Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO

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 to take a few years to get through this. And, and, and in the long run, you know, we might own some, we might rent some, maybe a combination, maybe do some other crazy stuff, joint mentors, who knows? Maybe some complex SPDs, whatever. Um, everything is on the table. How do you compete with open AI and enthropping and buying GPUs capacity? I’m assuming they’d want to like buy up literally everything that’s available. They are. And, and I think what you’re seeing is, um, you know, a lot of that, the, the price is going up. Is it a reflection of that? That being said, you know, NVIDIA is shipping a lot of stuff. I think also like to some extent, like NVIDIA is like kind of prefer a little bit of a diversification. Like they don’t want like, you know, small amount of large buyers in much, in the same way as you see, you know, hyperscalers getting a little bit of a constrained allocation. Uh, I think, you know, also NVIDIA prefers to see, you know, a lot of the neoclouds and a lot of alternative clouds, like getting more allocation. And I think a little bit, what we see in the market is also this is more speculation, but I think we see, you know, smaller clouds that are a little bit more sort of risky to work with being more of an opportunity for us. Like where, you know, maybe NVIDIA, sorry, enthropping and opening AI, my feeling is like they prefer to buy from like the, the, the try and through, uh, part of it is also because they had to do a lot of training runs. So they need to have like very large blocks, but there’s plenty of capacity out there, you know, you can get 32 nodes in Armenia.

22:30 You can get 128 nodes in Indonesia. Like, you know, like we love that kind of shit. Like, you know, so we can go out there and like get a lot of, you know, smaller capacity all over the world. Pretty good prices. Is it pretty much just like if someone offers you like a certain number of GPUs, like that’s all you care about. Is there nothing like, oh, you care about like how reliable it is, you know, is it just some like janky warehouse that someone has these in? I mean, sometimes it’s kind of a janky warehouse. It feels like, you know, but, but like, we do have like a spec sheet, basically. Like when we talk to vendors, the first thing we do is we send them a spec sheet. We say, we need this number of IOPS. We need, you know, like the CPU needs to be at least, at least, you know, have, have this many, you know, whatever the pass mark scores or whatever it is. We need to see like, you know, minimum, you know, North, North, South, bandwidth, egress and ingress. So we do have a list of requirements. Often we try to do like an acceptance testing period where we get like one node and, and runs and tests over time, you know, like to some extent, it also involves a little bit of a leap of faith. You know, sometimes we sign very big commitments, you know, 10s or 100s millions of dollars and we start working with the vendor that we still don’t fully know. And, you know, over time, like, you know, we’ll see, you know, and, and, and I think over, like, in a little bit of, like, there’s a little bit of sort of just, you know, our own sort of, like, you know, how like semi analysis has like a cluster max, I kind of feel like we almost like have our own, like modal max score internally. Like, I know which vendors I like more than others, you know, like, and, and,

24:00 and we sort of prefer to do additional deals with the vendors that we’ve seen, you know, very good reliability, not just that also, like it’s not just reliability, it’s also, are they very responsive when, when we have, you know, support issues, do the fixed issues we ask for to turn around time. If things go down, you know, how quickly do they fix it? Like things like that. Do you agree that the demand for intelligence is basically infinite? And if so, like, why wouldn’t you just buy up as much as you, as much capacity as possible? And then, like, if you over buy, you can just like sell it to somebody else. I think it’s very easy to say when there’s like a scarce resource, like, let’s just buy as much as you can. But I think, you know, first of all, like, if we bought it today, we couldn’t utilize all of it. Like we could use utilize a lot of it, right? But like, But you can sell it off to somebody else who wants to use it, right? I think the challenge is like, if there was like something in the market that happens, there are a lot of correlations that happen at the same time. If, you know, prices going down and utilization going down and the market for reselling capacity goes, vanishes at the kind of the same time. I’m sitting here and like, I’m extremely aggressive, like buying capacity every day and like raising money to buy, you know, so overall, I’m, I’m very bullish, but like, I do want to point out and like, you know, like there are tail risks here. And when those tail risks happen, like it could be get very bad. Part of it is like, maybe I’m, you know, I’m just old and I’ve seen this in the past, like I actually had my first job in 1999. So I saw the dot com crisis and I’ve seen the great financial crisis and things like that. And, you know, like, I want to build a company that, that is around for like 20

25:30 to 30, 50 more years, right? Like, and so, you know, I think we can certainly go out and like buying an insane amount of capacity right now and trying to just corner the market. But, you know, do you know about the Hunt Brothers? It’s like a legendary story in trading. They try to corner the silver market. It fails spectacularly. You know, so it’s a cornering the market. I think in general, like it can be not, not a good idea. And, but, but we are in general very, very aggressive, but we’re ramping up, you know, like at an aggressive plan that basically matches our demand growth and matches where we want to try to get to at the end of next year in terms of run runway or run rate. Are you only buying up NVIDIA stuff or also like, you know, maybe a capacity from other types of companies like training them and we’re looking at it. I think the alternative accelerators are starting to get very interesting. It’s, you know, ironically, like every, every year in the last five, maybe even 10 years I’ve been saying, you know, like next year, it’s going to be the year of like non NVIDIA accelerators. And then I’m always wrong. But, but I do think 2027 might be the year of non NVIDIA accelerators. Finally, so we’ll see if I’m wrong this year too. But so I, you know, I am these are starting to get like really good scores on certain things. I think, you know, TP use are obviously used very, you know, widely internally at Google. I seem to work really well. I don’t know much about training. I don’t feel like I see quite as, but, you know, the team is very good. I know they’re working on a lot of the software stuff.

27:00 You know, hardware is hard in a certain way. Like software is like easier. And that makes me feel like, you know, if we just fix the sort of software layer for these accelerators, like, I think there’s going to be a lot of demand for them. And I think fundamentally, you know, with NVIDIA being such a bottleneck, like, we’ll probably see, you know, more alternative accelerators. That being said, you know, there’s bottlenecks further down the pipe. I mean, you know, there’s like TSMC and ASML, maybe like a deeper bottleneck to all of this anyway. But, you know, long term, I think monopolies don’t always last. And so I think you’d be healthy for the market to have more alternative. As much as I love NVIDIA, like, to make that clear, like, I mean, they’re obviously an amazing company and within the amazing product. I do expect to see a little bit more diversity in the market as well. Also, things, you know, like Cerebras and Grock and these things. Yeah. What are your thoughts on like some of those providers? I think they’re pretty exciting. And, you know, and you see them doing really well on certain workloads. And, you know, there’s some interesting, you know, people doing these like hybrid, you know, disaggregated inference where they run, you know, NVIDIA for the pre-fill and Cerebras for the decode and things like that. Oh, I haven’t heard of that actually. Yeah. And, you know, Grock too, like, because a lot of these things, you know, certain things are compute bound, certain things are memory bound. So you can sort of mix and match. And I think that’s quite exciting. It starts to get very complex when you’re deploying these things in practice. But I do think there’s a lot of, there’s a lot of, you know, opportunity and these sort of, you know, more complex hardware setups. Are there any learnings that you get from some of these Neoclouds, like, I don’t know, like Nebius or whatever, and like the way that they’re doing things versus

