sector is an important customer for us from a lot of different states in Europe and outside. We bring them value. They bring value to the citizens. It creates offtake for companies like us. And that allows us to build further. And if you build further and faster, you build cheaper in this day of supply constraint.
--:-- Harry Stebbings: You said you sell to states there. Going back to that element of sovereignty, how far down does sovereignty need to go? What I mean by that is we obviously have Cohere in Canada. Does every country need their own model? Is it blocks that need their own model? How do we think about the granularity of sovereignty?
--:-- Arthur Mensch: I think the granularity that is allowed is you need a fairly large market to build relevant models. Today, Mistral is able to build relevant models because we operate not at national level. We're not a French company, but we serve Europe, we serve the US, we serve Asia. What we need is alternatives, but there will not be a single sovereign AI provider and model trainer at every state level. This is not going to happen.
--:-- Harry Stebbings: What percent of your customers are European?
--:-- Arthur Mensch: If we look at our revenue base, it's around 60%. The rest comes from the US and from APAC.
--:-- Harry Stebbings: And they use you to do post-training harnesses for their own models?
--:-- Arthur Mensch: European ones, they use us for multiple reasons. We help them build applications on either things that are related to the efficiency of their businesses or related to accelerating their R&D. We work with manufacturing companies, with financial services, we work with defense companies, and we have a pretty strong verticalization approach to all of these three domains. The fourth being public sector.
--:-- The way we work with manufacturing companies is, in general, we are with their R&D teams and we help them build better products. So we help ASML build better machines, operate better machines. We help Airbus build better planes. We bring reasoning models, but we also bring the physics models, the models that have a quantitative understanding of the world.
--:-- In financial services, we build models and harnesses that understand markets. We also bring models that understand quantitative time series to help do prediction. We come with a set of models that allows us to quickly build applications that bring value to our customers.
--:-- Back to your point on sovereignty, all of these companies have a sovereign AI strategy. They need deployment of the technology in a way where they have business continuity guarantees, affordability constraints, etc.
--:-- Harry Stebbings: We see OpenAI, Anthropic, DeepSeek move into the application layer and also move into the chip layer with Jalapeno, with Anthropic's rumor, DeepSeek are doing their own. Will we see a Mistral chip? How do you think about the importance of verticalization and owning every layer?
--:-- Arthur Mensch: No, it's not planned. We have gone down the stack, or I would say upstream if you look at it from an industrial perspective. We have gone from training models to serving the models we train on infrastructure that we own. That's already a pretty significant move. That's a move that took around 18 months to set up. And that is now pretty successful because we can serve the GPUs, we can serve tokens. We have tokens that are purely Europe generated in that it escapes any form of US extraterritorial law. It matters a lot for our customers.
--:-- That took us some effort. We are doubling down on this effort to scale that further. I think that's going to keep us busy for a while. We are pretty vertically integrated in that we have cloud services, infrastructure, and we go all the way to building the business applications for our customers.
--:-- Harry Stebbings: You mentioned the application layer.
--:-- Arthur Mensch: In AI, you bring the platform and you build custom applications for customers. That's how it works. That is what AI allows you to do.
--:-- Harry Stebbings: And sorry, this is through an FDE army?
--:-- Arthur Mensch: Yeah, it's FDE, but building on top of common primitives. So workflow management, agent management, management of sandboxes, observability of the deployment of different business applications. At the end, we are building cloud services that are adapted to run and build with AI agents.
--:-- Harry Stebbings: How do you think about doing it through Mistral FDEs versus a partnership economy, which we've traditionally had?
--:-- Arthur Mensch: It has been super helpful to have our FDEs front and center at the beginning of our journey, because it allowed us to understand exactly where enterprises were struggling. They started with individual productivity. It was not enough. Chatbots were not delivering any kind of value creation for them. We went and spent some time with the managers, the business unit owners, and understood the kind of core processes they needed to automate with artificial intelligence.
--:-- We did it in a way where we built business applications, custom applications that are managing state, applications where agents are operating the process end-to-end. It was super helpful for us to understand the primitives to build. So we built durable process execution, for instance. We understood how to orchestrate agents in a way that was very effective. Having this FDE sit with customers directly has been super useful.
