loading
loading
Warm welcome to Basil Chatha, our newest cohost from the Forward Deployed side of the AI Engineering world! We’ll be ramping up a new show with him and couldn’t pass up an interview with Modal’s CEO Eric on the occasion of their monster Series D! Two years ago, we spoke with Erik Bernhardsson about his vision for truly serverless AI infrastructure: developers should be able to run code without provisioning clusters, predicting load, or paying for idle resources. CTO Akshat then joined us to talk about Modal’s infra primitives and stack to offer those primitives and agent experience. A lot has changed in the past 5 months. Since their $5B series C, Modal now reports it crossed $1 billion in annualized run rate, raised a $750 million Series D at a $16B valuation, and has spun up nearly three billion sandboxes. In this conversation, Erik joins Vibhu to explain the infrastructure and economics behind that growth—and why demand for compute rarely looks like the contracts used to buy it. Watch the full conversation on YouTube → GPU demand isn’t rectangular A typical capacity contract gives a customer a fixed number of GPUs for a fixed period. Plot capacity against time and you get a rectangle. Real workloads behave differently: they grow, spike, and fall throughout the day. Buy enough for the peak and you pay for idle hardware; buy less and you risk missing demand. Erik describes Modal’s bet as a kind of duration transformation: pool demand across thousands of customers, secure capacity underneath, and let each customer use what they need when they need it. The smoother the aggregate demand, the more useful that pool can be. It’s a useful companion to our conversation with SF Compute’s Evan Conrad: Evan focuses on making capacity tradeable; Erik focuses on making it usable in smaller, elastic slices. There is a financial risk on the other side. If a provider locks in capacity at today’s prices and GPU prices later fall, it can be left holding expensive supply. Erik says he doesn’t expect an imminent collapse, but thinks the possibility matters when building the business. Jump to the GPU price discussion → The sandbox that almost didn’t make it Modal launched sandboxes after Erik and cofounder Akshat Bubna noticed that language models could generate code—and that someone would need a place to run it. Early adoption was modest enough that the team discussed shutting the product down. Then coding agents, background agents, and reinforcement learning created new demand. Erik says sandboxes have grown to roughly a third of Modal’s revenue, a result he didn’t predict at launch. That growth also changes what infrastructure has to do. An agent may create an environment, run code, inspect the result, and try again many times. The latency of starting and restoring those environments becomes part of the experience. In our recent interview with Akshat, we explored this shift from developer experience to agent experience; here Erik adds the product and business story. Jump to the sandbox discussion → Fast startup is an infrastructure feature Elasticity only works if capacity can arrive quickly enough to matter. Modal’s serverless GPU write-up describes the machinery: a buffer of available GPUs, a custom filesystem, process checkpointing, and GPU memory snapshots. Those pieces reduce the work needed to bring another model replica online. It is a concrete example of how infrastructure decisions show up in an application’s responsiveness and cost. We also discuss Erik’s path from Spotify’s recommendation system to Modal, how the company buys compute, alternatives to NVIDIA, forward-deployed engineers, and why code review is optional at Modal. Jump to engineering culture → Show notes - Modal — the AI infrastructure platform Erik founded. - Erik’s writing — his essays on infrastructure, markets, and building companies. - Modal’s Series D announcement — the company’s latest reported growth and sandbox milestones. - How Modal achieved truly serverless GPUs — the startup and scaling techniques behind elastic inference. - Modal’s resource solver — how it selects capacity across changing prices and availability. - Scaling to one million concurrent sandboxes — an engineering account of sandbox scaling. - Our first interview with Erik — the original serverless infrastructure thesis. - Our interview with Akshat Bubna — infrastructure and developer experience for agents. - The Programmable Data Center — Evan Conrad on GPU trading and physical settlement. Timestamps - 0:00 What Modal does - 1:25 From Spotify to building Modal - 4:19 Founding before the generative AI boom - 7:43 First customers and fundraising - 10:00 Building for inference and autoscaling - 13:21 Where Modal gets GPU capacity - 15:39 Renting vs. owning infrastructure - 19:45 The risk of GPU prices falling - 24:18 Is AI demand infinite? - 26:12 Alternatives to NVIDIA - 28:25 What Modal can learn from neoclouds - 30:26 GPUs as an asset class - 31:55 Modal’s products: functions and sandboxes - 36:03 The case for smaller training workloads - 41:21 How agent sandboxes are changing - 46:48 Anthropic and sandbox security - 49:29 Using agents to test security - 52:51 Developer experience in the age of agents - 57:45 What Modal’s forward-deployed engineers do - 59:55 Modal’s approach to product management - 1:03:29 Optional code reviews and engineering culture - 1:05:56 Cloud, on-prem and bring-your-own-cloud - 1:09:25 Capacity, fundraising and growth - 1:13:07 Scaling the team - 1:17:28 Erik’s tweets: ephemeral sandboxes - 1:18:31 The problem with rectangular GPU contracts - 1:21:20 Inference providers vs. model builders - 1:23:25 Where models improve next Transcript Automatically transcribed from the revised edit; names and wording may contain errors. 