28:30 like what you’re doing? I think one of the things that, you know, is very interesting with Neoclouds is like, how can they expand so quickly? I think actually the most crazy example is like N scale, which went from like nowhere to like, you know, having like, you know, whatever, hundreds of billions of dollars in the pipeline, despite barely having any capacity. How can Neoclouds grow so fast? I mean, there’s like an, you know, and basically the answer is like, you know, and this might be obvious, but like what they do is like, they go out to like, you know, enthropic or open the eyes and some big buyer or whatever. And basically say like, Hey, do you want to buy like five years of chips? You know, sign this contract, we give you a good rate. And then they take that contract and then they go to, you know, Wall Street and say, look, we got this like, you know, big buyer, you know, they committed to $10 billion worth of chips. We just need to monitor it right now. And then they go on to raise the money. And then they like, you know, go to like Super Micro and DAL or whatever in the video and like get the hardware and then they install it. And then, you know, now they have like, you know, delivered to the customer. Right. So you have this like very nice sort of financial construct where you can basically sort of pre-sell things that you haven’t even bought. And that means, you know, you can scale up extremely fast. And so that’s what we’ve seen with these Neoclouds is, is, is they can scale very, very well. I think modal because we’ve always had a little bit more of a PLG focus and, and not the sort of, you know, ability to lock in long-term contracts in the same way, it’s a little bit more challenging from a financial perspective to scale that fast, even though I think, you know, in the long run, like we’ll get to the

30:00 same point. But I think that’s been kind of a fascinating thing to see with these Neoclouds and especially like CoreWeave and those big ones. They’re very, very savvy with, you know, how they raise money and how to, you know, think about GPU as collateral for loans and all these things. What’s the relevance of, like, I forget who it was, but like some like financial company was like, oh, we’re going to make, oh, I think it was Nvidia. Actually, they were like, we are going to turn GPUs into an asset class. Is that just because like they need to raise money and they’re like trying to figure out where to get the money from? Like, what’s the relevance of that? I mean, there’s a bunch of startups. I don’t know if you were thinking about that. There’s like Silicon Data and computer exchange and like Orn and, and, and a few other ones that are like, basically like trying to get to like, you know, creating price indices and indexes and trading derivatives and things like that. I think it’s interesting. Like, I think with compute, like it does, there are a number of challenges with compute as an asset class. Like it’s definitely not as homogenous as other asset classes. You have different network and different locations and, and, and different disks and CPUs and all kinds of stuff and reliability. I mean, that being said, like you can say the same thing about oil and like we kind of created like, you know, relatively commoditized, you know, tradeable assets out of oil. It remains to be seen. Like I do think, you know, at the scale where we are with the sort of AI, like data center and GPU deployments, like we do need to be a little bit smarter with like risk hedging and, and, and how we share risks and, and, and, you know,

31:30 having more like sort of insurance and, and things like that. So I, I, I’m very happy that people are thinking a lot about this. That being said, like I’m, I’m not entirely sure if we’re going to see, you know, GPUs becoming sort of, you know, whatever it is, sort of Henry hub, like, you know, Brent crude type, like, you know, super commoditized, like tradeable asset. Yeah. I want to shift the conversation to modal sandboxes. Yeah. I guess like before we do that, what are the different products that modal has? It’s, I guess like the main product obviously is like the, the GPUs. Then you have modal sandboxes. Is there anything else? I think of the like the two big products that we have as basically number one is modal functions and number two is modal sandboxes. So, so with modal functions, we launched that, you know, back in 2021. It was the idea of like serverless functions that are very easy to deploy and very easy to scale and we managed all the capacity. That turned out to be really, really good for inference and it turned out to be really good for managing GPUs and running on things like that. But we actually never had like, you know, dedicated like inference product or like a GPU product per se. Like we never thought of modal as like a neocloud and I think there’s like a risk of like getting sucked in that direction and becoming a neocloud, like just selling, you know, sort of what I think was like rectangular, like here’s like a three year. You get this many GPUs. Models has always been very good at like you deploy something you can kind of scale it up. You have this like very cyclical or like bursty stuff. Like we manage all the capacity. And the beautiful thing is like, because we have many, many thousands of customers,

33:00 we can sort of aggregate that capacity and get very smooth workloads, you know, very smooth aggregate demand, which we can manage very, very efficiently. So modal functions was the core. We launched that in 2021. Like I mentioned, we started seeing product market fit in 2023. And now, you know, it’s about two thirds of our avenue and empowering a lot of, you know, in practice, it’s mostly inference, but you can use it for all kinds of other stuff. Modal sandboxes, we launched that in 2023. And it’s actually kind of, you know, took a while to see product market fit similar to functions. Why did we launch it? We realized a lot of people we talked to were using LLM to output code. This is like me and Akshar talking over lunch one day. We’re like, maybe they need a place to run that code. And we realized like, actually, we kind of have like a lot of the infrastructure already to just take code and run it. Because we’ve already built the primitives for like managing containers and isolation. We already had, you know, G visors and isolation primitive and things like that. So we get kind of excited about that idea. And Akshar wrote a prototype and launched it in basically one day or two days or something like that. Zero. Like we saw like very little like growth. I don’t want to like trivialize it. Because we did see some growth. Like there were like some sort of, you know, And it was 2023. So yeah, it was 2023. And like, you know, like some people picked it up. Like we had some early users who kind of enjoyed it for certain things. But like overall, like it was like a pretty insignificant source of revenue into going into the summer of 2023. So two years later, we even discussed like, do we even need this product?

34:30 Should we shut it down? Then it just started exploding. So 2023. And I think what really happened 2023 was like, LMS got really good at generating code. In 2023. 2023. I mean, it was like around that time. Like I feel like that was like when they went from like being able to like refactor functions and like write snippets, so like generating like full apps. Right. And so we started seeing a lot of growth coming from like vibe coding, you know, companies like Loveable and other ones. And then, you know, companies like ramps started using model for background agents. And then we started seeing tremendous growth coming from a number of Neolabs doing things like reinforcement learning on model sandboxes. So sandboxes is now about a third of our revenue. You know, we considered it like basically like our second product. And to me, it’s like really exciting because like even back in the early days of model, I always knew that model, you know, is about like creating a whole toolkit of products. Like we always wanted to be almost like a cloud, you know, you have the storage and the compute and a bunch of different ways to work with it. We always wanted to be a multi-product vendor, but that wasn’t necessarily true in practice until like now. We’re like, I think now it’s like kind of clear. We have functions, we have sandboxes. We’re working a bunch of other stuff too. We’re working on training, working on batch, we’re working a bunch of other stuff. So to me, that sort of evolution into becoming like a multi-product company, I think it’s been amazing because that was always so core to the vision. Even in the very early decks, I had a bunch of slides on this. But it wasn’t necessarily true to the external world until recently.