--:-- Now, what we have done since last year is to start propagating the tools to some of our partners, like Accenture, like TCS. System integrators that are great at scaling things, once you give them the right tools, once you give them the right training, they can help scale because they have just more people. But having people actually sitting hands on keyboard from Mistral with our customers is super useful for our customers and it's in high demand. It's super useful for us. It's not something we can scale indefinitely because we can't have tens of thousands of FDEs working for us. But it's something that now we are scaling through partners and the two motions coexist.
--:-- We are in many accounts, we have FDEs that are working directly with customers. They're operating the projects. They're making sure that the delivery is occurring. And we work hand in hand with system integrators to do the work, to then do the maintenance and these kinds of things.
--:-- Harry Stebbings: What line of revenue do you not have today that you think will be most significant in five years' time?
--:-- Harry Stebbings: When I was listening to you there, I was thinking, I bet you wouldn't have necessarily predicted these different motions when you started, when we first met, Arthur. I don't know if you remember, but you were sitting on a park bench just chatting to me about life.
--:-- Arthur Mensch: I was.
--:-- Harry Stebbings: Yeah, you did not invest. You should have. Dude, I fucking regret it so much. I saw Jean-Charles yesterday. And every time I see Jean-Charles, we talk about you. Yeah, it's a brilliant reminder of just how much money I lost. It's all missed out on. Sorry about that.
--:-- Arthur Mensch: No, what line of revenue? I mean, we have these two lines of revenue. We have one where we deploy the platform that is truly licensed revenue for building business applications. And then we have the consumption revenue coming from the infrastructure business that we built. And the two things go hand in hand because our customers, they come in general for the license motion, but then they realize that they don't have enough GPUs. And then we try to be helpful to them by providing them with either GPUs or token services. So the two things are very well combined together.
--:-- Harry Stebbings: Do you think you'll have an inference business, a big inference business in three to five years' time? Do you think you'll have a sandbox business? If you look at this granularity...
--:-- Arthur Mensch: I'd say the GPU side, like everything sitting on GPU, is going to continue to grow because the demand is enormous. You have what we call ancillary services, like running sandboxes, scaling up and down sandboxes very easily when you're running reinforcement learning algorithms, for instance, or when you're deploying many agents on the same tasks. That is a growing business for us because it comes from the demand of our customers. And in turn, that brings us to buy more and more CPUs. So I do expect what we call the runtime side — where agents execute tasks, call tools, compile code, and run the code — to grow.
--:-- It's pretty clear that when you were in synchronous tasks where you were only using GPUs to answer conversations, you didn't have that. When you start to have managed agents that are running in the background, doing a lot of tasks that are CPU intensive, you need more CPUs, you need more sandboxes, and you need more storage as well because they produce a lot of data. So those two things — CPU-related tasks, data-related tasks — are going to be growing lines of business for us.
--:-- Harry Stebbings: A big part of the business is selling to governments. What does no one know about selling to governments that everyone should know?
--:-- Arthur Mensch: The first thing is that there's no such thing as selling to governments in general. All governments are very different. Some governments are small enough so that it looks more or less like an enterprise sales motion. It's the case of governments where everybody in ministries knows each other, for instance. Other governments in larger countries are slightly more complex. So again, the complexity of finding where to create value, of understanding what the customer needs, grows with the size of the country, I should say.
--:-- Harry Stebbings: Can you help me on the inference market? It's something that I just totally didn't know enough about. What I know now that I wish I'd known 12 months ago was that it's just infinite demand for inference. The depth is astonishing. What happens with the inference market?
--:-- Arthur Mensch: The inference market behaves very much like a commodity market, which is pretty high margin today. If you have supply, if you have the right quality of service for certain kinds of models, you can sell them on demand because you have a lot of demand coming directly from customers that are non-committed. And then for customers that are sufficiently mature, they need to have guaranteed quality of service. They need to have service level agreements, and there they need to have reserved physical capacity. And that's something that we can provide because we have the right forecasting models and we have the right supply.
--:-- So you have these two things. You have one which is on demand, which very much behaves as a commodity market where pricing matters a lot. And then you have a premium side of the market where the customers actually want guarantees on latency, guarantees on the speed of generation for a certain number of requests. The more they can be precise about the shape of their demand, the better our pricing can be, the better we can shape our own infrastructure to make sure that they have the right quality of service for the right price.