0:00 Thanks for coming on, Eric Fern-Hartson from Modal. Could you explain the business a little bit and maybe how you got here? Yeah. So we build, the easiest way to put it is we build AI infrastructure. So we offer a compute platform and a storage platform that enables a lot of customers like Dacagon, and Suno, and Runway, and Cognition, and many others to essentially build AI applications. So what that means in practice is a lot of inference, a lot of sandboxes, sometimes training and fine-tuning, sometimes batch jobs and other things. So we have basically built a software SDK that enables customers to move really fast and build this. We manage all the capacity underneath, make it easier to deploy and scale and run these things in production. So you were on Light and Space like two years ago, a lot has happened since then. How big were you back then, like whatever numbers you can share and comparatively, how big are you now? I actually don’t remember where we were, but it’s probably like at least 50X, there’s revenue and users and users. Yeah. I use it actually for a side project that I have. Nice. Yeah. It’s like, yeah, turn 2D images into 3D models. Yeah. So yeah, just hosting a couple of models here. What do you think? Any feedback? I mean, I love it. Nice. Good. I mean, yeah, that’s why I use it over any competitors or anything. Good. So before you started Modal, you were at Spotify and also like better.com, I think. 1:30 That’s right. Yeah. Yeah. What were you doing at Spotify? I actually did a lot of different things at Spotify, but my main contribution was I built a music recommendation system. The first version I joined after school in 2008, 2009 maybe, and spent seven years at Spotify. And most of that time was building basically from scratch and music recommendation system and built up a small team and we launched a bunch of features like related artists and Discover Weekly and a bunch of other stuff. So a lot of, a lot of matrix factorization, a lot of compute intensive stuff. And arguably like a lot of what sort of later became the genesis of Modal was just building AI applications and realizing like the infrastructure fucking sucks. Like, there’s got to be a better way to do that. So a lot of sort of, you know, the rationale for Modal was like kind of building a tool I always wanted to have throughout my years at Spotify. Yeah. So Modal was more of an offshoot of the stuff you learn at Spotify, not much from Better.com. Better.com was a little bit of a side tour. It was kind of a roller coaster, a long story about the company. We talked about it separately. But I was a CTO for five years of better and grew to a tech team from, I think, you know, two to three, four hundred. So I learned a lot about management and building teams and hiring and also learned a lot about developer productivity. I thought a lot about tools and things like that. Did a lot more like full stack stuff. But yeah, I kind of came back to my, was close to my heart, which is infrastructure and AI and machine learning after that. What was Better.com doing again? Mortgages. Okay. Yeah. How did you get into that? 3:00 I mean, we don’t have to spend too much time on that. Yeah, no, it’s fine. Like this guy, Zach Frankel, who I didn’t know at that time, ended up recruiting me to Better as a CTO and the company was 10 people. And, you know, for whatever reason, you know, the founder of Vishal, very interesting guy, very, very smart, a little crazy in mostly positive ways. We got very well along. So we, yeah, we started building it in 2015 and I spent five years growing that company and then, you know, eventually turned out that doing mortgages is pretty hard and, you know, interest rates went up very quickly after the pandemic and that cost a lot of issues at the company. But by then I had already left. That was like 2020, 2021 when you left? Yeah, something like that. Yeah. So, OK, you left before you had the idea for a model or you left and then you started? It’s hard to say. I had a lot of random ideas when I left Better and I, I actually wasn’t sure if I wanted to start a company. I was kind of just, you know, during the pandemic, I sort of almost took like a sabbatical at two kids. I mean, I still have kids, but I mean, at that time there were there were I think two and four and so I decided to hang out and took some time off and was kind of working like part-time, like hacking on certain things and wasn’t sure maybe I should do some consulting and I started building stuff. And I just got like super pumped about this idea that eventually became model. But actually both, you know, model was like, I think the 30 I built that I got, you know, that so there were like a few attempts before then that went nowhere. 