36:00 Do you have anything in the works that you think is going to be like that next product? Any predictions maybe about the market? Anything like that? I mean, I’m very bullish on training. I think training is like kind of an underserved market. Obviously, there’s like a lot of focus on like very large training runs. But we actually see a lot of demand, the sort of smaller and mid-sized training segment. So I’ll be talking a little bit about that later this week. We’re organizing a conference. Batch is I think like a really interesting market. I think we see a lot of demand for sort of, you know, customers that need a... You know, we had a customer the other day that needed to transcribe, you know, 5,000 years of audio. They had like, you know, millions and millions of audio files. And we had a lot of capacity. So we let them do that, you know, and took a few days. I think there’s some really interesting use cases there. Very bullish on like low latency stuff. I think there’s going to be a lot of, you know, once we sort of get the audio, like the speech-to-speech models working, like the voice models. I sort of expect that to be very interesting market. Then of course, like all this like agente stuff, right? And in a way, like I have like mixed feelings about like the word agente, because I feel like in certain ways, like, I think all software will become agente. So it’s almost like to me, like, what does it mean to be agente? Like I think all software in a certain way, like will become like AI powered over time. But like clearly there’s like a set of like building blocks of like, you know, doing more sort of LLM powered software that seems to be sort of, you know, common across all the different use cases.

37:30 And we’re like, you know, the way I think about models, like we’re in the business of building building blocks, you know, we want to give you like amazing, generalizable, battle-tested, you know, general purpose, composable building blocks. And I think there’s some really interesting stuff also, like, you know, with the agent use cases. That’s interesting. You bring up training because I think in another podcast, maybe like a year ago, maybe like two years ago, you said training you were not focused on like specifically, like what changed in the market? I mean, we’ve been building a training product for multiple years on and off. So I wouldn’t say we’re like, we’ve never been focused on it. But it’s like, it’s like one of those things we’re like, I mean, now we’re like 150 engineers, but, you know, a year or two ago, we were a much smaller number of engineers. And so, but just virtue of, you know, being smaller, we had to focus a little bit more. And so inference became like our sole focus and was our sole focus for many years. But we’re bigger now. Like we, we, we do have opportunities to also place bets in other markets. I’m also really interested in, you know, kind of continuing the list of things. Post training, reinforcement learning. I think there’s some really exciting stuff around that. I think there’s also many other like stuff around like storage and building data warehousing and feature stores and real time streaming pipelines and all kinds of like, I don’t know. I think the longterm view of, of modal is like, basically like, you want to build some cool AI shit, like, you know, we got you covered, like, you know, we got all the building blocks, like comp tasks and like, we’ll help you, you know, take all these building blocks and compose them.

39:00 And that’s the beauty of software is like you, you can, you know, build almost anything if you have the right building blocks. So that, that’s very much the sort of company and the product that want to build. When you say training, are you focused on more of like the bigger enterprises doing huge training runs? No, no, they’re going to go and like, you know, do these like, you know, big, hyper-scaler or rather Neo, Neo cloud mega deals and get, you know, 100,000 GPUs or whatever. But, but, but there’s like a little bit of an underserved market of like, in my opinion, you know, like researchers iterating or like people training smaller models, there’s, you know, Texas beach and speeches, text models, computational biotech is actually a market that we see a lot of demand from. We have a lot of customers in that space training relatively, you know, modest models. You can train on 64 GPUs or whatever recommendation system, a lot of sort of traditional machine learning. There’s a lot of things like you don’t need a, you know, 100,000 GPUs. That market, I think is like, we’re not going to like focus on that for a while. But, but I think there’s a lot of stuff in the, you know, slightly smaller markets that that are relatively underserved. It’s very hard to set up training clusters. Yeah. Have you noticed like since, I don’t know, like GLM 5.2 or like, maybe like, can we 2.5 that, you know, a lot of people are using those and like fine tuning them? If anything, I’ve actually seen the other direction. I feel like fine tuning the value prop a couple of years ago was like, yeah, you know, open source models, they’re not quite as good. But, you know, if you take, you know, a small model and you fine tune it on your specific

40:30 domain, you can actually reach the frontier in terms of, you know, quality and cost. Today, I would say like the open source models are so good that like you can basically just like swap them out and just run, you know, instead of, you know, the frontier ones, you can run, you know, Kimi K3 or whatever, DeepSeq 4.1 or GLM 5.3. And they’re like basically almost as good as like the frontier model at a much lower cost. So we still see a lot of demand of for post-training. And I do think it’s a really interesting space and we’re working on some products in that space, you know, both sort of SFT and but also reinforcement learning and things like that. I would also say, you know, fine tuning is no longer the thing that you kind of have to do. Like it’s very optional. Like you can open source models like work really well just off the shelf too. Back to sandboxes actually. I forgot to ask you. So like what did you have to update about the sandboxes that you guys were offering that like existing sandboxes did not did not allow for? Not a whole lot. I think the thing we’ve always been very, very good at, I mean, we’re infrageeks, right? Like we’re very good at like, you know, scaling and reliability and doing a lot of stuff. And I think that’s why that explains to a large extent like why we have, you know, in my opinion, I don’t have numbers, but like I think we have by far the largest market share in this space. Like we’re just very good at running, you know, hundreds of thousands or even millions of sandboxes. Yeah. I will say we’ll have to, we’ve had to rewrite our systems, you know, a bunch of times in order to handle that scale.

42:00 But we were always like very good at it. There are other things like over time. I mean, the sandbox space in general, like, you know, like it’s like not the most like differentiated space, but you know, we’re working on some really cool stuff. Like over time, like we’ve had to add support for VMs, like virtual machines. So we previously bought our sandbox product on GVisor, but now we offer full virtualized machines as well. Turns out a lot of for certain use cases, you kind of need full Linux compatibility. There are other things we’ve had to build, you know, better primitives for like more interactive, you know, piping data back and forth between sandboxes, working with file system. We’ve had to spend a lot of time on snapshotting. I think a lot of the things that over in the long run will differentiate will be like, you know, really cool things like, you know, forking sandboxes, snapshotting, resuming snaps, like live migrations. In general, like I think sandboxes, the novelty of sandboxes is always, you know, this sort of ephemerality, like you can spin off sandboxes for a few seconds. And that’s something I like, that’s a string I want to pull more in. I think there’s a lot of really cool stuff you can do, you know, kind of decoupling the agent and the harness decoupling it from sandboxes, having agents control many sandboxes, forking sandboxes, you know, snapshotting things like, you know, like running basically like very short lived, maybe one per command, like things like that. So I think there’s a lot of cool stuff there. We’ve already spent a lot of time doing that, you know, around snapshots. But I think there’s a lot of cool stuff to be built there as well. You brought up snapshotting a couple of times.

43:30 Like what do you see as like the use case for snapshotting? Like what would that give us that we don’t get right now? I think the big thing for me is like today a lot of sandbox workloads are kind of like lift and shift. Like people think a lot about sandboxes almost like, you know, I have my dev box, now I’m going to shove it in the cloud, I’m going to have an agent control it, maybe I’ll run the harness inside the sandbox. I don’t know if like long term that’s like the right way to think about, you know, on like compute for agents. I think a much better model would be you’re going to have this like very long running agents, which almost like to me are like kind of durable execution workflows. Like they’re going to run, you know, 24 seven, you know, every once in a while they’re going to run commands and, and you know, having the ability to like spin up a sandbox in, you know, 100 millisecond, run a few commands and then like shut it down and like snapshot it. I think the faster you can get that like, you know, wake up, you know, go to sleep cycle, the more you can sort of create sandboxes that only run for like a very brief period and that will make compute much more efficient. And it will also enable you to do some really cool things, you know, like forking and moving things around and, and, and, you know, having like imagine, you know, like Claude code instead of, you know, having sort of a very linear flow, like sequential flow, like kind of branching and, you know, running hundreds of agents at the same time. I think a lot of like CI is like another like illustrative example, like for most of the, the history of CI, you know, talk about like GitHub actions and things like that.

