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 y...

• 0:03Speaker 00

  Thanks for coming on, Eric Fernhardson from Modal. Could you explain the business a little bit and maybe how you got here?

• 0:11Speaker 01

  Yeah, so we build, the easiest way to put it is we build AI infrastructure. So we offer a bunch of compute, a compute platform and a storage platform that enables a lot of customers like Decagon and Suno and Runway and Cognition and many others 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 bath shops 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 easy to deploy and scale and run these things in production.

• 0:49Speaker 00

  So you were on Layton Space like two years ago. A lot has happened since then. Like, how big were you back then, like whatever numbers you can share? And comparatively, yeah, how big are you now?

• 0:59Speaker 01

  I actually don't remember where we were, but it's like, it's probably like at least like 50x in terms of like revenue and users and usage and stuff like that, yeah.

• 1:08Speaker 00

  I use it actually for like a side project that I have where

• 1:10Speaker 01

  Nice.

• 1:11Speaker 00

  Yeah, it's like, yeah, turn like 2D images into 3D models.

• 1:14Speaker 01

  Yeah.

• 1:14Speaker 00

  So yeah, we're just hosting like

• 1:16Speaker 01

  That's awesome.

• 1:16Speaker 00

  Models here.

• 1:17Speaker 01

  What do you think? Any feedback?

• 1:18Speaker 00

  I mean, I love it.

• 1:20Speaker 01

  Good.

• 1:21Speaker 00

  I mean, yeah, that's why I use it over like any competitors or anything.

• 1:24Speaker 01

  Good.

• 1:24Speaker 00

  So before you started Modal, you were at Spotify and also like better.com, I think.

• 1:30Speaker 01

  That's right, yeah.

• 1:31Speaker 00

  Yeah, what were you doing at Spotify?

• 1:33Speaker 01

  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 a 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, you know, just, just building AI applications and realizing like the infrastructure fucking sucks. Like, there's gotta be a better way to do that.

• 2:12Speaker 01

  So a lot of sort of, you know, the rationale for, for Modal was like kind of building a tool I always wanted to have throughout my years at Spotify.

• 2:19Speaker 00

  Yeah, so Modal was more of an offshoot of the stuff you learned at Spotify, not much from better.com?

• 2:27Speaker 01

  Better, better.com was a little bit of a side tour. It was kind of a roller coaster, a long story about the company, talk about it separately. But I was a CTO for, for five years of Better and grew the tech team from, I think, you know, two to three, 400. So I learned a lot about management and building teams and hiring and also learned a lot about developer productivity. 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.

• 2:55Speaker 00

  What was better.com doing again?

• 2:57Speaker 01

  Mortgages.

• 2:58Speaker 00

  Okay.

• 2:59Speaker 01

  Yeah.

• 2:59Speaker 00

  How did you get into that? I mean, we don't have to spend too much time on that.

• 3:02Speaker 01

  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 Vishal, very interesting guy, very, very smart, a little crazy and mostly positive ways. We got very well along, so we, yeah, we, we started building it in, 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 caused a lot of issues at the company. But by then I'd already left.

• 3:39Speaker 00

  That was like 2020, 2021 when you left?

• 3:42Speaker 01

  Yeah, something like that, yeah.

• 3:43Speaker 00

  So, okay, you left before you had the idea for Modal? Or you left and then you started?

• 3:49Speaker 01

  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. I had two kids, I mean, I still have two kids, but I mean, at that time 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, that eventually became Modal. But actually I built, you know, Modal was like the, I think, the third thing I built that I got, you know, that. So there were like a few attempts before then that went nowhere. But Modal was like the first idea was like, this is kind of cool.

• 4:34Speaker 01

  Like I should do this, this is like, this could be a really cool platform.

• 4:37Speaker 00

  Were the other ideas like also in a similar vein, like developer tools?

• 4:41Speaker 01

  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.

• 4:47Speaker 00

  And was this before the ChatGPT launch?

• 4:49Speaker 01

  Yeah, yeah, 2021.

• 4:51Speaker 00

  Okay.

• 4:51Speaker 01

  So ChatGPT 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 called Luigi, which is like workflow orchestration. This is sort of the same category as airflow and prefect and DAX or sort of data category to some extent. Like people don't talk about it as much, but for a while it was like, you know, you got a lot of attention. Anyway, I never turned it into a 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 and, you know, like basically orchestrating code running in the cloud. And what I realized was like, you know, at the end of the day, like core puzzle piece is not just like orchestrating in the compute. It's like executing the compute.

• 5:36Speaker 01

  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 gonna 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. 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 was 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, could also be like data stuff.

• 6:18Speaker 01

  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 build it on file system, build it on container runtime. And did a lot of, you know, solved a lot of hard engineering challenges. And then you mentioned ChatGPT, 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 in Modal. And then we were, that, that made us realize like maybe Gen AI inference is actually a really good use case for us.

• 7:01Speaker 00

  How are they finding it?

• 7:02Speaker 01

  I'm this 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 always 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 is just like me just compulsively just like tweeting about Modal and like what I was building, 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. And like what I'm good at is like, I guess, like just shitposting and being termally online. And so, you know, like work, it's, 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.

• 7:43Speaker 00

  Nice. How long until you had your first customer?

