Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal

And arguably, like, a lot of what sort of later became the genesis of Modal was, you know, just building AI applications and realizing, like, the infrastructure fucking sucks. Like, there's got to be a better way to do that. So a lot of sort of, you know, the rationale for Modal was, like, kind of building a tool I always wanted to have throughout my years at Spotify. Yeah, so Modal was more of an offshoot of the stuff you learn at Spotify, not much from Better.com? Better.com was a little bit of a side tour. It was kind of a rollercoaster, a long story about the company. Talk about it separately.

But I was a CTO for five years of Better and grew to tech team from, I think, you know, two to three, four hundred. So I learned a lot about management and building teams and hiring and also learned a lot about developer productivity, thought a lot about tools and things like that. Did a lot more like full stack stuff. But yeah, I kind of came back to my, was close to my heart, which is infrastructure and AI and machine learning after that. What was Better.com doing again? Mortgages. Okay. Yeah. How did you get into that? I mean, we don't have to spend too much time on that. Yeah, no, it's fine.

Like this guy, Zach Frankel, who I didn't know at that time, ended up recruiting me to Better as a CTO. And the company was 10 people. And, you know, for whatever reason, you know, the founder of Vishal, very interesting guy. Very, very smart, a little crazy in mostly positive ways. We got very well along. So, yeah, we started building it in 2015, and I spent five years growing that company. And then, you know, eventually it turned out that doing mortgage 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 had already left.

That was like 2020, 2021 when you left? Yeah, something like that, yeah. so okay you left before you had the idea for modal or you left and then you started it's hard to say i i had a lot of random ideas when i left better and and i i actually wasn't sure if i wanted to start a company i was kind of just you know during a pandemic i 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 it was kind of working like part-time, like hacking on certain things and wasn't sure.

Maybe I should do some consulting. And I started building stuff. And I just got like super pumped about this idea that eventually became modal. But actually, modal was like, 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 but modal was like the first idea i was like this is kind of cool like i should do this this is like this could be a really cool platform were the other ideas like also in a similar vein like developer tools the first thing was like some sort of web scraping thing which went nowhere but actually you know in hindsight maybe could have gone somewhere and was this before the chat gbt launch yeah yeah 2021 okay so chat gbt was late 2022 right then i started building so maybe actually to rewind a little bit context like when i worked at spotify i released an open source framework called luigi which is like workflow orchestrations is sort of the same category as airflow and prefect index or sort of dated category to some extent like people don't talk about as much but for a while it was like you know got a lot of attention uh anyway i never turned it into a business uh but a couple years later when in 2021 when i was thinking about business ideas or things to work on i kind of

came back to workflow orchestration and i started hacking on a bunch of ideas of you know running code in the, you know, like 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 the compute. It's like executing the compute. And I was like, that's kind of a bigger problem actually to solve. So in a way, I actually like, you know, left it on the shelf and then said like, actually, I'm going to like go to this other thing over here and I'll go back, I'll get back to the workflow scheduling a little bit. But, but like, you know, I never got back to it long story short, and ended up building modal, which is like, the focus was more like, let's just figure out a way to make it easy to run code in the cloud. And the focus was initially like fairly general purpose. And actually, in a way, like it's always, we've kind of gone full circle more recently. But we focused on so many different applications. It's kind of the idea was like, let's just make it super easy to run code in the cloud, could be GPU code, AI, machine learning, And it could also be like data stuff and focus 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 StableFusion turned out to be a killer app for what we were 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 StableFusion model. And then that made us realize maybe Gen.AI inference is actually a really good use case for us. How were they finding it? I'm this compulsive tweeter. I was even more, now I'm too distracted by building a company. But I always tweeted a lot. I always blogged a lot. I think I had a little bit of a following. And so 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 and getting people excited and writing long blog posts about where the cloud is going and all these things and I call it a bit like founder-led marketing I always like feel like you kind of have to lean into like what you're good at and like what I'm good at is like I guess like just shit posting and being terminally online and so you know like work it's it's still like you know I think a lot of our growth is we're just kind of good at social media and just like being you know online a lot nice how long until you had your first customer it took a while also did you raise like early on we didn't raise for a while i actually self-funded it for a while uh even with up until the first few employees uh which

in a way was like kind of nice because it may be really like frugal about money and like wanting to see like ROI I wasn't taking out a salary for a while for a long time actually at some point it became a problem because I needed the money but we didn't raise for a while and until early 2022 raised the seed round so at that point I'd already been working on it for about a year. Akshay 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 uh revenue i guess that's when chadgbt came out yeah and stable diffusion then in our

Erik Bernhardssoncase uh 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 2022 and the next month we did like two thousand dollars a month

and then like january 2023 we did like 30 000 all of a sudden and you know so finally i felt this like massive just like anxiety thing off my chest i'm like maybe this is actually like we're onto something it feels like we're onto something and then 2023 we grew very fast and i guess we've been growing very fast since then basically that 30k a month moment was that just like a couple big customers or was it like very like diverse 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 suspect it it was a company in new york called mage they're doing like a stable diffusion company so at some point you In the first, I think, first quarter of 2023, I think there were like 80, 90% of our revenue.

But then after that, you know, it started diversifying a little bit. And late 2023, we got 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. For the stable diffusion stuff, I remember using it for like Comfy UI. I'm assuming a lot of people were using Comfy UI, like running Comfy UI workflows on it. Some people were, but I think the people that were using Modal, they tended to be more like low-level stuff. They did complicated stuff. What they like with Modal is it's always been pretty hackable.

So they deployed their own custom workflows with adapters and custom inference and a lot of optimizations. And all the stuff we had done in order to get the developer experience to be really good turned out to be really good for also running production workloads. Because autoscaling turns out to be actually a very similar problem. You want to scale up containers up and down very fast. So we just happened to be, like, very well positioned to capture a lot of those workloads in the early days. Did tools for autoscaling, like, not exist? Like, why did you have to build those yourself? In general, like, my thesis is, like, you know, every new sort of era of, like, you know, technology kind of runs up against limitations of tools.

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

You can build these things on top of Kubernetes and Docker and these things. But those technologies are built for a very different type of workload. They're built for a very sort of steady back-end workload, so you have an even 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. But now you're entering this new world, and you have 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 it turns out like a lot of these tools, like, you know, like they were just not built for that.