--:-- Harry Stebbings: We have so many different Neolab providers. There've been 102 different Neolab providers created. Every week, there's another DeepMind spin out and another. What will separate Neolabs that thrive and those that don't?
--:-- Arthur Mensch: Well, the first thing — I mean, we started as a Neolab ourselves. It wasn't called that way in 2023, but effectively, when you start a company, you have a bunch of silver bullets to hire the best. And we used all of our silver bullets to hire researchers. So effectively, we started as a research company. I think what has allowed us to scale and to now operate in a much wider way, still with this number one pillar, which is to deliver models — and that's why StarLarge4 has been very successful for us, and I think it's very exciting as well in the new releases that are coming because we're making pretty good progress — what has been successful is that we focused early on the market that would scale.
--:-- So we went for the enterprise market, which comes with a certain number of complexities because enterprise customers are demanding, as they should be. And we have adapted our product to meet those demands. But the fact that we were able to both operate at the research level, at the highest level, and able to constantly recruit and invest further in making stronger and stronger models, and the fact that we built a customer-centric motion where we understood the problems of our customers, has allowed us to scale. And that's something that every Neolab needs to think about when they start building their business.
--:-- Harry Stebbings: But how many people are there actually in Europe who truly have the ability to create these companies and have been and seen it like you have? I don't know, maybe a Jan LeCun has done. Is it 20? Is it 100?
--:-- Arthur Mensch: I mean, I'm not a VC investor, so you probably know it better than I do. There's a lot of talent in Europe generally. You have a lot of entrepreneurial talent. You have a lot of researcher talent and you have a pretty good combination of those two. So that's why you see a lot of Neolabs in Europe. The hard thing is it's not only about being talented. It's not only about being able to sell and having something that you can successfully create business with. It's also today about being able to access compute.
--:-- I think a big bottleneck today for many Neolabs that I know is that accessing GPUs to actually do interesting research in certain verticals is hard because there's an enormous supply crunch. And so that's why in the supply we have, we are reserving some capacity for certain Neolabs because we believe that they can grow and that we can grow with them. But effectively, if you're a founder today and you want to do something which is very research centric, the biggest problem you have is to get access to enough compute with a decent price.
--:-- Harry Stebbings: I see you've mentioned the word researchers there. How crazy is the researcher hiring market today?
--:-- Arthur Mensch: Well, it's pretty competitive, I should say. Again, if you have the right mission, if you have the right compensation structure, if you're doing something that matters for the world, people will join us. And I would say we're very grateful for the dedication of all of our employees to making Mistral successful. The quality of the Mistral Large 4 has been a success, has a lot of traction today, and is leading the pack in the open weight category in coding and in cyber defense. It's only due to the dedication of this team in the last three, four months. The resilience as well, because when you run a model — we've posted our tech report recently — oftentimes it doesn't work the first time. And so you need to be able to course correct in a way where, okay, you're going to spend two more months to actually make it work, and it's going to be painful. We're expecting to release, but we'll need to release a little later. It's going to be painful for everyone, but everyone needs to be all hands on deck and do the right thing. And I'm very grateful for the resilience of the team, given the scarcity of resources that we have.
--:-- Harry Stebbings: I just had Satya from Microsoft on the show and he talked about Mustafa's statement before that we'll see $100 billion training runs in the next few years. Do you think that's a little bit hyperbolic or is that actually well within reach?
--:-- Arthur Mensch: You do have a data wall before, I should say, in that at some point, if you want to make your model very strong at certain domains, like chip design, for instance, or if you want to make it very strong at operating industrial engineering workflows like we do, you do need to have the right environments to train them. And this part takes some skill and taste. It's not something that you can scale as easily as just stacking compute. So I would say the bottleneck that we're all running into today is effectively to be able to source enough quality data. It's compute. We need more compute. We're deploying more compute. Getting to $100 billion of training runs, I think, is not for tomorrow, clearly.
--:-- Harry Stebbings: If everyone's so short on compute, why is Nebius 40% down? Why is CoreWeave 40% down? Why is the AI trade cracking in public markets if it's so clear that everyone is so compute starved?