• 7:46Speaker 01

  It took a while.

• 7:48Speaker 00

  Also, did you raise like early on?

• 7:50Speaker 01

  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 taken out of 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, raised a seed round. So at that point, I'd already been working on it for about a year. Actually, I joined as the CTO and co-founder in about, 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.

• 8:30Speaker 00

  I guess that's when ChatGPT came out.

• 8:32Speaker 01

  Yeah, and stable diffusion then, in our case, what was the main thing that's sort of. So we went from like, you know, Hundred dollars a month in like, I think like November twenty twenty two, in the next month, we did like two thousand dollars a month. And then like January twenty twenty three, we did like thirty thousand 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 on to something. It feels like we're on to something. And then twenty twenty three, we grew very fast. And I guess we've been growing very fast since then, basically.

• 9:04Speaker 00

  That thirty K a month moment, was that just like a couple of big customers or was it like very like diverse?

• 9:10Speaker 01

  It's just one and I don't, I don't know if I ever talked about like who that was and if they even know it, but I think they suspected it was a company in New York called Mage. They're doing like a stable diffusion company. So at some point, you know, and in the first, I think first quarter of twenty twenty three, I think there were like eighty, ninety percent of our revenue. But then after that, you know, it started diversifying a little bit and late twenty twenty three, we got Suno 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.

• 9:42Speaker 00

  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.

• 9:51Speaker 01

  Some people were, but, but I, 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 Model is like, it's always been pretty hackable. So they, they deployed their own custom workflows with adapters and custom mean friends and a lot of optimizations. And, and all the, all the stuff we had done in order to get the developer experience to be really good, turned out to be really good for also running production workloads, uh, 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, we just happened to be like very well positioned to, to capture a lot of those workloads in the early days.

• 10:26Speaker 00

  Did tools for auto scaling like not exist? Like, why did you have to build those yourself?

• 10:30Speaker 01

  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, 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, or, you know, AI or whatever. So I, I think, you know, it's often the case that tools exist, but, but they're often not like particularly good. And, and, and often you, you kind of need to rethink a lot of those tools in order to reap the benefits of, you know, and really, you know, turbocharge and start building for this new world. And you saw this with cloud, you know, we had to invent things like Kubernetes and Docker and things like that. And in, in a similar way, you know, like with, with Gen AI, you could build these things on the sort of prior generation of tools.

• 11:14Speaker 01

  Like you can, you can build these things on top of Kubernetes and Docker and, and 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 backend workflows. 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 you'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, so we felt that, you know, we, 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

• 11:58Speaker 01

  building, you know, some really cool stuff around like CPU snapshotting, GPU snapshotting and all that. We built our own auto scaler and scheduler and, and and 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, of software on top of, you can't build like a wrapper on top of the old stuff in order to, to do the new stuff. You kind of need to start over a little bit from scratch.

• 12:21Speaker 00

  So I guess like the auto scaling stuff, like, did you, did you see any like, um, weird behaviors on the platform? Like, was there anything that like broke while you guys were scaling?

• 12:32Speaker 01

  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 spitting the bullet and building it. Like, we always thought of like our, our capacity management as like one big pool. Like, it's like one big pool over the, the entire world. Like, we don't think of it as like separate clusters running in each region. And so that, 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 Model to be a hundred percent uptime, you know, and I think recently we've basically hit four or five nines.

• 13:16Speaker 01

  But throughout the history of Model, of course, we've had, you know, run up against scaling issues and things like that.

• 13:21Speaker 00

  The GPU market and, well, actually before that, so you guys use AWS, like GCP, like all these cloud providers underneath? Like, do you also buy any infrastructure or no?

• 13:33Speaker 01

  Let's talk a bit about like cloud capacity, because I think it's actually changed a lot. Like in, 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, we started renting some capacity from Oracle pretty early because we wanted eight tens and eight one hundreds and we got really good rates on those.

• 14:02Speaker 01

  And, 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.

• 14:13Speaker 00

  Spot means you're just like renting it right there and then.

• 14:16Speaker 01

  Exactly. Yeah. And it's also preemptable, which we had to deal with. But, but it sort of works in aggregate 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 Model started growing like very fast, so we needed more capacity. But fundamentally, 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 twenty five clouds at this point. And in many cases, in order to get the capacity we want, like we, we have to go and do, you know, three plus year deals. You have to pay a meaningful amount of money upfront and, and you're locking in pricing on, on a three-year term, right?

• 15:00Speaker 01

  So, you know, like, and, and, and so we had to spend the entire, you know, last six to nine months, like, you know, kind of rethinking how we get capacity. We had 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 typically these three-year deals. And that's like a big decision, like should we buy, should, should we, you know, or should we keep renting?

• 15:44Speaker 01

  I, I'm actually like, not like mega dogmatic about it. I, 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 an obvious like operational complexity of it, like you have to, you know, deal with vendors and suppliers and, and, you know, power 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. You can raise debt, but you know, if you raise debt, you know, you typically want to have like locked in long-term revenue to, to, to pay the interest rates. So that's also not trivial.

• 16:25Speaker 01

  But, you know, like, we look at the economics, we look at the, the, the operational complexity, we look at the risks. It's a complex trade-off and, and we're open to, to, to buying. We're sort of, there's other constructs too, like we're, it's to get the capacity we need for, for twenty twenty seven, it's going to take a lot of work on, on, you know, going a lot of different avenues to, to lock it up.

