The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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.
00:10:57 · Harry Stebbings, Arthur Mensch
[00:10:57] S1: What percent of token traffic is your own models versus other open source? [00:11:02] S0: I would say around 80% comes from our own models. [00:11:05] S1: Wow. That... That's a lot more than I thought. Okay. On the 20%, everyone tells me that, oh my god. American CIOs and CIOs would never use Chinese open source models, and, actually, that fear really is there. You have them as customers. You have amazing customers. Are they fearful of using Chinese open source models?
00:11:23 · Arthur Mensch
[00:11:23] S0: It depends how critical their industry is in general to governments. If you work with defense, for instance, we work with defense customers in Europe, they would never use Chinese technology. If you work on the finance sector, again, because it's critical infrastructure, they are very reluctant to do it. If you work in retail, if you work in certain domains of manufacturing, again, it depends where the reluctance is different. Again, think there is a common understanding that open weight models, if they are being deployed on sovereign infrastructure like ours, the only valid threat is that they go rogue and have weird behaviors in certain domains because you may have a supply chain attack on it. There is a common understanding that this is very theoretical and it's certainly not the case today. But indeed, it's part of the conversation. I'd say what is also part of the conversation is not this, is it a threat to deploy third party models that I don't fully understand. What is part of the conversation is, am I going to be able to get updates in six months from now? Because what's going on is models have gotten so strong that they're able to do cyber attacks, that they're... You can use that for pretty much advanced purposes, let's say. And so the consequence is that they are more and more being treated as defense systems. It's being treated de facto by The US as a defense system because at some point in May, they started to ban them from outside of The US. And if you look at what China is doing today, they're also looking at ways of restricting exports beyond a certain threshold. The reason for that is that effectively, this is a dual use technology. And so as any dual use technology, security of supply matters even more. And so the companies we work with realize that they need security of supply not only on the infrastructure side, but also on the model IP side.