The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:32:00 · Arthur Mensch, Harry Stebbings
[00:32:00] S0: Well, the first thing... I mean, we started as a neolab ourselves. It wasn't called that way in 2023. But effectively, when you start a company, you have a bunch of silver bullets to hire the best, and we used all of our silver bullets to hire researchers. And so effectively, we started as a research company. I think what has allowed us to scale and to now operate in a much wider way, still with this number one pillar, which is to deliver models, and that's why Mistral Large 4 has been very successful for us. And I think it's very exciting as well in the new releases that are coming because we're making pretty good progress. What has been successful is that we focused early on, on the market that would scale. So we went for the enterprise market, which comes with a certain number of complexity because, of course, enterprises customers are demanding as they should be, and we have adapted our product to meet those demands. But the fact that we were able to both operate at the research level, at the highest level and able to constantly recruit and invest further in making stronger and stronger models. And the fact that we built a customer centric motion where we understood the problems of our customers have allowed us to scale. And that's something that every Neolab needs to think about when [00:33:09] S1: they start building their business. But how many people are there actually in Europe who truly have the ability to create these companies and have have been and seen it like you have, I don't know, maybe a Jan LeCun has done. Is it 20? Is it 100?
00:33:09 · Harry Stebbings, Arthur Mensch
[00:33:09] S1: they start building their business. But how many people are there actually in Europe who truly have the ability to create these companies and have have been and seen it like you have, I don't know, maybe a Jan LeCun has done. Is it 20? Is it 100? [00:33:23] S0: I mean, I'm not a VC investor, so I would you probably know it better than I do. There's a lot of talent in Europe generally. You have a lot of entrepreneurial talent. You have a lot of researcher talent, and you have a pretty good combination of those two. So that's why you see a lot of new labs in Europe. The hard thing is it's not only about being talented. It's not only about being able to sell and having something that you can successfully create business with. It's also today about being able to access compute. I think a big bottleneck today for many neo labs that I know is that accessing GPUs to actually do interesting research in certain verticals is hard because there's an enormous supply crunch. And so that's why in the supply we have, we are reserving some capacity for certain neo labs because we believe that they can grow and that we can grow with them. But effectively, if you're a founder today and you wanna do something which is very research centric, the biggest problem you have is to get access to it off compute with a decent price. [00:34:19] S1: I ask you, you've mentioned the word researchers there. How crazy is the researcher hiring market today?
00:34:19 · Harry Stebbings, Arthur Mensch
[00:34:19] S1: I ask you, you've mentioned the word researchers there. How crazy is the researcher hiring market today? [00:34:26] S0: Well, it's pretty competitive, I should say. Again, if you have the right mission, if you have the right compensation structure, if you're doing something that matters for the world, people will join us. And I would say we're very grateful for the dedication of all of our employees to making Mistral successful. The quality of the Mistral Large Four has been a success, has a lot of traction today and is leading the pack in the open weight category and coding and in cyber defense. It's only due to the dedication of this team in the last three, four months. The resilience as well because when you run a model, we've posted our tech report recently, oftentimes it doesn't work the first time. And so you need to be able to course correct in a way where, okay, you're going to spend two more months to actually make it work, and it's going to be painful. We're expecting to release, but we'll need to release a little later. So it'll be painful for everyone, but everyone needs to be all hands on deck and do the right thing. And I'm very grateful for the resilience of the team given the scarcity of resources that we [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?
00:35:30 · 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?