Unsupervised Learning with Jacob Effron
00:36:39 · Erik Bernhardsson
[00:36:39] S1: We we do think about it, but we... I think we think about it more from an infrastructure perspective. I I think it's very easy to kinda get lost in, like, you know, whatever is, like, the latest today. When I look at, like, the architecture of, like, agent harnesses and, like, how we're building agents and stuff like that, I I still kinda feel like we're so early in these architectures, like, evolving so fast. And, like, what our job to some extent is, like, trying to figure out is, like, what are, like, the sort of core building blocks that we can put out today that, like, will last, you know, like, people would need them for the next five years. And and that to me is, like, less obvious. Like, we we are interested in, like, sort of agent orchestration as a category. I think there's a lot of interesting stuff there. We're interested in, like, you know, evals and model gateways and all kinds of other stuff around, like, you know, like, the the whole, like, you know, life cycle of of of learning from agents and and innovating on agents. But but I think it's, like, less clear exactly, like, how the architecture. My my view is, like, it will be more cloud centric, and it will be probably more, you know, like like, durable objects, like, kind of workflow engine type driven. And we have some ideas of, like, you know, stuff to build in that space. But but I I I I find it, like, harder to, like, you know, lay out exactly what agentic development will look like in five years. Partly because, like, I also think, like, to some extent, like, all software will, like, basically be agentic. Like, I I kind of hate hate the word agentic in certain ways because I I feel like it's like, okay. So it's using AI to make decisions. Like, well, shouldn't all software do that? So, like, I I almost, like, feel like if you look at it on a five year horizon, like, I I think, like, software... Like, all software will become agentic. And then what does software look like then? Like, I I don't know, but I, you know, I I I hope we can build a lot of the building blocks to support all kinds of aspect of like, know, in general, just like how software uses AI.
00:38:22 · Jacob Effron, Erik Bernhardsson
[00:38:22] S0: Yeah. I mean, it's still like one of the hardest parts of building AI for a company, right, of... You know, across the board for many people, you know, building AI for companies of, what actually persists versus is a pattern is like rag from two years ago or whatever it is. [00:38:35] S1: Rag is a good example, exactly. Like if we had made a bet on like rag like two years ago, it would have been kind of a bad thing to do. But but I I would also... So so it's hard. But I would also say, like, one of the benefits of building an infrastructure is, like, you can take a longer view, and you don't have to, like, listen to all the noise. I mean, you you you have to listen to the noise, but, you kinda have to, like, ignore a lot of it trying and to figure out, okay, what are the patterns here? [00:38:58] S0: You kind of alluded to some of these other workloads that like low latency workloads, like voice to voice and real time video. I feel there's a lot of chatter in the air about multimodal inference, robotics inference, these other categories. I'd love to hear you just riff a little bit on like, to what extent are those problems similar to the set of problems the inference markets had before? To what extent are they like different and new?
00:39:18 · Erik Bernhardsson
[00:39:18] S1: I think they're all quite similar, but all... I mean, they're like snowflakes, unique in their own way. Robotics is gonna be a lot of it on on device, of course. So that's a big difference. And and, you know, I I don't sort of envision, like, we're gonna support, you know, most of what robotics looks like because a lot of it is gonna be on the devices themselves. But I think we're gonna be able to support a lot of the testing and development and training of the... Of robotics and and a lot of the simulation environments. So that's something I'm very excited about. On on the multimodal stuff, I mean, there's audio and music and image and stuff like that. Like, people don't talk about as much, but that keeps growing like crazy too. I I feel like LMs have kinda dominated the the airspace in the last few years, but I... I'm just incredibly, you know, excited about all the different applications. Like, why are, like, voice assistants so bad in 2026? Like, I don't get it. Like, they're just horrible. Like, I have a Google Home, and and, like, every question, it just ungulates. It does not understand what I'm... Like, you know, so I just go to ChatGPT. Like, why can't you just connect all of it, like, end to end and train, like, a a system so that that that can, you know, do, like, speech to speech? I I I think there's a lot of... We... We're gonna see a lot of sort of incredibly cool applications in that space. I always think about, you you look back like, know, four years ago, three years ago, whatever, know, could you have a guess that like things like Instinct or Sumo or like Cognition or like none of those things like are very easy to guess, like, you know, three years ago. So I kind of feel like also, like, if you look down the road, like, three years, like, most people don't have an idea of, like, what the world is gonna look like in three years. It's so hard to to know. But, like, what I do know is, like, kinda judging by the past three years, it's gonna be, like, there's gonna be some really cool applications. I just don't know what it's gonna be, but they're gonna be very cool. And hopefully, they'll use modal.
