The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:11:23 · Arthur Mensch
[00:11:23] S0: It depends how critical their industry is in general to governments. If you work with defense, for instance, we work with defense customers in Europe, they would never use Chinese technology. If you work on the finance sector, again, because it's critical infrastructure, they are very reluctant to do it. If you work in retail, if you work in certain domains of manufacturing, again, it depends where the reluctance is different. Again, think there is a common understanding that open weight models, if they are being deployed on sovereign infrastructure like ours, the only valid threat is that they go rogue and have weird behaviors in certain domains because you may have a supply chain attack on it. There is a common understanding that this is very theoretical and it's certainly not the case today. But indeed, it's part of the conversation. I'd say what is also part of the conversation is not this, is it a threat to deploy third party models that I don't fully understand. What is part of the conversation is, am I going to be able to get updates in six months from now? Because what's going on is models have gotten so strong that they're able to do cyber attacks, that they're... You can use that for pretty much advanced purposes, let's say. And so the consequence is that they are more and more being treated as defense systems. It's being treated de facto by The US as a defense system because at some point in May, they started to ban them from outside of The US. And if you look at what China is doing today, they're also looking at ways of restricting exports beyond a certain threshold. The reason for that is that effectively, this is a dual use technology. And so as any dual use technology, security of supply matters even more. And so the companies we work with realize that they need security of supply not only on the infrastructure side, but also on the model IP side.
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?