Unsupervised Learning with Jacob Effron
00:27:48 · Danila Shtan, Jacob Effron
But I strongly believe that for for real AI adoption, when we are talking about serious enterprise business, when we are talking about, like, practical AI that actually solves problems, You are getting more and more into these specialized agents, limited domains, where you even stop caring about the cost. You care more about predictability of the outcome, and you care about time, as in, like, real seconds and minutes till you get that outcome. Yeah. And I think there is a lot of potential there, and we still... We'll we'll we'll still see a couple of nice breakthroughs there. [00:28:31] S1: There's a ton of work around inference optimization, and a lot of that gets done in open source, right, or, like, you know, contributions that that share throughout the community. There's obviously a bunch of work on on on, like, smaller purpose built models and and compaction and a bunch of these things. Across all these problems, which do impact, you know, how enterprises consume inference, which ones like Nebius should have a solution or offer a product around and which ones you're like, hey. It's great that that exists, and we'll benefit from that, but we don't need to be at the forefront.
00:28:59 · Danila Shtan, Jacob Effron
[00:28:59] S0: We want to be at the forefront of everything. In a sense, these are additive and even maybe multiplicative. Right? Like, if you have a right subset of things, optimizations that you introduce for a particular use case, it means that you can reach the targets easier, better, faster, in a more predictable fashion. So we invest in all avenues. Like, one of our acquisitions back in, like, the beginning of summer was targeting specifically that. Like, we basically bought the company for the talent in model optimization, model compression, speculative decoding, etcetera, etcetera, etcetera, combined it with existing talent. So, like, we have, like, team that is two x this size. And we... Like, we have serious manpower, like, as in tens and tens of people who are working on these problems. Like, we have specialized groups for, like, kernel optimization, specialized groups for, like, model compression problems. Because this is... Like, I feel if you want to talk about, like, running inference as a service at a serious level, [00:30:09] S1: this is the [00:30:09] S0: only way you can do it nowadays. [00:30:11] S1: And I guess, like, you know, maybe along those lines, you guys have done, you know, a few acquisitions, and and one of them that that caught a few people's eyes was, you know, Tavily in the kind of, like, web search space. Maybe talk a little through a little bit, like, you know, the rationale for that, how you think about that fitting into the broader platform.
00:30:11 · Jacob Effron, Danila Shtan
[00:30:11] S1: And I guess, like, you know, maybe along those lines, you guys have done, you know, a few acquisitions, and and one of them that that caught a few people's eyes was, you know, Tavily in the kind of, like, web search space. Maybe talk a little through a little bit, like, you know, the rationale for that, how you think about that fitting into the broader platform. [00:30:27] S0: So first, we were very impressed by the product and their go to market motion. It was almost a year ago now. Right? But we felt like we knew how to help them run even faster with our expertise and technology and low level stuff. Because actually running a web search engine is not a very simple task, not neither from, like, the software development perspective nor from the It's [00:30:56] S1: a a lot of... You used to run one.
00:30:56 · Jacob Effron, Danila Shtan
[00:30:56] S1: a a lot of... You used to run one. [00:30:59] S0: And of us dabbled a little bit in our previous lives. Yes. Right? So this was the premise. Like, we have a great team that is running real fast in terms of like market penetration and in general go to market motion. And we know how to make it complementary with our expertise on the low level stuff infrastructure and, for example, large scale web index. It's not exactly, like, embedded into the rest of the offerings that we have now today. Like, we are exploring and we have something cooking for, like, for the managed inference combined with, like, agentic tools. Because, for example, we also develop sandboxes nowadays. Right? So this is another, like, building blocks for p... Building... One of one of the building blocks that people use to build their agents. We have several other products that are in the earlier stages of development in that regard too.
00:31:55 · Jacob Effron
[00:31:55] S1: Yeah. I mean, it seems that there's obviously a set of tools that folks are gonna use to build agents and build on open source. And it seems like you wanna have a bunch of those tools to be able to offer to Yep. To your end customers. And web search kind of being a a a key primitive for any any long running agent. We'll consume a decent amount of tokens on on just pulling information in. So Yeah. I guess I'd love to switch gears to just like the broader, you know, industry build out and and kind of the... You know, I I think there's lots of questions folks are asking about that. Figured one place to start that actually might just be interesting for our listeners is to what extent are, like, the data centers that you work with similar and, you know, like, there's a reference design and they all kind of, like, know, I don't know. To you, it doesn't really matter which one's which. And, like, what are some of the differences in them that actually impact the business of, you know, running an AI cloud on on top of them?
00:32:41 · Danila Shtan
[00:32:41] S0: To be frank with you, I have no idea what the walls of the facility are made of. So they're probably different, but that doesn't really help. But in all seriousness, what's within the walls is something that we fully control. We do not rent data centers when someone like have built the, like the network on their own or brought some GPU GPUs or servers, etcetera. If we don't own the facility and we rent it, for example, we rent, well, the building, electricity, but that's it. All the internal build out is controlled by us. It's like, it's done according to our standards. So from the software side of things, there is literally no difference which data center we run. Is it our own facility? Is it something that is built within a rented facility? Or for example, we've recently announced the so called asset light model when a third party brings capital in order to set up the facility and GPUs, and we run our platform on top of that. It's gonna be the same. These data centers are built according to our... Like fitted out according to our spec. Hardware is something that is familiar to us because like unknown hardware is quite hard to adopt and make sure that you provide proper SLAs. And that's it. So we don't compromise there. We never rent something from people and present it as our own cloud. If you launch a virtual machine in Nebius cloud, we can be sure that it runs on our stack top to bottom.