Unsupervised Learning with Jacob Effron
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.
00:06:57 · Jacob Effron, Danila Shtan
[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. [00:07:27] S0: First, we had and still have the core team that supports the hardware side of business that came from Yandex that had experience operating hardware, like, infrastructure at scale, like, real scale. Hundreds of thousands, millions of servers, span over multiple facilities, etcetera, etcetera, etcetera. These people, like, were at the core of our build outs, like initial build outs and expansion, and they're still with the with the company. So this definitely helped. I think, like, we would be way less successful if not for that core team. [00:08:04] S1: Yeah.
00:08:05 · Danila Shtan
[00:08:05] S0: On the software side, it's interesting because it's a two fold. Like, because like with hardware, it's very straightforward. We needed people with experience. We had people with experience. They started building out from the get go. On the software side, it's... It was a weird one. First of all, we had to rewrite everything from scratch. So our previous experience was kinda like garbled. Second, we had to grow so fast that by, for example, by the end of twenty twenty four, when the new platform launched, like a significant portion of our engineering team, software engineering team, was not from Yandex anymore. Because we had to hire during this period extensively. As a team, we were working on this cloud platform from, like, from the initial idea. Right? So we kinda just went with it and doubled down on software. Like, we are not doing a managed bare metal. We are not doing a patchwork of third party services. We want to build a holistic cloud experience, the holistic cloud platform. And we kind of doubled down on that even with the whole rewriting of platform, like doing it from scratch, etcetera, etcetera, etcetera. And of course, like having a ton of talented engineers on the team obviously helped. Like, just for the scale of it. Right? You've mentioned, like, people who, like, came from crypto or came from other avenues of life. They are mostly concentrated on how to... Like, their idea is like, you need to buy GPUs, you need them to run, and then you'll find the buyer. In our case, for example, in 2024, when our hardware team that actually, like, procured GPUs, did the build outs, we're working on our own servers and chassis, writing firmware and stuff, building the network. That whole team was roughly, like, 40 to 50 people. Yeah. The software organization was already 200. Right? So we kind of concern... Like, think of ourselves as a software first company. We are building the cloud on the solid foundation of hardware. Yeah. We're not a hardware first company.