Unsupervised Learning with Jacob Effron
00:23:35 · Erik Bernhardsson, Patrick Chase
You know, having all the models and and and and a lot of, you know, stuff like around reinforcement learning maybe or or other things. [00:23:41] S2: You know, the biggest modal customers are are on multiple products. Maybe can you touch on a few of the more interesting ones? [00:23:49] S1: So, yeah, we we have some incredibly cool customers like Cognition is using us at large scale for both for inference and and sandboxes to do a lot of reinforcement learning on model. These sandboxes for all the rollouts. We have customers like Ramp using this for background agents for the internal coding agent that they actually launched as a as a standalone product today that they're selling. We have customers like Suno and and Runway that uses regenerative video and audio. Decagon is another customer that uses us for a lot of customer support stuff, voice and and text. DoorDash uses us for a bunch of internal applications, mostly LMs, but also all kinds of other stuff. We actually have a lot of customers in in in computational biotech using us for for various types of drug discovery, using, you know, molecular simulations and sequence alignment and all kinds of stuff. We have a bunch of customers in robotics, one x and and a few other ones using us to train robot models, like doing a lot of training, but also batch processing of video and things like that. A lot of customers in the data generation space. So so customers who... They're building environments and and running benchmarks in model often using sandboxes.
00:24:55 · Jacob Effron
[00:24:55] S0: One thing you kind of mentioned is portfolio of products, and I think that's like one of the most interesting parts of business you're building. I imagine, honestly, one of the hardest parts must be just prioritization of all the different things you could build. There's probably so many new things that come up of really exciting tools you can build for developers, and I guess I'm curious, obviously, Sandboxes, you have incredible products around coding agents, reinforcement learning, how do you think about building down that vector? There's also the fine tuning or all as a service stuff that some of your competitors seem like they they prioritize. Like, how do you think about, I guess, you know, where to prioritize within that portfolio?
00:25:34 · Erik Bernhardsson
[00:25:34] S1: I I think it's an incredibly hard problem at, I mean, most startups, but especially multiproduct companies. Right? Like, the sort of balance of, like, you know, first of all, like, when do you go multiproduct, and and then where do you put your bets? Like, we're a 150 engineers or something like that to... Today, and and I struggle with it every time, like, every day. Like, you know, I wish we could, you know, do that and then, you know, and whatever. And and, like... But I kinda know that even if we had a thousand engineers or 10,000 engineers, like, it's all, you know There's lots of build. Dollars. Like, know, I wish we could build a rocket and go to the moon and whatever. Like, it just never goes away. So so, like, you kinda have to be just, like, ruthless with prioritization and and really think about, you know, what's the highest ROI and, like, where do you draw the line? And you have to be sometimes, like, pretty aggressive also saying, like, actually, like, I know we started on this project. I know you're really excited about it, but, like, this other thing kinda came up. So, like, we're gonna have to pause this for a second. We'll get back to it later. We've had to do that many times too. It sucks when it happens, but I I really think, you know, that sort of prioritization and, like, how do you how do you allocate resources? I mean, resource allocation is, like, basically, like, you know, the number one thing that I think a CEO has has to has to really, you know, be ruthless about it, a company, and like so much value is being created, like allocating the the work to the right resources.
00:26:50 · Patrick Chase
[00:26:50] S2: The modal roadmap in each board meeting is extensive and, you know, very daunting. I'm always impressed by the amount of software that the team can ship and hardcore software while keeping the system running. How much is... You guys were building the company pre ChatGPT and then post ChatGPT moment. How much has that changed, you know, maybe on the product side, you know, what you guys were doing, but then also when you were thinking about building and the velocity that you can ship software at?
00:27:24 · Erik Bernhardsson
[00:27:24] S1: Yeah. I I mean, we've always benefited from, you know, being quite well positioned at a time where, like, AI kinda blew up, and that's been incredible to see. And we're powering so many different AI applications today that, like, I had, like, no idea would exist, like, know, four years ago or five years ago when we started models. So that's been kind of incredible. And and and like you said, like, software in itself is also... Like, how we build software has changed a lot. People write, like... I I mean, like, I I think probably, like, at this point, like, 95% of the lines of code that gets merged as Modal is probably written by agents to some extent. Infrastructure cell, feel like, relies a lot on, like... Agents are, like, kinda good at it. I've actually been on a tier, like, the last few days, like, building... Or last few months, actually, like, writing, like, an insane amount of code using agents. And, like, it's kind of incredible, like, what they can build, but, like, you kinda still need humans to, like, supervise them quite a lot. And, like, you know, infrastructure has a lot of tail risks around the reliability and taking... And making sure that things scale and, like, stuff like that. So so so so at least for now, I I think software engineers are pretty safe, and and I think we we still benefit from having incredibly good engineers that can supervise these agents, and I think probably for the next few years. Another thing I I think is is being kind of interesting to see is, like, actually, like, I think the adoption pattern is actually the opposite of what I would have expected. Like, I thought it would be the junior people that would adopt agents the most. I've actually seen almost the other way around. It's like the very senior people. Because for them, it's like giving them, like, 10 interns. Like, they've been kinda constrained by, like, the typing speeds for a long time, but they have all these ideas in their heads. And I think using agents, now they can get it, like... They can get it out, like, much faster. They're really good at building a test suite. Like, you know, give the agents, like, here's an extensive text... Test suite. It implements all of, you know, the post six standard for file systems. Now go write a new file system. And it just goes on like just writes a file system and takes like three days to do it.
00:29:08 · Erik Bernhardsson, Patrick Chase
We've seen some of the very senior engineers like go like complete agent maxing and I don't know. I I mean, it's kind of incredible, like, what you could do with agents. You can move very fast with them. At the end of the day, it seems like humans are still safe as software engineers for the next few years. I don't know. We'll see. [00:29:23] S2: How have you found the velocity of shipping software has changed what you guys need to do with code review or CICD or deployment? Have those pieces of your development life cycle changed at all? [00:29:36] S1: I think a little bit. I think we're coming up against some of those constraints. Like, we've had to spend a lot more time on CICD. Like, I always, like, you know, even long before agents, I always, like... You know, I was a CTO for many years. Like, I was always, like, obsessed with CICD as, like, you know, very, very important to to figure out. So, like, in that sense, like, I don't know if it's, like, changed, like, my view as, like, you know, you, like, you really need to have, like, automated deployments, automated tests, like, these things. Now, by way, agents are pretty good at writing tests too. They weren't, like, a year ago. So I I I don't know if it, like, we've necessarily, like, yet, like, run up against the foundational limits. But, I mean, Anthropic, like, you know, a couple days ago, they announced they're moving off Git, I believe, or at least GitHub. We might we might run up against those limits at some point when we have, you know, 10 x the amount of commits every day.