--:-- Arthur Mensch: It's a little unclear to me how markets are reacting today. What we see is that we're fully able to finance our own compute, given the quality of the collateral that we have in terms of revenue commitment, take-or-pay commitments. We as a company operate as a vertically integrated company. We build compute, but for higher level purposes, for serving the enterprises, for building business applications. So we are anchored on the value creation of our customers, which means that at the financing level, we haven't had to go and leverage the company enormously on debt financing. And so that gives us a lot of confidence in what is going to happen. But effectively, as in any industrial buildout, it's tempting to get a lot of leverage, but at some point leverage breaks. And so that's why we're very happy to be vertically integrated and to operate from business application creation all the way to the infrastructure part.
--:-- Harry Stebbings: What did you not do with the benefit of focus that with hindsight, you go, "I wonder if we should have done that"?
--:-- Arthur Mensch: We did not go deep into the consumer market, but I think we're very grateful that we did not do it. We did not go into chip design. I think that's...
--:-- Harry Stebbings: Pause. Why do you feel grateful you didn't go into the consumer market?
--:-- Arthur Mensch: I think it's a very packed market and it's very hard to operate at the highest level with enterprises and also to run what is inherently going to be an ads business. So that part, that decision to focus on the enterprise segment — also that decision to focus on the four verticals that we have decided to focus on: manufacturing, financial services, defense, public sector — has been very critical to focus the company. Because the problem with artificial intelligence is that it can do everything. And you need a source of focus. And so the source of focus we've picked comes from the market. So we want to have the models that serve the best agentic engineering workflows, that serve the best financial decision making, that serve the best defense purposes and everything related to state sovereignty as well.
--:-- Harry Stebbings: With RSI, do you need a source of focus?
--:-- Arthur Mensch: I mean, using AI to improve AI models does require focus as well because you have a limited amount of resources to spend on problems. So at the end, the bottleneck here is indeed compute. The bottleneck is also that everything that you do which is recursively self-improving — or just self-improving, because it means the same thing — requires having well-defined problems. If you have a well-defined problem today with artificial intelligence, you can later climb it. In many cases in enterprises, the problems are not well-defined. And so the true friction to economic growth is not the fact that we cannot solve hard problems that are well-defined. It's that it's hard to solve ambiguous problems where you have ambiguous objectives, where you have humans in the loop. And that part you cannot really solve with self-improvement. You can only solve with models being deployed and interacting with humans.
--:-- And that's the part that we are focusing on, I would say, that we are investing a lot on — making self-improving loops on well-defined problems as well. But that is the part that is constraining the explosion of AI usage in the enterprise: the poorly defined problems on which you want to deploy AI, but you have no way to lay the climb because the definition of the problem is not proper. And so you need to spend some time. That's why you have FDEs. That's why we have FDEs, to try and better define the problem.
--:-- Harry Stebbings: Arthur, I've got to be blunt with you. I joked at the beginning that you're like Europe's last hope. There is — and I don't mean that super dramatically — but there is a very big chasm between you and anyone else in terms of Neolabs that will aspire to be the next Mistral. Do you and the team feel the pressure?
--:-- Arthur Mensch: We do feel some pressure, but as anyone in the field, this is fast growing, fast moving. You need to, as an entrepreneur in AI, you need to deliver on the model side because that's the one thing that shows the progress you're making. Then enterprises need to see the value. So you need to have people that are understanding the true business problem. So it's a fairly different problem to solve. So we do feel some pressure, but I think it's a very healthy pressure.
--:-- It's a very important moment in time and in history. We need to be meeting the expectations of the general opinion. At the end, we need to show that AI is useful. The kind of deployment we're making, the kind of noise this is making in the market — this technology needs to appear as a positive technology. We need to show the positivity of that technology. And the only way in order to do so is to promote sovereign AI as a category, is to make sure that it creates economic growth and opportunities for everyone, to make sure that the gains of AI are well distributed. And so, of course, it creates some pressure because it's not a given. If we don't succeed, I don't know what's going to happen in the world. I think so. We do have a role to play. I think the mission we play is important and it's pressure, but it's also quite exciting.
--:-- Harry Stebbings: Will AI inflect GDP growth by more than 2% a year?
