Mistral AI vs. Silicon Valley: The Rise of Sovereign AI

0:00I think the expectation is that demand and amount of tokens generated for the enterprise will completely jump once you are not bound anymore by humans asking questions or reading them. As soon as you have enough trust to have agents running in the background, you're not really limited by the number of tokens. The term we use is control. The software stack, once deployed, is in the hands of our customers. they own the model changes that we make. And I think it's really important as a customer to consider that your expertise and what makes your company valuable stays yours. Hi, I'm Matt Turk. Welcome back to the Matt Podcast.

0:43Today, we have a special episode with Timothée Le Croix, the CTO and co-founder of Mistral, the company that proved that you could build frontier models with a fraction of the compute of the US giants. But recently, Mistral has quietly evolved into a much more ambitious full-stack industrial power, building not just the models, but the platform, the deployment stack, and their own massive supercomputing clusters. We covered a lot of ground in this one, the engineering behind Mistral 3, what sovereign AI actually means in practice, and Tim's contrarian view on why trust matters more than autonomy for agents.

1:17If you're tired of the AI hype, Tim is refreshingly no-nonsense. Please enjoy this great conversation with Timothee LeCroix. Hey, Timote, welcome. Hey. So as I was prepping for this, I was struck by how much has been going on at Mistral over the last few months. I think most people probably know Mistral as a provider of open source models. It seems that you guys evolved from an AI lab to more of a full stack solution focused on enterprise and sovereign customers. So just to set it up, in the last year, you guys raised a 1.7 billion euros Series C led by ASML at an 11.7 billion post-money valuation.

2:04You launched a bunch of models, which we're going to talk about. Is the big vision behind all of this that enterprises and sovereign states are going to need their own AI infrastructure and Mistral is going to be the provider? So the big vision has been evolving. And as you stated, we started as a company that built models, because with Arthur and Guillaume, this was what we knew how to do at the start. The premise on which we built Missful.ai was immediately solving for enterprise needs. And we started with OpenWeights model. After this and working with enterprise, we realized the need for basically the rest of the stack.

2:45So we built the serving platform because infrastructure was needed. And then all of the tooling around it was also something that we saw was missing. More than the tooling, it also requires a lot of work and expertise still to get deep into an enterprise workflows and really help that transformation. And so we built that FDE function. And more recently, with Mistral Compute, we're going a bit lower in the stack as well. So we've done all of this because it was required for enterprise success while still continuing on our models journey. All of this stack being modular is really important to us as it gives full control to enterprise and our clients as to which part of the stack they decide to own and control, which is maybe more involved, or that they decide to have serverless or basically this modularity that we like.

3:43All right. So let's take some of those modular components in order. Let's start with Mistral Compute. So that was a big announcement, I guess, in June of 2025, putting a big partnership with NVIDIA to help with this effort. What's the current status? Is that live yet? Are you building it? You know, how does one go about building data centers or leveraging data centers in Europe? Maybe first to go into the reasons why we decided to start building our own data centers. We tried a lot of different partners over the years and we realized that our use of the AI compute for large scale training was not necessarily well understood by a lot of providers.

4:31and our need for stability, especially like when you run inference on a few GPUs or when you run small scale trainings on hundreds of GPUs, margin for error is a lot larger than when you run trainings on thousands of GPUs at the same time. And so to address this need for stability, we saw a way for us to basically build our own data centers and maintain it with our understanding of what quality looks like. And so that was why we launched Mistral Compute. And when we decided to do it, we also realized, well, maybe others will benefit from it. We launched into a bigger, basically, development than what was previously intended.

5:13And so this was announced in June, as you said. Since then, the building of the facility has progressed quite well. It's in the south of Paris. And we are right now running through the stabilization of the first trench. So it's quite a large data center. So delivery doesn't happen in one day. And the first part of this data center is something that we are working on as we speak. We have a few jobs running and we're fine tuning basically all of the last things to run at speed and with the right stability. Okay, great. And did I understand correctly, it's going to be for your customers and your own needs around training, but also you'll be providing it as a service to others in Europe and beyond?

