Unsupervised Learning with Jacob Effron
00:30:20 · Jacob Effron
[00:30:20] S0: But it's interesting to think about kind of the the... You know, how different the world is with these really powerful coding agents. And obviously, think, you know, part of of of what I believe your your co founders talked about is is kind of the shift even in who the consumer of modal is over time, right, from a from a developer to, you know, to agents themselves, right? I'm curious, like, how you think about what that means for model as a business and some of the decisions you might make differently as, you know, more and more of of of folks interacting with model are either doing it via an agent or or purely end to end an agent.
00:30:48 · Erik Bernhardsson
[00:30:48] S1: It's actually changed, a lot less than than you... Than I think I would have expected. It turns out agents... I mean, like, in in the early days, like, we we built model in order to give engineers a better developer experience and the ability to move faster. It turns out, like, agents kinda want the same thing. Like, they want fast feedback loop. They want, like, good error messages, good documentation, a lot of the stuff like that that in... Engineers want too. So so so most of the stuff is actually quite similar. I I do think it's, certain things are a little bit harder. Like, one of the things, like, we found, for instance, like, deprecations is much more annoying because agents are trained often on, like, a lot of old code that exists online. So removing features is much harder for us. Like, we can't just rely on, like, you know, remove it from the documentation and wait three months because, like, it's in the training data. People will use, you know, LMs to generate model code, and that will be, like, you know, using deprecated features. And and that's, like, quite hard to change. So so we've had to change our deprecation window quite... For quite a lot. Another thing we... We've started doing quite a lot is also setting up a lot of automated pipelines to to basically have, like, LMs continuously write model code and making sure it it compiles and... Or or not compiles, but it builds and and runs. Trying to just, like, catch, like, things that agents struggle with. And and then maybe another thing we we spend a lot of time on is more like kinda like agent optimization stuff, which is actually more like similar, like, search engine optimization. Like, if you you you can go and ask, you know, ClockCode, like, something like, hey. Like, I wanna do something with inference. I wanna deploy this model and and not say anything about model and just, like, see, like, which tool does it recommend. And and and that obviously is something we we wanted to recommend Bodle as much as possible. So so that's something we track internally to make sure that, you know, over time, like... And I actually think that's, like, something that that will change software engineering a little bit. Maybe I was, like, to the worse. Like, I like...
00:32:39 · Erik Bernhardsson, Jacob Effron, Patrick Chase
I I feel like, in a way, like, because of the training data and and the training loops, like, there's more, like, you know, like, incumbency power in a way. Like, there's more staying power in software today. Like, if you get inside the training data and you get the models to recommend, you know, a a certain framework, I... I've heard people say, like, oh, I'm just gonna use React for everything because agents are really good at it. I'm not gonna try these, like, new fancy front end frameworks. Right? Like, I I think there's, like, something similar with, like, a lot of the frameworks today. It's like, that staying power is actually very, very valuable now. So so we obviously wanna make sure that agents recommend modal as much as possible because that creates, a little bit of flywheel. [00:33:19] S0: And one thing I'm I'm struck by is you obviously had this, like, very prescient bet in 2023 on on sandboxes. Right? It sounds like you guys had kind of this instinct of, like, hey. This is gonna be something that's required for coding agents, and then it took a few years for it to really take off, and then it became this really important primitive. I'm curious, is there anything else that you guys are working on today that you feel like you're a little early on, but you're equally as excited. And and how much of your product portfolio is, like, those kind of bets versus, like, okay. We know this is a hair on fire, like, here and now problem. [00:33:48] S2: Jacob, are you are you fishing for the next thing we should be investing in?
00:33:48 · Patrick Chase, Jacob Effron, Erik Bernhardsson
[00:33:48] S2: Jacob, are you are you fishing for the next thing we should be investing in? [00:33:51] S0: A 100%. A 100%. I'm nothing if not shameless. [00:33:54] S1: I I I feel like Sandboxes, they did create a little bit of, like, a high bar for us. Like, everyone's like, what's the next thing? Like, you know, what's the secret sauce you're working on? And, like, what's cooking in the lab? You know? And and I I I wish I had, like, an easy answer. Like, yeah. Like, a 100% confidence, you know, this thing is [00:34:09] S0: Would you have answered sandboxes in '23, or was it just, like, part of a bunch of things you were doing?
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?