4:30 But my model was like the first idea was like, this is kind of cool. Like I should do this. This is like, this could be a really cool platform. Were the other ideas like also in a similar vein like developer tools? The first thing was like some sort of web scraping thing, which went nowhere. But actually, you know, in hindsight, maybe could have gone somewhere. And was this before the chat GBT launch? Yeah. Yeah. 2021. Oh, OK. So chat GBT was late 2022, right? Then I started building, so maybe actually to rewind a little bit context, like when I worked at Spotify, I released an open source framework, Kaluigi, which is like workflow orchestration. So sort of the same categories, airflow and pre-effect index or sort of dated categories to some extent, like people don’t talk about as much. But for a while, it was like, you know, got a lot of attention. Anyway, I never turned it into business. But a couple of years later, when in 2021, when I was thinking about business ideas or things to work on, I kind of came back to workflow orchestration. And I started hacking on a bunch of ideas of, you know, running code in the, you know, like basic orchestrating code running in the cloud. And what I realized was like, you know, at the end of the day, like core puzzle pieces, not just like orchestrating the computer, it’s like executing the computer. And I was like, that’s kind of a bigger problem actually to solve. So in a way, I actually like, you know, left it on the shelf and then said, like, actually, I’m going to like go to this other thing over here and I’ll go back. I’ll get back to the workflow scheduling a little bit. But, but like, you know, I never got back to long story short and ended up building modal, which is like the focus is more like, let’s just figure out a way to make it easy to run code in the computer, in the cloud. And the focus was initially like fairly general purpose. 6:00 And it actually in a way like it’s always, we’ve kind of gone full circle more recently, but, but we focused on so many different applications. It’s kind of the idea was like, let’s just make it super easy to run code in the, in the cloud. Could be GPU code, AI, machine learning. It could also be like data stuff. And focused a lot on, you know, how do we scale it up? How do we make the developer experience awesome? Realized super early that like in order to do that, we’d have to go very deep in a stack and built on file system, built on container runtime and did a lot of, you know, solved a lot of hard engineering challenges. And then you mentioned chat, CPT, that was actually not quite the killer app. But like, if you remember, like that fall, another thing came out around the same time, which was stable diffusion and stable diffusion turned out to be a killer app for our, for what we’re building at that time. We were only four or five people at that time. So we started seeing a lot of people coming to us and wanting to run a stable diffusion model. And then we were real, that, that, that made us realize like maybe gen AI inference is actually a really good use case for us. And how are you finding it? I’m just like compulsive tweeter. I was even more like now, like I’m too distracted by building a company, but I always like tweeted a lot. I was blogged a lot. So I think I had a little bit of a following. And so, you know, in the early days, I like a lot of what drove the growth was just like me just compulsively just like tweeting about model and like what I was building and getting people excited and writing long blog posts about where the cloud is going and all these things. And I call it a bit like founder led marketing. I always like feel like you kind of have to lean into like what you’re good at 7:30 and like what I’m good at is like, I guess like just shit posting and being terminally online. And so, you know, like work, it’s still like, you know, I think a lot of our growth is we’re just kind of good at social media and just like being, you know, online a lot. Nice. How long until you had your first customer? It took a while. Also, did you raise like early on? We didn’t raise for a while. I actually self funded it for a while, even with up until the first few employees, which in a way was like kind of nice because it may be really like frugal about money and like wanting to see like ROI. I wasn’t taking out a salary for a while for a long time. Actually, at some point it became a problem because I needed the money, but we didn’t raise for a while. And until early 2022, we raised the seed rounds. So at that point I’d already been working on it for about a year. Akshah joins the CTO and co-founder in about five years ago, about now August, maybe 2021. But then it took until like basically like late 2022 for us to see any meaningful revenue. I guess that’s why I’m proud of you. Yeah. And stable diffusion then in our case was the main thing that’s sort of, so we went from like, you know, $100 a month in like, I think like November, 2022. And the next month we did like $2,000 a month. And then like January, 2023, we did like 30,000 all of a sudden. And, you know, so finally I felt this like massive, just like