Unsupervised Learning with Jacob Effron
00:46:06 · Danila Shtan
[00:46:06] S0: In a sense, I think it will, like, it will kind of mimic what's happened with like the legacy generation of clouds, previous generation of clouds. Right? When your growth kind of dictates that you need to start addressing new and like new sectors, new markets, etcetera. Because otherwise your growth is like bounded and you can't like can't show your projections, can justify your valuation, etcetera, etcetera, etcetera. So there will be a natural push for the larger companies to start like increasing their portfolio, like trying to get into new products, into new services, etcetera, etcetera, etcetera. It doesn't mean that there will be a one size fits all solution. Because in a sense, if you're concerned by that, you will... Like, we just need to stop doing whatever we are doing and just wait for like OpenAI to rule the world, Anthropic to rule the world. Because these companies will eventually... Like, you do realize that OpenAI has models, runs inference. They have their own search that they... Like, they don't exactly expose it. But if you're building the agent on their platform, it has access to search. They have all kinds of like sandboxing, local agents, cloud agents, etcetera, etcetera, etcetera. They both... OpenAI and Anthropic are now investing in forward deployed organizations and are gonna go and do the work that was traditionally like, I don't know, Deloitte's or like Accenture's piece of pie. Right? So they will be everywhere. And I think this is a natural motion for the large company to start getting into a broader markets. But it doesn't mean this is... They are gonna be the only ones. So even nowadays, there is a place for independent object storage players all of a sudden. Right? Or independent compute players or independent small scale inference players for that matter. Independent database players. Like like, on one... Like, you've seen, like, Google has, like, big BigQuery and whatever, all kinds of data stuff. But then Snowflake kinda, like, appears from out of nowhere. AWS has, like, all kinds of, like, managed databases, large scale databases, like Aurora, Redshift, etcetera, etcetera, etcetera. But then MongoDB appears. And it's pretty successful. I can't say it's, like, super large scale and probably by nowadays standards will never be. Because nowadays standards for a large company is like trillion plus. Right?
00:48:55 · Danila Shtan, Jacob Effron
But like two years ago it was a very solid and very successful company. Or someone like Clickhouse appears with their cloud offering. Right? Also a massive managed database, an independent company that finds its niche and works it real well. So I think, like, larger company, larger clouds will grow. They will have, like products in every sector and will be pretty successful at selling them. Those because of bundling, because of their sales power, etcetera, etcetera, etcetera. But there will always be a place for niche players. And because the market is huge, niche doesn't mean small. Niche doesn't mean small scale or low revenue or low valuation. This can be solid companies. [00:49:43] S1: Yeah. I mean, as you mentioned, you know, OpenAI and Anthropic having this kind of broad suite of offerings too. I mean, in many ways, they're obviously building their own AI clouds. And I thought one thing that was interesting recently was, you know, obviously, limitation of those AI clouds versus others is is the the, you know, having just one model family, right, to be able to call from. But I thought it was really interesting that OpenAI announced that partnership with Baseten. I'm curious what your take on... Was was on that. I mean, is that them just trying to get into this general, you know, hyperscaler game across models?
00:50:10 · Danila Shtan
[00:50:10] S0: In a sense, we see a lot of push and concerns about people like claiming that OpenAI models or Anthropic models are too expensive. Everyone is puzzled or surprised by the bills they get, they get to the end of the month. So this might be like an answer to that. Well, if you're like, oh, go try open open models. At the same time, open models are still behind the closed source... Closed models. Right? However good those models are, they're still lagging behind. And giving people an option to try, like, lower scale with the ability to switch back to the top frontier model, it also be a good incentive. Like, anyway, I think like OpenAI is doing a very interesting move with that partnership. And I like it really much because like, this kind of shows flexibility. Right? They're not set and so. Like, they're exploring different options, different things that they can or cannot do. Like looking for different options to like so... But I I I think in a sense, this all plays into one single thing. Like OpenAI wants... Same as Anthropic. They want to be one single, like touch point. Like they want to be one single thing for everything. Even that is like, okay, but sometimes I want to use open source models within my codex controlled environment, by the way. Right? So like, well, good for them. But if the company, the person, the user is still within your ecosystem, you still have a lot of options to steer them, to upsell them, to introduce them to new services, etcetera, etcetera, etcetera. So if that, like, alleviates one of the concerns, so be it. Why not?
00:52:07 · Jacob Effron, Danila Shtan
[00:52:07] S1: Well, I always like to end these interviews with some, like, overly broad questions that I stuff into the to the very end. And so if you'll if you'll humor me, I guess Sure. First question is just, what's one thing that you've changed your mind on in AI in the last year? [00:52:19] S0: In the last year? Oh, a lot a lot has happened in the year. No. Like, the obvious answer would be software engineering. Like, a lot of people forget, but a year ago, we lived in a totally different world. [00:52:34] S1: It's amazing. Like dog years in AI world. [00:52:36] S0: Less Way less like capable. Yes. Way less capable in terms of the model capabilities and in terms of how good the modern harnesses and tools are. It's like the AI coding. Because like a year ago, it was total bullshit. Right now, it's it's a solid part of, like, your everyday tooling. But for example, if you're not talking about the industry trends or, like, talking about more, like, my personal thing, I've started relying more on the, like, AI enabled, like, local agents and harnesses to offload a part of my cognitive load, to be honest. [00:53:18] S1: I mean, obviously, like, run a really large technical organization. And and I think everyone's trying to figure out, you know... I mean, obviously, the models only recently got really good on the coding side. So everyone's trying to figure out what it means to build teams given the new model capabilities and run an organization. I'm curious what you've... Where you've settled and how you've thought about that.
00:53:39 · Danila Shtan
[00:53:39] S0: Cautiously. Conservatively. I'm very concerned that modern models and the attractive user experience that they provide, like modern agents, modern models, etcetera, etcetera, etcetera, is something that will lead us to two issues. You like... You see slop, like, being revealed in, like, in chats, in merge requests, all over. And I, like, I try to pace that, and I try to, like, be very observant and vigilant about that. So for example, like, don't restrict talking usage. Like, people can use whatever they want. Our budget is quite significant. We don't impose, like, per person limitations or per department limitations. But the... At the same point, we try to, like, look at the usage, figure out what it is used for, look at the most ineffective ways people use it. We, like, constructed a set of policies that doubles down in the idea that whatever you've done with the agent, it's still your result. It's your responsibility. It's your pull request. It's your code. It's you who is on call. It's you who's gonna wake up during... In the middle of the night if something goes awry, etcetera, etcetera, etcetera. And if something was created by a completely autonomous agent, it is marked like that. Completely autonomous agents will not touch, like, the core services for a while because, like, this is, like, this is, like, out of bounds. Like, sometimes people are surprised that, like, I work in a company that's kind of participates in this whole Yeah. AI revolution, and I'm very conservative. But at the same time, like, I've seen how this sausage is made, and I, like, I'll be cautious.
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.