The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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?
00:25:41 · Arthur Mensch
[00:25:41] S0: It has been super helpful to have our FTEs front and center at the beginning of our journey because it allowed us to understand exactly how... Where enterprises were struggling. They started with individual productivity. It was not enough. Chatbots were not delivering any kind of value creation for them. And so 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. And it was super helpful for us to understand the primitives to build. So we build durable process execution, for instance. We understood how to orchestrate agents in a way that was very effective. So having this FDE or 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, to system integrators that are great at scaling things and that 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 FTEs 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 FTEs that are working directly with customers. They are operating the projects. They are making sure that the delivery is occurring, and we work hands in hands with system integrators to do the work, to then do the maintenance and these kind of things.
00:27:27 · Harry Stebbings, Arthur Mensch
[00:27:27] S1: What line of revenue do you not have today that you think will be most significant in five years' time? 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 Mensch, I don't know if you remember, but you were sitting on a park bench just chatting to me about life. [00:27:45] S0: I was. Yeah. You did not invest. You should have. [00:27:48] S1: 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. [00:28:00] S0: Sorry about that. Now what line of revenue? I mean, we have these two lines of revenue. We have one which where we deploy the platform that is truly licensed revenue for billing 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 we are able to our customers, they come in general for the license motion, but then they realize that they don't have enough GPUs. And then we are trying to be helpful to them by providing them with either GPUs or token services. So the two things are very well combined together. [00:28:34] S1: Do you think you'll have an inference business, a big inference business in three to five years' time? Do think you'll have a sandbox business?
00:28:34 · Harry Stebbings, Arthur Mensch
[00:28:34] S1: Do you think you'll have an inference business, a big inference business in three to five years' time? Do think you'll have a sandbox business? [00:28:40] S0: Yes. If you look at this granularity, I would 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, running 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 of things, so where agents execute tasks, call tools, compile code and run the code to grow. It's pretty clear that when you are in synchronous task where you are only using GPUs to answer conversations, you didn't have that. When you start to have managed agents that are running on 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, I would say, CPU related tasks, data related tasks are going to be growing line of businesses for us. [00:29:47] S1: A big part of the business is selling to governments. What does no one know about selling to governments that everyone should know?