45:00 Like you had a very sequential flow, like, you know, you slowly install a bunch of stuff, then you run the test suite and then, you know, generates some reports or whatever. And it’s like, you can break it up into shards, but it’s kind of annoying. Like what I would love to do is like take your entire test suite and just run every single test in its own sandbox, right? Like, and just paralyze the shit out of that around like a thousand sandboxes. Why can’t that be done? I mean, the problem is like there’s too much, there’s too much overhead around like, you know, starting and stopping sandboxes. So I think the more like you create like, you know, frictionless, like starting and stopping, the more you can enter a world where like you can do this like extremely bursty, like large scale, like run as much as you think you want in parallel. So, you know, I’m an infrageek, like I said, like I get very excited about these things. You know, once we figure out all these primitives and the right building blocks, I think it’s going to unlock a lot of really cool stuff that, you know, we previously could not do with software. Why hasn’t that already been built? I would assume even previously people would want to parallelize as much of their CI as possible. You can’t, but the overhead, it’s like Amdahl’s law or whatever. Right? Like the overhead of parallelization right now is very high because like right now, like you can get up actions, for instance, like we parallelize in the modal test suite, right? But like what that basically means is like now if you take your test suite and you split it up in two, like now each of those shards both need to do all the installs and the test setup. And that takes a long time. Like it can take minutes, but let’s say you can do the test set up once

46:30 and then you snapshot the container and now you clone it and now you can run the test suites independently. I think a lot of the benefits cannot be, you can’t materialize those benefits until you get the friction and the overhead down, the transaction cost. I think you guys have a partnership with Anthropic and they’re using sandboxes for something, like modal sandboxes. Yeah, I mean the managed agents with Anthropic, this is sort of bring your own sandbox integration. So you can use modal for those, yeah. How did that come out? Like come to be? I think Anthropic is like trying to create like a thriving ecosystem and community and so I think they make money from inference, right? Okay. I think it’s very much in their interest to promote, for everything else, as much of an ecosystem as possible and they want to have the largest possible surface area as long as you use Anthropic for inference. So I think it makes like very rational sense for them to partner with us as a sandbox provider. Also, I feel like we have to bring up the hugging face incident. Are you okay talking about that? Yeah, yeah, I mean it’s all there. Yeah, yeah. So yeah, could you just briefly describe like what happened and I think they were using the modal sandbox but it wasn’t your, you know, it wasn’t like a fault of the sandbox. Yeah, exactly. I forget all the details of the exploit but I mean, you know, I think what you’re referring to is the fact that like modal was involved. Yeah. And so, you know, just to comment on that, like basically the agents running inside,

48:00 you know, that exploited hugging face, they discovered a vulnerability in an app that was deployed on modal and they used that to sort of, it’s almost like a springboard for getting access, getting full internet access is my understanding. I don’t remember the full details. To be clear, this is an app running a modal that was hacked. Like it was not modal that was hacked. So it’s like AWS, a lot of people run stuff on AWS, occasionally those apps get hacked. Does that mean AWS got hacked? Like no, of course not, right? And so there’s a lot of people running apps on modal and, you know, one of those apps got hacked. And I think, but like there are some lessons we can learn and I think one of the things we’ve been reflecting on is like, could we have more sensible, you know, security restrictions by default, I think to use the AWS analogy and just as another analogy, you know, S3 used to be this, like, you know, it used to be very easy to accidentally make an S3 bucket public and AWS realized like, you know, it doesn’t necessarily cause it, you know, poses security problem for AWS, per se. But a lot of their customers accidentally would, you know, like leak data. And so S3, it’s much harder now to make S3 buckets public. And so in a similar way, it’s like, we’re exploring like, you know, can we make security a little bit more restricted by default without necessarily, you know, destroying the magic that makes modal good. And I think there’s also, you know, it’s kind of a separate sidetrack is like, agents are getting really good at cybersecurity. So we’ve actually also, as a sort of, you know,

49:30 separate sort of learning from this incident, like we started using agents internally quite a lot to try to find vulnerabilities so far hasn’t found anything, but like, they’re very good at like, probing into like very deep parts of the system and exploiting, trying to exploit things and trying to escalate and get out of sandboxes. So, you know, it’s, it’s, it’s something we’re exploring very actively right now. How exactly do you get the agents to like run through the code base? You’re just like, hey, like find security vulnerabilities? Well, no, you just like, you just put them in a sandbox and tell them like, can you try to explain? Just run through the escape from the sandbox. It’s like, escape the room, but for agents. Cool. When did you start doing that? We started doing it a few months ago. And it’s pretty exciting. Like when Fable, I guess, came out. Yeah, roughly around the same time. Okay. I mean, my view on like cybersecurity is that, you know, I actually think they’re very good at finding vulnerabilities, but I think you can also use them defensively very well. So I, I, I think, you know, companies like modal and many other companies right now, like, I think, you know, if you go to talk to like pretty much every system, I’m sure they’re all thinking about the same thing. Like how do we use agents defensively to, to, to, you know, fix all our vulnerabilities? So over time, actually, in a way, like I’m not that nervous. I think if anything, like agents and cybersecurity, all the breakthroughs in cybersecurity will, will make the world safer, if anything. Do you think that like all of the cybersecurity, the big cybersecurity companies are just cooked? I don’t know if that’s true, because I mean, you’re asking something that’s like very far

51:00 outside of my like core expertise. But when I think about like cybersecurity, a lot of what you’re selling is effectively fear. And that’s almost like a branding and like marketing strategy, as much as, you know, obviously the core products. But I think, you know, brand and, and things like that are actually like arguably like more valuable in the air of AI. So I wouldn’t necessarily jump to the conclusion that they’re cooked. I think they have, you know, deep relationships with the enterprise customers and they have, you know, big sales teams are very good at telling, you know, spooky stories and, and things like that. And I think they’re going to continue to be very good at that. I just ask, because like, yeah, you’re doing it yourself, you’re not going to one of like the Palo Alto networks or whatever. But I guess like it’s only like giant enterprises that care about that. Also, they can blame somebody if something goes wrong. That is true. Yeah. Yeah. You kind of want to hire in many cases, just like an established vendor in order to blame them. I mean, to some extent, it does reduce a little bit the need for us to do, to bring in pen testers like human pen testers, which most startups like do on a regular cycle. So maybe previously, you know, we would do it, I actually don’t know what the cycle was, but, but I think we used to do it like once every six months, roughly. Maybe now we only need to do it once every year. I don’t know. But, but like, I think with all of these things, you know, humans can also use them to get more efficiently. So like, you know, like, I don’t know, I’m kind of a believer also that like humans with AI is even more powerful now. So like, maybe there’s an argument that like we can bring in pen testers

