The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:19:18 · Harry Stebbings, Arthur Mensch
[00:19:18] S1: That 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? [00:19:28] S0: We would build much more capacity. I think that's the big bottleneck in building capacity is, of course, capital. And we see the demand, and we know that our customers are running out of compute. As we take some of our use cases and we help them scale it, suddenly, they realize they don't need eight GPUs, but they need 1,000 GPUs, and that's for one use case. And so 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. And so 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. And 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, that's what we're working on. Well, you said, hey. There's a role to [00:20:25] S1: 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?
00:20:33 · Arthur Mensch
[00:20:33] S0: And we've been quite vocal about the kind of ways in which they can intervene in a way which is market neutral, that just 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. And I would say France, 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. And so 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 ten or fifteen years are super useful for companies like us that have relatively small balance sheet and that knows how to build and who to build it for. So that's the role they can play by giving some guarantees, putting some guarantee on the table to build further ahead of time. And then of course, public offtake. We see public sector is an important customer for us from a lot of different states in Europe and outside. And again, we bring them value. They bring value to the citizens. It creates off take 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.
00:22:02 · Harry Stebbings, Arthur Mensch
[00:22:02] S1: You said you sell to the states that... And kinda going back to that element of kinda sovereignty, how far down does sovereignty need to go? And what I mean by that is, like, we obviously have Cohere in Canada. Does, like, every country need their own model? Is it blocks that need their... How do we think about like the granularity of sovereignty? [00:22:19] S0: I think the granularity that is allowed is that you need a fairly large market to build relevant models. And 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. And 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. [00:22:44] S1: What percent of your customers are European? [00:22:46] S0: If we look at our revenue base, it's around 60%, and the rest comes from The US and comes from APAC. [00:22:53] S1: And they use you to do post training harnesses for their own models?
00:22:53 · Harry Stebbings, Arthur Mensch
[00:22:53] S1: And they use you to do post training harnesses for their own models? [00:22:57] S0: European ones, they use us for multiple reasons, but we help them build applications on either things that are related to the efficiency of their businesses or related to accelerating their R and 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. And the fourth being public sector. The way we work with manufacturing companies is, in general, we are with their R and 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. And 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 predictions. So we come with a set of models that allow us to quickly build applications that bring value to our customers. So that's how we serve them. And I guess that back to your point on sovereignty, all of these companies have a sovereign AI strategy. They need deployments of the technology in a way where they have business continuity guarantees, affordability constraints, etcetera.
00:24:04 · Harry Stebbings, Arthur Mensch
[00:24:04] S1: We see OpenAI, Anthropic, DeepSeek move into application layer and also move into chip layer with Jalapeno, with Anthropic's room and DeepSeek are doing their own. Will we see a Mistral chip? How do you think about the importance of verticalization and owning every layer? [00:24:19] S0: 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. So that's already a pretty significant move. That's a move that took around eighteen months to set up, and that is now pretty successful because we can sell the GPUs, we can sell tokens. We have tokens that are purely Europe generated in that it escapes any form of U. S. Extraterritorial law. It matters a lot for our customers. So that took us some effort. We are doubling down on this effort to scale that further, and 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. You mentioned the application layer. 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. [00:25:14] S1: And sorry, this is through an FD army?
00:25:14 · Harry Stebbings, Arthur Mensch
[00:25:14] S1: And sorry, this is through an FD army? [00:25:17] S0: FD? It's FD, but building on top of common primitives through workflow management, agent management, management of sandboxes, observability of the deployment of different business applications. So at the end, we are building cloud services that are adapted to run and build with AI agents. [00:25:35] S1: How do you think about doing it through Mistral FTEs versus partnerships economy, which we've traditionally had?