The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:04:06 · Arthur Mensch, Harry Stebbings
[00:04:06] S0: Thank you, Harry, for having me today. [00:04:07] S1: 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. [00:04:25] S0: 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. And so AI is going to be a big enabler of economic growth. And if there's no AI being produced in Europe in particular, Europe will struggle to actually produce the growth it needs to produce. [00:04:51] S1: 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 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?
00:05:08 · Arthur Mensch
[00:05:08] S0: 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 Europe, in the world, which can be The U. S. And so there is a macroeconomic problem to not being able to produce artificial intelligence, to do R and D in your territory. That is not linked to a certain way of saying that Europe is falling behind or a certain way of using soft power, let's say. 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 in that as a company, as a state, wherever you are, whether in The U. S, 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. And sovereignty precedes twenty twenty five election. So it turned out to be true, what we said in 2023. It turned out to become quite emotional when we start to speak with Greenland, but it's mostly an economic problem. It's not really a political problem. It's just too important a technology.
00:06:40 · Harry Stebbings
[00:06:40] S1: When I hear you speak, I think of Lin at Fireworks, who came on the show and said, actually, 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?
00:06:59 · Arthur Mensch
[00:06:59] S0: And what the category we define and the reason why companies work with us is that we bring them sovereign AI technology. And so in sovereign AI technology, you need three things. You need to have a source of model that is secured. So you need a company that can train the models itself. So we have that model factory. Mistral Largefur is actually leading the open weight models in agent decoding. It's leading in cyber defense and cybersecurity. So 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. So it's the reason why Mistral has been created, and it's the main part where we're investing money. Now you also need to have control over your data. And so in general, you don't want that data to flow back to the AI providers. And so we've made sure at the technological level that our platform to run the agents, that our platform for customizing models actually sit on our customer 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 processor. Now that's not enough in general because AI is consuming a lot of energy and because 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 basically. You need a combination of data that sits in your infrastructure and tokens that are produced in managed infrastructure at a lower cost. And so 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, what is intense in energy, and that's the reason why we've been building our own infrastructure. So we have these three things, and those are the three ingredients that are necessary to serve sovereign AI workloads.
00:08:50 · Harry Stebbings, Arthur Mensch
[00:08:50] S1: Can I ask if we start on one, 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. Okay? 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. [00:09:17] S0: Well, at Mistral, we we have a... I mean, one of our business is to really run the inference for open model. So we run ours, but we also run others. [00:09:26] S1: Why do you run others?
00:09:28 · Arthur Mensch
[00:09:28] S0: Because customers want choice. And when you have infrastructure, you want to provide as much choice as possible to saturate the infrastructure. That is the reason why that brought us to actually serve more and more of open models. Because 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. So the more choice you have, the better usage you can make of the infrastructure, the lower your cost can be. And it's in a way, it's a commodity market, so you want the cost to be as low as possible. So 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. And so 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. And so our customers, they 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 are 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.