• 16:47Speaker 00

  So do you have to hire a lot of like finance people to do all this stuff or not really?

• 16:51Speaker 01

  You know, I've had to spend a lot of time on this and we, we've had to, to, to spend a lot of time on this. And I, I do think over time, I mean, there, there is a lot of, you know, these are very expensive assets, the hardware. And, you know, I think, I mean, it's kind of staggering. You're starting to see like AI investment started to become like several percentage points of, of GDP of the US and you know, starting to see like interest rates in, in America shooting up in the last few weeks. And, and I mean, I like at this stage, like, you know, and, 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 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,

• 17:36Speaker 01

  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, I don't think that's necessarily the right solution for, for most companies. But of course, like in order to get the capacity we want, like, you know, everything is on the table.

• 17:57Speaker 00

  Yeah. And I guess the supply is going to be constrained for a while. 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?

• 18:06Speaker 01

  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, I don't want to say like the market is like hundred percent efficient, but like, you know, in general, like if you're willing to pay a high price, uh, you can get GPU capacity, right? Like, if you're willing to pay more than everyone else, 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, and so I don't, 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 is like, the normal state of hardware is like, it's like kind 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.

• 18:52Speaker 01

  And, you know, it, it's hard to say, you know, how, how long is this going to last? You know, it could be another six months, could be another thirty six months. Like, it's really hard to, to know how, how, how long. And, but, but I fundamentally don't know if like it's, it's true to say like, you know, you need to buy capacity in order to, to me, that's a little bit to sort of, almost like the great toilet paper shortage of, you know, 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, 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 scarcity also means like we can charge more from our customers and the fact that we have a lot

• 19:33Speaker 01

  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.

• 19:53Speaker 00

  Why would that be bad?

• 19:54Speaker 01

  I think it would be bad for if for anyone who's locked in a lot of supply.

• 20:00Speaker 00

  Yeah, because they paid way too much.

• 20:01Speaker 01

  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 that much. Like, I don't, 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 worth, you know, reflecting on that, right? Like, right now, I think, I think your capacity is constrained and that leads to sort of, you know, in economic terms, there's a scarcity rent, right? Like, who captured a scarcity rent? I think it 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.

• 20:42Speaker 01

  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 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 in the long run, you know, we might own some, we might rent some, maybe a combination, maybe do some other crazy stuff, joint ventures, who knows? Maybe some complex SPDs or whatever. Everything is on the table.

• 21:19Speaker 00

  How do you compete with OpenAI and Anthropic in buying GPU capacity? I'm assuming they'd want to like buy up literally everything that's available.

• 21:25Speaker 01

  They are, and I think what you're seeing is, you know, a lot of the prices going up is 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. Much in the same way as you see, you know, hyperscalers getting a little bit of a constrained allocation. I think, you know, also Nvidia prefers to see, you know, a lot of 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

• 22:07Speaker 01

  to work with, being more of an opportunity for us. Like where, you know, maybe Nvidia, sorry, Anthropic and OpenAI, my feeling is like they prefer to buy from like the tried and true. Part of it is also because they do a lot of training rounds, 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, 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.

• 22:40Speaker 00

  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?

• 22:52Speaker 01

  I mean, sometimes it's kind of a janky warehouse, it feels like, you know? 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 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 run some 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, tens or hundreds of millions of

• 23:35Speaker 01

  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 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 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 they fix the issues we ask for at turnaround time? If things go down, you know, how quickly do they fix it, like, things like that?

• 24:18Speaker 00

  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 overbuy, you can just like sell it to somebody else?

• 24:31Speaker 01

  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 utilize a lot of it, right? But like-

• 24:44Speaker 00

  But you can sell it off to somebody else who wants to use it, right?

• 24:46Speaker 01

  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, price is 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.

• 25:29Speaker 01

  And, you know, like, I want to build a company that, that is around for like 20, 30, 50 more years, right? Like, and so, you know, I think we can certainly go out and like buy an insane amount of capacity right now and try to just corner the market. But, you know, do you know about the Hunt Brothers?

• 25:44Speaker 00

  No.

• 25:45Speaker 01

  It's like a legendary story in trading, they tried to corner the silver market. It failed spectacularly. You know, so it's a cornering the market, I think in general, like, can be not, not a good idea. 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 runway, or run rate.

• 26:12Speaker 00

  Are you only buying up Nvidia stuff or also like, you know, maybe a capacity from other types of companies, like, I guess like Trinium and-

• 26:21Speaker 01

  We're, 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 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, AMDs are starting to get like really good scores on certain things. I think, you know, TPU's are obviously used very, you know, widely internally at Google, seem to work really well. I don't know much about Trinium, 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. You know, hardware is hard in a, in a certain way, like software is like easier, and that

• 27:03Speaker 01

  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 Azeval, 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'm, I think it'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 with an amazing product. I do expect to see a little bit more diversity in the market as well, also things, you know, like Cerebras and Grok and these things.

• 27:46Speaker 00

  Yeah, what are your thoughts on like some of those providers?

• 27:49Speaker 01

  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.

• 28:03Speaker 00

  I haven't heard of that, actually.