So we felt that, you know, we had to rebuild a lot of that, you know, building our own. I mentioned the file system, but we also spent a lot of time over the last few years, like building, you know, some really. cool stuff around like CPU snapshotting, GPU snapshotting, and we built an autoscaler and scheduler 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 software on top of, you can't build like a wrapper on top of the old stuff in order to do the new stuff. You kind of need to start over a little bit from scratch. So I guess like the autoscaling stuff, like, Like, did you see any, like, weird behaviors on the platform?

Like, was there anything that, like, broke while you guys were scaling? I mean, capacity has always, from time to time, been a challenge. Like, how do you get, like, you know, hundreds and thousands and tens of thousands of GPUs? So, that's always challenging. We had to go multi-region very early. So, I think that was, like, kind of a fateful decision in hindsight, like, doing that and kind of just biting the bullet and building it. Like, we always thought of, like, our capacity management is, like, one big pool. Like it's like one big pool over the entire world. Like we don't think of it as like separate clusters running in each region.

And so that was like one of the like very consequential decisions we made earlier 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 modal to be 100% uptime, you know. And I think recently we've basically hit four or five nines. But throughout the history of modal, of course, we've had, you know, run up against scaling issues. Yeah, the GPU market and well, actually before that. So you guys use AWS, like GCP, like all these cloud providers underneath.

Like, do you also buy any infrastructure or no? Let's talk a bit about like cloud capacity, because I think it's actually changed a lot. Like in the first few years, we did, we got almost all the capacity pretty much from the hyperscalers, spot and on-demand capacity, mostly spot capacity. running in all the different regions, using initially AWS, but also GCP and Overtime Azure. We started renting some capacity from Oracle pretty early because we wanted A10s and A200s, and we got really good rates on those. And over time, we added a couple more clouds, like doing reservations. But still up until actually end of last year, most of our capacity was either spot or one-year reservations.

Spot means you're just like renting it right there and then that's it. Exactly, yeah, and it's also preemptible, which we had to deal with. But it sort of works in aggregate because you can sort of just aggregate capacity from many, you know, hundreds of regions, basically. So that model worked really well until we started running up again. I mean, first of all, like, modal started growing, like, very fast, and 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 have to work with a much larger set of clouds.

We actually work with almost 25 clouds at this point. And in many cases, in order to get the capacity we want, like, we have to go and do, you know, three plus year deals. You have to pay a meaningful amount of money up front. And you're locking in pricing on a three-year term, right? So, you know, like, 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 all these things. We don't own capacity. We rent all the capacity so far, you know, on typically these three-year deals. And that's like a big decision. Like, should we buy? Should we, you know, or should we keep renting? I'm actually like, not like mega dogmatic about it. I think renting is nice because there's a certain amount of capital efficiency to it. If you're buying, you know, like, I mean, first of all, there's an obvious like operational complexity of it.

Like you have to, you know, deal with vendors and suppliers. and, you know, power 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 you can raise a billion dollars and go buy hardware for it. That's a very diluted 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 pay the interest rates. So that's also not trivial. But, you know, like, we look at the economics, We look at the operational complexity, we look at the risks.

It's a complex trade-off and we're open to buying. There's other constructs too. To get the capacity we need for 2027, it's going to take a lot of work on going a lot of different avenues to lock it up. So do you have to hire a lot of finance people to do all this stuff or not really? I've had to spend a lot of time on this and we've had to spend a lot of time on this. And I do think over time, I mean, 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 investments starting to become like several percentage points of GDP of the U.S.

and, you know, starting to see like interest rates in America shooting up in the last few weeks. And I mean, like at this stage, like, you know, and you go and look at the neoclots, like these are very, very sophisticated financial engineering companies. And so, yeah, 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, like a very lean software company, like doing the things we're good at.

Erik BernhardssonI don't think it's good in general for software companies to own hard assets, you know, in the same way as like, should you own your office?

Like, I don't think that's necessarily the right solution for most companies. But of course, like in order to get the capacity we want, like, you know, everything's on the table. 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 agree with that? Or do you think this is just like a moment in time? I don't necessarily agree with that. I mean, like, NVIDIA is delivering, you know, several hundred thousand black walls, you know, a month, right? And I don't want to say, like, the market is, like, 100% efficient, but, like, you know, in general, like, if you're willing to pay a high price, you can get GPU capacity, right?

Like if you're willing to pay more than everyone else and of course, Anthropic and OpenAI is buying up as much as they can. But fundamentally, like there's constantly supply coming on the market. And so I don't necessarily think it's like a fully cornered resource. Prices are going up. And I do think it's worth kind of reflecting on that as 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 and you know it's hard to say you know how long this is going to last you know could be another six months could be another 36 months like it's really hard to know how long and but but i fundamentally don't know if like it's true to say like you know you need to buy capacity in order to me that's a little bit 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 because you know scarcity it also means like we can charge more from our customers and and you know and the fact that we have a lot of reservations locked in at a rate means like to some extent we're effectively like long gpu prices already. So I don't necessarily worry too much about prices going up. I do worry more about actually prices collapsing. I don't think that's going to happen anytime soon. But I think that would be a quite bad thing for a lot of the players in the market. Why would that be bad? I think it would be bad for anyone who's locked in a lot of supply.

Yeah, because they paid way too much. They paid too much. So there's a risk of buying at the top, right? And again, I don't worry about that that much. I don't think it's going to happen anytime soon. But like I said, hardware prices in the very long run, they tend to go down. And so it's worth reflecting on that. Right now, I think your capacity is constrained. And that leads to, in economic terms, there's a scarcity rant, right? Who captured the scarcity of rent? I think that primarily goes to two places, mostly NVIDIA, of course, but also to some extent, NeoClouds. NeoClouds, in general, anyone owning capacity has better economics.

That's undeniably true today with capacity being constrained. Long term, in the very long run, I don't think that's necessarily entirely true. There's always going to be a little bit of a premium you can get from owning GPUs, but is it worth the risk? Is it worth the risk holding these very expensive assets on your balance sheet? I'm not so sure. But we're going to see. It's going to take a few years to get through this. And in the long run, we might own some. We might rent some. Maybe a combination. Maybe do some other crazy stuff. Joint ventures. Who knows? Maybe some complex SPVs or whatever.

