Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal

[00:00:03] [music] >> Thanks for coming on, Eric Burn Arson from Modal. Could you explain the business a little bit and maybe how you got here? >> Yeah, so we we build these as we put it as we build AI infrastructure. So we offer a bunch of compute a compute platform and a storage platform that enables a lot of customers like Deck of Gone and Suno and Runway and Cognition and many others to to essentially build AI applications. So what that means in practice is a lot of inference, a lot of sandboxes, sometimes training and fine-tuning, sometimes batch jobs and and other things. So we have basically built a a software SDK that enables customers to move really fast and and build this. We manage all the capacity underneath, make it easy to deploy and and scale and and run these things in production. >> So you were on Latent Space like 2 [music] years ago. A lot has happened since then. Like how big were you back then? Like whatever numbers you can share and comparatively, yeah, how big are you now? >> I actually don't remember where we were, but it's like it's probably like at [00:01:03] least like 50x in terms of like revenue and users and usage and stuff like that, yeah. >> I use it actually for like a side project that I have where Yeah, yeah, it's like yeah, turn like 2D images into 3D models. >> Yeah. >> So yeah, I'm just hosting like a couple models there. >> do you think? Any feedback? >> I love it. >> [laughter] >> Nice. Good. >> Um I mean, yeah, that's why I use it over like any competitors or anything. >> Good. >> So before you started Modal, you were at Spotify and also like better.com >> That's right, yeah. >> Yeah, what were you doing at Spotify? >> I actually did a lot of different things at Spotify, but my main contribution was I built a music recommendation system, the first version. I I joined after school in 2008 2009 maybe and spent 7 years at Spotify and and most of that time was building basically from scratch a music recommendation system and built up a small team and we launched a bunch of features like related artists and discover weekly and and a bunch of other stuff. So a lot of a lot of matrix factorization, a lot of compute-intensive stuff. And and and arguably like a lot of what sort of layer became the genesis of [00:02:04] Modal was you know, just just building AI applications and realizing like the infrastructure sucks. Like, there's got to be a better way to do that. So, a lot of sort of you know, the rationale for for Modal was like kind of building a tool I always wanted to have throughout my years at Spotify. >> Yeah. So, Modal was more of an offshoot of the stuff you learned at Spotify, not much from better.com? >> Better better.com was a little bit of a side tour. It was kind of a roller coaster. Long story about the company. Talk about it separately. But, I was the CTO for for 5 years of better and grew the tech team from I think, you know, two to three 400. So, I learned a lot about management and building teams and hiring and also learned a lot about developer productivity. Thought a lot about tools and things like that. Did a lot more like full stack stuff. But, yeah, I kind of came back to my what 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. Just >> Yeah, no, it's fine. I like this guy Zach Frankel [00:03:05] who I didn't know at that time ended up recruiting me to better as the CTO and the company was 10 people and you know, for whatever reason, you know, the the founder of Vishal very interesting guy, very very smart, a little crazy in in mostly positive ways. We got very well along. So, we yeah, we we started building it in in 2015 and I spent 5 years growing that company and then you know, eventually turned out that doing mortgages is pretty hard and you know, interest rates went up very quickly after the pandemic and and that 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 out on your own? >> Yeah, it's It's to say. I I had a lot of random ideas when I left Better and and and I I I actually wasn't sure if I wanted to start a company. I was kind of just, you know, during the pandemic I I sort of almost took like a sabbatical. I had two kids. I mean I still have two kids, but [00:04:05] I mean at that time they were they were I think two and four and so I decided to hang out and took some time off and was kind of working like part-time, like hacking on certain things and wasn't sure maybe should do some consulting and I started building stuff and I just got like super pumped about this idea that that eventually became Modal. But actually both, you know, Modal was like the I think the first thing I built that I got, you know, that So so there were like a few attempts before then that that went nowhere. But but Modal was like the first idea was like, "This is kind of cool. Like I should do this. This is like this could be a really cool platform." >> Were the other ideas like also in a similar vein, like developer tools? >> The first thing was like some sort of web scraping thing, which went nowhere. But actually, you know, in hindsight maybe could have gone somewhere. >> And was this before the ChatGPT launch? >> Yeah. Yeah, 2021. >> Okay. >> was late 2022, right? Then I I started building So So maybe actually to rewind a little bit context, like when I worked at Spotify I released an open-source framework called Luigi, which is like workflow orchestration. It's sort of the same category as Airflow and and Prefect and Daxter. Sort [00:05:05] of dated category to some extent, like people don't talk about it as much, but it for a while it was like, you know, got a lot of attention. Uh anyway, I I never turned it into a business. Uh but a couple years later when in 2021 when I was thinking about business ideas or 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 in a little bit, but but like you know, I