• 28:04Speaker 01

  Yeah, and, you know, Grok 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 in these sort of, you know, more complex hardware setups.

• 28:24Speaker 00

  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 like what you're doing?

• 28:34Speaker 01

  I think one of the things that, you know, is very interesting with, 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, Anthropic or OpenAI or some, some big buyer or whatever, and just 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

• 29:19Speaker 01

  of chips, we just need the money right now. And then they go and raise the money. And then they like, you know, go to like Super Micro and Dell or whatever, and Nvidia and like get the hardware, and then they install it. And then, you know, now they have like, you know, deliver it 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 they can scale very, very well. I think Modal, because we've almost 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 same point.

• 30:04Speaker 01

  But I think that's been kind of a fascinating thing to see with these NeoClouds. And especially like CoreWeave and those big ones. Very, very savvy with how they raise money and how to think about GPU as collateral for loans and all these things.

• 30:18Speaker 00

  What's the relevance of, like, I forget who it was, but like some like financial company was like, oh, we're gonna 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?

• 30:37Speaker 01

  I mean, there's a bunch of startups. I don't know if you're thinking about that. There's like Silicon Data and Computer Exchange and like ORN 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 homogeneous as other asset classes. You have different network and different locations 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,

• 31:16Speaker 01

  tradable assets out of oil. It remains to be seen. Like, I do think, you know, at the scale where we are with this sort of AI like data center GPU deployments, like, we do need to be a little bit smarter with like risk hedging and and how we share risks and, you know, having more like sort of insurance and things like that. So I'm very happy that people are thinking a lot about this. That being said, like, I'm not entirely sure if we're gonna 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 tradable asset.

• 31:54Speaker 00

  Yeah. I want to shift the conversation to Model Sandboxes.

• 31:58Speaker 01

  Yeah.

• 31:59Speaker 00

  I guess like before we do that, what are the different products that Model has? It's, I guess, like, the main product obviously is like the GPUs. Then you have Model Sandboxes. Is there anything else?

• 32:10Speaker 01

  I think of the like, the two big products that we have is basically, number one is Model Functions and number two is Model Sandboxes. So with Model 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 manage 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 Model 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.

• 32:51Speaker 01

  Model's 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, 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 Model 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 revenue and empowering a lot of, you know, in practice, it's mostly inference, but you can use it for all kinds of other stuff. Model 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.

• 33:32Speaker 01

  Why did we launch it? We realized a lot of people we talked to were using LMS to output code. This is like me and Akshat 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, Gvisors and isolation primitive and things like that. So we got kind of excited about that idea and Akshat 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. We did see some growth. Like, there were like some sort of, you know.

• 34:12Speaker 00

  And we have 2023, so.

• 34:13Speaker 01

  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? Like, 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.

• 34:41Speaker 00

  In 2023?

• 34:42Speaker 01

  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 Ramp started using Model for background agents. And then we started seeing tremendous growth coming from a number of Neo Labs doing things like reinforcement learning on Model Sandboxes. So Sandboxes is now about a third of our revenue. You know, we considered 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.

• 35:26Speaker 01

  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. Where like, I think now it's like kind of clear, we have Functions, we have Sandboxes, we're working on a bunch of other stuff too. Working on training, working on batch, working on a bunch of other stuff. So to me, that's sort of evolution into becoming like a multi-product company, I think has been amazing. Because that was, 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.

• 36:02Speaker 00

  Do you have anything in the works that, you know, you think is gonna be like that next product? Any predictions maybe about the market? Anything like that?

• 36:10Speaker 01

  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, you know, training runs. But we actually see a lot of demand in 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, it took a few days. I think there's some really interesting use cases there. Very bullish on like low latency stuff.

• 36:50Speaker 01

  I think there's gonna 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 agentic stuff, right? And in a way, like, I have like mixed feelings about like the word agentic because I feel like in certain ways, like, I think all software will become agentic. So it's almost like, to me, like, what does it mean to be agentic? Like, I think, 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 L M powered software that seems to be sort of, you know, common across all the different use cases. We're like, you know, the way I think about models, like, we're in the business of building building blocks.

• 37:34Speaker 01

  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.

• 37:48Speaker 00

  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, like, training you were not focused on like specifically. Like, what changed in the market?

• 37:59Speaker 01

  I mean, we've been building a training product for multiple years, so on and off. So we, 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 where 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 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

• 38:45Speaker 01

  real-time streaming pipelines and all kinds of like, I don't know. I think the long-term view of Model 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, come to us and like, we'll help you, you know, take all these building blocks and compose them. And that's the beauty of software. It's like you, you can, you know, build almost anything if you have the right building blocks. So that's very much the sort of company and the product that we want to build.

• 39:07Speaker 00

  When you say training, are you focused on more of like the bigger enterprises doing huge training runs?

• 39:12Speaker 01

  No.

• 39:13Speaker 00

  Okay.

• 39:14Speaker 01

  They're gonna go and like, you know, do these like, you know, big hyperscaler or rather NeoCloud.