anxiety thing off my chest. I’m like, maybe this is actually like we’re onto something. It feels like we’re onto something. And then 2023 we grew very fast. 9:00 And I guess we’ve been growing very fast since then basically. That 30 K a month moment, was that just like a couple of big customers or was it like very diverse? It’s just one. And I don’t know if I ever talked about like who that was and if they even know it, but I think they suspect it. It was a company in New York called Mage. They’re doing like a stable diffusion company. So at some point, you know, in the first, I think first quarter of 2023, I think there were like 80, 90% of our revenue. But then after that, you know, it started diversifying a little bit. And late 2023, we got soon as a big customer. They were a big chunk of our revenue for a while too. But then, you know, it’s over time, like we actually have really good revenue diversification now. Yeah. For the stable diffusion stuff, I remember using it for like comfy UI. I’m assuming a lot of people were using comfy UI, like running comfy UI workflows on it. Some people were, but I think the people that were using model, they tended to be more like low level stuff. Like they did like complicated stuff. What they like with models, like it’s always been pretty hackable. So they deployed their own custom workflows with adapters and custom inference and a lot of optimizations and all the stuff we had done in order to get the developer experience to be really good, turn out to be really good for also running production workloads. Cause auto scaling turns out to be actually a very similar problem. Like you want to scale up containers up and down very fast. So we just happened to be like very well positioned to capture a lot of those workloads in the early days. Did tools for auto scaling like not exist? Like why did you have to build those yourself? 10:30 In general, like my thesis is like, you know, every new sort of era of like, you know, technology kind of runs up against limitations of tools. You know, whether that’s you look back, you know, like the web or the cloud or, you know, mobile or PCs or, you know, AI or whatever. So I think, you know, it’s often the case that tools exist, but, but they’re often not like particularly good. And often you kind of need to rethink a lot of those tools in order to reap the benefits of, you know, and really turbo charge and start building for this new world. And you saw this with cloud, you know, we have to invent things like Kubernetes and Docker and things like that. And in a similar way, you know, like with, with gen AI, you could build these things on the sort of prior generation of tools. Like you can, you can build these things on top of Kubernetes and Docker and in these things, but, but, but those technologies are built for a very different type of workload. They’re built for a very sort of steady work, steady back end workloads. They have an even, you know, steady number of requests, like running a pretty low utilization because CPUs are normally pretty cheap. And so people don’t really worry so much about cost. And, but now we’re entering this new world and you have, you know, very expensive GPUs you want to manage capacity and utilization. You have very large models that you need to load over network into CPU into GPU. And, and it turns out like a lot of these tools, like, you know, like they were just not built for that. So we felt that, you know, we had to rebuild a lot of that, you know, building our own, I mentioned the file system, but we also spent a lot of time over the last few years, like building, you know, some really cool stuff around like CPU 12:00 snapshotting, GPU snapshotting and audit, we built on autoscaler and scheduler and, and, and in all kinds of capacity management tools. And we kind of feel like you have to do that because like you can’t build, you know, for this new generation of software on top of, you can’t build like a wrapper on top of the old stuff in order to do the new stuff. You kind of need to start over a little bit from scratch. So I guess like the auto scaling, like did you, did you see any like weird behaviors on the platform? Like was there anything that like broke while you guys were scaling? I mean, capacity is always from time to time been, been a challenge. Like how do you get like, you know, hundreds and thousands and tens of thousands of GPUs? So that’s, that’s always challenging. We had to go multi region very early. So I think that was like kind of a fateful decision in hindsight, like doing that and kind of just biting the bullet and building it. Like we always thought of like our capacity management as like one big pull. Like it’s like one big pull over the entire world. Like we don’t think of it as like separate clusters running in each region. And so that was like one of the like very consequential decisions we made early to kind of think about it that way. So I don’t know if there’s like been like, you know, specific issues. I mean, we’ve obviously had bugs and, you know, outages and I hate, you know, as much as I, you know, would love for a model to be a hundred percent uptime, you know, and I think recently we’ve basically hit four or five nines, but throughout the history of model, of course, we’ve had, you know, some run up against scaling issues. Yeah. The GPU market and well, actually before that. So you guys use AWS like GCP, like all these cloud providers underneath. 