Everything is on the table. How do you compete with OpenAI and Anthropik and buying GPUs in capacity? I'm assuming they'd want to buy up literally everything that's available. 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 neoclouts and a lot of alternative clouds, like, getting more allocation. And I think a little bit of what we see in the market is also, this is more speculation, but I think we see, you know, smaller clouds that are a little bit more sort of risky to work with being more of an opportunity for us. Like where, you know, maybe NVIDIA, sorry, 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 runs, so they need to have like very large blocks.

but there's plenty of capacity out there you know you can get 32 nodes in armenia you can get 128 nodes in indonesia like you know like we love that kind of shit like you know so we can go out there and like get a lot of you know smaller capacity all over the world pretty good prices is it pretty much just like if someone offers you like a certain number of gpus like that's all you care about is there nothing like oh you care about like how reliable it is you know is it just some like janky warehouse that someone has these in i mean sometimes it's kind of a janky warehouse it feels like you know but but like we do have like a spec sheet basically like when we talk to vendors the first thing we do is we send them a spec sheet we say we need this number of iops we need you know like the cpu needs to be at least at least you know have have this many you know whatever the pass mark scores or whatever it is we need to see like in a minimum you know north north south bandwidth, egress and ingress. So we do have a list of requirements. Often we try to do an acceptance testing period where we get one node and run some tests. Over time, to some extent, it also involves a little bit of a leap of faith. Sometimes we sign very big commitments,

tens or hundreds of millions of dollars, and we start working with the vendor that we still don't fully know. And over time, we'll see. And then I think 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

Erik Bernhardssonsemi-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 one there's I like more than others, you know, like and

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 we have, you know, support issues? Do they fix the issues we ask for, the turnaround time? If things go down, you know, how quickly do they fix it? Like things like that. Do you agree that the demand for intelligence is basically infinite? And if so, like why wouldn't you just buy up as much capacity as possible? Well, and then like if you overbuy, you can just like sell it to somebody else. I think it's very easy to say when there's like a scarce resource, like let's just buy as much as you can.

But I think, you know, first of all, like if we bought it today, we couldn't utilize all of it. Like we could utilize a lot of it, right? But you can sell it off to somebody else who wants to use it, right? I think the challenge is like if there was like something in the market that happens, there are a lot of correlations that happen at the same time. If, you know, prices going down and utilization going down and the market for reselling capacity goes, vanishes at the kind of the same time. I'm sitting here and like, I'm extremely aggressive, like buying capacity every day and like raising money to buy, you know.

So overall, I'm very bullish, but like, I do want to point out and like, you know, like there are tail risks here. And when those tail risks happen, like it could be get very bad. Part of it is like, maybe I'm, you know, I'm just old and I've seen this in the past. Like I actually had my first job in 1999. So I saw the dot-com crisis and I've seen the great financial crisis and things like that. And, you know, like I want to build a company that is around for like 20, 30, 50 more years, right? And so, you know, I think we can certainly go out and like buy an insane amount of capacity right now and trying to just corner the market.

But, you know, do you know about the Hunt brothers? It's like a legendary story in trading. They try to corner the silver market. It failed spectacularly. So it's cornering the market, I think, in general, can be not a good idea. But we are, in general, very, very aggressive. But we're ramping up at an aggressive plan that basically matches our demand growth and matches where we want to try to get to at the end of next year in terms of run rate. Are you only buying up NVIDIA stuff or also maybe a capacity from other types of companies? I guess like Trinium and... We're looking at it. I think the alternative accelerators are starting to get very interesting.

It's, you know, ironically, like 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, you know, AMDs are starting to get like really good scores on certain things. I think, you know, TPUs are obviously used very, you know, widely internally at Google seem to work really well. I don't know much about Tranium. 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 certain way, like software is like easier. And that makes me feel like, you know, if we just fix the sort of software layer for these accelerators, like I think there's going to be a lot of demand for them. And I think fundamentally, you know, with NVIDIA being such a bottleneck, like we'll probably see, you know, more alternative accelerators. That being said, you know, there's bottlenecks further down the pipe. I mean, you know, there's like TSMC and ASML, maybe like a deeper bottleneck to all of this anyway.

But, you know, long term, I think monopolies don't always last. And so I think 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. Yeah, what are your thoughts on some of those providers? I think they're pretty exciting. And you see them doing really well on certain workloads. And there's some interesting people doing these hybrid disaggregated inference where they run.

NVIDIA for the pre-fill and Cerebras for the decode and things like that. Oh, I haven't heard of that, actually. Yeah, and, you know, Grok, too. 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, you know, opportunity in these sort of, you know, more complex hardware setups. Are there any learnings that you get from some of these NeoClouds? like, I don't know, like Nebius or whatever, and like the way that they're doing things versus like what you're doing? I think one of the things that, you know, is very interesting with neoclouds is like, how can they expand so quickly? I think actually the most crazy example is like Nscale, 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, 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 it's not big or

court or opening out some some big buyer or whatever is it 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 dollars worth of chips uh we just need the money right now and then they go and raise the money and then they like you know go to like Supermicro and Dell or whatever and NVIDIA and like get the hardware and then they install it

Erik Bernhardssonand 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 they can scale very very well I think modal because we've always had a little bit more of a PLG focus and and not the sort of you know ability to lock in long-term contracts in the same way, it's a little bit more challenging from a financial perspective to scale that fast, even though I think, you know, in the long run, like we'll get to the same point. But I think that's been kind of a fascinating thing to see with these NeoClouds and especially like CoreWeave and those big ones, very, very savvy with, you know, how they raise money and how to, you know, think about GPU as collateral for loans and all these things.

What's the relevance of, like, I forget who it was, but like some like financial company was like oh we're 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 i mean there's a bunch of startups i don't know if you're thinking about that there's like silicon data and compute exchange and like orn and and a few other ones that are like basically like trying to get to like you know creating price indices and indexes and and trading derivatives and things like that i think it's interesting like i think with compute like it does there are a number of challenges with compute as an asset class like it's definitely not as homogenous as other asset classes uh you have different network and different locations and and different disks and cpus and all kinds of stuff and reliability i mean that being said like you can say the same thing about oil and like we kind of created like you know relatively commoditized you know tradable assets out of oil it remains to be seen. I do think at the scale where we are with the sort of

AI data center GPU deployments, we do need to be a little bit smarter with risk hedging and how we share risks and having more insurance and things like that. So I'm very happy that people are thinking a lot about this. That being said, I'm not entirely sure if we're going to see GPUs becoming whatever, the Henry Hub-like like, you know, Brent crude type, like, you know, super commoditized, like, tradable asset. Yeah. I want to shift the conversation to modal sandboxes. Yeah. I guess, like, before... Before we do that, what are the different products that Modal has? It's, I guess, like the main product, obviously, is like the GPUs.

