The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:27:27 · Harry Stebbings, Arthur Mensch
[00:27:27] S1: What line of revenue do you not have today that you think will be most significant in five years' time? When I was listening to you there, I was thinking, I bet you wouldn't have necessarily predicted these different motions when you started. When we first met Arthur Mensch, I don't know if you remember, but you were sitting on a park bench just chatting to me about life. [00:27:45] S0: I was. Yeah. You did not invest. You should have. [00:27:48] S1: Dude, I fucking regret it so much. I saw Jean Charles yesterday. And every time I see Jean Charles, we talk about you. Yeah, it's a brilliant reminder of just how much money I lost. It's all missed out on. [00:28:00] S0: Sorry about that. Now what line of revenue? I mean, we have these two lines of revenue. We have one which where we deploy the platform that is truly licensed revenue for billing business applications. And then we have the consumption revenue coming from the infrastructure business that we built. And the two things go hand in hand because we are able to our customers, they come in general for the license motion, but then they realize that they don't have enough GPUs. And then we are trying to be helpful to them by providing them with either GPUs or token services. So the two things are very well combined together. [00:28:34] S1: Do you think you'll have an inference business, a big inference business in three to five years' time? Do think you'll have a sandbox business?
00:28:34 · Harry Stebbings, Arthur Mensch
[00:28:34] S1: Do you think you'll have an inference business, a big inference business in three to five years' time? Do think you'll have a sandbox business? [00:28:40] S0: Yes. If you look at this granularity, I would say the GPU side, like everything sitting on GPU is going to continue to grow because the demand is enormous. You have what we call ancillary services, like running sandboxes, running scaling up and down sandboxes very easily when you're running reinforcement learning algorithms, for instance, or when you're deploying many agents on the same tasks. That is a growing business for us because it comes from the demand of our customers. And in turn, that brings us to buy more and more CPUs. So I do expect what we call the runtime side of things, so where agents execute tasks, call tools, compile code and run the code to grow. It's pretty clear that when you are in synchronous task where you are only using GPUs to answer conversations, you didn't have that. When you start to have managed agents that are running on the background, doing a lot of tasks that are CPU intensive, you need more CPUs, you need more sandboxes and you need more storage as well because they produce a lot of data. So those two things, I would say, CPU related tasks, data related tasks are going to be growing line of businesses for us. [00:29:47] S1: A big part of the business is selling to governments. What does no one know about selling to governments that everyone should know?
00:29:47 · Harry Stebbings, Arthur Mensch
[00:29:47] S1: A big part of the business is selling to governments. What does no one know about selling to governments that everyone should know? [00:29:53] S0: The first thing is that there's no such thing as selling to governments in general. All governments are very are very different, I'd say, depending [00:30:00] S1: Which is the best? [00:30:02] S0: Some governments are small enough so that it looks more or less like an enterprise sales motion. It's the case of governments where everybody in ministries know each other, for instance. Other governments in larger countries are slightly more complex. So again, the complexity of finding where to create value, of understanding what the customer needs, grow with the size of the country, I should say. [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?
00:30:25 · 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