52:30 who are really good at using agents and, you know, find vulnerabilities even faster. I don’t know. I’m not that, you know, I’m an optimist. I started a company. So like, you know, I’m generally like optimistic about everything. But like, I certainly don’t think humans are cooked or companies are cooked. Like I think in the era of AI, a lot of companies are going to figure out, you know, how to use AI to be even more efficient. I want to transition a little bit to like developer experience, because like early on, I mean, even now, like you guys focus a lot on that. I mean, you’ve talked a lot about that. Does developer experience even matter anymore? I think it still matters. And I think you’re sort of, you know, like the context, I think you’re official, like the thing you’re talking about is like, now with agents, right? Like the, the developer experience, like the docs are already there. The agents are going to look at the docs, like all that stuff. To that point, though, like almost all the things that make developer experience good, also makes the agent experience good is kind of what we’ve seen in practice, which is like things like having good docs, having descriptive error messages, having fast feedback loops, right? Like, I do think there’s certain things have changed. The other day, I was like setting something up with Cloudflare, and it’s like, I find Cloudflare like notoriously annoying to work with sometimes. And, and I threw an agent at it, and it was able to like just configure a bunch of like, you know, things and pollute me for me. And, you know, and so I will say like agents does, they do make it a little bit easier to work with things that are like harder to work with in the past, you know, like the sort of human toll of dealing with the AML as arguably gone down a little bit, because you can just like,

54:00 you know, tell your agent to deal with it. But, but for the most part, I think, you know, the things that make developer experience good also make agent experience good. I think to some extent, also like infrastructure over time, anyway, becomes, it needs to be more programmatic. I think that’s always been the thesis about sandboxes, is that, you know, like things that you previously had to configure using YAML, that should just be an SDK. That should be more like imperative, like launch a sandbox, like do stuff with it, tear the sandbox down, right? Like, rather than sort of declarative using, using, you know, some sort of DSL, it also pushes like a little bit of a different interface that maybe previously didn’t exist. I think in either way, like SDKs are as relevant as they were, as they always were, if not more. Is there anything you changed in your product? Just because like, you know, most people are just running agents on it? We do. I mean, we also, we actually do run a lot of internal tests where we have agents, we tell agents to like, build a mobile app and run it. And I mean, a very concrete thing that is kind of funny is that we have actually seen that like, because of agent training data, it’s very hard to deprecate things now. We see that like, agents often like, love using, you know, old versions of modal syntax and it’s kind of harder to nudge them away from that. So we’ve actually had to be a little bit more conservative going forward, like, you know, over the last year or so with, with how we deprecate and remove features, it takes, we have to be very careful to update every single example that’s out

55:30 there online. Yeah. And, and, and, you know, skills, right? Yeah, there’s like stuff like that. But, but like, there’s also an insane amount of like modal code that’s now like online too that goes into the training data. I’ve seen like that to be like kind of a, you know, a little bit of like an interesting example where like, another thing by the way too is like, there’s obviously like to sort of a similar to like search engine optimization, there’s like agent engine, you know, whatever people call it, agent optimization. We often like, run internal benchmarks, we’re like, ask an agent, like, hey, like, can you deploy a stable diffusion for us? Use whatever tool you want. And we see if it recommends modal or not. And those things are very useful for us to sort of track over time. Or like, hey, spin up some sandboxes or like, hey, like rank all the sandbox vendors. So we spent a lot of time like doing those things to make sure. What do you do about that? Yeah, you say, hey, like, you know, build this stable diffusion thing, does it recommend modal or not? Like, yeah, what do you do about that? Yeah, I think that’s harder. I think, you know, it’s like search engine optimization, I think it’s primarily through content is like, you just have to put out a lot of content. You can also to some extent, like sort of see the perception, I think, a little bit more like you can ask an agent like, you know, how does, you know, rank all the sandbox vendors by reliability or rank all the sandbox vendors by price and sort of see, and it does feel like agents are a little bit more prone to like, misunderstanding pricing and, and, you know, things like that. So we also spend a lot of time like just making content more clear

57:00 and more like, you know, understandable for agents, because we know they’re all indexing, like everything that’s out there and that’s talking about modal. Have you thought about just like, making a bunch of GitHub repos and having the agents like write a bunch of modal code in there? Have you tried that? Slop cannon. I think the truth is like, that is probably something you can do. My guess is like LM training data today is probably quite insensitive to like, what’s like AI output in the first place and you can probably like create a lot of slop. Like we thought about it, like, I don’t love that idea. But like in theory, like much like, you know, SEO in the early days and still to some extent, like auto-generated content is like a very effective strategy. I think it’s possible. Like, you know, you could, you can do those things too. Yeah. I want to switch to FDEs. Apparently, you guys hire like forward-appoid engineers. Like, what do you do? We’re in a new era of like, building applications and, and, and, you know, things that we couldn’t build, you know, just a few years ago. And we see a lot of companies that, you know, they need a little bit of help, right? Like, you know, and so we started investing in FDEs a couple years ago, but it’s really been this year, it was kind of scaled it up a lot. I think we started this year having like four FDEs now. I think we have like 24 or something like that. So we’ve grown it a lot. And they do it, you know, it’s more, it’s not exactly the pound term model, but they do a lot of like heavy, you know, sort of customer engagement. Like they work with a lot of customers on performance tuning, especially with LMS. We see that there’s so much benefit from like, you know, sitting down

58:30 with customers and training draft models and tweaking, you know, parameters and DLM and SG Lang. They often, you know, fix bugs or not fix bugs, but like, you know, when customers run into issues, like they often work with customers trying to figure out like, how do we work around that? Or another thing we’ve used FDEs a lot for is like, you know, kind of proactively, you know, if we see certain customers, you know, over provisioning with model, like customers requesting too much CPU or too much memory, for instance, like that’s kind of expensive. And like, we try to catch those things and we try to sit down with those customers like, yo, like you’re spending a lot of money on model right now, we can actually help you save money. And then of course, like with sort of active deals, you know, we helped them migrate code from other platforms, things like that. I think, you know, we never have the intention of becoming like service heavy business. I think that the goal for FDEs is like, you know, A is to be a part of, you know, the sort of sales process and like closing deals. B is to serve customers and do some like, you know, customer support. But like, I actually think like the most important thing is like, C is like bridging gaps in the product that we later want to turn into like real features. So I think to a large extent, you know, anything an FDE does, you know, my question is always like long term, how do we turn this into product so we don’t need an FDE for this? Yeah. Do you guys hire PMs?