Then you have Modal sandboxes. Is there anything else? I think of the two big products that we have is basically, number one is Modal Functions, and number two is Modal Sandboxes. So with Modal Functions, we launched that back in 2021. It was the idea of 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 a dedicated inference product or a GPU product per se. We never thought of modal as a NeoCloud and I think there's a risk of getting sucked in that direction and becoming a NeoCloud, like just selling what I think was rectangular, like here's a three-year, you get this many GPUs.

Modal's always been very good at, you deploy something, you can kind of scale it up, you have this very cyclical or bursty stuff. 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 modal functions was the core. We launched that in 2021. Like I mentioned, we started seeing product market fit in 2023. And now, you know, it's about two thirds of our avenue and empowering a lot of, you know, in practice, it's mostly inference, but you can use it for all kinds of other stuff.

Modal sandboxes, we launched that in 2023. three. And it's actually kind of, you know, took a while to see product market fit similar to functions. Why did we launch it? We realized a lot of people we talked to were using LLMs to output code. This was 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 and primitive and things like that.

So we got kind of excited about that idea and Nakshara 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. And it was 2023, so. Yeah, it was 2023. And like, you know, like some people picked it up. Like we had some early users who kind of enjoyed it for certain things. But like overall, it was a pretty insignificant source of revenue going into the summer of 2023.

Erik BernhardssonSo two years later, we even discussed, do we even need this product? Should we shut it down?

Then it just started exploding. So 2023, and I think what really happened in 2023 was LM's got really good at generating code. In 2023? 2023. I mean, it was around that time. I feel like that was when they went from being able to refactor functions and 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 Lovable 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 consider it like basically like our second product. And to me, it's like really exciting because like even back in the early days of modal i always knew that modal you know is about like creating a whole toolkit of products like we always wanted to be almost like a cloud you know you have the storage and the compute and a bunch of different ways to work with it we always wanted to be a multi-product vendor but that wasn't necessarily true in practice until like now 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 we're working on a bunch of other stuff so to me that sort of evolution into becoming like a multi-product company i think it's been amazing because that was that was always so core to the vision even in the very early decks i had a bunch of slides on this uh but but it wasn't necessarily true to the external world until recently.

Do you have anything in the works that, you know, you think is going to be like that next product? Any predictions maybe about the market? Anything like that? I mean, I'm very bullish on training. I think training is like kind of an underserved market. Obviously, there's like a lot of focus on like very large, 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. I think there's going to be a lot of, you know, once we sort of get the audio, like the speech-to-speech models working, like the voice models, I sort of expect that to be a very interesting market. Then, of course, all this agentic stuff, right? In a way, I have mixed feelings about the word agentic because I feel like in certain ways, I think all software will become agentic.

So it's almost like, to me, what does it mean to be agentic? I think all software in a certain way will become AI-powered over time. But clearly, there's a set of building blocks of doing more sort of LLM-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. You know, we want to give you like amazing generalizable battle-tested, you know, general purpose, composable building blocks. And I think there's some really interesting stuff also, like, you know, with the agent use cases.

That's interesting. You bring up training, because I think in another podcast, maybe like a year ago, maybe like two years ago, you said like training you were not focused on like specifically like what changed in the market i mean we've been building a training product for multiple years so you know on and off so 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 we're like i mean now we're like 150 engineers but you know a year or two ago we were a much smaller number of engineers and so but just virtue of you know being smaller we had to focus a little bit more and so inference became like our sole focus and was our sole focus for many years but bigger now like we we do have opportunities to also place bets in other markets i'm also really interested in you know kind of continuing the the list of things post-training reinforcement learning i think there's some really exciting stuff around that um i think there's also many other like stuff around like storage and building data warehousing and feature stores and real-time streaming pipelines and and all kinds of like i don't know i think the long-term view of of modal is like basically like you want to build some cool

AI shit like you know we got you covered like you know we got all the building blocks like 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 is 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 I want to build When you say training are you focused on more of like the bigger enterprises doing huge training runs? No they're going to go and like you know do these like you know big hyperscale or rather NeoCloud mega deals and get, you know, 100,000 GPUs or whatever.

But there's like a little bit of an underserved market of like, in my opinion, you know, like researchers iterating or like people training smaller models. There's, you know, text-to-speech and speech-to-text models. Computational biotech is actually a market that we see a lot of demand from. We have a lot of customers in that space. Training relatively, you know, modest models. You can train on 64 GPUs or whatever. Recommendation system. A lot of sort of traditional machine learning. There's a lot of things where you don't need 100,000 GPUs. That market, I think, we're not going to focus on that for a while.

But I think there's a lot of stuff in the slightly smaller market. markets that are relatively underserved. It's very hard to set up training clusters. Yeah. Have you noticed, like, since, I don't know, like, GLM 5.2 or, like, maybe, like, Kami 2.5, that, you know, a lot of people are using those and, like, fine-tuning them? If anything, I've actually seen the other direction. I feel like fine-tuning the value prop a couple years ago was like, yeah, you know, open source models, they're not quite as good, but you know if you take you know small model and you fine-tune it on your specific domain you can actually reach the frontier in terms of you know quality and cost uh today i i would say like the open source models are so good that like you can basically just like swap them out and just run you know instead of you know the the frontier ones you can run you know kimi k3 or or whatever deep seek 4.1 or glm 5.3 and they're like basically almost as good as like the frontier model at a much lower cost so we still see a lot of demand of for for posturing and i do think it's a really interesting space and we're working on some some products in that space you know both sort of sft and but also reinforcement learning and things like that i would also say you know fine-tuning

is no longer the thing that you kind of have to do like it's it's very optional like you can open source models like work really well just off the shelf too back to sandboxes actually i forgot to ask you so like what did you have to update about the sandboxes that you guys um were offering uh that like existing sandboxes did not uh did not allow for 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 like you know scaling and reliability and doing a lot of stuff and i think that's why that explains to a large extent like why we have you know in my opinion i don't have numbers but like i think we We have by far the largest market share in this space.