13:30 Like do you also buy any infrastructure? No, let’s talk a bit about like cloud capacity. Because I think it’s actually changed a lot. Like in the first few years we did, we got almost all the capacity pretty much from, from the hyperscalers spot and on demand capacity, mostly spot capacity, running in all the different regions, you know, using initially AWS, but also GCP and overtime measure. We started renting some capacity from Oracle pretty early because we wanted the eight tens and eight 100s and we got really good rates on those. And over time we added a couple more clouds, like doing reservations, but, but like still up until like actually end of last year, like most of our capacity was either spot or one year reservations. Spot means you’re just like renting it right there and then. Exactly. Yeah. And it’s also preemptible, which we had to deal with. But, but it sort of works in Agria because you can sort of just, you know, aggregate capacity from many, you know, hundreds of regions basically. So that, that, that model worked really well until we started running up again. I mean, first of all, like modal started growing like very fast as we needed more capacity, but fundamental also like the cloud market started changing a lot. So in the last year, basically now in order to get the capacity we need, we had to work with a much larger set of clouds. We actually work with almost 25 clouds at this point. And in many cases, in order to get the capacity we want, like we, we have to go into, you know, three plus year deals and you have to pay a meaningful amount of money upfront. And you’re locking in pricing on a three year term, right? 15:00 So, you know, like, and, and, and so we have to spend the entire, you know, last six to nine months, like, you know, kind of rethinking how we get capacity. We have to build a whole procurement like desk, like we have like four guys working full time basically, all they’re doing is like procuring capacity, calling Neoclouds, you know, doing deals, vetting the capacity. We’ve had to build a whole engineering team, basically 10 people just like managing like bare metal capacity from all over the world and dealing with network issues and bad drives and GPUs falling off the bus and, and all these things. We don’t own capacity. We rent all the capacity so far, um, you know, on, on, on typically this three year deals. And that’s like a big decision. Like, should we buy, should, should we, you know, or should we keep renting? I’m actually like not like mega dogmatic about it. I think renting is nice because there’s a certain amount of capital efficiency to it. If you’re buying, you know, like, I mean, first of all, there’s not this like operational complexity of it. Like you have to, you know, deal with vendors and suppliers and, and, you know, power and, and, and cables and all kinds of stuff. But there’s also, you know, financial complexity to it. Like, how do you fund it? Like, should you raise equity, you know, and, and you can raise a billion dollars and go buy hardware for it. That’s a very dilutive way to do it. Um, you can raise debt, but you know, if you raise that, you know, you typically want to have like locked in long-term revenue to, to, to pay the interest rates. That’s also not trivial. But you know, like we look at the economics, we look at, uh, the operational 16:30 complexity, we look at the risks. Uh, it’s a complex trade-off and, and we’re open to, to, to buying. We’ll, we’ll sort of, there’s other constructs too. Like we’re, it’s to get the capacity we need for, for 2027. It’s going to take a lot of work on, on, you know, going a lot of different avenues to, to lock it up. So do you have to hire a lot of like finance people to do all this stuff or not? Really? You know, I’ve had to spend a lot of time on this and we’ve had to, to, to spend a lot of time on this. And I do think over time, I mean, there, there is a lot of, you know, these are very expensive assets, uh, the hardware. And, you know, I think, I mean, it’s kind of staggering. You starting to see like AI investments starting to become like several percentage points of, of, of GDP of the U S and, you know, starting to see like interest rates in, in America, shooting up in the last few weeks. And I mean, I like, at this stage, like, you know, and you go and look at the Neoclouds, like these are very, very sophisticated financial engineering companies. And so yeah, we, we look at that and, you know, we look at all the options and, and it’s possible we might have to do similar things ourselves. I wouldn’t want to like, I would rather focus on being, you know, like a very lean software company, like doing the things we’re good at. I don’t think it’s good in general for software companies to own hard assets. You know, in the same way as like, should you own your office? Like I don’t think that’s necessarily the right solution for most companies. But of course, like in order to get the capacity you want, like, you know, everything is on the table. Yeah. And I guess the supply is going to be constrained for a while. 