• 39:21Speaker 01

  Mega deals and get 100,000 GPUs or whatever. But there is a little bit of an underserved market of like, in my opinion,

• 39:30Speaker 01

  like researchers iterating or like people training smaller models. There's text to speech and speech to text models. Competition on biotech is actually a market that we see a lot of demand from. We have a lot of customers in that space. Training relatively modest models. You can train on 64 GPUs or whatever. Recommendation system, a lot of traditional machine learning. There's a lot of things like you don't need 100,000 GPUs. That market, I think, is like, we're not going to focus on that for a while. But I think there's a lot of stuff in the slightly smaller markets that are relatively underserved. It's very hard to set up training clusters.

• 40:05Speaker 00

  Yeah. Have you noticed like since, I don't know, like GLM 5.2? Or like maybe like KIMI 2.5? That a lot of people are using those and like fine tuning them?

• 40:17Speaker 01

  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 if you take a small model and you fine tune it on your specific domain, you can actually reach the frontier in terms of quality and cost.

• 40:39Speaker 01

  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. Instead of the frontier ones, you can run KIMI K3 or whatever, DeepSeek 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 for post-training. And I do think it's a really interesting space. And we're working on some products in that space, both sort of SFT and but also reinforcement learning and things like that. I would also say 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.

• 41:21Speaker 00

  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 allow for?

• 41:34Speaker 01

  Not a whole lot. I think the thing we've always been very, very good at. I mean, we're infra geeks, right? Like, we're very good at 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, 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 hundreds of thousands or even millions of sandboxes.

• 41:55Speaker 00

  Yeah.

• 41:56Speaker 01

  I will say, we'll have to, we've had to rewrite our systems a bunch of times in order to handle that scale. 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, it's like not the most 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 built 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, 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.

• 42:40Speaker 01

  I think a lot of the things that over in the long run will differentiate will be like, you know, really cool things like forking sandboxes, snapshotting, resuming snap, like live migrations. In general, like I think sandboxes, the novelty of sandboxes was always this sort of ephemerality, like you can spin up 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, the 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 around snapshots.

• 43:26Speaker 01

  But I think there's a lot of cool stuff to be built there as well.

• 43:29Speaker 00

  You brought up snapshotting a couple of times. 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?

• 43:37Speaker 01

  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, 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 24/7. You know, every once in a while, they're going to run commands. And having the ability to like spin up a sandbox in 100 milliseconds, run a few commands,

• 44:21Speaker 01

  and then like shut it down and like snapshot it. I think the faster you can get that like, you know, wake up, 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, like forking and moving things around and 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 history of CI, you know, talk about GitHub Actions and things like that. Like, you had a very sequential flow.

• 45:04Speaker 01

  Like, you slowly install a bunch of stuff, then you run the test suite, and then, you know, generate 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 parallelize the shit out of that. Run like 1,000 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 want in parallel. So, you know, I'm an infra geek, like I said. Like, I get very excited about these things.

• 45:49Speaker 01

  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.

• 45:57Speaker 00

  Why hasn't that already been built? I would assume even previously, people would want to parallelize as much of their CI as possible.

• 46:04Speaker 01

  You can't, but the overhead, it's like Amdall's Law or whatever, right? Like, the overhead of parallelization right now is very high. Because like right now, like GitHub 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 setup once, 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.

• 46:48Speaker 00

  I think you guys have a partnership with Anthropic, and they're using sandboxes for something, like modal sandboxes?

• 46:54Speaker 01

  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.

• 47:03Speaker 00

  Oh, cool. How did that come out, like, come to be?

• 47:06Speaker 01

  I think Anthropic is like trying to create, you know, like a thriving ecosystem and community. And so I think, you know, they make money from inference, right? Like, so I think it's very much in their interest to promote, you know, like for everything else, you know, as much of, you know, 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.

• 47:30Speaker 00

  Also, I feel like we have to bring up the hugging face incident. Are you okay talking about that?

• 47:35Speaker 01

  Yeah, yeah, I mean, it's all there.

• 47:36Speaker 00

  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, it wasn't like a fault of the sandbox.

• 47:45Speaker 01

  Yeah, exactly. I know, I forget the, 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.

• 47:54Speaker 00

  Yeah.

• 47:55Speaker 01

  And so, you know, just to comment on that, like, basically, the agents running inside 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 on 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 app 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,

• 48:40Speaker 01

  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 a, you know, poses a 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 similar ways, like, we're exploring like, you know, can we make security a little bit more restrictive 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,

• 49:26Speaker 01

  agents are getting really good at cybersecurity. So we've actually also, as a sort of, you know, separate sort of learning from this incident, like, we started using agents internally quite a lot to try to find vulnerabilities. So far, it 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 something we're exploring very actively right now.

• 49:52Speaker 00

  How exactly do you get the agents to like run through the codebase? You're just like, hey, like, find security vulnerabilities?

• 49:58Speaker 01

  Well, no, you just like, you just put them in a sandbox and tell them like, can you try to explain?

• 50:02Speaker 00

  Just run through the?

• 50:03Speaker 01

  Yeah. Can you just try to escape from the sandbox? It's like Escape the Room, but for agents.

• 50:08Speaker 00

  Cool. When did you start doing that?

• 50:10Speaker 01

  We started doing it a few months ago and it's pretty exciting.

• 50:13Speaker 00

  Like when Fable, I guess, came out?

• 50:15Speaker 01

  Yeah, roughly around the same time.

• 50:16Speaker 00

  Okay.