6:00Yeah, exactly. So we will use part of that capacity for ourselves as one of our training clusters, but we will also provide a managed Kubernetes and managed Slurm stack on top. Okay. Any lessons learned so far? I mean, as you said, you guys come from a very deep background in AI and AI research. It's a whole different thing to build a whole data center facility. How have you gone about it? And what are some things that surprised you and any lessons so far? As most new experiences as a founder, I relied on the knowledge of others. And so I was lucky to have a few seasoned HPC experts and a lot of cloud software experts as well to build that solution.

6:45For me personally, and it's one of the things I love about my position at Mistral is that I get to discover so many new things and so many new problems I hadn't thought possible. having to learn to like all of the different parts of building a data center all of the different trades that you have to coordinate all of the potential synchronization between all of the different trades I mean it's a huge building it involves hundreds of people working on it you have this then when you stand up the thing you have to question what works you have to filter through the blades that are faulty it's just an entire new area of work where I get to see experts in their field go through things and try to explain to me what their daily work is.

7:32It's always fascinating to see an expert in this field do something that you don't know how to do. I think the logistics of it and the timelines are also quite different from what I'm usually dealing with in software and research. For new capacity to be built, you have to plan around having energy available. You have to plan for the space to be available and on time. And so it's a lot more long-term planning than a few software features. How do you guys go about power since you mentioned energy? What we've been doing in Europe so far hasn't been a huge blocker, although there is constraint. I think the grid in various parts of Europe is not necessarily easily extensible.

8:20I know it's an issue in France. A lot of the sites are contended. So we'll see how it all develops. We are lucky in Europe to have very clean and affordable energy, either with green energy in the Nordics and nuclear in France. So it's been relatively okay for us today. As you describe this, what comes to mind is the gigantic amounts of money that are being invested in the U.S. around data centers. How do you guys go about that from a financing standpoint and perhaps even more taking a step back? If you think about the race between the big AI labs globally, whether that's the Open AI and Anthropica of the World and XAI, it seems that all of them are affiliated with a gigantic pocket of money somewhere.

9:13Obviously, there's Gemini and Google to add to the list and Meta. I'm just curious, where do you guys stand on that? You have a bunch of partnerships with SAP and NVIDIA, but you don't have one of those gigantic companies on your cap table. So how do you think about competing in that general context? So with those companies, so the hyperscalers, there are two parts to the game, and we've played the partnership part quite well with them, and were integrated within Google's Vertex, Amazon Bedrock, and Azure AI Studio. And that is the choice that we've made. In terms of having access to gigantic pockets of monies, we've been focused on efficiency from the start.

10:00And I think we've done quite well at building models that are competitive with the investments that we've put in. For us, it's important to build the company as efficiently as we can. And I deeply believe that with the capabilities that we have today in the models, there is so much to be unlocked in enterprise that I don't think my main focus today would be into going into the gigawatts of power. We still need to build so much with our clients and unlock so much values with the capacities that we have. All right. So let's go into the enterprise reality of all of this. So if I'm an enterprise or if I'm a sovereign and I want to deploy a Mr.

10:50Open Source model, what is it that I do these days with everything that you've built? The way we work with enterprise, I mean, as you mentioned, like we have a few of our models that are open source and Apache and all of our clients are welcome to use them as they need. What we have seen in terms of success is that given the current stack, it still requires a lot of expertise to manage to come to actual value and things that go to production, basically. The way we interact is that we usually stand up our Misrule AI Studio, which is our platform, and we can deploy all of our stack on the client's choice of deployment methods.

11:39So it can be on-prem, it can be on their VPC, it can be in several places. The reason we do this is that it lets clients build where their data is and without having to shuffle things around, which as I've learned as a CTO is something that you don't want to do ever because it raises a lot of questions and it's quite a stressful thing to do. So once this is deployed, we then work with the business units to understand where their pain points are. Sometimes it's knowledge management. And I think it's the most well-known use case from the output, from the outside of the enterprise world. But it's also around automating core workflows for the enterprise.

