The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:13:03 · Harry Stebbings
[00:13:03] S1: With that in mind, do you think your OpenAI's and your Anthropics withholding their best models is good or bad for society? And is that just an inherent feature that will be prevalent within this industry?
00:13:14 · Arthur Mensch
[00:13:14] S0: Is it good or bad for society? I don't know. What is clear is that there is immense leverage to centralised AI providers in a world where there's no providers that is allowing decentralized deployment, that is allowing sovereign deployment. So what we are doing is extremely important for companies. If we end up in a world where all companies need to rely on two or three providers that are centralized AI providers, that give them enormous leverage. They will use the data that is being produced by the systems to make better systems and to go after the business of their customers because at the end, this is the end game. This is pretty clearly what is being priced in today. So the approach we are taking is to make sure that this world in which you have an oligopoly of AI providers that are leveraging all data being produced by enterprises to make better systems that can be intermediate with enterprises is not the world we live in. And we're very aligned in that with all of our customers that, of course, don't want to live in such a world. So I would say the fact that there are internal models that are being used to do certain research, for instance, is an early sign of what can happen in a world where you have massive concentration of power in artificial intelligence. Luckily, it's not that complex to build models. It's about deploying capital. It's about having the right team that is training the right thing. And so we've been able to do it. The open weight frontier is catching up with the closed frontier. In many domains, it's actually above. And so that means that I don't think we'll end up in a world where if you wanna do mathematics, you need to work at OpenAI. Luckily, the... There is enough people in the world that don't want this to occur.
00:14:41 · Harry Stebbings
[00:14:41] S1: You said the word energy a couple of times, and one of the core underlying tenants of being successful is having the energy and the infrastructure to power European AI. We have a very different energy system to The US, and we have a very different energy system to China. In China, they'll move 3,000,000 people to put some form of energy infrastructure. And in The UK, we won't move a a birdhouse. It's quite concerning despite the joke. How do you think about ruminate on the energy supply challenge?
00:15:08 · Arthur Mensch
[00:15:08] S0: Well, I think the first thing to realize is that energy is around 10% of the bill in the total cost of ownership of compute. So we are not talking about the place where we can make most of the optimization. So the cost of energy, at the end of the day, is not the one thing that matters in the economic of a project. What matter is the cost of the chips, and what matters is the cost of capital. This is dominating the bill of material. So it's a problem. It will become more and more of a problem as we deploy more compute in Europe because we'll run into supply constraints. Again, it's not evenly distributed. So if you look at France, for instance, there are excessive energy, so you can build capacity. If you look at Nordics, it's also the case. In The U. K, it's a little less the case. Germany, it's a little less the case. But there is a realization that AI is going to drive energy consumption forward. I'd say in the next two or three years, the true fight, the true challenge is to actually make sure that for Mistral, make sure that we build as fast as possible to use the excess of energy that is already there. And so that is what we are at work doing, raising capital, building more, building the team to build all of this capacity. There is excess of energy. It's not the bottleneck today. It will become the bottleneck in 2019, 2030. But right now, the question is how can we build on existing energy? Now we need to think ahead of time in starting in 'twenty nine, 2030, as multiple gigawatt of capacity are being deployed every year on AI. We do need to ramp up the production. What we are hopeful for is that in France, for instance, there is an enormous EPR program where nuclear energy is going to be produced much more. You have large efforts on the solar side as well. So it's going to be a problem. It's mostly a coordination problem because if you have demand coming from AI on energy, the business case for building new nuclear plants is pretty well made. It totally is, and
00:16:57 · Harry Stebbings, Arthur Mensch
[00:16:57] S1: I agree with you. I am a big believer in Adam Smith's invisible hand, but the lead time is very real. And so that is just... The '29, 2030 is a challenge. [00:17:06] S0: Yes. Nuclear plants take longer than that to build, so our plants can go faster. And, say, I do believe in that invisible hand. The states have to play a role in facilitating the build out, but also in planning ahead of time. They they have a longer term vision than enterprises in general. And so we are spending some time, I'm spending some time explaining that the energy demand is going to grow and that it needs to be addressed somehow. It needs to be part of the business plan of any energy supplier. [00:17:35] S1: You said about the cost of chips being important. What do you know now about compute buying, forecasting demand of compute chips that you didn't know?
00:17:45 · Arthur Mensch
[00:17:45] S0: It's quite a process. So we've learned to run that process to match supply and demand. It's a complex industrial process, it has nothing to do with software. You need to understand the latency in the supply chain. You need to understand that the supply chain comes from I mean supply chain is fairly complex because you have GPUs, but you also have CPUs, you have storage, you have electrical parts, you need to make sure that your own suppliers have no problem with their own suppliers. So that aspect is something we actually learned, but it's a fairly complex one where you need to manage resources, you need to have multiple forecasts, you need to be able to forecast your demand for multiple buckets. So enterprises, which are our main ICP but also AI natives that can take larger piece of GPUs but want them for a shorter period of time. You have companies like Microsoft that are off taking capacity from us that we can use to finance more capacity. So at the end, the problem of building a cloud is about matching supply and demand. It's about managing the supply chain ahead of time, giving visibility to your suppliers. And it's about matching all of this with the finances. You need project financing. You need people that are strong at understanding credit. It's actually quite interesting to build such a business. What we've learned is that at the end, if you're able to front capacity ahead of demand, you get something that is pretty easy to sell, and it's going to be the case for the next few years. And so we're very grateful that we anticipated the fact that we would run out of supply fairly quickly, that our customers would have supply chain problems for their own GPUs. Because now that we have those GPUs, we can serve them.