• 50:17Speaker 01

  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 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, 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.

• 50:51Speaker 00

  Do you think that like all of the cybersecurity, the big cybersecurity companies are just cooked?

• 50:56Speaker 01

  I don't know if that's true because, I mean, you're asking something that's like very far 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 things like that are actually like arguably like more valuable in the era 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 who 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.

• 51:38Speaker 00

  I just ask, cause 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.

• 51:49Speaker 01

  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, 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 who are really good at using agents

• 52:33Speaker 01

  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.

• 52:51Speaker 00

  I want to transition a little bit to like developer experience. Cause like early on, I mean, even now, like you guys focus a lot on that.

• 52:58Speaker 01

  Yeah.

• 52:59Speaker 00

  You've talked a lot about that. Does developer experience even matter anymore?

• 53:02Speaker 01

  I think it still matters. And I think you're sort of, you know, like the context. I think you're fish, or like the thing you're talking about is like now with agents, right? Like, does developer experience?

• 53:13Speaker 00

  Like the docs are already there. The agents are going to look at the docs, like all that stuff.

• 53:17Speaker 01

  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 found 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, ask, you know, things in Palumi 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?

• 53:58Speaker 01

  Like the sort of human toll of dealing with YAML has arguably gone down a little bit because you can just like, 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.

• 54:41Speaker 01

  I think in either way, like SDKs are as relevant as they, as they always were, if not more.

• 54:48Speaker 00

  Is there anything you changed in your product just because like, you know, most people are just running agents on, on it?

• 54:53Speaker 01

  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 Modal app and run it. And I mean, a very concrete thing that is kind of funny is that we've 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, and it's kind of hard to nudge them away from that. So we've actually had to be a little bit more conservative going for it, 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 there online.

• 55:32Speaker 00

  Yeah.

• 55:33Speaker 01

  And, and, you know.

• 55:34Speaker 00

  You have skills, right?

• 55:35Speaker 01

  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, is obviously like to sort of, 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, doing those things to, to make sure.

• 56:19Speaker 00

  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?

• 56:25Speaker 01

  Yeah, I think that's harder. I think, you know, it's like search engine optimization. I think it's primarily through content. It's like, you just have to put out a lot of content.

• 56:34Speaker 00

  Interesting.

• 56:35Speaker 01

  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 and more like, you know, understandable for agents. Cause we know they're all indexing, like everything that's out there and that's talking about Modal.

• 57:09Speaker 00

  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?

• 57:17Speaker 01

  Slop cannon. I think the truth is like, that is probably something you can do. My, 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 can, you can do those things too.

• 57:47Speaker 00

  Yeah. I want to switch to FTEs. Apparently you guys hire like forward deployed engineers. Like what do they do?

• 57:54Speaker 01

  We're in a new era of like building applications 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 FTEs a couple of years ago, but it, but it's really been this year. We've kind of scaled it up a lot. I think we started this year having like four FTEs. Now I think we have like 24 or something like that. So we've grown it a lot. And they do a, you know, it's more, it's not exactly the counter 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 with customers and training draft models and tweaking,

• 58:39Speaker 01

  you know, the 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 FTEs a lot for is like just kind of proactively, you know, if we see certain customers, you know, over provisioning with Modal, 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 Modal right now. We can actually help you save money. And then of course, like with, with sort of active deals, you know, we helped them migrate code from other platforms, things like that.

• 59:20Speaker 01

  I think, you know, we never have the intention of becoming like a service-heavy business. I think that the goal for FTEs is like, you know, A is to be a part of, you know, the 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 FTE does, you know, my question is always like, long-term, how do we turn this into product so that we don't need an FTE for this?

• 59:57Speaker 00

  Yeah. Do you guys hire PMs?

• 1:00:00Speaker 01

  Product managers?

• 1:00:00Speaker 00

  Yeah.

• 1:00:01Speaker 01

  No.

• 1:00:02Speaker 00

  Would you say these FTEs are kind of like pseudo-ish PMs?

• 1:00:06Speaker 01

  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? Model 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 just showing strong aptitude on doing, thinking very strategically about what we have to build. So I'm actually not like anti-product management.

• 1:00:51Speaker 01

  I do think that a lot of companies in the past hired 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 widget-y things and just ship them. I think doing product management at an infra company really well is very, very hard. Cause 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? Cause 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 kind of similar

• 1:01:33Speaker 01

  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.

• 1:01:45Speaker 00

  Yeah, that makes sense. How would you say your teams have been evolving? Maybe like since GenAI? Cause 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?

• 1:02:00Speaker 01

  In a funny way, like, yes, I do think team structures have changed a lot, but I'm actually not convinced it was like GenAI 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 an 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 a management, because that was the way you would get more money and more, you know, more recognition and more prestige. I think that shift actually started even before GenAI, but I do think it's like heavily accelerated with AI. Like, you know, we, we make it very clear to everyone at Model that like manager or RC, it's like, there's no difference in prestige.

• 1:02:44Speaker 01

  Like, everyone is just a member of technical staff. You know, it's about ultimately your contributions and we see a lot of ex-managers, you know, becoming ICs and, because they want to go back and build code. And so, and you know, and there's been, there's been sort of shift away from like, you know, non-technical managers, everyone, including me. I still write a lot of code at Model and Akshat 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 caused by GenAI, but I think GenAI has like greatly accelerated it, because it made it even lower, the cost of like writing code is even easier.