1:00:00 Product managers? Yeah. No. Would you say these FDEs are kind of like pseudo ish PMs? It’s a little bit of a different function. Like I think certainly on the more like, you know, customer facing side they are, but there’s like, you know, on the more like inward, you know, internal side, like this is sort of separate part of like product management, which is more like, what are the next building blocks Modal should invest in and like, you know, and then there’s like something in between like, you know, looking at like maybe 10, 20 different customers that are all asking for like slightly different things, but maybe what are the patterns? Like how do we turn this into product? We do have people doing product with us and it’s sort of evolved, you know, we’ve had people basically like become product managers after previously being either DevRel or engineers and showing strong aptitude on doing thinking very strategically about what we have to build. So I’m actually not like anti product management. I do think that a lot of companies in the past hired product management product managers too eagerly. And I think there’s a risk that you kind of, I can sort of see it in a lot of products like certain products like you can tell like, there’s just like eager product managers that just thought of like, kind of like, wigity things and just ship them. I think doing product management at an infra company really well is very, very hard because I think it takes like a very deep, you know, level of strategic thinking thinking about like, what are the foundational building blocks? Where do we want to be in one to two years? Because the truth is building infrastructure is very hard. And how do we make sure we don’t end up with like 35 features that are like,

1:01:30 kind of similar looking, but all do like a little bit slightly different things. That sort of level of thinking is something that, you know, is very rare to find. And for that reason, we haven’t aggressively tried to build up a product management team yet. Yeah, that makes sense. How would you say your teams have been evolving maybe like since Genialite? Because I would assume, yeah, I mean, you obviously worked at like Spotify, you had like a certain team structure, have your team structures like evolved a lot now that, you know, we have coding agents. In a funny way, like, yes, I do think team structures have changed a lot, but I’m actually not convinced it was like Genialite driving that. I think in particular, like one thing I’ve seen a lot is that there’s been sort of a focus, like a swing back towards being in IC that I actually think is very, very positive for the space as a whole. I think, you know, if you look back at the space like 10 years ago, 20 years ago, everyone wanted to be in management because that was the way you would get more money and more recognition and more prestige. I think that shift actually started even before Gen AI, but I do think it’s like heavily accelerated with AI. Like, you know, we make it very clear to everyone at Modal that like manager or RC, it’s like there’s no difference in prestige. Like everyone is just a member of technical staff. You know, it’s about ultimately your contributions and we see a lot of X managers, you know, becoming ICs and because they want to go back and build code. And so, and you know, and there’s been sort of shift away from like, you know, non-technical managers,

1:03:00 everyone, including me, I still write a lot of code at Modal and Akshath too and pretty much everyone in the tech team and a lot of people who are not in the tech team still write code. So that to me is like, you know, a recent phenomenon, but I actually don’t know if it’s like necessarily cost by Gen AI, but I think Gen AI has like greatly accelerated it because it made it even lower the cost of like writing code is even easier. So if you have like non-technical people like committing code, like how do you, how do you review that? Like how do you guys do code reviews? We do code reviews, but it’s, we think of it as optional. I mean, for most, my view is like code reviews in large part is like people, in many cases, trying to just escape the accountability of like, you know, merging things into production. And if you have that like, you know, if you have that culture, like then to me, that’s a sign that you need to invest way more in, you know, CD and CI and things like that is CI in particular, but also CD, containers integration, because it’s deployment. We do code reviews. Like, I mean, like when I write code, like occasionally, like I’m asking, you know, authors, like, is this in line with how you think about things? I hope I’m not changing the architecture fundamentally, but we also make it clear internally that like code reviews are quite optional. And, you know, if you’re just changing docs, if you’re just doing, you know, some basic stuff, like don’t wait for a human to look at it, especially in the era of AI, why do you need a human to look at every piece of code? You don’t have to. But that does require you to have, you know, very good controls, you know,

1:04:30 internally around, you know, testing things. Yeah, and exactly. Like, you know, infra companies, like we need one more nine than the average startup, right? In terms of uptime. But like, you know, these are investments you just have to make. Like, you have to build systems that are very resilient to like, you know, you have to have rate limits everywhere. You have to decouple systems. You have to make sure that like, nothing is a single point of failure. They have to have extremely good test coverage. Agents are actually really good at writing tests these days, which was like, you know, a few years ago, I thought like, one of the major things missing in agents, now they’re actually quite good at like, because I think it takes a little bit of like, sort of kind of like, need to step out of what you’re trying to do for a second and be almost a little bit like devil’s advocate to write good tests. But now they’re actually quite good at writing tests too. So I think, I think all these things help. Do you guys use like frontier models for coding or do you guys have you guys experimented with like, open source ones? We experiment with everything. But yeah, I mean, you know, initiative is a lot of cursor, then, you know, of course, Claude and Codex and now a lot of people are using, you know, night shift and all these open source stuff or other stacks. So I don’t necessarily have like a strong point of view, like I want people to experiment and try different things like we, you know, use frontier models, use open source models, we’re trying to do a little bit more dog floating internally. Now that we’re actually running inference for a lot of the open source models, like a very large scale. I think it’s also helpful to like use certain internal endpoints for a lot of agentic development. So we try to promote that a little bit. Something I read maybe like, I don’t know, a year ago, maybe like two years ago,

1:06:00 you were saying that you didn’t think that there was going to be like a big on-prem business for you guys. Have you maybe like changed your opinion on that? No, I have not changed my opinion. I think, moral to me, I’m a Claude maximalist and maybe to take a step back, like when the cloud came in 2008, 2009 or whatever, my first reaction was like, that’s crazy. Like why would I want to take my code and run it on someone else’s computer? That’s like insane. And then like six months later, I was doing it. And I think, you know, like took me six months to change my mind. It took probably enterprises like another 15 to 20 years. But like you look at, you know, vendors like Snowflake or I mean AWS and like, I think there’s many examples of Datadog, et cetera. Like the cloud is clearly winning, right? Like and the multi-tenant systems are clearly winning. And you know, to me, on-prem looks increasingly like sort of a legacy business model. And I actually think, you know, BYOC, you know, sort of VPC peering, like all of these things, you know, like to me, they reflect sort of an outdated security model of like, we need to protect everything inside our own network, right? Like using the network as a mode. And that I don’t think is true anything anymore, right? Like we’ve sort of moved on to more like actually robust security paradigms, like, you know, zero trust and things like that. And so to me, BYOC and those things, like they look increasingly

1:07:30 as like outdated, at least from a security perspective. From our perspective also, like the fact that we have full control over our entire infrastructure stack, we rent all the hardware, we deploy, we manage all the infrastructure, we deploy and like manage health and observe, you know, all the metrics and all the hosts. It means we can iterate very fast and build a greater product. And I think that’s very, very valuable versus, you know, customers in the past who had enterprise editions that they had to deploy on customers environment, they had to be very, you know, they have to think very carefully about release management and having the like, we don’t care. Like if we want to introduce a new database, like we can introduce a new database because we have control over it. Like we don’t need to think about what customers have access to. So to me, multi-tenancy is, is, yeah, has so many different benefits. I mean, that being said, like, you know, if we were to work with like, you know, some very large Fortune 500 customer in the future and, you know, they ask for BYOC, you know, we’ve done this in the past, we’ll give them a number. Like, you know, that number is very high and no one said yes to it so far. But like, who knows? Like, you know, at the right price, like I would still build it. And, and I think there’s like maybe separately, like another thing which I think is interesting, which is like, in theory, BYOC might also be a way to work with and get capacity in certain ways. Like we sometimes work with Neolabs. And those Neolabs have already have very large capacity reservations. And, you know, the question is like, you know, how do we, how do we get them to run modal when they already have capacity? And BYOC might be a solution. We can still keep the control plane in our environment.