never got back to it. Long story short and and and I ended up building Modal, which is like the the focus is more like let's just figure out a way to make it easier to run code in the computer in the cloud. And and the focus was initially like fairly general purpose and it actually in a way like it's always we've kind of gone full circle more recently. [00:06:06] But but we we focused on so many different applications. It was kind of the idea was like let's just make it super easy to run code in the in in the cloud. Could be GPU code, could be AI machine learning, it could also be like data stuff. Um and uh focused a lot on, you know, how do we scale it up? How do we make the developer experience awesome? Realized super early that like in order to do that we'd have to go very deep in the stack and built our file system, built our container runtime. Uh and 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 it like if you remember like that fall another thing came out around the same time, which was stable diffusion. And stable diffusion turned out to be a killer app for our for what we're building at that time. We were only four or five people at that time. So uh we started seeing a lot of people coming to us and wanting to run uh stable diffusion model. And then we realized that that that made us realize like maybe gen AI inference is actually really good use case for us. >> And how were you finding it? >> I'm just like compulsive tweeter. I was even more like now like I'm too [00:07:06] distracted by building a company, but I always like tweeted a lot. I was blogged a lot. So I think I had a little bit of a following and so, you know, in the early days uh 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 was 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 posting and being terminally online and and so, you know, like work it 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 cuz we started >> Also, did you raise like early on? >> We didn't raise for a while. I actually self-funded it for a while. Even with up until the first few employees. Which in a way was like kind of nice cuz it [laughter] made me 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 [00:08:06] point it became a problem because I needed the money. But we didn't raise for a while and um until early 2022 raised a seed round. So so at that point I'd already been working on it for about a year. Akshat joined us as CTO and co-founder in in about about 5 years ago, about now, August maybe 2021. But then it took until like basically like late 2022 for us to see any meaningful revenue. >> I guess that's when ChatGPT came out. >> Yeah, and and Stable Diffusion then in in our case was the main thing that sort of So so we went from like, you know, $100 a month in like I think like November 2022, and the next month we did like $2,000 a month. And then like January 2023 we did like $30,000 all of a sudden. And you know, so 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 [00:09:07] 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 know, in in in the first I think first quarter of of 2020 three, I think they were like 80-90% of our revenue. But but then after that, you know, it started diversifying a little bit and late 2023 we 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. >> Yeah. For the stable diffusion stuff, I remember using it for like comfy UI. I'm assuming a lot of people you were using comfy UI, like running comfy UI workflows on it. >> Some people were, but but I I think the people that were using Modal, they tended to be more like low-level stuff. Like they did like complicated stuff. What they like with Modal is like it's always been pretty hackable. >> Mhm. >> So, they they deployed their own custom workflows with adapters and custom in friends and and a lot of optimizations [00:10:07] and and all the all the stuff we had done in order to get the developer experience to be really good turned out to be really good for also running production workloads. Uh cuz auto scaling turns out to be actually a very similar problem. Like you want to scale up containers up and down very fast. Uh so, we we just happened to be like very well positioned to to capture a lot of those workloads in the early days. >> Did tools for auto scaling like not exist? Like why did you have to build those yourself? >> In general, like my thesis is like, you know, every new sort of era of like, you know, technology kind of runs up against limitations of of tools. Uh you know, whether that's you look back, you know, like the web or the cloud or, you know, mobile or PCs or or, you know, AI or whatever. So, so I I I think, you know, it's of often the case that tools exist. But but they're often not like particularly good. And and and often you you kind of need to rethink a lot of those tools in order to reap the benefits of, you know, and and really, you know, turbocharge and start building for this new world. And then you saw this with cloud, you know, we had to invent things like Kubernetes and Docker and things like that. And then in in in [00:11:07] a similar way, you know, like with with gen AI, you could build these things on the sort of prior generation of tools. Like you you can't you can build these things on top of Kubernetes and Docker and and these things. But but but those technologies are built for a very different type of workload. They're built for a very sort of steady work steady back-end workloads. They have an even, you know, steady number of requests, like running at a pretty low utilization cuz CPUs are normally pretty cheap. And so, people don't really worry so much about cost and but now you're entering this new world and you have you know very expensive GPUs you want to manage capacity and and utilization you have very large models that you need to load over network into CPU into GPU and and it it turns out like a lot of