So my question is, what would you consider an appropriate breakfast food? I was standing in the station in London today and I had what I can only describe as the most insane sandwich. It was like fata bread. It was filled with all sorts of things and it was amazing. Problem was it was so messy and the most inappropriate thing to eat while standing around waiting for a train. So messy. Why was I waiting for a train? Well, it's because I needed to take that train to come to Paris because this is the first ever tech download on the road. I am here in Paris. I'm about to speak to Arthur Mensch who's the CEO of Mistral. Now, if you don't know Mistral, they're basically Europe's answer to OpenAI, Anthropic. They make various AI models. Some of them are open source or open weight and they commercialize these as well. And they're very much focused on the enterprise, i.e. big businesses integrating AI into those businesses. Their valuation sits somewhere above $13 billion as well. And the company is racing to try to grow revenue. And before we get into this, there's a couple of terms I want to put on your radar because we're going to speak into them. We're going to get deep into those terms. The first one is compute. Now, you may have heard this when talking about AI. And effectively what it refers to is computing power in the form of these data centers that are running chips that are powering and running all of this AI. So let's set the scene. That's it right now. And I'm so excited to dig into this conversation with Arthur Mensch, the CEO of Mistral.
Arthur, thanks so much for joining me on the tech download.
Thank you for having me, Arin.
So Arthur, I want to kick off first by talking about compute and I just want to set the context for our viewers and our listeners as well. You've committed I think 4 billion euros to investment in data centers across France and Sweden. I think capacity wise it was 200 megawatt was the capacity aim by 2027, a gigawatt by 2030. And you've now announced a new site specifically for inferencing as well. Can you just run us through exactly kind of what that new site entails and how it fits into your broader plans here around building out this computing infrastructure?
So the new site that we have is a high availability site. So we're going to be using it to serve our customers. Our customers are in need of tokens. It's actually in short supply at the moment and so we've been at work building more capacity for them in 2026 and there will be much more in 2027. So why do we do that? Because inherently the business we're in is about transforming electrons into tokens. In order to do that, you need to train the models. We're building everything on top of open source models which makes it easier and I think more healthy for our customers. But then once you have the models, you need to put them on GPUs. And we've built that expertise on serving efficiently GPUs and turning them into token generators through the acquisition of COAB that we talked about together a couple of months ago and through those investments that we are doing on the infrastructure. Europe is lagging behind when it comes to the buildout of infrastructure and so we are investing to close that gap. So that's a new site. It's based in France so it's low carbon tokens I should say. And it's going to be there to serve our customers and to both with our studio offering and public cloud services and private cloud services.
And there are others who have taken the approach of perhaps renting capacity from elsewhere. What's the need here to build out what is very expensive infrastructure at this point?
Well, you know, we have a few customers I should say that look at their dependency to tokens as a critical supply chain problem. And one of the things that we have in Europe is that we're a European company, I should say. And as a European company, when we build European capacity, we can actually serve APIs that are fully under our control. And so that decoupling from other providers is actually quite important for our defense customers, for our manufacturing customers. So that's the reason why we really decided to invest is that from a product perspective, owning the full stack in Europe is extremely attractive. And what's more is that because we built that software stack that can fit on any hardware we are also bringing it to Asia. We've announced with Singtel the partnership where we're actually bringing our public cloud platform on top of the hardware they're building so that they become a full stack provider. So we're all about building full stack AI providers that are resilient that are not dependent on foreign entities when it comes to the digital services and so that investments when we own the compute is important for us because that allows to have a big product offering. Now we also run compute, we work with hyperscalers in other parts of the world. But in Europe our customers are asking us to actually own the full stack.
So just walk me through the strategy in terms of the compute that you're building. Obviously you're using it for yourselves. You've trained your models on some of the compute you've built renting it from others etc. The compute you're building now is this specifically for some of the customers you work with, you know ASML for example or Singtel as you mentioned, or is the view also you know you could rent this out to other kind of customers who aren't necessarily you're working, I'm talking other labs for example.
So there's really the two categories. So on one side when we work with enterprises among our customers they don't really care about the GPUs themselves what they care is the services on top so the high value services that allows to deploy the agents that allows to connect of course generate the tokens but then use the tokens to call tools, record what the systems are producing in term of data. We use that data to train new models that are going to be more efficient. And so enterprise customers they don't want to see the infrastructure. They want to have serverless offerings and they just want a comprehensive suite of tools that when stitched together allows to build business applications. So that's what they need and that's how we address their needs. Now you also have AI labs and companies that are maybe quite advanced in using their own data flywheel using their own data to build their own models and those may actually want to actually get the compute itself. And so we do offer GPU as a service through managed Kubernetes to AI labs and to some of our customers that have part of their R&D teams that are actually doing AI research. So the two things are available but when it comes to enterprises high value services and token generation is the thing that we provide.