We're just very good at running hundreds of thousands or even millions of sandboxes. I will say, we've had to rewrite our systems a bunch of times in order to handle that scale. But we were always very good at it. There are other things over time. I mean, the sandbox space in general, it's not the most differentiated space, but we're working on some really cool stuff. Over time, 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, you know, better primitives for like more interactive, you know, piping data back and forth between sandboxes, working with file system. We've had to spend a lot of time on snapshotting. I think a lot of the things that over in the long run will differentiate will be like, you know, Really cool things like forking sandboxes, snapshotting, resuming snaps, like live migrations. In general, I think sandboxes, the nullity of sandboxes is always this sort of ephemerality. You can spin up sandboxes for a few seconds. And that's something, that's a string I want to pull more in.

I think there's a lot of really cool stuff you can do. You know, kind of decoupling the agent and the harness decoupling it from sandboxes, having agents control many sandboxes, forking sandboxes, you know, snapshotting things like, you know, like running basically like very short lived, maybe one per command, like things like that. So I think there's a lot of cool stuff there. We've already spent a lot of time doing that, you know, around snapshots. But I think there's a lot of cool stuff to be to be built there as well. You brought up snapshotting a couple 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?

I think the big thing for me is like, today, a lot of sandbox workloads are kind of like lift and shift like people think a lot about sandboxes almost like you know i have my dev box now i'm going to shove it in the cloud i'm going to have an agent control it maybe i'll run the harness inside the sandbox i don't know if like long term that's like the right way to think about you know on like compute for

Erik BernhardssonI think a much better model would be you're going to have this like very long running agents, which almost like to me are like kind of durable execution workflows, like they're going to run, you know, 24 seven, you know, every once in a while, they're going to run commands and, you know, having the ability to like spin up a sandbox in, you know, 100 millisecond, run a few commands, and then like shut it down and like snapshot it.

I think the faster you can get that like you know wake up you know go to sleep cycle the more you can sort of create sandboxes that only run for like a very brief period and that will make compute much more efficient and it will also enable you to do some really cool things you know like forking and moving things around and you know having like imagine you know like clawed code instead of you know having sort of a very linear flow like sequential flow like kind of branching and running hundreds of agents at the same time. I think a lot about CI is another illustrative example. For most of the history of CI, talk about GitHub actions and things like that, you've had a very sequential flow.

You slowly install a bunch of stuff, then you run the test suite, and then generate some reports or whatever. And it's like you can break it up into shards, but it's kind of annoying. What I would love to do is take your entire test suite and just run every single test in its own sandbox, right? like and just paralyze the shit out of that around like a thousand sandboxes why can't that be done i mean the problem is like there's too much there's too much overhead around like you know starting and stopping sandboxes so i think the more like you 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 want in parallel so you know i'm an infra geek Like I said, like I get very excited about these things.

You know, once we figure out all these primitives and the right building blocks, I think it's going to unlock a lot of really cool stuff that, you know, we previously could not do with software. Why hasn't that already been built? I would assume even previously people would want to parallelize as much of their CI as possible. You can't, but the overhead, it's like Amdahl's law or whatever, right? Like the overhead of parallelization right now is very high because like right now, like in GitHub Actions, for instance, like we parallelize in the modal test suite, But what that basically means is now if you take your test suite and you split it up in two, now each of those shards both need to do all the installs and the test setup.

And that takes a long time. 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. I think you guys have a partnership with Anthropic and they're using sandboxes for something, like modal sandboxes. Yeah, I mean, the managed agents with Anthropic, there's a sort of bring your own sandbox integration. So you can use modal for those, yeah.

Oh, cool. How did that come to be? 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. Also, I feel like we have to bring up the hugging face incident.

Are you okay talking about that? Yeah, I mean, it's all there. Yeah, yeah. So, yeah, could you just briefly describe, like, what happened? And I think they were using the modal sandbox, but it wasn't your, you know, it wasn't, like, a fault of the sandbox. Yeah, exactly. I forget all the details of the exploit, but, I mean, you know, I think what you're referring to is the fact that, like, modal was involved. Yeah. And so, you know, just to comment on that, like, basically, the agent. running inside that exploited hugging face, they discovered a vulnerability in an app that was deployed on modal.

And they used that as 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. It was not modal that was hacked. So it's like AWS, a lot of people run stuff on AWS, occasionally those apps get hacked. Does that mean AWS got hacked? Like, no, of course not, right? And so there's a lot of people running apps on modal and, you know, one of those apps got hacked. And I think, but like, there are some lessons we can learn. And I think one of the things we've been reflecting on is like, could we have more sensible, you know, security restrictions by default?

I think to use the AWS analogy, 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 it doesn't necessarily pose a security problem for AWS per se, but a lot of their customers accidentally would leak data. And so S3, it's much harder now to make S3 buckets public. And so in a similar way, it's like we're exploring, can we make security a little bit more restrictive by default without necessarily destroying the magic that makes modal good? And I think there's also, you know, it's kind of a separate sidetrack is like, agents are getting really good at cybersecurity.

So we've actually also, as a sort of, you know, separate sort of learning from this incident, like we started using agents internally quite a lot to try to find vulnerabilities. Software 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. How exactly do you get the agents to run through the code base? You're just like, hey, find security vulnerabilities? Well, no, you just put them in a sandbox and tell them, can you try to explain?

Just run through the entire thing? Yeah, can you just try to escape from the sandbox? It's like escape the room, but for agents. Cool. When did you start doing that? We started doing it a few months ago. And it's pretty exciting. Like when Fable, I guess, came out? Yeah, roughly around the same time. Okay. I mean, my view on 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 citizen, 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. Do you think that like all of the cybersecurity, the big cybersecurity companies are just cooked? I don't know if that's true. Because, I mean, you're asking something that's like very far outside of my core expertise. But when I think about like cybersecurity, a lot of what you're selling is effectively fear. And that's almost like a branding and like marketing strategy, as much as you know, obviously the core products but i think you know brand and and things like that are actually like arguably like more valuable in the era of ai so i 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 i just ask because like yeah you're doing it yourself you're not going to one of like the palo alto networks or whatever but i guess like it's only like giant enterprises that care about that also they can

blame somebody if something goes wrong that is true yeah yeah you kind of want to hire in many cases just like an established vendor uh in order to blame them i i mean to some extent it does reduce a little bit the need for us to 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 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 Like, we can bring in pen testers who are really good at using agents and, you know, find vulnerabilities even faster.

I don't know. I'm not that, you know, I'm an optimist. I started a company, so, like, you know, I'm generally, like, optimistic about everything. But, like, I certainly don't think humans are cooked or companies are cooked. Like, I think in the era of AI, a lot of companies are going to figure out, you know, how to use AI to be even more efficient. I want to transition a little bit to, like, developer experience. Because, like, early on, I mean, even now, like, you guys focus a lot on that. Yeah. And you've talked a lot about that. Does developer experience even matter anymore? I think it still matters.