12:27It's, you know, some tooling that you wouldn't expect where one thing that we've done is around code modernization, where you turn a bunch of Excel sheets into an actual like Python app. And if you have many, many of those sheets, then potentially you want to use AI for this. So once the infrastructure is built, then we basically look for what's the most valuable to the customer and we start accruing value inside a stack of AI assets that then accelerates all of the other developments with that customer. And is part of the idea that you do actual model work at the customer and for the customers, in particular, fine-tuning?

13:08Yes, we customize in various ways. So we have done continued pre-training, and this is most useful when you want to change the capabilities of a model more deeply. So we've done this to sometimes change the mix of languages in a model to get something that's a lot better at Southeast Asian languages, for example. or you could require this if your internal data, which doesn't happen on the public web, is something that's so new that you need a large amount of tokens to get a model that understands it and becomes fluent with it. So we do these kinds of continued pre-training. Fine-tuning, we also like, and this is more for an efficiency reason.

13:58When you get to smaller models, you have to make trade-offs. The models won't be as good in their knowledge of the world. And so when you lose a lot of things, you have to focus on what you really care about. And so this is typically important if you want really fast, really cheap models that will be really good at a specific task. It's also useful if you want models that run on the edge that get very, very tiny. And so for all of these, fine tuning is a tool of choice. Another reason to do fine tuning, it can be to adapt to data that's not necessarily massive, but that's also not available on the web.

14:40So typically in coding, what happens is that you will have massive code bases, sometimes accrued over decades, that the model will need to be able to work with in terms of having like Vibe deployed on it typically. And so being able to come in, not move the code base and learn an actual coding agent for that code base is really powerful as well. And who does all of this? You have evolved towards an FDE model? So we have indeed a large FDE section. It's a mix of software and FDEs. And we split our FDEs into what we call AI engineers and applied scientists. and so applied scientists will tend to use the tools that we've just talked about.

15:31So fine-tuning, continued pre-training and the likes where AI engineers will focus more on adaptation to the enterprise environment and figuring out what workflows to automate and all of this. They work with the customers to make sure that the use cases are indeed providing values and going to production but it's also a fantastic way for us to understand what matters in an enterprise context and be faster at building the right platform. And again, those customers are the kind of customer for whom customization and privacy is essential. How do you position, again, open AI is the topic of the world that are going very hard at the enterprise?

16:13Is it data sovereignty? Is it customization? The term we use is control. The value that we see is both in our expertise and the software stack that we provide. The software stack, once deployed, is in the hands of our customers and they can change it. They can add to it the own model changes that we make. And I think it's really important as a customer to consider that your expertise and what makes your company valuable stays yours. And so in working with us and building, because it takes effort to build an AI advantage today. And so having this effort built into something that you own is, I think, a choice that makes sense.

16:57Let's talk about agents. Obviously, part of the overall effort at Mistral, how does that work? How do you build an agent and what key use cases have you seen so far? Personally, I think I've moved from agents to workflows, which is, I guess, an abstraction on top. um so agents are i think the building blocks uh where you have a given expected inputs a set of tools and you are trying to reach a uh set of you have a goal that you want to reach the set of inputs uh that we've enabled are um images text and audio when you build an agent to me it's really important that you build it on a focused task with a data set that you understand and that you can iterate on and that you can improve.

17:56What we see in enterprise is rarely things that are solved with agents because that's not necessarily where you would expect an FDE to be most useful. Those ideally would be built on our platform by the customers directly. Where there is more values, value is in more complex workflows where you will have several agents interact through a workflow to automate something slightly more complex. And so that's what we've been focusing on. What would be an example? An example is something that we've built with the shipping company, CMA CGM, where we've automated the container release process. And so it's a use case where I don't know how familiar you are with shipping.

