Unsupervised Learning with Jacob Effron
00:43:58 · Danila Shtan
[00:43:58] S0: Listen, I think the whole, like, AI consumption is gonna grow. And it will be like universally used everywhere. Like AI is everywhere. Like right now, like web services are everywhere. AI services will be everywhere. So in a sense, like you have AWS, right? But you also have Hetzner. You also have DigitalOcean. You also have Linode. You also have like tons and tons and tons of local hosting providers that are giving you virtual machines, sometimes bare metal, etcetera, etcetera, etcetera. So this market is gonna be vast. Will all of the current three to 500 companies survive the next five years? I'm not sure. But I am pretty sure that there will be a place for the very long tail of providers down the line. Like, some of this, the current players will consolidate, some of them will die off, new new players will be launched, etcetera, etcetera, etcetera. It's a... Like, it's not a bad market right now. It's not a bad business to be in. So they will constantly attract capital and attention and investments, etcetera, etcetera, etcetera. And I trust that, like, in five years' time, there's still gonna be, like, hundreds of players in this market who found the investment related to and several $100,000,000, and they can offer something to someone in the market. Same as it is with, like, with the the small scale clouds like DigitalOcean these days. Right? Like, DigitalOcean did not die because AWS is growing.
00:45:42 · Jacob Effron
[00:45:42] S1: You're obviously doing a lot of things in house like sandboxes and, you know, web search and, you know, helping people with their their models over time. And, you know, I I think one question a lot of people are asking is, you know, what parts of the AI infrastructure end up just being subsumed into these future AI hyperscalers and, like, live, you know, in just one combined offering? And then what are, like, the standalone pieces of AI infrastructure that will persist as companies?
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.