18:00 So you kind of have to do that. Like, do you, do you agree with that? Or do you think this is just like a moment in time? I don’t necessarily agree with that. I mean, like Nvidia is delivering, you know, several hundred thousand black walls, you know, a month, right? And, and I don’t want to say like the market is like 100% efficient, but like, you know, in general, like if you’re willing to pay a high price, you can get GPU capacity, right? Like if you’re willing to pay more than everyone else, and, and of course, Anthropic and OpenAI is buying up as much as they can. And, but fundamentally, like there’s constantly supply coming on the market. And so I don’t necessarily think it’s like a fully cornered resource. Prices are going up. And I do think it’s worth kind of reflecting on that. It’s like the normal state of hardware is like, it’s like out of like gravity. Like eventually, hardware prices always go down. So we’re a little bit in like a weird sort of period of time where like hardware prices are going up. And, you know, it’s hard to say, you know, how long this is going to last, you know, could be another six months, could be another 36 months. Like it’s really hard to know how long. But I fundamentally don’t know if like it’s true to say, like, you know, you need to buy capacity in order to me, that’s a little bit to sort of almost like the great toilet paper shortage of the pandemic or whatever. Right. Like, you know, at least this like, you know, run on the bank type situations. And, but that being said, like, you know, capacity is going to be tight for a while. And, you know, prices are going up. And, you know, luckily, I think, you know, it’s not even necessarily bad for us. If prices go up, because, because, you know, scarcity also means like we can 19:30 charge more from our customers. And, you know, and the fact that we have a lot of reservations locked in at a rate means like, to some extent, we’re effectively like long GPU prices already. So I don’t necessarily worry too much about prices going up. I do worry more about actually prices collapsing. I don’t think that’s going to happen anytime soon. But I think that would be a quite bad thing for a lot of the players in the market. Why would that be bad? I think it would be bad for if, for anyone who’s locked in a lot of supply. Yeah. Because they paid way too much and they paid too much, you know, so there’s a risk of buying at the top, right? And again, like, I don’t worry about that much. Like, I don’t think it’s going to be like, you know, it’s going to happen anytime soon. But like, you know, like I said, like hardware prices in the very long run, they tend to go down. And so it’s, it’s worth, you know, reflecting on that, right? Right. Like right now, I think, I think your, your capacity is constrained and, and that leads to sort of, you know, in economic terms, there’s a scarcity rent, right? Like who captured the scarcity rent? I think the primarily goes to two places, mostly Nvidia, but of course, but also to some extent, Neoclouds, like Neoclouds and in general, like, you know, anyone owning capacity has better economics. Like that’s undeniably true today with capacity being constrained. Long term, you know, in the very long run, I don’t think that’s necessarily entirely true, right? Like there’s always going to be a little bit of a premium you can get from owning GPUs, but is it worth the risk? You know, is it worth the risk of like holding this like very expensive 21:00 assets on your balance sheet? I’m not so sure, but you know, we’re going to see, like it’s going
Warm welcome to Basil Chatha, our newest cohost from the Forward Deployed side of the AI Engineering world! We’ll be ramping up a new show with him and couldn’t pass up an interview with Modal’s CEO Eric on the occasion of their monster Series D! Two years ago, we spoke with Erik Bernhardsson about his vision for truly serverless AI infrastructure: developers should be able to run code without provisioning clusters, predicting load, or paying for idle resources. CTO Akshat then joined us to talk about Modal’s infra primitives and stack to offer those primitives and agent experience. A lot has changed in the past 5 months. Since their $5B series C, Modal now reports it crossed $1 billion in annualized run rate, raised a $750 million Series D at a $16B valuation, and has spun up nearly three billion sandboxes. In this conversation, Erik joins Vibhu to explain the infrastructure and economics behind that growth—and why demand for compute rarely looks like the contracts used to buy it. Watch the full conversation on YouTube → GPU demand isn’t rectangular A typical capacity contract gives a customer a fixed number of GPUs for a fixed period. Plot capacity against time and you get a rectangle. Real workloads behave differently: they grow, spike, and fall throughout the day. Buy enough for the peak and you pay for idle hardware; buy less and you risk missing demand. Erik describes Modal’s bet as a kind of duration transformation: pool demand across thousands of customers, secure capacity underneath, and let each customer use what they