The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:38:34 · Harry Stebbings, Arthur Mensch
[00:38:34] S1: With RSI, do you need a source of focus? [00:38:38] S0: I mean, using AI to improve AI models do require to focus as well because you have limited amount of resources to spend on problems. So at the end, the bottleneck here is indeed compute. The bottleneck is also that everything that you do that which is recursively self improving or just for self improving because it means the same thing, requires to have well defined problems. If you have a well defined problem today with artificial intelligence, you can later climb it. In many cases in enterprises, the problems are not well defined. And so the true friction to economic growth is not the fact that we cannot solve hard problems that are well defined, that we it's hard to solve ambiguous problems, where you have ambiguous objectives, where you have humans in the loop. And that part you cannot really solve with self improvement. You can only solve with models being deployed and interacting with humans. And that's the part that we are focusing on, I would say, that we are investing a lot on making self improving loops on well defined problems as well. But that is the part that is constraining the explosion of AI usage, I would say, the enterprise. The poorly defined problems on which you want to deploy AI, but you have no way to lay lay the climb because the definition of the problem is not proper. And so you need to spend some time. That's why you have FDEs. That's why we have FDEs to try and better define the problem.
00:39:55 · Harry Stebbings
[00:39:55] S1: Arthur, I've got to be blunt with you. I joked at the beginning that you're like Europe's last hope. There is a... And I don't mean that, like, super dramatically, but there is a very big chasm between you and anyone else in terms of Neolabs that will aspire to be the next Mistral. Do you and the team feel the pressure?
00:40:10 · Arthur Mensch, Harry Stebbings
[00:40:10] S0: We do feel some pressure, but as anyone in the field, this is fast growing, fast moving. You need to, as an entrepreneur in AI, you need to deliver on the model side because that's the one thing that shows the progress you're making. Then enterprises need to see the value, so you need to have people that are understanding the true business problem. So it's a fairly different problem to solve. So we do feel some pressure, but I think it's a very healthy pressure. It's a very important moment in time and in history. We need to be meeting the expectations of the general opinion. At the end, we need to show that AI is useful, the kind of deployment we are making, the kind of noise this is making in the market. This technology needs to appear as a positive technology. We need to show the positivity of that technology. And the only way in order to do so is to promote sovereign AI as a category, is to make sure that, that creates economic growth and opportunities for everyone, to make sure that the gains of AI is well distributed. So of course, it creates some pressure because it's not a given. If we don't succeed, I don't know what's going to happen in the world. I think so we do have a role to play. I think the mission we play is important, and it's pressure, but it's also quite exciting. [00:41:22] S1: Will AI inflate GDP growth by more than 2% a year?
00:41:22 · Harry Stebbings, Arthur Mensch
[00:41:22] S1: Will AI inflate GDP growth by more than 2% a year? [00:41:26] S0: Well, that's the that's the entire question. What kind of bottleneck do you go and solve to actually inflate that GDP? That's the reason why we were very bullish on industrial engineering. At the end, if you want GDP growth, you need more production. You don't need more intelligence, you need more production. In order to have more production, you need better factories, you need objects that are more efficient, you need better planes that are flying for longer with the same amount of oil, you need better energy production that are more efficient, less carbon intensive, you need better ASML machines, you need better services also, in financial services. In industrial engineering in particular, using AI allows you to do better design of objects. So if you set up the agentic loop to design safer cars, for instance, you can have your car design team go much faster and iterate faster on that product. That means that you can sell more. That means that you can sell more cheaply. That means that you're building safer products. In the aerospace industry, if you design a better plane, it's going to be out there for forty years. If it runs faster with less fuel, then it's going to create GDP growth. So at the end, what truly matters is to enable AI to have a physical effect. This effect we find mostly in manufacturing and financial services. [00:42:45] S1: I'm aware of time. I'm gonna do a quick fire round with you. Okay? [00:42:48] S0: Please do. What do you
00:42:48 · Arthur Mensch, Harry Stebbings
[00:42:48] S0: Please do. What do you [00:42:50] S1: believe today that most around you would disagree with? [00:42:53] S0: I think we strongly believe that this is a fragmenting market and that sovereign AI is going to be the most important category of AI deployment in the enterprise. In that when enterprises are going to deploy the technology, business continuity, cost and control over their data and IP is going to be the one thing that matters. So I think that's a very strong conviction that we have. That is shared by a lot of enterprises and shared by a significant part of the market, but, course, not shared with some of our competitors. [00:43:21] S1: What is a extremely wild prediction today that will be very common in five years' time about the way that we live, the way that we work? [00:43:29] S0: I think the way we work as engineers has already changed for software engineers, is going to completely change for any kind of engineers. So I'm super excited by that approach of designing better products in a way which is fairly autonomous, thanks to artificial intelligence. So it does not concern everybody, but everyone with a technical background, and not only the software engineers that were the first affected and the mathematicians, everyone with a technical background will be operating AI agents in a way that will give them enormous leverage. So this in itself is going to be a major shift in the way the labor market is actually working. [00:44:08] S1: What do you do about going public? Because one day you will want to. Okay? But you you can't go public in Europe.
00:44:08 · Harry Stebbings, Arthur Mensch
[00:44:08] S1: What do you do about going public? Because one day you will want to. Okay? But you you can't go public in Europe. [00:44:16] S0: You can absolutely go public in Europe about... Because they... Yeah. You need to go public in France? No. What used to be true ten years ago, I think, is no longer true. There's a huge appetite from investors from across world to invest in Europe, both on the private equity side but also on the public equity side. And so the bottlenecks that used to be there are no longer there. So we at Mistral are, of course, getting prepared on the you have a lot of operational constraints when you go and become public. We're also pushing and delivering on the commercial milestone that we need to deliver on. But I don't think this is anymore a bottleneck, and there are widely successful companies. Some of them are stakeholders that are firstly listed in Europe. So there is no reason why not to go and and and and IPO in Europe at some point. [00:45:04] S1: Final one for you, dude. What are you most excited about when you look forward to the next five, seven, ten years that not many people think about?