The Exchanges

Q than I thought it would be. I thought that there was going to be a race where some companies would leap out further ahead and would take others some time to catch up. But it looks like right now you have lots of model builders with their frontier models, uh, exhibiting performance that's so similar is difficult to tell which is the best. So what do you make of that?

A I would say that, uh, inherently this is a technology that is going to get commoditized. Uh, the reason for that is that it's actually not hard to build. Uh, you have around 10 labs in the world. That know how to build that technology, that get access to similar data, uh, that follows the same recipes and algorithms, which are very, uh, it's very short actually, like the knowledge you need to actually train a model is fairly short. So because it's short, it actually circulates. Uh, so there's no IP differentiation gap that you can create. So it's very hard to actually leapfrog and to be way ahead of the competition because there's some diffusion of knowledge that is just making everybody do the same things. And so The question there is therefore, where is the value accruing? Uh, and what kind of business model should you pursue to actually make sure that in the end you're turning profitable? Uh, and then the challenge that we see with some of our competitors is that they're investing billions or hundreds of billions into creating assets that are deprecating very fast because those are commodities. And so for us, it has always been at Mistral, it has always been question Like one of the biggest question of the industry, uh, is that you need to invest enough to actually bring value to enterprises, but you also need to invest, uh, reasonably so that you can build unity economics t…

AI assessment note: “inherently this is a technology that is going to get commoditized.”

Q to be a moat forever. And the other way is, you know, the model is actually not the value. It's the know-how of, and, and the implementation side of things. So you can make the model open source, but then provide a service. To businesses to be able to figure out how to take that model and put it into action and actually get results. Are those the two choices?

A Uh, yeah, that's, uh, it's kind of the fork that we see in the industry. Uh, and, uh, our view there has been to be at on the second one, uh, to really the open source, the open source implementation side, which brings customization, but it also brings decentralization in that, uh, If you assume that the entire economy is going to run on AI systems, well, enterprises will just want to make sure that nobody can turn off their systems. So it's the same way if you have a factory, you connect it to, to the grid, you want to make sure that nobody's going to turn off the grid because they don't like you. If AI effectively becomes a community, which is what's happening, and if you treat intelligence as electricity, then you just want to make sure that your, your access to intelligence cannot be throttled. And so that's also one of the things that Open source technology can bring.

AI assessment note: “yeah, that's, uh, it's kind of the fork that we see in the industry”

Q Uh, you mentioned that there's a saturation effect. So, uh, without getting too technical, are, are the models sort of done with getting better? Like are, are, let me put it this way. Are AI models going to continue to get better given the fact that they all seem to be hitting saturation?

A They will get better in more and more specific domains. Uh, in that, uh, I think we've really collectively made them very clever and able to reinvent about long context and able to Call multiple tools, etc. But if you go and want to effectively put them into production in a bank or in a manufacturing company, well, the models need to learn about the, all of the knowledge that is contained into the companies themselves. And so what it effectively means is that for very precise directions, let's say I want to make my model extremely good at discovering materials or extremely good at Designing, uh, plane, uh, designing planes. I will need to go and sweat it a little bit and, and get the right reward signal and get the right experts and ask them to make my model specifically good in that very precise direction. And so we are definitely not done doing that because what we are all racing for is the right environment and the right signal provider for specific capabilities. Uh, but the broad horizontal reasoning capabilities Still going to improve them, but nobody's going to improve them in a way that is creating strong, that is creating a strong gap versus its competitors. So the strong gap is actually in the, in the, in working with vertical experts that know exactly how they design a plane and that actually explain to the model how to do it. And you have like a wealth of directions …

AI assessment note: “They will get better in more and more specific domains.”

Q So it's really working for you. This pitch being like, we are not an American company. We're based in Europe. We'll be able to help you build, whether it's something Uh, with like important data protection or national security like defense?

A Well, it's a technological differentiation we've built. So because we can build on the edge, because we can deploy wherever our customers wants us to deploy, we effectively can die, and the system is going to still be up, which is, which actually matters for many, many industries, and the more critical it gets, the more it matters. And so what that also means is that we can serve the US, uh, US customers. Uh, we can serve US customers that want to Depend less on certain providers. Uh, we can serve banks that wants to have more customization, more control that are more regulated. It also means we can serve, we can of course serve the European industry, uh, where historically that's where we were based. We, you, you sell next door when you start your company. And that's what we did. Uh, but we also serve Asian countries, uh, and Asian countries, they have similar problems. They want to have a technology that they can rely on. Even if we were to die, uh, they want to have a technology that they can customize to their own cultural needs. Uh, and so that's, uh, that has, that has been driving our business for sure. That aspect, that technological differentiation that we've built around control, open source, uh, like a technology built on open source models and around customization.

AI assessment note: “that has been driving our business for sure. That aspect, that technological differentiation”

Q What do you think China's strategy is? And do you think that there's like in the U S there's often this kind of very large conversation about, um, the need to stay ahead of China. Um, do you, do you think there's a risk of China runs away with this?

A I think China is very strong. It's vertically integrated. Uh, they have strong engineers, they have compute, they have energy, they have everything they need to compete. Uh, Europe also has everything it needs to compete. I don't think we'll be in a setting where anyone is going to have one artificial intelligence ahead of the others. And if you look at like the world in it, like in its entirety, uh, every large enough sovereign entity, which is a big economy is going to want Uh, some form of autonomy, uh, in its usage of AI and its deployment of AI. So that does justify the emergence of multiple centers of excellence, I would say. One of them, which is in Europe, which is led by us. One other, which is more in Nangzhou, uh, in China. And then you have a bunch of companies, uh, here in the west coast.

AI assessment note: “I don't think we'll be in a setting where anyone is going to have one”

Q it? The Neo, the Neo humanoid robot, uh, where there's like a person controlling it, teleoperating it. Kind of weird. Um, there, they might be in your house. So, uh, we haven't seen progress in robotics, you know, start to move as fast as we've seen it, uh, in the software side, in the large language model side. So where does that, when does that come if it ever does?

A I think in robotics, you have the combination of two things that needs to work, uh, hardware platforms, uh, that needs to be, you need to have the right actuators with the right haptic signals, uh, that needs to be built at scale with, uh, with good economics. And this is starting to be true. Uh, and we've, we are, we're not the one working on it, but, but the, the industry has made a lot of progress on that domain. Then the other thing is that you need to be able to have control system that are sufficiently intelligent, To be deployed on those, on those, uh, robots. And so that's where actually we, we come in, in that again, you need to, to have custom models, uh, because the problem is the model needs to be customized to the platform, to the, to whether it's a humanoid robotic or whether it's something on wheel or whether it's a flying drone, and it needs to be customized to the mission, uh, because the mission is going to bring different kinds of images, the kind of actions that can be taken, Are going to vary across the mission. Maybe the guardrails are different. And so that adaptation to the world and to the wealth of data that the hardware platform that is being deployed is bringing does require the right platform, uh, and the right training platform. And so our bet in robotics and what we've been doing with multiple companies, uh, in defense in particular is, uh, to bui…

AI assessment note: “I believe we'll see deployment of such systems first in areas where you don't want to send humans.”