And I think you're sort of, you know, like the context, I think you're official, like the thing you're talking about is like now with agents, right? Like does developer experience. Like the docs are already there. The agents are going to look at the docs, like all that stuff. To that point though, like almost all the things that make developer experience good also makes the agent experience good. It's kind of what we've seen in practice, which is like things like having good docs, having descriptive error messages, having fast feedback loops, right? Like, I do think there's certain things have changed.

The other day I was like setting something up with Cloudflare and it's like, I find Cloudflare like notoriously annoying to work with sometimes. And I threw an agent at it and it was able to like just configure a bunch of like, ask, you know, things in Pulumi 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, So 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 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 and needs to be more programmatic. I think that's always been a thesis about sandboxes is that, you know, like things that you previously had to configure using YAML, that should just be an SDK. that should be more like imperative like launch a sandbox like do stuff with it tear the sandbox down right like rather than sort of declarative using using you know some sort of dsl it also pushes like a little bit of a different interface that maybe previously didn't exist i think in either way like sdks are as relevant as they as they always were if not more is there anything you changed in your product just because like you know most people are just running agents on on it? We do. I mean, we also, we actually do run a lot of internal tests where we have agents, we tell agents to like build a modal app and run it. And I mean, a very concrete thing that

Erik Bernhardssonis 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 it's kind of hard to nudge them away from that. So we've actually had to be a little bit more conservative going forward. Like, you know, over the last year or so with how we deprecate and remove features, it takes, we have to be very careful to update every single example that's out there online. Yeah. And, you know. You have skills, right? Yeah, there's like stuff like that. But like, there's also an insane amount of like modal code that's now like online too, that goes into the training data. I've seen like that to be like kind of a, you know, a little bit of like an interesting example where like, another thing, by the way, too, is like, there's obviously like to sort of 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 so 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 spend a lot of time, like, doing those things to make sure. I mean, what do you do about that? Yeah, you say, hey, like, you know, build a stable diffusion thing. Does it recommend modal or not? Like, yeah, what do you do about that? Yeah, I think that's harder. I think, you know, it's like search engine optimization. I think it's primarily through content. It's like you just have to put out a lot of content.

You can also, to some extent, like sort of see the perception, I think a little bit more. Like you can ask an agent, like, you know, how does, you know, rank all the sandbox vendors by reliability or rank all the sandbox vendors by price and sort of see. And it does feel like agents are a little bit more prone to like misunderstanding pricing and, 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 because we know they're all indexing like everything that's out there and that's talking about modal. Have you thought about just like making a bunch of GitHub repos and having the agents like write a bunch of modal code in there?

Have you tried that? Slop cannon. I think the truth is like that is probably something you can do. my guess is like lm training data today is probably quite insensitive to like what's like ai output in the first place and you can probably like create a lot of slop like we thought about it like i don't love that idea but like in theory like much like you know seo in the early days and still to some extent like auto-generated content is like a very effective strategy i think it's possible like you know you could you can do those things too yeah i want to switch to fdes apparently you guys hire like forward deployed engineers like what do you do we're in a new era of like building applications and and and you know things that we couldn't build you know just a few years ago and we see a lot of companies that you know they need a little bit of help right like you know and so we started investing in fdes a couple years ago but it but it's really been this year we've

Erik Bernhardssonkind of scaled it up a lot i think we started this year having like four fdes now i think we have like 24 or something like that so we've grown it a lot and they do it you know it's more it's not

exactly the pound term model but they do a lot of like heavy you know sort of customer engagement like they work with a lot of customers on performance tuning especially with lms we see that there's so much benefit from like you know sitting down with customers and training draft models and tweaking uh you know the parameters and dlm and sglang um they often you know fix bugs or not fixed bugs, but when customers run into issues, they often work with customers trying to figure out how do we work around that. Another thing we've used FDs a lot for is kind of proactively, if we see certain customers over-provisioning with modal, like customers requesting too much CPU or too much memory, for instance, that's kind of expensive.

And we try to catch those things and we try to sit down with those customers like, yo, you're spending a lot of money on modal right now, we can actually help you save money. And then, of course, with active deals, we help them migrate code from other platforms, things like that. I think we never have the intention of becoming a service-heavy business. I think the goal for FDs is A, is to be part of the sales process and closing deals. B, is to serve customers and do some customer support. but like i actually think like the most important thing is like c it's like bridging gaps in the product that we later want to turn into like real features so i think to a large extent you know anything an fde does you know my question is always like long term how do we turn this into products so we don't need an fd for this yeah do you guys hire pms Product managers? No.

Would you say these FTEs are kind of like pseudo-ish PMs? It's a little bit of a different function. I think certainly on the more customer-facing side they are, but there's on the more inward internal side, there's a separate part of product management, which is more like, what are the next building blocks Modo should invest in? And then there's something in between looking at maybe 10, 20 different customers. They're 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. I do think that a lot of companies in the past hired product management, product managers too eagerly. And I think there's a risk that you kind of, I can sort of see it in a lot of products. Like certain products, like you can tell, like there's just like eager product managers that just thought of like kind of like widgety things and just ship them. I think doing product management at an infra company really well is very, very hard. Because I think it takes like a very deep, you know, level of strategic thinking, thinking about like, what are the foundational building blocks?

Where do we want to be in one to two years? Because the truth is, building infrastructure is very hard. And how do we make sure we don't end up with like 35 features that are like kind of similar looking, but all do like a little bit slightly different things? That sort of level of thinking is something that, you know, is very rare to find. And for that reason, we haven't aggressively tried to build up a product management team yet. Yeah, that makes sense. How would you say your teams have been evolving maybe like since Jenny and I? Because I would assume, yeah, I mean, you obviously worked at like Spotify, you had like a certain team structure, have your team structures like evolved a lot now that, you know, we have coding agents?

In a funny way, like, yes, I do think team structures have changed a lot, but I'm actually not convinced it was like Gen AI driving that. I think in particular, like one thing I've seen a lot is that there's been sort of a focus, like a swing back towards being in IC that I actually think is very, very positive for the space as a whole. I think, you know, if you look back at the space like 10 years ago, 20 years ago, everyone wanted to be in management because that was the way you would get more money and more, you know, more recognition and more prestige. I think that shift actually started even before Gen.AI.

