Unsupervised Learning with Jacob Effron
00:55:48 · Jacob Effron, Danila Shtan
[00:55:48] S1: What's the biggest mistake you feel like you've made in in building Nebius so far? Obviously, a lot's gone right. But I'm curious, you know, something that in retrospect you wish you guys had done differently. [00:55:56] S0: It might sound funny these days, but we underestimated the scale. Like... And it's not about the growth of the company. We were pretty optimistic from the very beginning. But I personally... And that was, like, my strong bias for the first, like, significant part. I underestimated the scale that a single client operates at nowadays. Because, like, five years ago, if you're talking about a 30 megawatt data center, it was a serious facility. It hosted, like, hundreds of thousands of servers, like, run glorious... Gloriously large foreclosed. By now, 30 megawatts sometimes is not enough for a single client, which I'm still getting used to. Right? The storage I've mentioned. Right? Like, four years ago when you were talking about network attached storage, you were talking about, like, hundreds of megabytes, maybe gigabytes per second of throughput. Right now, some of our clients routinely fetch, I don't know, seven hundreds of gigabytes per second from a single shared file system. I'm still... I underestimated it in the beginning, and I'm still getting used to this scale. Like, it's... Like, I'm an infrastructure person. I've worked and have seen these numbers throughout my career multiple times. And all of a sudden, you add a couple of zeros to the end and call it a new norm. Yeah. Still puzzling.
00:57:31 · Jacob Effron
[00:57:31] S1: It's crazy. And I guess lastly, like, there's obviously a lot of, you know, unsettled questions in the in the AI field today that, you know, lots of people trying to figure out where where the world goes. I'm curious, like, you know, what you're paying attention to. And I guess, you know, with the lens toward, you know, if it goes one way or another, it would really change kind of the strategy for for Nebius as a whole. What are you kind of most paying attention to on on, like, these these unsettled questions of where the AI world's going?
00:57:57 · Danila Shtan
[00:57:57] S0: Well, I stopped caring about, like, the new models being larger or smarter. I just accepted that it's a given. I'm kind of trying to look out for, like, new novel architectures and approaches, but I don't see any as of today. I hope somewhere deep down in like a stealth lab, something new is cooking. But for now, I frankly don't see anything that like is a thing that I would be, you know, like would place even a small bets on. But I'm more and more interested in the whole outcome optimization part on the agent side. Right? Because, like, we we are used to talking about models and size of models and families of models, etcetera, etcetera, etcetera. But now that we've shifted to this multi turn, long running agents that we are talking not about requests, but rather outcomes, companies are talking about bidding for per outcome. Right? I'm very interested in how that part will shape up. And we have, like, a small research group internally with the Nebius that's working on this problem, actually. How is, like, how do you increase the chances of getting to that outcome? Not by increasing the model or, like, changing the model. That that is, of course, part of the discussion. But I believe there is a lot of optimization on the part where you introduce more sophisticated or maybe more simplified tools. You introduce the harnesses that start to evolve according to a particular task. And this kinda gets us back to, like, a significant CPU consumption. Because, like, a lot of that... A lot of, like, modern AI workloads actually go back to becoming code. Like, this qualified. It's, like, very formal. You see, like, quality gates. You see sophisticated tools, etcetera, etcetera, etcetera. And sometimes your agent works for minutes and minutes minutes without even issuing a back end request to an LLM because the workloads start to, like, to sit on this part. And this is the part that really interests me. Because I think, like, a lot of future AI adoption lies in how reliable your agentic scenarios are and how reliably you reach those outcomes. And I think, like, the whole optimization loop outside the model is important part of that.
01:00:28 · Jacob Effron, Danila Shtan
[01:00:28] S1: 100%. Well, this has been an awesome conversation. Really appreciate you coming on the show. And, yeah, thanks so much for for coming on. [01:00:35] S0: Thank you for having me. [01:00:36] S1: I'm Jacob Effron, and this has been Unsupervised Learning, a podcast where I get to talk to the smartest people in AI and ask them tons of questions about what's happening with models and what it means for businesses in the world. As I hope is clear, I have a ton of fun doing this. It's a nights and weekends project in addition to my day job as an investor at Redpoint. But our ability to get these incredible guests on really comes from folks like you subscribing to the podcast, sharing it with friends. It's really what ultimately makes this whole thing work. And so please consider doing that, and thank you so much for your support and listening. We'll see you next episode.