The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:30:25 · Harry Stebbings, Arthur Mensch
[00:30:25] S1: Can you help me on the inference market? It's something that I just totally didn't know enough about. What I know now that I wish I'd known twelve months ago was that the... It's just infinite demand for inference. It's just the depth is astonishing. What happens with the inference market? [00:30:40] S0: I mean, the inference market behaves very much like a commodity market, which is pretty high margin today. In that if you have supply, if you have the right quality of service for certain kind of models, you can sell them on demand because you have a lot of demand coming directly from customers that are noncommitted. And then for customers that are sufficiently mature, they need to have guaranteed quality of services. They need to have service level agreements. And there, they need to have reserved physical capacity. And that's something that we can provide because we have the right forecasting models and we have the right supply. So you have these two things. You have one, which is on demand, which very much behave as a commodity market where pricing matters a lot. And then you have, let's say, premium side of the market where the customers actually want to have access to want guarantees on the latency, want guarantees on the speed of generation for a certain number of requests. And the more they can be precise about the shape of their demand, the better our pricing can be, the better we can shape our own infrastructure to make sure that they have the right criteria of service for the right price.
00:30:40 · Arthur Mensch, Harry Stebbings
[00:30:40] S0: I mean, the inference market behaves very much like a commodity market, which is pretty high margin today. In that if you have supply, if you have the right quality of service for certain kind of models, you can sell them on demand because you have a lot of demand coming directly from customers that are noncommitted. And then for customers that are sufficiently mature, they need to have guaranteed quality of services. They need to have service level agreements. And there, they need to have reserved physical capacity. And that's something that we can provide because we have the right forecasting models and we have the right supply. So you have these two things. You have one, which is on demand, which very much behave as a commodity market where pricing matters a lot. And then you have, let's say, premium side of the market where the customers actually want to have access to want guarantees on the latency, want guarantees on the speed of generation for a certain number of requests. And the more they can be precise about the shape of their demand, the better our pricing can be, the better we can shape our own infrastructure to make sure that they have the right criteria of service for the right price. [00:31:45] S1: We have so many different NeoLab providers. There've been a 102 different NeoLab providers created. Every week, there's another DeepMind spin out and another. There's so many. What will separate NeoLabs that thrive and those that don't?
00:31:45 · Harry Stebbings, Arthur Mensch
[00:31:45] S1: We have so many different NeoLab providers. There've been a 102 different NeoLab providers created. Every week, there's another DeepMind spin out and another. There's so many. What will separate NeoLabs that thrive and those that don't? [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
00:32:00 · 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?