But I do think it's like heavily accelerated with AI. Like, you know, we make it very clear to everyone at Modal that like manager IRC, it's like there's no difference in prestige. Like everyone is just a member of technical staff. you know it's about ultimately your contributions and we see a lot of ex-managers you know becoming ICs and 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 pretty including me I still write a lot of code at modal and Akshat too and 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 but i actually don't know if it's like necessarily cost by gen ai but i think gen ai has like greatly accelerated it because it made it even lower the cost of like writing code is even easier so if you have like non-technical people like committing code like how do you how do you review that like how do you guys do code reviews we do code reviews but it's we think of it as optional i mean for most my view is like code reviews in large part is like people in many cases trying to just escape the the accountability of like

you know merging things into production and 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 and things like that or 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. 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 especially at an infra company yeah and 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 they have to have extremely good test coverage agents are actually really good at writing tests these days which was like you know a few years ago i thought like one of the major things missing in agents now they're actually quite good at like because i think it takes a little bit of like sort of kind of like need to step out of what you're trying to do for a second and be almost a little bit like devil's advocate to write good tests but now they're actually quite good at writing tests too so i think i think all these things help do you guys use like frontier models for coding or do you guys have you guys experimented with uh like token source ones we experiment with everything

But yeah, I mean, you know, initially it was a lot of cursors and, you know, of course, Claude and Codex. And now a lot of people are using, you know, Nightshift and all these open source stuff or other stacks. So I don't necessarily have like a strong point of view. Like I want people to experiment and try different things. Like we, you know, use frontier models, use open source models. We're trying to do a little bit more dog floating internally now that we're actually running inference for a lot of the open source models, like a very large scale. I think it's also helpful to like use our internal endpoints for a lot of agentic development.

So we try to promote that a little bit. Something I read maybe like, I don't know, a year ago, maybe like two years ago, you were saying that you didn't think that there was going to be like a big on-prem business for you guys. Have you maybe like changed your opinion on that? No, I have not changed my opinion. I think, modal to me, I'm a cloud maximalist. And maybe to take a step back, like when the cloud came in 2008, 2009 or whatever, My first reaction was like, that's crazy. Why would I want to take my code and run it on someone else's computer? That's insane. And then six months later, I was doing it.

And I think it took me six months to change my mind. It took probably enterprises another 15 to 20 years. But you look at vendors like Snowflake or AWS, and I think there's many examples of Datadog, etc. The cloud is clearly winning. right like and and the multi-tenant systems are clearly winning and you know to me on-prem looks increasingly like sort of a legacy business model and 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 moat 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 from our perspective also like the fact that we have full control over our entire infrastructure stack we rent all the hardware we deploy we manage all the infrastructure we deploy and like manage health and observe you know all the metrics and all the hosts it means we can iterate very fast and build a greater product and i think that's very very

valuable versus you know customers in the past who had enterprise editions that they had to deploy on customers environment they had to be 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. 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 I think there's, like, maybe separately, like, another thing which I think is interesting, which is, like, in theory, BYOC might also be a way to work with and get capacity in certain ways. Like, we sometimes work with NeoLabs, and those NeoLabs already have very large capacity reservations. And, you know, the question is, like, you know, how do we get them to run modal when they already have capacity? and BYOC might be a solution. We can still keep the control plane in our environment.

And the other flip side of that is like, we could maybe even run like other people's stuff on that capacity. So there's some like kind of fun stuff you could do in theory where like you can sort of, you know, like kind of aggregate all that capacity into one big pool. So we're excited about some of those ideas, but in general, like I feel like on-prem, I don't know if we're ever going to do, but BYOC like maybe bring your own cloud. I keep saying BYOC, I should explain it. executives um let's say hypothetically you are going to raise i should tend more money like soon what would you use that money for i mean like i mentioned like in in this capacity situation the a lot of the money a lot of the cash flow i should say that's required is is capacity prepayments right and so that to me represents the best use of of cash today uh we're ramping up capacity very aggressively, maybe we move into, you know, other types of deployment models in the future, ownership or whatever. But I think there's always going to be a cash component of that, like some sort of down payment or prepayment or some sort of that. So that to me is the greatest use of cash right now. There's of course other things like, you know, salaries and office spaces and

stuff like that. But that actually, in the grand scheme of like our financials, like it's basically a rounding error, right? Yeah, interesting. Do you think that's going to change? No, I think, you know the sort of operating leverage that we have in this business is kind of insane like you know we have 200 employees you know they cost a lot of money but you know the the gpus cost a lot more money yeah if you had to say like what are the top like i don't know like three things that are your biggest focus or the business's biggest focus right now like what would you say those are capacity and the sort of financial complexity and you know in general like how do we how do we think about that space how do we think about pricing and packaging how do we think about the sort of tension between ownership and renting?

Should we go 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 we, you know, do more like agentic stuff, like what are the things we need? You know, are there certain storage primitives or compute primitives or in general, like what is the thing that the world is going to wake up and realize they need over the next few years.

And ideally, we have that at that point. We've been pretty good in general at like, you know, anticipating demand and building products in advance of it with the serverless functions 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 on like culture and company building and stuff like that. I mean, we recently crossed 200 people. We're growing extremely fast. We'll probably hit 250 by the end of the year. Like when you're growing very fast, you have 250 people that are all trying to figure out how they can 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 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 it's 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 ceo and like all this stuff so like you know how do i remain approachable and how do i like it's it's there's like the dunbar number which i think is like more sort of you know 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 where like laying the foundation of the culture is very very important how many people are you guys trying to hire over the next year we've grown about 4x 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 can double the team every year, and that's not super hard to do. But 4X, what you start to see is cultural drift. You start to see islands of different working styles. You start to see people doing very speculative things. That's something I always watch out for in the engineering team. You go and talk to an engineer, and I'm like, what are you working on? And it turns out they're building something kind of like fringe. I'm like, is this really what you should spend time on. So, growing very fast has that cost and, you know, it has the cost of ramping people up. So, I don't know.

I think by the end of the year, maybe 250, by the end of the next year, you know. But, like, you know, we're going to keep growing, like, maybe two and a half, three XO. So, it's very likely we'll end up, like, next year, like, 700 people or something like that. And I'm going to assume most of those are engineers? Mostly engineers. We're wrapping up sales a lot, too. Okay. What kinds of engineers? It's primarily systems engineers. I mean, we've always been, like, very deep into sort of, you know, Linux and file systems and scalable systems and networking and cloud and stuff like that. So that's the core of what we're looking for.