--:-- Arthur Mensch: Well, that's the entire question. What kind of bottleneck do you go and solve to actually inflect that GDP? That's the reason why we were very bullish on industrial engineering. At the end, if you want GDP growth, you need more production. You don't need more intelligence. You need more production. In order to have more production, you need better factories. You need objects that are more efficient. You need better planes that are flying for longer with the same amount of oil. You need better energy production that is more efficient, less carbon intensive. You need better ASML machines. You need better services also in financial services.
--:-- In industry and engineering in particular, using AI allows you to do better design of objects. So if you set up the agentic loop to design safer cars, for instance, you can have your car design team go much faster and iterate faster on that product. That means that you can sell more. That means that you can sell more cheaply. That means that you're building safer products. In the aerospace industry, if you design a better plane, it's going to be out there for 40 years. If it runs faster with less fuel, then it's going to create GDP growth. So at the end, what truly matters is to enable AI to have a physical effect. This effect we find mostly in manufacturing and financial services.
--:-- Harry Stebbings: I'm aware of time. I'm going to do a quick fire round with you.
--:-- Arthur Mensch: Okay, please do.
--:-- Harry Stebbings: What do you believe today that most around you would disagree with?
--:-- Arthur Mensch: I think we strongly believe that this is a fragmenting market and that sovereign AI is going to be the most important category of AI deployment in the enterprise. When enterprises are going to deploy the technology, business continuity, cost, and control over their data and IP is going to be the one thing that matters. So I think that's a very strong conviction that we have. That is shared by a lot of enterprises and shared by a significant part of the market, but of course not shared with some of our competitors.
--:-- Harry Stebbings: What is an extremely wild prediction today that will be very common in five years' time about the way that we live, the way that we work?
--:-- Arthur Mensch: I think the way we work as engineers has already changed for software engineers, and it's going to completely change for any kind of engineers. So I'm super excited by that approach of designing better products in a way which is fairly autonomous, thanks to artificial intelligence. So it does not concern everybody, but everyone with a technical background — and not only the software engineers that were the first affected and the mathematicians — everyone with a technical background will be operating AI agents in a way that will give them enormous leverage. So this in itself is going to be a major shift in the way the labor market is actually working.
--:-- Harry Stebbings: What do you do about going public? Because one day you will want to, okay? But you can't go public in Europe.
--:-- Arthur Mensch: You can absolutely go public in Europe.
--:-- Harry Stebbings: Oh, come on.
--:-- Arthur Mensch: Yeah, you need to be on the rest of the... Are you going to go public in France? No. What used to be true 10 years ago, I think is no longer true. There's a huge appetite from investors from across the world to invest in Europe, both on the private equity side, but also on the public equity side. And so the bottlenecks that used to be there are no longer there. So we at Mistral are, of course, getting prepared on the operational constraints when you go and become public. We're also pushing and delivering on the commercial milestones that we need to deliver on. But I don't think this is any more a bottleneck. And there are widely successful companies. Some of them are stakeholders that are firstly listed in Europe. So there is no reason why not to go and IPO in Europe at some point.
--:-- Harry Stebbings: Final one for you, dude. What are you most excited about when you look forward to the next five, seven, 10 years that not many people think about?
--:-- Arthur Mensch: I'm quite excited by how AI is going to enable foundational research in domains where we lack understanding of the world. So in physics, for instance, the fact that we can both run AI simulations at a scale that was nowhere reached — and that's the team we bought called Amy, a specialist in that — is going to enable new discoveries. And so the fact that we can build simulators and that we can have agents operating those simulators to understand better the world is going to be an enormous enabler of the understanding of the world. So I come from a math and physics background, and so I'm quite excited by the opportunity ahead of us in the next generation of researchers.
--:-- Harry Stebbings: Do you know what, Arthur? I blame you. I blame you when you sat on the bench and you didn't speak at all like you speak now. Like the Arthur then and the Arthur today are just paradoxically different, with respect. It's amazing to see the leader that you are and you've become. I honestly do. I have so much respect, admiration, gratitude for you and all you've done for Europe. But wow, it's amazing.
--:-- Arthur Mensch: Thank you for the praise. There's still a lot of things we need to deliver on, but we are quite happy with the progress we've done. I think the mission is important. And so if anyone watching us wants to join us in that journey, please contact me.
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