need when they need it. The smoother the aggregate demand, the more useful that pool can be. It’s a useful companion to our conversation with SF Compute’s Evan Conrad: Evan focuses on making capacity tradeable; Erik focuses on making it usable in smaller, elastic slices. There is a financial risk on the other side. If a provider locks in capacity at today’s prices and GPU prices later fall, it can be left holding expensive supply. Erik says he doesn’t expect an imminent collapse, but thinks the possibility matters when building the business. Jump to the GPU price discussion → The sandbox that almost didn’t make it Modal launched sandboxes after Erik and cofounder Akshat Bubna noticed that language models could generate code—and that someone would need a place to run it. Early adoption was modest enough that the team discussed shutting the product down. Then coding agents, background agents, and reinforcement learning created new demand. Erik says sandboxes have grown to roughly a third of Modal’s revenue, a result he didn’t predict at launch. That growth also changes what infrastructure has to do. An agent may create an environment, run code, inspect the result, and try again many times. The latency of starting and restoring those environments becomes part of the experience. In our recent interview with Akshat, we explored this shift from developer experience to agent experience; here Erik adds the product and business story. Jump to the sandbox discussion → Fast startup is an infrastructure feature Elasticity only works if capacity can arrive quickly enough to matter. Modal’s serverless GPU write-up describes the machinery: a buffer of available GPUs, a custom filesystem, process checkpointing, and GPU memory snapshots. Those pieces reduce the work needed to bring another model replica online. It is a concrete example of how infrastructure decisions show up in an application’s responsiveness and cost. We also discuss Erik’s path from Spotify’s recommendation system to Modal, how the company buys compute, alternatives to NVIDIA, forward-deployed engineers, and why code review is optional at Modal. Jump to engineering culture → Show notes - Modal — the AI infrastructure platform Erik founded. - Erik’s writing — his essays on infrastructure, markets, and building companies. - Modal’s Series D announcement — the company’s latest reported growth and sandbox milestones. - How Modal achieved truly serverless GPUs — the startup and scaling techniques behind elastic inference. - Modal’s resource solver — how it selects capacity across changing prices and availability. - Scaling to one million concurrent sandboxes — an engineering account of sandbox scaling. - Our first interview with Erik — the original serverless infrastructure thesis. - Our interview with Akshat Bubna — infrastructure and developer experience for agents. - The Programmable Data Center — Evan Conrad on GPU trading and physical settlement. Timestamps - 0:00 What Modal does - 1:25 From Spotify to building Modal - 4:19 Founding before the generative AI boom - 7:43 First customers and fundraising - 10:00 Building for inference and autoscaling - 13:21 Where Modal gets GPU capacity - 15:39 Renting vs. owning infrastructure - 19:45 The risk of GPU prices falling - 24:18 Is AI demand infinite? - 26:12 Alternatives to NVIDIA - 28:25 What Modal can learn from neoclouds - 30:26 GPUs as an asset class - 31:55 Modal’s products: functions and sandboxes - 36:03 The case for smaller training workloads - 41:21 How agent sandboxes are changing - 46:48 Anthropic and sandbox security - 49:29 Using agents to test security - 52:51 Developer experience in the age of agents - 57:45 What Modal’s forward-deployed engineers do - 59:55 Modal’s approach to product management - 1:03:29 Optional code reviews and engineering culture - 1:05:56 Cloud, on-prem and bring-your-own-cloud - 1:09:25 Capacity, fundraising and growth - 1:13:07 Scaling the team - 1:17:28 Erik’s tweets: ephemeral sandboxes - 1:18:31 The problem with rectangular GPU contracts - 1:21:20 Inference providers vs. model builders - 1:23:25 Where models improve next Transcript Automatically transcribed from the revised edit; names and wording may contain errors. 0:00 Thanks for coming on, Eric Fern-Hartson from Modal. Could you explain the business a little bit and maybe how you got here? Yeah. So we build, the easiest way to put it is we build AI infrastructure. So we offer a compute platform and a storage platform that enables a lot of customers like Dacagon, and Suno, and Runway, and Cognition, and many others to essentially build AI applications. So what that means in practice is a lot of inference, a lot of sandboxes, sometimes training and fine-tuning, sometimes batch jobs and other things. So we have basically built a software SDK that enables customers to move really fast and build this. We manage all the capacity underneath, make it easier to deploy and scale and run these things in production. So you were on Light and Space like two years ago, a lot has happened since then. How big were you back then, like whatever numbers you can share and comparatively, how big are you now? I actually don’t remember where we were, but it’s probably like at least 50X, there’s revenue and users and users. Yeah. I use it actually for a side project that I have. Nice. Yeah. It’s like, yeah, turn 2D images into 3D models. Yeah. So yeah, just hosting a couple of models here. What do you think? Any feedback? I mean, I love it. Nice. Good. I mean, yeah, that’s why I use it over any competitors or anything. Good. So before you started Modal, you were at Spotify and also like better.com, I think. 