1:09:00 And the other flip side of that is like, we could maybe even run like other people’s stuff on that capacity. So there’s some like, kind of fun stuff you could do in theory where like, you can sort of, you know, like kind of aggregate all that capacity into one big pool. So we’re excited about some of those ideas. But in general, like I feel like on-prem, I don’t know if we’re ever going to do. But BYOC, like maybe bring your own cloud. I can say BYOC. I should explain. Let’s say hypothetically, you are going to raise, I should tend more money like soon. What would you use that money for? I mean, like I mentioned, like in this capacity situation, the, a lot of the money, a lot of the cash flow, I should say, that’s required is capacity, prepayments, right? And so that to me represents the best use of cash today. We’re ramping up capacity very aggressively. Maybe we move into, you know, other types of deployment models in the future, ownership or whatever. But I think there’s always going to be a cash component of that, like some sort of down payment or prepayment or some sort of that. So that to me is the greatest use of cash right now. There’s of course other things like, you know, salaries and office spaces and stuff like that. But that actually in the grand scheme of like our financials, like it’s basically a rounding error, right? Yeah, interesting. Do you think that’s going to change? No, I think, you know, the sort of operating leverage that we have in this business is kind of insane. Like, you know, we have 200 employees, you know, they cost a lot of money, but, you know, the

1:10:30 GPUs cost a lot more money. Yeah. If you had to say, like, what are the top, like, I don’t know, like three things that are your biggest focus or the business’s biggest focus right now, like, what would you say those are? Capacity and the sort of financial complexity. And, you know, in general, like, how do we, how do we think about that space? How do we think about pricing and packaging? How do we think about the sort of tension between ownership and renting? Should we be going a more neocloud direction? Should we go in the opposite direction? The sort of counter position, like, that’s probably the biggest thing that I think a lot about. The second biggest thing I think a lot about is probably around, like, more like product strategy. Like, I think a lot about, like, what is the next building blocks? Like, if we, you know, do more like, agentic stuff, like, what are the things we need, you know, are there certain storage primitives or compute primitives? Or in general, like, what is the thing that the world is going to wake up and realize they need over the next few years? And ideally, we have that at that point. We’ve been pretty good in general at, like, you know, anticipating demand and building products in advance of it with the serverless functions in the sandboxes. So what’s the next thing? Then I would say, like, actually the third thing I think a lot about is, like, more on, like, culture and company building and stuff like that. I mean, we recently crossed 200 people. We’re growing extremely fast. We’ll probably hit 250 by the end of the year. Like, when you’re growing very fast, you have 250 people that are all trying to figure out how they can have the most value to modal and, you know, making sure that they have the right context, that they have the right motivation, that they’re excited to come into work every day because they get to build something incredibly cool

1:12:00 and they know, you know, what makes modal successful, what they can do, aligning the incentives and making sure that, you know, we, we, people have like a very positive culture where they have the sort of implicit trust to do a lot of stuff, but also the accountability to like, you know, deliver the best things and, you know, and be held accountable to like, if they’re not doing it, to me, those are like, you know, super critical things around this scale. Like, when you’re like 10, 20, 30 people, you can all just like, kind of sit in a room and like, it’s all kind of implicit and you kind of just like, feel it. People can sort of read, read me like, they can tell me when I get a little bit annoyed. They can tell when I’m happy, but like, now we’re 250 people and like, people don’t necessarily see it and, you know, of course, there’s like a lot of new people that are like, maybe a little bit nervous to like, talk to CEO and like all this stuff. So like, you know, how do I remain approachable and how do I, like, it’s, it’s, there’s like the Dunbar number, which I think is like more sort of, you know, faked than, than anything, like at least like the story behind it. But like, it is actually kind of real still, like there’s something around like 150, 200, we’re like, laying the foundation of the culture is very, very important. How many people are you guys trying to hire over the next year? We’ve grown about four acts on an annualized basis in the last 12 months. I think that’s like an unsustainable growth rate on a relative basis. I’ve seen that, you know, you can double the team every year and that’s not super hard to do, but four acts, what you start to see is, you know, cultural drift, you start to see islands of different working styles.

1:13:30 You start to see people doing like very speculative things like that. That’s something I always watch out for in the engineering team. You go and talk to the engine and engineer and I’m like, what are you working on? And it turns out they’re like building something like kind of like fringe and like, is this really what you should spend time on? So growing very fast has that cost and you know, it has the cost of ramping people up. So I don’t know. I think by the end of the year, maybe 250, about the end of the next year, you know, but like, you know, we’re going to keep growing like maybe two and a half, three acts. So it’s very likely we’ll end up like next year, like 700 people, something like that. I’m going to assume most of those are engineers. Most engineers. We’re wrapping up sales a lot too. Okay. What kinds of engineers? It’s primarily systems engineers. I mean, we’ve always been like very deep in the sort of, you know, Linux and file systems and scalable systems and networking and cloud and stuff like that. So that’s the core of what we’re looking for. Like people are really good at those things. We’re also looking a lot at like, you know, more like, because we moved up in the stack a little bit, like we’re doing a lot of work in the LMA in front of space. So now we’re dealing with a separate set of concerns around, you know, CUDA and like GPUs and and and MFU, you know, like maxing out the tokens that you can get per GPU and a lot of that things. And, you know, even though I actually like will say a lot of the work in that space, in my opinion, turns out is like more systems driven still, because it turns out like KV caching and routing is actually like, is still like very like systems like like these are very like systems have problems. But yeah, a little bit

1:15:00 more like sort of GPU performance, that kind of stuff. We’re starting to build up a research team. We’re very excited about, you know, moving into stuff around like, you know, post training and distillation and better methods for quantization for your customers or for internally. Both. I think it’s like a little bit like with the FDEs. Like I think we initially will do a lot of stuff with our customers to optimize their models. But over time, figure out how do we turn those into products? I think we’ll sort of arguably fail in a certain way. If every time a customer needs to optimize a model, we have to go and work with them. And, you know, long term, it has to be turned into products. And so with the FDEs and with the research team, initially, you’re going to have to do a lot of work with the customers and we are that. But over time, we’re also building a lot of internal tooling and a lot of over time internal products that are fully self-serve. So we’re working a lot of stuff right now in post training space, but also in the GPU performance space. So a lot of cool stuff. A lot of cool stuff. Back to the product strategy stuff that you were talking about. Yeah. Yeah. Like what are your thoughts? Like what do we need over the next year, two years? I think it’s incredibly hard to say. And if I said like, I know exactly what we need to build, I think I would be like delusional. I think what we’ve seen so far, and what I kind of believe in on a more general level is like, we need better building blocks. Like this is a new era of software and we’re unlocking a whole new type of applications that could not be built a few years ago.