So everyone I'm back in our podcast studio in CNBC's London offices now and you're going to hear me jumping in throughout episodes a couple of times as you listen more and more to the tech download because these are moments for me to help flesh out some of the context around the conversations I'm having. The first term I want to pick up on is something called tokens or you've heard that in this episode, you're likely going to hear it in many more episodes going forward. But tokens are effectively the basic data unit that AI models process. So whenever you put your query into a chatbot, something like Gemini or ChatGPT or Claude, it's broken down into tokens that the AI can then process and then of course you get your output. Then the chatbot gives you that output, but also the longer the request, the more tokens that are effectively used. And this is a way for AI companies to do a couple things. One is to measure how much their service is being used and then secondly to charge for that service as well.
And part of the conversation and we'll get on to this is around sovereignty right here in Europe and building out European infrastructure as well. So is there a world in which you know US labs, I'm talking OpenAI and Anthropic, could be customers of yours for your compute?
Absolutely. I mean the AI labs are in sore need of compute and we have some of it and some of them are actually asking us for a lot of compute today. We actually need to prioritize the access and so we're giving it to some of the AI labs but more importantly we're prioritizing our customers that see a surge in usage as they are moving toward the systems that are running on the background and agents are producing much more tokens. So we're at work building as much capacity as we can to address the large amount of customers that want compute for different kind of use.
But you are getting inquiries from US customers, Frontier Labs, OpenAI, Anthropic.
I mean every lab wants to have inference in Europe because you have latency problems etc. So that's one use case. But then when it comes to training the question is it's not really a geographical question. It's an availability question. And as we have availability and as we've have a lot of proof points on training on this kind of hardware because we train on this kind of hardware ourselves. So we've made a lot of improvements on the training software that we expose to our customers today.
When we look at sort of the pure numbers, Arthur, and we see, you know, your 4 billion euro investment into data centers, and we look over the pond to the US and we look at the hyperscaler spending north of $700 billion or, you know, 800 billion, whatever the figure is this year, in terms of AI infrastructure going into data centers, into chips, etc. Is there a concern that Europe is so far behind this, or is there actually more of a concern that, you know, those guys are overspending?
Well, I think the two things may be slightly true in that European companies are adopting and we help them adopt the technology and increase the ROI so that they can justify more spending. And then on the other side there's effectively pretty high aggressivity when it comes to deployment everywhere. What Europe has is a very good grid. So availability of energy is high and it's actually fairly easy to build data centers that are in the hundreds of megawatts. So that's an asset that we have. Now what our customers are telling us is that AI is becoming so important that they actually need to think about where they actually source the technology itself. So the same way in energy you actually import energy sources but you also produce your own energy. AI is really looking like energy at this point in time. You do need to have affordability of energy. So building on an open source foundation is the way also to do more customization to create your own models so that they can run on smaller hardware. You need security of supply. If your provider is actually under certain legal constraints that may come from foreign entities, you never know what can happen. And so when it comes to your resilience plan if you're in the board of a company globally well you do want to make sure that your tokens may come from different places of the world. And so the fact that we can provide fully independent token generation to all of these companies is actually useful not only in Europe but in the entire world.
And just on that overspending part of the equation, do you think sort of some of the hyperscalers over in the US are spending too much at this point?
I mean it's hard to tell. What we see is there's a very big increase in demand. And today there's actually not enough chips, there's not enough memory. There is enough electricity in Europe, there's not enough electricity in the US. So today the demand is actually way above the supply. Now we all anticipate the rise in demand in slightly different ways. We're at work with our customers to make sure that when they spend a euro in tokens, they actually get like two euros in return. Because if that's not the case, the entire thing is going to collapse. They will have too much compute because at some point everybody is going to ask I'm spending 10% of my opex in AI. Is it bringing me more than 10% in growth? So at the end of the day what really matters when it comes to estimating whether we're overspending or under spending is whether the enterprises are effectively adopting the technology to build real world use cases. What is working today and what is driving the demand is coding. Where if you're a developer it brings you a lot of productivity but at the end of the day if you want to have an impact on the real economy well you do need to bring the technology to the real world objects, to manufacturing etc and so that is also something that we're really investing in making sure that AI systems are affecting engineers but not only the software engineers the industrial engineers as well.
Yeah that return of investment kind of piece is incredibly important I think going forward and such a big focus. I want to get on to that in a minute. Just want to spend a couple minutes more on this sort of infrastructure piece of the equation here and particularly around sovereignty and you. I want to pick up on some comments you made recently to some of the lawmakers here in France around the requirements for Europe right now and you warned Europe has you know a 2 year window to build independent AI infrastructure or sort of risk losing control to some of these American tech giants. I think that the comments were translated as you know Europe could risk becoming a vassal state to the US. Just lay out some of your thoughts around what your concerns are when it comes to kind of AI infrastructure and the need I guess as you see it for Europe to have a little bit more of a sovereign and independent infrastructure.
