Unsupervised Learning with Jacob Effron
00:00:00 · Danila Shtan
[00:00:00] S0: If NVIDIA could have bought... Like, could have produced, like, 10 x the chips, this whole market would have been shaped differently. Yeah. Because there is not enough capacity for their scale, this is a relatively small business for them. For the hyperscalers, it's not just a matter of, like, sales. It's also a matter of changing the infrastructure that was built at scale for other kinds of workloads. This is a massive investment on their part that needs to be justified by the size of a potential business, and it's not that there yet.
00:00:33 · Jacob Effron
[00:00:33] S1: The emergence of Nebius and the other hyperscaler competitors these past years has been a fascinating part of the AI story. It's something I've really been interested in, and so it was a lot of fun to have Nebius' CTO, Danila, on the podcast because we're able to dig into a bunch of interesting things about the business and how the space has unfolded. We talked about Nebius' really interesting origin story and the original relationship with the index and how the company came to be. We talked about what it's like to be inside one of these companies, how Nebius works, you know, with NVIDIA, what it looks like to get their next generation of chips live. We talked about why the opportunity actually existed and why AWS, GCP, Azure, didn't just capture this entire market. We also talked about Danila's view on a bunch of new technologies like Disag, Inference, among other things. All in all, just awesome to get to talk with someone who's at the epicenter of the AI infrastructure market about his thoughts on everything about where the space is headed. I think folks will really enjoy it. Without further ado, here he is. I guess I'd love to talk just maybe about the broader, like, AI cloud market, you know, today. And I think that there's a few maybe larger and prominent public, you know, I guess, neo clouds. And then, you know, it feels like there's a new one getting funded every day. I know you don't like that term. But maybe just talk a little bit about how you think about what differentiates... Like, some of our listeners are probably thinking like, you know, all that really matters is just access to GPUs and like, you know, you... Wherever you can get them, you go. Maybe maybe use that as a straw man and and tell us why that's not the case.
00:01:54 · Danila Shtan, Jacob Effron
[00:01:54] S0: Well, I'm obviously biased. Because like, first of all, I'm a software person. And our product and business was launched on the idea that software is the mode. Right? So your ability to serve the customers that consume, I don't know, tens of thousands GPUs, of GPUs, and then one interconnected cluster with the same platform as you serve, like a self-service customer that runs one GPU for half an hour, was our value prop from the very beginning. So we believe that this is extremely important. Being able to build large scale, but have the possibility almost like sell it in retail in a sense. This is what we think like definitely differentiates us from the market, from everyone on the market. Like a small remark, Neo Clouds. Hate that term, like with a passion. Because like, it started as a distinction, as in these are kind of cloud companies that are doing this weird, strange new thing. But then everyone wants it in that market. So like, when we started, there were like, I don't know, five, seven companies that were in this area that were called NeoClouds. Now I think it's closer to, like, three to 500. [00:03:16] S1: Yeah.
00:03:16 · Jacob Effron, Danila Shtan
[00:03:16] S1: Yeah. [00:03:16] S0: Depending on what you even are ready to call a Neo Cloud to the scale. And we we really don't like this stretched umbrella term anymore. We are the AI Cloud. We don't care. Like, let others... Other guys beat Neo Clouds. We are the AI Cloud, And this is our mental model. We are not competing with the Neo Cloud markets. We kind of believe we are past that. Our next competition point is taking on AWS and Google Cloud. Yes, we are way smaller. Yes, our revenue is like, what, I think, 1% of AWS nowadays. But we are running fast. We serve a very particular market. We have a bet that this is the market that is going to grow, that is going to explode. So people are concerned that these investments are huge. People put a lot into the infrastructure, etcetera, etcetera. My view, if I compare what we see in the market with what was there two years ago, we are barely scratching the surface. I think the overall demand in the next three years will increase tenfold, hundredfold maybe. So we are at the verge of another explosive growth. Right now, we see these huge facilities, right? But a lot of those are privately owned for internal needs of certain companies. Right? So they're like, Elon Musk is talking about his Colossus cluster. And it's like, it's a man's scale. Like, really, it's like real solid, large scale thing. But it's not for the broader market. Right? It's for their internal needs. Maybe for some partners that they do something. [00:05:07] S1: Maybe for Anthropic if... Yeah.
00:05:07 · Jacob Effron, Danila Shtan
[00:05:07] S1: Maybe for Anthropic if... Yeah. [00:05:09] S0: Yeah. Anthropic and OpenAI are messing the, like, the capacity, but they do it for their own product. And this is like a wholly separate discussion. These companies will essentially want to be everything. They will want to be the internet and contain everything and every product and every capacity angle within themselves. There is a question of Google, Meta, and Microsoft who have a lot of capacity but are constantly juggling and struggling between, like, giving it out to the market for... Like, to rent or giving it out to internal teams. Because all of all of these companies are extremely, like, machine learning AI enabled. They need a ton of capacity for internal usage, for runtime and for research. So they have to constantly juggle this. So our bet is that there is a niche for an independent, as in not affiliated with a large company with huge internal consumption player that will serve the markets of the mid tier players who want to be the large companies. [00:06:16] S1: I thought it'd be interesting for our listeners to talk a little bit about the history of Nebius. I feel like it's a really interesting path that you've you've, and, you know, had to end up here. And so maybe just talk a little bit about how the business came to be.
00:06:16 · Jacob Effron, Danila Shtan
[00:06:16] S1: I thought it'd be interesting for our listeners to talk a little bit about the history of Nebius. I feel like it's a really interesting path that you've you've, and, you know, had to end up here. And so maybe just talk a little bit about how the business came to be. [00:06:28] S0: I will start with the, like, with the moment, I've joined. Right? So I am CTO of the company, but I've been with the company from the very beginning, like, since before it was called Nebius. So the new company is established, I think, during 2024 also. The deal was finalized. We finally announced our separation from Yandex. No connections to the Russian assets anymore, clean slate, etcetera, etcetera, etcetera. And that was it. [00:06:57] S1: You know, people have come to these AI cloud businesses in different ways. And, obviously, you know, I felt like the index team was, like, legendarily really, really strong, you know, on on the engineering side. You know, a lot of folks building AI clouds came from, like, crypto or energy or, like, not not necessarily the same, you know, maybe technical heritage. To what extent, I guess, was the Yandex experience really relevant? And to what extent was it like... You know, it turns out no one knows how to build a GPU cloud because it's all kinda new and, you know, there's there's... Everyone's learning on the fly.