Like people are really good at those things. We're also looking a lot at like, you know, more like, because we moved up in the stack a little bit. Like we're doing a lot of work in the LM inference space. So now we're dealing with a separate set of concerns around, you know, CUDA and like GPUs and MFU, you know, like maxing out the tokens that you can get per GPU. and a lot of that things. Even though I actually will say a lot of the work in that space, in my opinion, turns out it's more systems-driven still, because it turns out KV caching and routing is actually still very systems-like. These are very systems-heavy problems. But yeah, a little bit more GPU performance, that kind of stuff.

We're starting to build up a research team. We're very excited about moving into stuff around post-training and distillation and better methods for quantization? For your customers or for internally? Both. I think it's a little bit like with the FTEs. 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 FDs and with the research team initially you're going to have to do a lot of work with the customers and we are that but over time we're also building a lot of internal tooling and a lot of over time internal products that are fully self-serve so we're working on a lot of stuff right now in the post-training space but also in the GPU performance So a lot of cool stuff.

A lot of cool stuff. Back to the product strategy stuff that you were talking about. Yeah. Yeah, like what are your thoughts? Like what do we need over the next year, two years? I think it's incredibly hard to say. And if I said like, I know exactly what we need to build, I think I would be like delusional. I think what we've seen so far, and what I kind of believe in on a more general level is like, we need better building blocks. Like this is a new era of software. and we're unlocking a whole new type of applications that could not be built a few years ago. You know, doing, you know, all the JNI 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 work with GPUs and work with LMs. And, you know, we built some building blocks so far and, you know, that's unlocked, you know, 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, there's going to be so many more cool things. And so I mentioned some stuff. I'm very bullish on training. I'm very bullish on best jobs. I'm very bullish on certain things around agents and low latency and things like that. But in the end, we consider ourselves like an infrastructure shop.

We're not trying to build very narrow things. We try to build very general purpose building blocks and put them in front of customers. And then those customers will combine those building blocks and build some incredible applications that we could have never anticipated. I want to go through some of your tweets. This will be the last part of it. Okay. And we can just like, maybe you want to double click on anything. A lot of long running persistent sandbox stuff right now. Today feels lift and shift. I suspect sandboxes will evolve from dev box in the cloud to something more ephemeral. One environment for every command and snapshotting in between.

So you can pause, resume clone. Yeah, I mean, we talked about this a lot. Yeah, we talked a bit about it, right? Like, I'm fully... I think 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 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 massively paralyze things and reducing the overhead of starting and stopping sandboxes?

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 pace the arms race a bit. No comment. The biggest downside of GPU scarcity is not that it's hard to get GPUs, but that almost all capacity is sold as a three-year rectangle is starting three to six months in the future. That's not what demand looks like for most users. I think we talked about that a little bit as well. Yeah, and maybe just to go a little bit deeper now. One thing I think Model has always been very, very good at is fully on-demand capacity, right? Like if a customer comes to us and needs 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 workloads in real life, like they don't look like these like flat, you know, rectangular workloads. Like they scale up and down throughout the 24 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. And I do think it's a legit problem for the space. It's like the prime way to get capacity 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 that's why I think modal can play a very large role 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. Would you say that's a differentiator? I think so. Oh, really? Okay. Yeah, massive differentiator. Actually, in fact, I think modals, if you want to be very sort of reductive, you can break down modals value prop into two things. One is the developer experience, the ability to iterate and build things and not have to think about infrastructure. Sure. The other thing is the capacity stuff. The more and more I think about model 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 snapshotting, 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. Every month I keep saying our sandbox revenue might apply to it, but it keeps going up 30% every month. It's kind of nuts. Like, I don't understand, you know, like every month I'm like, well, you know, sandboxes grew a lot, but like clearly they kind of plateaued. And then they keep growing even more. So 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 it's insane and you know we're obviously very happy about it we 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 for seeing sandboxes for what it is today a little bit back in even 2023 but i i think you know if like i probably would have never you know taken a bet that revenue would be this large from sandboxes and the market demand would be so much there's an interesting structural reason and why inference providers should exist separately from model builders.

The best model keep changing a lot. Inference providers can easily reallocate resources between models since overall demand is quite smooth. So, I mean, if you think about all the Chinese labs and, you know, in the future, hopefully more U.S. 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.

But of course, in the background, they're working on the next version. So I think there's a structural reason, like I said, why you're seeing demand kind of shifting to the latest and greatest Chinese lab model. I mean, it's not like everything just moves overnight because a lot of models and prompts and stuff like that, 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 is built to handle. No one even needs to tell us to do that. It just magically does that. So you agree that basically the LLMs are commodities? And if you agree, then where do you think the business for the big labs is going to be? I'm not sure if that's necessarily true, right? Like, is Taylor Swift 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, this is the best analogy. And then she releases a new album, you know.

So I don't know, like, she still has like a lot of brand and there's a lot of, you know, power and like makes a lot of margin. So, like, as long as the labs, like, repeatedly can launch new models, and as long as the margin that they have on those models are such that they can sort of pay off, amortize, like, the cost of training those models, it could still be, like, a decent business, I think. So, you don't think that the intelligence is, like, plateauing? No, I don't think so. I mean, I think it remains to be seen, right? But models today are very good at doing things that can be trained very efficiently using reinforcement learning.

So we've seen that initially with 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. These are all quite verifiable things. I still think it remains to be seen. Our model is going to be really good at... whatever, cooking food or, I don't know, like things that have like more like diffused targets and maybe longer feedback loops or whatever. I think humans probably like, my feeling is like will be quite good and quite competitive with models for a long time in those domains.

But, you know, certainly, you know, AI models, they're getting very, very good. Cool. As I'm sure everyone can agree with. Yeah, so obviously like the models are getting really good at coding. They're already really good at coding. What other applications do you think, maybe that aren't as big right now are going to become huge over the next few years for the LLMs? I don't know, but I think anything that has kind of a verifiable... A computational bio, I think you mentioned. Yeah, a computational bio. I think those are not exactly verifiable, but there's certain ways you can sort of run... You can use an LLM to output predictions, then you can do computational methods to simulate the molecular dynamics and get kind of feedback loops.

I think material science is 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. Cool. I mean, those are all the questions that I had. Is there anything you wanted to talk about that I haven't? I'm good. This is great. This is a lot of fun.

Awesome. Thank you. Thank you.