1:30 That’s right. Yeah. Yeah. What were you doing at Spotify? I actually did a lot of different things at Spotify, but my main contribution was I built a music recommendation system. The first version I joined after school in 2008, 2009 maybe, and spent seven years at Spotify. And most of that time was building basically from scratch and music recommendation system and built up a small team and we launched a bunch of features like related artists and Discover Weekly and a bunch of other stuff. So a lot of, a lot of matrix factorization, a lot of compute intensive stuff. And arguably like a lot of what sort of later became the genesis of Modal was just building AI applications and realizing like the infrastructure fucking sucks. Like, there’s got to be a better way to do that. So a lot of sort of, you know, the rationale for Modal was like kind of building a tool I always wanted to have throughout my years at Spotify. Yeah. So Modal was more of an offshoot of the stuff you learn at Spotify, not much from Better.com. Better.com was a little bit of a side tour. It was kind of a roller coaster, a long story about the company. We talked about it separately. But I was a CTO for five years of better and grew to a tech team from, I think, you know, two to three, four hundred. So I learned a lot about management and building teams and hiring and also learned a lot about developer productivity. I thought a lot about tools and things like that. Did a lot more like full stack stuff. But yeah, I kind of came back to my, was close to my heart, which is infrastructure and AI and machine learning after that. What was Better.com doing again? Mortgages. Okay. Yeah. How did you get into that? 3:00 I mean, we don’t have to spend too much time on that. Yeah, no, it’s fine. Like this guy, Zach Frankel, who I didn’t know at that time, ended up recruiting me to Better as a CTO and the company was 10 people. And, you know, for whatever reason, you know, the founder of Vishal, very interesting guy, very, very smart, a little crazy in mostly positive ways. We got very well along. So we, yeah, we started building it in 2015 and I spent five years growing that company and then, you know, eventually turned out that doing mortgages is pretty hard and, you know, interest rates went up very quickly after the pandemic and that cost a lot of issues at the company. But by then I had already left. That was like 2020, 2021 when you left? Yeah, something like that. Yeah. So, OK, you left before you had the idea for a model or you left and then you started? It’s hard to say. I had a lot of random ideas when I left Better and I, I actually wasn’t sure if I wanted to start a company. I was kind of just, you know, during the pandemic, I sort of almost took like a sabbatical at two kids. I mean, I still have kids, but I mean, at that time there were there were I think two and four and so I decided to hang out and took some time off and was kind of working like part-time, like hacking on certain things and wasn’t sure maybe I should do some consulting and I started building stuff. And I just got like super pumped about this idea that eventually became model. But actually both, you know, model was like, I think the 30 I built that I got, you know, that so there were like a few attempts before then that went nowhere. 4:30 But my model was like the first idea was like, this is kind of cool. Like I should do this. This is like, this could be a really cool platform. Were the other ideas like also in a similar vein like developer tools? The first thing was like some sort of web scraping thing, which went nowhere. But actually, you know, in hindsight, maybe could have gone somewhere. And was this before the chat GBT launch? Yeah. Yeah. 2021. Oh, OK. So chat GBT was late 2022, right? Then I started building, so maybe actually to rewind a little bit context, like when I worked at Spotify, I released an open source framework, Kaluigi, which is like workflow orchestration. So sort of the same categories, airflow and pre-effect index or sort of dated categories to some extent, like people don’t talk about as much. But for a while, it was like, you know, got a lot of attention. Anyway, I never turned it into business. But a couple of years later, when in 2021, when I was thinking about business ideas or things to work on, I kind of came back to workflow orchestration. And I started hacking on a bunch of ideas of, you know, running code in the, you know, like basic orchestrating code running in the cloud. And what I realized was like, you know, at the end of the day, like core puzzle pieces, not just like orchestrating the computer, it’s like executing the computer. And I was like, that’s kind of a bigger problem actually to solve. S
document fetched 2026-10-24 11:26 · canonical https://latent.space/p/modal-d