It's not only a need for Europe. I think it's a need for every state that wants strategic autonomy. I think the main reason is economical in that this is a technology that is going to be a line in the budget that is maybe 10% of the wages. So if you look at the wages in the world it's around 50 trillion. So we're talking about something that is worth 5 trillion tokens I would say in the next five years. It all depends on how fast it goes. But I believe that with the right enablement and with the right models, we can actually get there. Now, if you take Europe, Europe is around 9 trillion in wages. So we're talking about roughly a little north of 1 trillion in spending in AI in the next 5 years. The amount of money that goes back to the US because of digital services today in Europe is 250 billion. That's a lot because all of this money is actually reinvested in R&D in the US and not in Europe. So in a way you have some compounding effect of depending too much on technology from one region to another and that compounding effect is going to increase if there is no alternative. That's the reason why you see states like India also start to think about their full stack strategy. That's the reason why we work with the Singaporean states to develop a full stack AI strategy in which if we are to disappear well they can still produce the technology. And that's the reason why Europe is starting to look at AI as a strategic asset the same way it has looked at gas and that's I would say there is a realization even in the policy maker side that something needs to be done but really the companies I would say are the ones that are making it happen. What we see with all of our customers in the US, in Europe, in Asia is that the kind of proposition that we bring which is centered around open source models that can be customized is resonating with them and that brings demand and we believe that that window which is fairly short actually because there's only a limited amount of chips, a limited amount of memory and limited amount of electricity. We believe that the demand we see allows us to take a very meaningful position in everything that is related to mission critical AI deployment. So that I think is the hope that we have but really what I'm regretting and that's the reason why I was asked to actually go to see lawmakers in France and I wanted to share that this is not only a technological problem it's actually a macroeconomic problem. You can't afford to have a commercial deficit of a trillion if you actually want to stay competitive and in the race and so that's something I think that people are realizing that we're talking about something that should be concerning for any one of us.
And I think if I'm hearing it correctly, what you're talking about here is the digital services because I mean the reality of the situation is, you know, physical infrastructure. The chips are being made in Taiwan, right? The Nvidia is designing and AMD, the US company's designing some of the most advanced, you know, chips for AI workloads. You know, some of the memory is so heavily concentrated in South Korea and that doesn't seem like it's going to change, right? But it...
It will not change short term. And it's not that much of a problem. I mean we live in a globalized economy. That is the reason why we've been growing so fast in the last 50 years and it's great. The question is how do we maintain the equilibrium that we have in order for every part of the world to actually thrive. Today goods are being exchanged between the US, Europe, China, between South Korea, Taiwan. When it comes to building the system that I'm used to deploy AI, I would say on the good side, this is fairly balanced in that the semiconductor chain is completely intertwined. You have ASML which is a critical piece of it which is a European company. You have TSMC, you have Samsung, you have SK Hynix, you have a bunch of fabs in the US as well and then you have Nvidia of course and more and more I would say chip designers that are trying to disrupt the space. So on the good side it's of course Europe could actually build more but for this it needs a market and for it to have a market it actually needs to have cloud providers. So we go where we think we have an edge which is the digital services, the deployment of AI serverless systems that allows to build AI applications, the deployment of a high value skilled workforce that allows to turn those serverless services into AI applications that deliver value and we build value for that. Then we reinvest in R&D. We actually buy chips. And eventually we think that the tech ecosystem in Europe in particular can grow to a point where it becomes a good idea for a fab to set up for a company like Samsung for instance or a company like SK Hynix or a company like TSMC to set up a fab in Europe. But for this to happen, you actually need for those companies to have a market and the market is the infrastructure that is getting built. So let's start where we are strong. That's what we shared. And then let's create something that allows every country of the world to get enough leverage and to participate into the AI revolution in a way that is not creating unfair dependencies.
And just a final one on infrastructure just because you mentioned chip disruptors. How much work is going on at Mistral into designing your own ASICs? This has been a hot topic for a lot of the cloud providers in the US. We've seen it for Google, Amazon, Microsoft. Any work going on there?
So we don't do it yet. This is of course interesting. No, we're not ruling it out. Because if you look at the chip design space, there are really it's not low hanging fruits, but there are fruit. So you can really lower the cost of deploying tokens to a meaningful extent. And so we're working with a few chip designers that are really building custom ASICs. They do much better job than we do. They take our models and they try to make it work. We deploy our systems or serverless infrastructure so that we can actually create tokens with a multitude of chips. We think it's going to matter for our customers. It's going to matter for the total cost of ownership. Today we're really focused on making the models and operating the chips. We're focused on turning the models into things that are useful for enterprises. So with the right enterprise context etc. That's already a significant part of the stack. That means we have multiple business units. But really when we look at our markets and when we look at our opportunity of building on top of open source models really owning the infrastructure owning the product is very important. Owning the chips may come I think it should come at some point but for now we are relying on Nvidia which is a great partner to us and we're testing a few things here and there.
So in terms of self-designed chips that's more right now just research phase seeing how they work with your models rather than obviously being deployed in your data centers.
