Nebius Group N.V. Class A Ordinary Shares Goldman Sachs Communacopia + Technology Conference 2026

Thank you. Thank you. Great.

Before we get started, just like to state that this conversation is not intended for the media and is off the record. Fantastic. So let's get straight into it and perhaps start, Arkady, with a bit of a discussion on AI compute demand. One of the key investor debates has been around how durable the current AI infrastructure cycle will ultimately prove to be. Can you talk about what you're seeing in the market today, and what does that tell us about the duration of underlying AI compute demand? To what degree has your view beyond 2026 strengthened?

Well, I doubt I will say something new here. Everybody sees what's going on with the prices. It's obvious that demand today is much higher than the real world can build for this demand, and the prices always reflect this unbalance. What's going on is that AI is creating a lot of added value in some way in real life and in the industries, be it coding or recently security or office work and many other areas which are to come. There is a lot of value created there, which goes down to people who provide tools to serve this demand, who provide models, which goes down from there to people like us who provide compute and the cloud to serve these models, and then going down to people who build the infrastructure, which we also do. Demand is huge. It grows, let's say, times a year, if not tens of times a year.

The physical world is limited. It cannot grow 10 times a year. That's why we have this imbalance. We probably have said that a couple of months ago, that if we wanted, we could sell all the 2027 demand today. We have much more ask than we can physically serve. We do not pre-sell much. We are focusing on free capacity, which we will be selling later. We're building capacity which will be sold later. But since we last publicly spoke, actually this sign of this demand went even further. Now we receive orders for first, second quarter of 2028. People are demanding tens of thousands of vGPUs and GPUs now. So we see demand today as unlimited.

Nothing is unlimited in this world, but for now, the demand in all the visible perspective is much higher than anybody can serve. For how long it will go, we were talking about 18 months. Now I think we can easily talk about 24 months, maybe more. But today is the situation that AI is working. AI creates real value in real life. It needs to be served. We just, as an industry, we can't build as fast. That's why this pricing grows.

Well, what Palantir calls sovereign is sovereign for the companies. The companies, they use AI. By using AI, they produce a lot of data. These data go back to the models, improve the models, produce even more data, and this loop continues. The companies have actually today a choice. They can go to commercial models and start feeding their data back to them and improve their models, or they can actually use open source, open weights model under their own control, feed data to these models, keep improving these models for themselves, not sharing that data with the whole world. The practice shows that if you do this with generally open weight models are lower quality than commercial models. But commercial models are very universal. They are expensive. Open weight models could be trained with this data in repetitive loops, so that in this specific narrow domain of this enterprise, could be trained better to a higher level of intelligence than general models.

There is a lot of cases in our practice which it works. Just recently, one of our clients, Shopify, there was a famous tweet of CEO of Shopify which showed that they used open-weight models, trained it with their own data repetitively, and they achieved the quality which is higher than they had with GPT-5 and 6. So open model, your own data, repeated many times in the loop, better quality in your domain. That is what enterprises should be doing, and by this, they do not share their data. They keep their data. They do not train the whole world, potentially their competitors.

This is what Palantir, actually, and NVIDIA as well, and us see as the vision as the main mechanism how enterprises should be using their AI. To implement this, Palantir have instruments to create this loop, open models data back and forth, but they need infrastructure on which it could be run. They used to use hyperscalers for that, sending data there. Their clients do not like it because, again, their data is shared with everyone. They needed a controlled stack which could run open-weight models, which is controlled down through the models to cloud, to racks, and the data centers. Palantir can control the whole stack. We provide this, and the synergy is that we provide this whole stack up to our token factory, where we run a variety of different open models. They take it, add their tools on top of it, their customer base.

For us, it is an excellent channel to enterprises. Together, hopefully it will be a nice tool to serve the market.

Well, as Arkady just shared, there's a very powerful value proposition that we share with Palantir with regards to how we believe enterprise adoption is likely to take place. So working together, combining the full stack capabilities we have and the tooling that they have, and more importantly, the credibility and momentum that Palantir has in the enterprise, we're confident that we're going to be able to jointly develop opportunity that goes well beyond the existing market adoption in the enterprise, and actually will accelerate our overall enterprise business and contribute to the customer diversification that we have at Nebius.