00:41:04 · Patrick Chase, Erik Bernhardsson
[00:41:04] S2: I was looking back at an email you sent me with the modal series a pitch deck, and this is what you said. This was after we had signed a term sheet. By the way, we never actually reviewed a modal deck. But here's something we made earlier that I just updated with the latest numbers. Could be good to review at some point. When you sent this, you were at roughly $2,000,000 in run rate. You now you've crossed $1,000,000,000 run rate, which is insane, in three years. What's been the biggest kind of change to supporting that kind of scale in just, you know, a couple years? [00:41:38] S1: So so first of all, I I actually realized, like, the other day, I I said on on another podcast that I've never made a pitch deck. And then I was actually thinking about it. I was like, maybe I did make a pitch deck after the series a because we had a couple of angels that came in, and they were asking about it. So I was like, oh, fine. I guess I'll put something together. So I I I I realized I did lie. Like, I did make a pitch deck, but it was after they rolled it across [00:41:59] S2: the showed it to us, so I don't know that it counts.
00:41:59 · Patrick Chase, Erik Bernhardsson, Jacob Effron
[00:41:59] S2: the showed it to us, so I don't know that it counts. [00:42:01] S1: Yeah. So I don't know if it counts. Like, I never, like, sent a pitch deck to an investor. Sent it to few angels, actually. But that... And that's the only pitch deck I've ever made, actually. But in terms of the scale, yeah, I mean, we're we're grown, you know, 100 x, I guess, in terms of revenue, something like that since then over the last three years. So a lot of growth. I think I think in a way, like... I don't know. Maybe I'm, just... You know, my my experience has mostly been at hyperscale startups. Like, I was a Spotify for seven years and some other places. So so so for me, like, this, like, fast growth is kind of all I've seen. Like, you know, this, like, you know, every every six months, you have to rewrite all the systems. You have to kind of rethink everything you're doing. You have to kind of change all the processes. Like, you always feel like you're behind. Like, I don't know. I I kinda like the mask. [00:42:51] S0: I guess we would love to, you know, since since we have your your mind on the podcast, we'd love to kinda zoom out, maybe talk a little bit about the broader info market and some things that have been in the news of late. You know, maybe maybe most top of mind these last few weeks, I feel like there's been tons of discourse about neo clouds. Right? They like, you know, how the debt's trading, they've traded down, they're kinda back up a little bit now. And I'm curious, like, what do you think happens to this landscape over time?
00:43:12 · Erik Bernhardsson
[00:43:12] S1: I think the neo clouts are, like, fine business models. Like, I I I think people have been, like, confusing it, like, a little bit. They're like, wow. There's so much demand. Like, revenue's gonna grow, like, so fast. And and then, you know, there's a little bit of sort of a... Almost like a VC mindset of, like, oh, like, thinking in, like, revenue multiples. But but I think all of these, like, these NeoClouds, like, growth is actually quite expensive for them because they have to go and raise a lot of debt. So And I I don't know if necessarily, like, you know, just because, like, they grow a lot, like, you know, like, it's very, capital inefficient. Like, these companies are at at their core, like, you know, financing vehicles. Like, they're very... Like, they're doing leasing, like, of hard assets, And and that's, you know, and that's like a... Inherently, you know, harder to scale the capital. Now that that market is kinda saying, woah. We deployed a lot of capital into NeoCloud. It's like, maybe maybe hold on for a second. Like, you know, we don't have that much more capital to lend, and so the interest rate is starting to go up. But... Yeah. Also, by the way, like, these companies have incredibly high leverage. So so, of course, they're gonna have more volatility. Like, the the the residual equity value is gonna go up and down much faster on the stock market with GPU prices and stuff like that. But they're, like, fine business models. Like, you know, they kinda locked in customer contracts and raised debt against it, and, like, you know, now they're paying off that debt. And and and in many cases, it turns out the residual value has been higher than people expected. Life time of GPUs has been longer than people expected. So they can resell once they paid it off, like, at a higher price than people thought. So, like, they're gonna make a bunch of money from that. Like, it takes a few years to get there. But I think in that sense, like, Neoclass will do well. Not particularly differentiated. So I I I... You know, long term, I think they're gonna start