The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:35:30 · Harry Stebbings, Arthur Mensch
[00:35:30] S1: Microsoft on the show, and he talked about Mustafa's statement before that we'll see a $100,000,000,000 training runs in the next few years. Do you think that's a little bit hyperbolic, or is that actually well within reach? [00:35:41] S0: You do have a data world before, I should say, in that... Some point, if you want to make your model very strong at certain domains like chip design, for instance, or if you want to make it very strong at operating industrial engineering workflows like we do, you do need to have the right environments to train them. And this part takes some skill and taste. It's not something that you can scale as easily as just stacking compute. So I would say the bottleneck that we're all running into today is effectively to be able to source enough quality data. It's compute. We need more compute. We are deploying more compute. Getting to a 100,000,000,000 of training runs, I think, is not for tomorrow, clearly. [00:36:20] S1: If everyone's so short on compute, why is Nebius 40% down? Why is CoreWeave 40% down? Why is the AI trade cracking in public markets if it's so clear that everyone is so compute starved?
00:36:20 · Harry Stebbings, Arthur Mensch
[00:36:20] S1: If everyone's so short on compute, why is Nebius 40% down? Why is CoreWeave 40% down? Why is the AI trade cracking in public markets if it's so clear that everyone is so compute starved? [00:36:34] S0: It's a little unclear to me how markets are reacting today. What we see is that we're fully able to finance our own compute, given the quality of the collateral that we have in term of revenue commitment, take or pay commitments. We, as a company, operate as a vertically integrated company. So we build compute but for higher level purposes, for serving the enterprises, for building business applications. So we are anchored on the value creation of our customers, which means that at the financing level, we haven't had to go and leverage enormously the company on debt financing. And so that gives us a lot of confidence in what's going to happen. But effectively, as in any industrial build out, it's tempting to get a lot of leverage, but at some points, break. And so that's why we're very happy to be vertically integrated and to operate from business application creation all the way to the infrastructure part. [00:37:30] S1: What did you not do with the benefit of focus that with hindsight, you go, I wonder if we should have done that? [00:37:36] S0: We did not go deep into the consumer market, but I think we're very grateful that we did not do it. We did not go into chip design. [00:37:44] S1: I think that's Oh, pause. What... Like, why why do you feel grateful you didn't go into the consumer market?
00:37:44 · Harry Stebbings, Arthur Mensch
[00:37:44] S1: I think that's Oh, pause. What... Like, why why do you feel grateful you didn't go into the consumer market? [00:37:51] S0: I think it's a very packed market, and it's very hard to operate at the highest level with enterprises and also to run what is inherently going to be an ad business. So that part, that decision to focus on the enterprise segment, also that decision to focus on the four verticals that we have decided to focus on, manufacturing, financial services, defense, public sector, has been very critical to focus the company because the problem with artificial intelligence is that it can do everything, and you need a source of focus. And so the source of focus we've picked comes from the market. So we wanna have the models that serve the best agentic engineering workflows, that serve the best financial decision making, that serves the best defense purposes and everything related to state sovereignty as well. [00:38:34] S1: With RSI, do you need a source of focus?
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?