Unsupervised Learning with Jacob Effron
00:34:13 · Erik Bernhardsson
[00:34:13] S1: No. Because what sandboxes represented back back then was, a little bit of, like, an asymmetric bet. Like, sandboxes was not, like, a very hard thing for us to build back then. Like, to getting out, like, a prototype of sandboxes took, like, Akshat, like, a few days. And then I spent a little bit of time, you know, like, improving the SDK, some other people, like, started building. And then we started seeing some customers. We rewrote it. And then in 2025, when it started exploding, then we had to rewrite the system, like, five times just to handle, like, more and scale and make it faster and and and more reliable and and things like that. So... But but but the initial investment was actually pretty pretty basic. So in in that sense, like, it was a little bit like a sort of asymmetric bat, like a a kind of a low downside, high upside bat. I don't know if there's, like, anything similar today that that we're working on. I am... I mean, like, just just kind of called out, like, a couple of, like, you know, things that I'm quite bullish on, and and these probably represent, like, way larger investments, like, you know, larger risks and and, you know, in terms of, like, the the bat size, Low latency. Like, I I think, you know, there's gonna be a lot of voice to voice, a lot of real time video type stuff that requires us to rethink a lot of how we manage to compute, like, globally. How do we get down the number of hops, like how do we scale up and down in different regions, like almost like building a CDN for inference. So I'm pretty excited about that. I think training is actually kind of an underrated thing. I think there's a lot of opportunity in in in training, like mid sized models and reinforcement learning. That's been a big bet for the next... For for the last couple of years, like, getting all the RMA InfiniBand type stuff working. What else? I I think a lot of VM, like, stuff is is quite interesting. Like, I think, you know, supporting full blown operating systems like Mac and Windows and Android and iPhone could be kinda cool. I think there's lot of upside there. So I I I have a lot of those ideas in my head, and we we we... We're, like, building a lot of those things. I I don't know if there's anything that represents the same, like, sort of explosive potentials like sandboxes.
00:36:05 · Erik Bernhardsson, Jacob Effron
I mean, maybe, like, if we have this podcast again in, like, three years, like, we'll we'll kinda look back and [00:36:11] S0: We're we're gonna make it recurring at this point, so we'll we'll we'll look we'll look forward to to revisiting that. You know, I I guess obviously sandboxes have become this fundamental primitive for coding agents, and it feels like that then brings you into this world where there's like, I imagine you look at the current state of coding agents, you're like, God, there's so many more tools that would be helpful for these agents to make them more effective for people that use them. How do you think about, like, you know, whether you further build into that world or, like, the the set of the things that are interesting for you there?
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.