• 1:03:25Speaker 00

  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?

• 1:03:32Speaker 01

  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 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. It's CI in particular, but also CD, continuous integration, continuous 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.

• 1:04:17Speaker 01

  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, internally around, you know.

• 1:04:32Speaker 00

  Especially at an infra company.

• 1:04:34Speaker 01

  Yeah, exactly. Like, you know, infra companies, like we, 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, you 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.

• 1:05:18Speaker 00

  Do you guys use like frontier models for coding? Or do you guys, have you guys experimented with like the open source ones?

• 1:05:24Speaker 01

  We experiment with everything. But yeah, I mean, you know, initially it was 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 at a very large scale. I think it's also helpful to like use our internal endpoints for a lot of agentic development. So we try to promote that a little bit.

• 1:05:56Speaker 00

  Something I read, maybe like, I don't know, a year ago, maybe like two years ago, 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?

• 1:06:09Speaker 01

  No, I have not changed my opinion. I think Model to me, I'm a cloud maximalist. And maybe to take a step back, like when, 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 it 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.

• 1:06:52Speaker 01

  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 as like outdated, at least from a security perspective.

• 1:07:34Speaker 01

  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 on 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 additions 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, 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, it has so many different benefits.

• 1:08:17Speaker 01

  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, 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, BYLC might also be a way to work with and get capacity in certain ways. Like, we sometimes work with Neo Labs and those Neo Labs 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?

• 1:08:59Speaker 01

  And BYLC might be a solution. We can still keep the control plane in our environment. 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 gonna do. But BYLC, like, maybe. Bring your own cloud.

• 1:09:23Speaker 00

  Yeah, yeah, yeah. It's okay, it's okay.

• 1:09:25Speaker 01

  I should explain the acronyms.

• 1:09:27Speaker 00

  Let's say hypothetically, you are going to raise, I should tend more money, like, soon. What would you use that money for?

• 1:09:35Speaker 01

  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, 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 of 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, our financials, like, it's basically rounding error, right?

• 1:10:17Speaker 00

  Yeah, interesting. Do you think that's gonna change?

• 1:10:20Speaker 01

  No, I, 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, the GPUs cost a lot more money.

• 1:10:32Speaker 00

  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?

• 1:10:41Speaker 01

  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 in 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, 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 gonna wake up and realize they need over the next few years? And ideally, we have that at that point.

• 1:11:25Speaker 01

  We've been pretty good in general at like, you know, anticipating demand and building products in advance of it with the serverless functions and 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 around like, culture and company building and stuff like that. I mean, we recently crossed 200 people. We're, 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 add 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 and they know, you know, what makes Modal successful, what they can do.

• 1:12:05Speaker 01

  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. They're like, maybe a little bit nervous to like, talk to the CEO and like, all this stuff.

• 1:12:49Speaker 01

  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, fake 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.

• 1:13:07Speaker 00

  How many people are you guys trying to hire over the next year?

• 1:13:09Speaker 01

  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. 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 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, I don't know. I think by the end of the year, maybe 250, by the end of the next year, you know.

• 1:13:53Speaker 01

  But, but like, you know, we're gonna 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.

• 1:14:00Speaker 00

  And I'm gonna assume most of those are engineers?

• 1:14:02Speaker 01

  Mostly engineers. We're ramping up sales a lot, too.

• 1:14:05Speaker 00

  Okay. What kinds of engineers?

• 1:14:08Speaker 01

  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, and cloud and, and stuff like that. So that, that's, 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 LM inference space. So now we're dealing with a separate set of concerns around, you know, CUDA and like GPUs and, and MFU, you know, like, maxing out the tokens that you can get per GPU and, and a lot of that things. And, you know, even though I actually like, will say a lot of the work in that space,

• 1:14:49Speaker 01

  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 heavy problems. But yeah, a little bit 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.

• 1:15:15Speaker 00

  For your customers or for internally?

• 1:15:17Speaker 01

  Both. I, I think it's like a little bit like with the FTEs. Like, I, 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 FTEs and with the research team, initially, you're gonna 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 on a lot of stuff right now in the post-training space, but also in the GPU performance space.

• 1:16:01Speaker 01

  So a lot of cool stuff, a lot of cool stuff.

• 1:16:03Speaker 00

  Back to the product strategy stuff that you were talking about?

• 1:16:07Speaker 01

  Yeah.

• 1:16:07Speaker 00

  Yeah, like, what are your thoughts? Like, what do we need over the next year, two years?

• 1:16:11Speaker 01

  I think it's incredibly hard to say. And, and if I said like, I know exactly what we need to build, like, I think I would be like, delusional. I think what I've, what we've seen so far, you know, 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, of software. And we're unlocking a whole new type of applications that could not be built a few years ago. You know, doing, you know, all the GNI stuff, code that, you know, LMs that output code that, you know, it needs to run like in an isolated way. Like, there's so many like new primitives that are needed to, to work with GPUs and work with LMs. And, you know, we built some building blocks so far and, and, you know, that's unlocked, you know,

• 1:16:53Speaker 01

  a bunch of applications that the world, you know, has seen over the last few years, not just us, of course. But I think over the next few years, like, there's gonna be so many more cool things. And so I, I mentioned some stuff, like, I'm very bullish on training. I'm very bullish on fast jobs. I'm very bullish on, you know, certain things around like agents and, and low latency and, and things like that. But, 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.