18:43I wasn't at first. But a container reaches a port and you have to harbor, probably in English. Some decision has to be made that this container is ready for release to the next person on the line to handle this container. And so there are lots of checks that need to be run and data to be accessed in the backend before that decision is made. So as you can imagine, some of those containers are extremely valuable and you can't really afford a mistake. And so what we've done in this case is an application that's integrated into how these harbor workers work. And it automates a lot of the manual work that they did to check the data.

19:29And they make the final decision, given all of the evidence. Okay, this is super interesting. Obviously, the key question about agents these days, especially when they are combined into workflows, is the question of autonomy. How do you guys think about it? How autonomous are those agents in your deployments? I don't know if it's the way I think about it. To me, the better question usually is how much you trust the agents. And there are a few dimensions around this. What worries me when building those kind of workflows is that typically if you want the value to accrue and if you want to build faster and faster, the more workflows that you build, what you will want to do is reuse assets and make them reusable by others.

20:16As soon as you do this with agents, you then start to ask the question, well, this agent has access to some data that is privileged, but maybe this other agent is publishing it to something that's public. You might have governance concerns where some agent is acting on something very critical and you don't know necessarily that the data that it got has been approved or something like this. It's really a new way to develop where the parts of your workflows have to be trusted. Each of them to be trusted requires quite a lot of tooling and quite a lot of observability to get confidence and to basically enable this at scale in an enterprise.

20:59So the question that you're asking about autonomy, to me, this is something that I see happening when I vibe code. Sure, like longer running tasks and making and improving on this is going to be critical and we're working on it daily. But today, the problems that we're solving on the software side of things are really about how you trust what you've built and how you improve it and how you allow an entire company to build on it with confidence. Maybe describe some of the things that you guys have built in iStudio around governance, as you mentioned, and truckability and registry, all the things.

21:35What are the key components of a modern agent suite? So workflows, as I mentioned, is something that we've worked a lot on with our customers, and it's not GA yet. So look out for this sometime in the future. But it's also one of the benefits of working with enterprise. We can have a lot of design partners. And once we're confident with the solution, we make it GA. So a workflow solution is critical. Workflows are built on various model capabilities. So vision, audio, and text, and reasoning. It is important to have a registry of connectors and MCPs. And so for this, we have our connections. The observability is an area where we're still working on.

22:23it's important for me to be able to iterate and really define precisely what an agent does and control each of its goal and see how it's progressing, being able to maintain evaluations and build on them. What is difficult in this entire sea of complexity is that you also have to maintain proper versioning and tagging and think about how you're going to deploy and improve upon what you've built. So let's say you've built a kick-ass workflow based on a lot of agents and models that Mistroll has released in the past. Then a few months pass and there are new sets of models that are out. Maybe you can simplify that workflow.

23:11Maybe the next Mistroll 4 is good enough that you can factor out a few agents. Basically, what you need to be able to do is create a new agent, run it on the same set of inputs and outputs and control that you haven't broken anything, and then deploy it in the wild. All of this software suite, basically, which has been built for software development over years, I feel it isn't there yet in the AI world, and that's what we're building. As I'm sure you've seen, there was, for the last few weeks, in startup and venture circles, there's been this whole idea of the context graph as an infrastructure that made the rounds.

23:49Is that something that you think about or a layer that would basically enable one to know how the agents made a decision and how those decisions relate to one another? I've seen this indeed. And I think there are two levels to that discussion. The part that you mentioned at the end where it's interesting to know how an agent came to. So in that discussion when when we talk about understanding how an agent came to a decision or an action the game is really to understand how a human uh agent really made this decision it's understanding how an enterprise does what it does and it's certainly interesting uh what keeps me up at night and what i really want to solve first is just the basic idea of gathering a workable enterprise context Right now, with any model and with a lot of effort, you will be able to get some connections to tools and you will ask questions and your agent will do a bunch of things.

