Nebius Group N.V. Class A Ordinary Shares Citi’s 2026 Global TMT Conference

Okay. Good morning, everybody. We got a packed house here, so hope everyone can get in and hear okay. Appreciate everyone joining for the session. I'm Tyler Radke, Citi's co-head of U.S. Software. Welcome to day two of the conference. Really pleased to have Nebius here speaking with us. We got Arkady Volozh, who's the founder and CEO. I know there's been a lot of announcements and exciting news in your space. Arkady, maybe for folks that are newer to the story, give us an overview, I think, of your background, which is also really interesting, but how you would position Nebius in the AI landscape.

Nebius is a young startup, a little bit more than two years old but not so young by experience. It was formed with several hundred of very well-educated and experienced engineers with a very small one, small data center, 10 megawatt, and some pocket cash of $2.5 billion. That was our initial state. That's how we separated from our previous company, and we started this business in summer 2004, understanding that with our experience of building big infrastructure projects, being a hyperscaler in the past, there is a new opening on the market. AI will need a lot of infrastructure to be built, and we know how to do it, and there's not too many other people, teams who know how to do it, and there is a business to do, and that's how we started. $40 billion raised later, five gigawatts contracted later. We're now here. A company with real business, real revenues, engineers, products, sales, marketing teams.

What we actually are building is a small, yet another hyperscaler. We think what we are doing, there's not too many other teams, again, can do it. There's, if you look around on the market, who builds all this infrastructure from the full stack, from the ground, from the plots to data center, to racks, to software, cloud layer, application layers, and so on. There's not too many companies in the world who do it, this full stack. We do everything, just like, again, we're not as big, but just like AWS, Azure, GCP today.

Got it. Given there's a number of different players out there, hyperscalers, kind of more GPU infrastructure, neoclouds, that primarily focus on providing capacity on the GPU side. Maybe let's dive a little bit deeper into the differentiation that you have that maybe puts you more in that hyperscaler camp versus the bare metal GPU providers.

Yeah. The infrastructure layer AI is actually, it's many layers. There's different layers. There's people who build, develop plots. They take plots, build electricity, build shell, and then lease these data centers to somebody else with profits. Then there's people who lease these data centers, bring active equipment on the site, GPUs, racks, all these big systems, network, connect all together, and then they sell it to the next layer on, I guess it's called bare metal. It's a basic infrastructure, almost not virtualized, basic virtualization. Then people, bigger companies buy it. Usually the same hyperscalers and/or big labs buy this infrastructure and then run their product on this infrastructure and resell it to the next layer, which is application developers, corporate users, people who build applications to build AI applications, which then on the next level generate ultimate value. AI brings value to this world in different areas.

Generates new coding, security, pharma, whatever, retail. AI generates a lot of value. Actually, the food chain is the opposite. AI generates a lot of value in real life. It goes down to people who provide tools. They buy infrastructure capacity, and capacity buys the land and electricity, and it goes like a waterfall down. But it starts there, generating value. If AI stops generating value, all this is death. So far, it generates more and more value, and nobody knows when it stops.

Yeah. You're on the road and phone, I'm sure, talking with customers every day. Give us a sense of the level of the demand environment right now. Maybe compare and contrast that versus what you've seen in prior quarters. How would you describe it to this packed room?

Supply and demand is a well-known problem now, and it's a good problem to have because demand exceeds supply significantly today. That's because AI found a lot of niches to grow and to bring new value, to generate value, and these niches are growing exponentially. Whereas infrastructure supply is grounded in the real world, and it cannot multiply this fast. You cannot make physical things 10 times more a year of physical things. It's not software which you can multiply very quickly. So supply is scarce, demand is growing, and the situation, at least for now, is getting, I don't know, worse or better. More and more severe. There is more and more demand, and unfortunately, with all this growth in infrastructure, it's not enough to cope with growing demand. So the difference is increasing. And it all reflects in prices, in the whole waterfall of pricing.

Prices go up, and we can see it. You can see it on all the expert management systems. Prices are going up. In our business, we just reported last quarter that we started selling like everybody else, bare metal prices were at the level of 10, maybe $15 million per megawatt. We realized that demand is much higher. We tried manually to adjust this pricing, and until we found the next level of $20, $25 million per megawatt for consumer level, retail level. And on even shorter contracts, the demand is even higher. There's the people who need capacity now, not ready to wait until it's available in 2027, 2028, and they're ready to pay much higher, $40, $50 million per megawatt for these shorter-term contracts. This is what we discovered, not only we as an industry, we as a company as well discovered this year.

We discovered that kind of manually, and we understood that on this disproportionate market where demand is high and supply is not enough, the best way for price discovery to balance it through the price, you need some automated procedure to always balance growing demand versus limited supply. We were among the first, I think, who went to develop this system of auctions. We first experimented. Again, it's why we were able to do it, because in the past, for the previous 20 years, we worked on consumer markets of internet advertising or transportation services and all those industries, consumer industries, they're all balanced by some kind of auctioning systems. Both advertising, surge pricing in Uber, taxi business, and many, many areas. The balance, it's huge industries, and the balances in those industries are all found in some kind of auctioning systems. They're not just simple auctions.

It's very complicated systems. Started simple and eventually became very much complicated with a lot of people serving this area of price discovery. It's a separate business, actually. We think that probably something like this will start happening here in our industry, which is B2B now. Still, price discovery should be determined by some kind of this auctioning system, probably like with commodities or financial markets. Every day somebody trades stock, it's also kind of an auction system.

Yeah, I thought the auction comments last quarter were really interesting and just sort of revolutionary in this space. How do you think? You described this dynamic, a good problem to have. Demand way more than supply.

Yeah. You are going to be bringing on a lot of capacity.

You still have a lot of capacity to sell. You have talked about 5 gigawatts by 2030. How do you think about what makes most sense for Nebius in terms of the types of contracts you want to sign? For the next gigawatt of capacity, how much do you want to be auction, maybe short-term capacity through Token Factory versus multi-year committed deals?