1:16:30 Doing all the GNI stuff, code that, LMS that output code that needs to run like in an isolated way. Like there’s so many like new primitives that are needed to work with GPUs and work with LMS. And we built some building blocks so far and that’s unlocked a bunch of applications that the world has seen over the last few years. Not just stuff with us, of course. But I think over the next few years, there’s going to be so many more cool things. And so I mentioned some stuff. Like I’m very bullish on training. I’m very bullish on fast jobs. I’m very bullish on certain things around like agents and low latency and things like that. But like in the end, we consider ourselves like an infrastructure shop, right? Like we’re not trying to build, you know, very narrow things. Like we try to build like very general purpose building blocks and put them in front of customers. And then those customers will combine those building blocks and build some incredible applications that we could have never anticipated. I want to go through some of your tweets. This will be the last part of it. Okay. And we can just like, maybe you want to double click on anything. A lot of long running persistent sandbox stuff right now. Today feels lift and shift. I suspect sandboxes will evolve from dev box into cloud to something more ephemeral, one environment for every command. And now I’m shouting in between so you can pause, resume, clone. Yeah. I mean, we talked about this a lot. Yeah, we talked a bit about it, right? Like I’m fully, I think, I think the median sound box in modal today is like eight seconds long or something like that. Like, which is kind of incredible

1:18:00 when you think about it. And over time, I think it might get even shorter. We want to support long running sandboxes too. Like I think they’re awesome, like very valid use cases around long running sandboxes. But like my vision is like, you know, the world needs also, we need to figure out this like how can we just massively parallelize things and reducing the overhead of starting and stopping sandboxes? I don’t know if I ran an IP already start up engaged in catfax arms race for the competitor, it would be nice to coordinate and paste arms race a bit. No comment. The biggest downside of GPU scarcity is not that it’s hard to get GPUs, but that almost all capacity is sold as three years as a three year rectangle is starting three to six months in the future. That’s not what demand looks like for most users. I think we talked about that a little bit as well. Yeah. And you know, maybe just go a little bit deeper now. Like one thing I think modal has always been very, very good at is fully on demand capacity, right? Like if a customer comes to us and need 100 GPUs, like we can typically give them that like immediately, you don’t even have to talk to salesperson. And a lot of workloads in real life, like they don’t look like these like flat, you know, rectangular workloads, like they scale up and down throughout the 24 cycle, they maybe grow over time, they maybe have certain burst periods. And to me, that is a challenge with a lot of customers today. Like the, and I do think it’s a legit problem for, for the space is like the only, the prime way to get capacities to, to buy these rectangular blocks. It’s not great. I think the world needs to, and that’s fundamentally, I mean, I’m very biased, but like that’s why I think modal can play a very large role in that sort of, you know, whatever you want

1:19:30 to call it, sort of duration transformation of offering more flexible capacity. And we can do that because we can aggregate many, many thousands of customers and the demand gets very smooth and aggregate. So that’s a lot of the value prop of modal is exactly that. Would you say that’s a differentiator? Like the, oh really? Okay. Yeah, massive differentiator. Actually, in fact, like, I think, modals, like if you want to be like very sort of reductive, like you can break down modal’s value prop into two things. What is a developer experience, the ability to like iterate and like build things and not have to think about infrastructure. The other thing is the capacity stuff. The more and more I think about modal and like where we’re going in the long run is like we’re building, like we’re aggregating the world’s compute capacity. And we’re very good at slicing it out like very granularly, because we’ve built all this work around, you know, scaling up and down extremely fast, you know, all this stuff around GPU, snapshotting, working some, some ways to make it much, much faster. And that model works at very large scale, like really, really well, because you can aggregate lots of customers. Every month I keep saying or saying box revenue might apply to it, but it keeps going up 30% every month. It’s kind of nuts. Like I don’t understand, you know, like every month I’m like, well, you know, sandboxes grew a lot, but like clearly the kind of plateau and then they keep growing even more. So I, I, I don’t know. And a lot of VCs, like they’re like, how did you know sandboxes was going to be big? Like, and the truth is like, I didn’t like, like, I thought it was going to be bigger than it was in 2023 for sure. But like the fact that sandboxes is a third of our revenue today, it’s, it’s insane. And, you know, we’re obviously very happy about it. And we have like a very large team investing

1:21:00 in sandboxes. You know, I don’t want to like trivialize it. Like we obviously, I do want to give ourselves like a lot of credit for, for seeing sandboxes for what it is today a little bit back in even 2023. But I think, you know, if like, I probably would have never, you know, taken a bet that revenue would be this large from sandboxes and the market demand would be so much. There’s an interesting structural reason why inference providers should exist separately from model builders. The best model keep changing a lot inference providers keep can easily reallocate resources between models since overall demand is quite smooth. So I mean, if you think about all the Chinese labs and, you know, in the future, hopefully more US labs too, every time a new lab releases, you know, the latest and greatest open source models, like you see big shifts in demand, you know, for these models. And so I think a challenge with, you know, if all these labs independently like held capacity, they would see when they launched a new model demand would go up a lot. And then another lab launches a better model and then the demand goes down. You know, but, but of course, like in the background, they’re working on the next version. So like, I think, you know, there’s like a structural reason, like I said, like why you’re going to start, you’re seeing demand kind of shifting to like the latest and greatest Chinese lab. I mean, not like, it’s not like, you know, this is like, everything just moves overnight because, because a lot of models and prompts and stuff like, like a lot of things that get sort of there’s like a certain stickiness to it. But I do think, you know, there’s an argument that it’s very easy for model to like, you know, right, today we’re running, you know, X number of 1000 GPUs for one model. Tomorrow we need to shift like, you know, 50% of that to another model.

1:22:30 Sure, that’s just like, that’s what the system is built to handle. No one even needs to tell us to do that. It just magically does that. So you agree that basically the LLMs are commodities? And if you agree, then where do you think the business for the big labs is going to be? I’m not sure if that’s necessarily true, right? Like, is Taylor Swift the commodities? Like, she releases a big album and goes on a tour and makes a lot of money. And then, you know, then there’s like a down the air when she goes and like records, I don’t know, this is best analogy. And then she releases a new album, you know, so I don’t know, like she still has like a lot of brand and there’s a lot of, you know, power and like makes a lot of margin. So like, as long as the labs like repeatedly can launch new models, and as long as the margin that they have on those models are such that they can sort of pay off amortized like that the cost of training those models, it could still be like a decent business, I think. So you don’t think that the intelligence is like plateauing? No, I don’t think so. I mean, I think it remains to be seen, right? Like, models are today are like very good at like doing things that can be trained very efficiently, you know, using reinforcement learning. So we’ve seen that initially with, you know, coding and writing simple snippets over time, it’s gotten really good at also writing production like level code. It’s gotten really good at math. It’s gotten really good at cybersecurity. Like these are all like very quite verifiable things. I still think it remains to be seen, you know, our model is going to be really good at whatever, cooking food or I don’t know, like things that I have like more

1:24:00 like diffuse targets, maybe longer feedback loops or whatever, I think humans probably like my feeling is like will be quite good and quite competitive with models for a long time in those domains. But you know, certainly, you know, AI models, they’re getting very good as I’m sure everyone can agree with. Yeah, so obviously, like the models are getting really good at coding, they’re already really good at coding. What other applications do you think maybe that aren’t as big right now are going to become huge over the next few years? For the LLMs? I don’t know, but I think anything that has like kind of a verifiable computational bio, I think you mentioned. Yeah, computational bio, I think those are like, not exactly verifiable, but there’s like certain ways you can sort of run simulate, like you can use an LLM to output predictions, then you can do like computational like, like methods to like simulate molecular dynamics and get like kind of feedback loops. I mean, like I think like material science is like maybe another area where like you have a little bit of that loop where you can sort of, you know, synthetically manufacture certain materials and measure the properties and use kind of put an agent in the loop and like, you know, do things. So, you know, I have a physics background, I’m pretty excited about that. So drugs, materials, what else? I’m drawing a blank, but I’m sure there’s many other things. Cool. I mean, those are all the questions that I had. Is there anything you wanted to talk about that I haven’t? I’m good. This is great. This is a lot of fun. Awesome. Thank you.