24:59it will realize that, oh, by doing five API calls and three joins, I can probably get what Timothy asked. Immediately, what should happen is that all of that discovery and all of that intelligence should be stored somewhere to be reused. It's not really how things happen. It's just basic knowledge about what the infrastructure of the company is. So knowing where the tables are, what they contain, how they're joined. So all of this is compute that should be amortized, basically. And to me, it's really the entire game with the context engine, as we call it internally, is to be in a setup where over time, knowledge of the company and the context that's available to the agent accrues and is maintained.

25:48The second order thing of, oh, how was that decision reached? Sure, it's going to be super interesting and it's important. But right now, I feel we're not even in a place where it's easy for an enterprise to have any worker in it be able to build an agent that has access to the right context. For this to happen, you have huge data privacy concern. If you want this to be efficient, you need to give access to the agent system to the entire data of your enterprise. and there is going to be RBACs everywhere and you need to make this safe. Speaking of which, what's the current reality of enterprise deployments of Generative AI from your perspective?

26:32Just listening to some of the concern since we very early. To me, we are still in the building phase and I think it's kind of the frustrating thing for enterprise is that when you come to a chat assistant, you feel that it's magic and it's all going to work. But as most things that have value in life, there is still work to be done to get to them. And so most of the enterprise value of AI will happen once you've gone through that first building phase of just setting up all of the machinery. You've got to set up all of the connections. You've got to make all of that data available. And the reality is, even despite a lot of work recently to make data more available in enterprise, it's still not easily available in the format and at the scale that we need for the true ROI of AI to happen.

27:25And so when we come in, there is still that phase of work that is just work to connect everything and then be able to build on it. So do you think we are years away from General AI actually being deployed in the enterprise? Not years. I think years singular. uh it's uh also to be fair to us we've started working i mean the company started two years ago and so most of our uh it's a good reminder right it's a good reminder that like you guys have done all of this and the company was started in right yeah june 23 right if i recall yeah and so for most of our clients uh we we started working with them recently the tooling uh for everyone is still in its infancy.

28:10And so I hope that the tooling will stabilize. And I hope that we will have true value. True value to me is really, okay, we've gone through that first phase of building connections. And now employees of that enterprise are able to use everything that we've built. Right now, I think we're in a phase where we build siloed things, because we're scared of data going through walls and everything. And so to me, the real success is when you're confident enough to give all of that control back to the company's employees at large and they start really building on it you're talking about mistro in particular by the industry in general right is that do i understand this correctly uh because obviously that's that's the big question right we all collectively building this whole thing and data centers and models and pouring uh billions and i think it's pretty clear that from a personal use case or uh from uh maybe some discrete like coding use cases The demand is very clear.

29:06But the big question is whether demand is going to materialize at the same level as the extraordinary level of supply we're building. Yeah, around this, I think the expectation is that demand and basically amount of tokens generated for the enterprise will completely jump once you are not bound anymore by humans asking questions or reading them. As soon as you have enough trust to have agents running in the background, as soon as you've set them to run a bunch of ETLs, as you've got them running lots of workloads and you've got them consolidating data and knowledge across your entire company, then you're not really limited by the number of tokens that humans can create or read.

29:57And so I think everyone in the industry expects the daemon to jump at that point. And the reality is for this to happen, you just need a lot of boring software and control and things like this. It's amazing how much all of this is engineering, right, versus just sheer performance of models. Yeah, it's a lot of plumbing. And the goal is to make all of this plumbing easy and easier and to make it faster. All right. And you said we're about a year away. I'm not the most optimistic person. It might be faster. Who knows? And we talked about use cases a bit already, but let's just put that one to bed because it's such an important question.

30:33What do you think are the kind of the banger use cases in the enterprise? Let's assume like all agents work in a workflow kind of way that you described based on either your industry watch or more specifically talking to your customers. What is it that is going to generate an amazing ROI beyond coding, which is pretty established at this stage? Yeah, there are several dimensions to this. Coding is an obvious one. And to me, to get the full ROI of coding, you need customization because a lot of ROI is unlocked on sprawling code bases that are completely impossible to know for something that's been trained on the web.