• 1:17:30Speaker 00

  I want to go through some of your tweets. This will be like the last part of it.

• 1:17:33Speaker 01

  Okay.

• 1:17:34Speaker 00

  And we can just like, maybe if 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 in the cloud to something more ephemeral. One environment for every command and snap shotting in between, so you can pause, resume, clone. Yeah, I mean, we talked about this a lot.

• 1:17:52Speaker 01

  Yeah, we talked a bit about it, right? Like, I, I'm fully, I think, I think the median sandbox in Modal today is like eight seconds long or something like that, like, which is kind of incredible when you think about it. And, and over time, I think it might get even shorter. We want to support long-running sandboxes too. Like, I think there are some 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 mess with paralyzed things and reducing the overhead of starting and stopping sandboxes?

• 1:18:21Speaker 00

  I don't know. If I ran an IPO-ready startup engaged in CapEx arms race with a competitor, it would be nice to coordinate and paste arms race a bit.

• 1:18:29Speaker 01

  No comment.

• 1:18:31Speaker 00

  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, 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.

• 1:18:44Speaker 01

  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 a salesperson. And a lot of workflows in real life, like, they don't look like these, like, flat, you know, rectangular workflows. Like, they scale up and down throughout the 24-hour 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, and I do think it's a legit problem for this space. It's like, the only, the prime way to get capacity is to buy these rectangular blocks. It's not great. I think the world needs to evolve. And that's fundamentally, I mean, I'm very biased, but like, that's why I think Modal can play a very large role

• 1:19:29Speaker 01

  in that sort of, you know, whatever you want to call it, the 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 in aggregate. So that's, a lot of the value prop of Modal is exactly that.

• 1:19:44Speaker 00

  Would you say that's a differentiator? Like, the

• 1:19:45Speaker 01

  I think so.

• 1:19:46Speaker 00

  Oh, really? Okay.

• 1:19:47Speaker 01

  Yeah, massive differentiator. Actually, in fact, like, I think Models, like, if you want to be, like, very sort of reductive, like, you can break down Models' value prop into two things. One is the 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 built all this work around, you know, scaling up and down extremely fast, you know, all this stuff around GPU snapshoting, working 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.

• 1:20:26Speaker 00

  Every month, I keep saying, or sandbox revenue might apply to it, but it keeps going up 30% every month.

• 1:20:31Speaker 01

  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 they kind of plateaued. And then they keep growing even more. So 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, 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 insane. And, you know, we're obviously very happy about it. We have, like, a very large team investing 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 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,

• 1:21:15Speaker 01

  taken a bet that revenue would be this large from sandboxes and the market demand would be so much.

• 1:21:20Speaker 00

  There is 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.

• 1:21:32Speaker 01

  So, I mean, if you think about all the Chinese labs and, you know, and 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 launch a new model, demand would go up a lot. And then another lab launches a better model and then the demand goes down.

• 1:21:58Speaker 00

  Yeah.

• 1:21:59Speaker 01

  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 model. 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 Modal to like, you know, right? Today, we're running, you know, X number of thousand GPUs for one model. Tomorrow, we need to shift, like, you know, 50% of that to another model. Sure, that's just like, that's what the system was built to handle. No one even needs to tell us to do that. It just magically does that.

• 1:22:39Speaker 00

  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?

• 1:22:49Speaker 01

  I'm not sure if that's necessarily true, right? Like, is Taylor Swift a commodity? 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 year when she goes and, like, records, I don't know if this is the best analogy. And then she releases a new album, you know. So I, 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 amortize, like, the cost of training those models, it could still be like a decent business, I think.

• 1:23:24Speaker 00

  So you don't think that the intelligence is, like, plateauing?

• 1:23:27Speaker 01

  No, I don't, 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 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's going to be really good at whatever. Cooking food or, I don't know, like, things that have, like, more, like, diffuse targets and maybe longer feedback loops or whatever. I think humans probably, like, my feeling is, like, will be

• 1:24:12Speaker 01

  quite good and quite competitive with, with models for a long time in those domains. But, you know, certainly, you know, AI models, they're, they're getting very, very good.

• 1:24:21Speaker 00

  Cool.

• 1:24:21Speaker 01

  As I'm sure everyone can agree with.

• 1:24:23Speaker 00

  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?

• 1:24:34Speaker 01

  I don't know, but I think anything that has, like, kind of a verifiable.

• 1:24:38Speaker 00

  A computational bio, I think you mentioned.

• 1:24:39Speaker 01

  Yeah, computational bio. I think those are like, not exactly verifiable, but there's like certain ways you can sort of run, like, you can use an LLM to output predictions. Then you can do, like, computational, like, like, methods to, like, simulate the 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 you just 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.

• 1:25:16Speaker 00

  Cool. I mean, those are all the questions that I had. Is there anything you wanted to talk about that I haven't?

• 1:25:21Speaker 01

  I'm good. This is great. This is a lot of fun.

• 1:25:23Speaker 00

  Awesome, thank you.