these tools like you know like they're they're just not built for that. So so we felt that you know we we had to rebuild a lot of that you know building our own I mentioned the file system but we also spent a lot of time over the last few years like building you know some really cool stuff around like CPU snapshotting GPU snapshotting and auto we built our own auto scaler and scheduler and and and and all kinds of capacity management [00:12:08] tools and we kind of feel like you have to do that because like you can't build you know for this new generation of of software on top of you can't build like a wrapper on top of the old stuff in order to to do the new stuff. You kind of need to start over a little bit from scratch. >> So I guess like the auto scaling stuff like did you did you see any like um weird behaviors on the platform like was there anything that like broke while you guys were scaling? >> I mean capacity is always from time to time been been a challenge like how do you get like you know hundreds and thousands and tens of thousands of GPUs so that's that's always challenging. We had to go multi-region very early so I think that was like kind of a fateful decision in hindsight like doing that and kind of just biting the bullet and building it like we always thought of like our our capacity management is like one big pool like it's like one big pool of it over the the entire world like we don't think of it as like separate clusters running in each region and so that that was like one of the like very consequential decisions we made early to kind of think about it that way so I don't know if there's like been like you know specific issues. I mean we've obviously had bugs and you know outages [00:13:09] and I hate you know as much as I you know would love for Modal to be 100% uptime you know and 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 things like that. >> The GPU market and well actually before that. So >> So, 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 in the first few years, we did we got almost all the capacity pretty much from from the hyperscalers, spot and on-demand capacity, mostly spot capacity, running in all the different regions, you know, using uh initially AWS, but also GCP and and over time Azure. Uh we we started renting some capacity from Oracle pretty early cuz we wanted um 8 tons and 8 100s, and we got really good rates on those. Uh and and over time we added a couple more clouds like doing reservations, but but like still up until like actually end of last year, like [00:14:09] most of our capacity was either spot or or 1-year reservations. >> Spot means you're just like renting it right there and then >> Exactly. Yeah, and it's also preemptible, which we had to deal with. Um but but it sort of works in aggregate cuz you can sort of just, you know, aggregate capacity from many, you know, hundreds of regions, basically. So, that that that model worked really well until we started running up again. I mean, first of all, like Modal started growing like very fast, and so we needed more capacity. But fundamentally, also like the cloud market started changing a lot. Um 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, um in order to get the capacity we want, like we we have to go and do, you know, 3-plus year deals, and you have to pay a meaningful amount of money up front. Uh and and you're locking in pricing on on a 3-year term, right? So, you know, like and and and so we have to spend the entire, you know, last 6 to 9 months, like, you know, kind of rethinking how we get capacity. We had to build a whole [00:15:09] procurement like desk, like we have like four guys working full-time, basically. All they're doing is like procuring capacity, calling the clouds, you know, doing deals, vetting the capacity. We had to build a whole engineering team basically you have people just like managing like bare metal capacity from all over the world and dealing with network issues and bad drives and GPUs falling off the bus and and all these things. We don't own capacity. >> Mhm. >> We rent all the capacity so far, um you know, on on on typically these 3-year deals. And that's like a big decision. Like should we buy? Should Should we you know, you know, or should we keep renting? I I'm actually like not like mega dogmatic about it. I I think renting is nice because there's a certain amount of capital efficiency to it. If you're buying, you know, like I mean, first of all, there's an obvious like operational complexity of it. Like you have to, you know, deal with vendors and suppliers and and, you know, power and and and cables and and all kinds of stuff. But there's also, you know, financial complexity to it. Like how do you fund it? Like should you raise equity, you know, and and you can raise a billion [00:16:10] dollars and and go buy hardware for it. That's a very diluted way to do it. Um you can raise debt, but you know, if you raise debt, you know, you you typically want to have like locked-in long-term revenue to to to pay the interest rates. So that's also not trivial. But, you know, like we look at the economics, we look at the the the operational complexity, we look at the risks. Uh it's a complex trade-off and and we're open to to to to buying. We we're we're sort of There's all other constructs, too. Like we're it's To get the capacity we need for for 2027, it's going to take a lot of work on on, you know, going a lot of different avenues to to to lock it up. >> So, do you have to hire a lot of like finance people to do all this stuff or not really? >> You know, I've had to spend a lot of time on this and we we've had to to to spend a lot of time on this. And I I do think over time, I mean, there there is a lot of, you know, these are very expensive assets, uh the hardware. And, you know, I think I mean, it's kind of staggering. You're starting to see like AI investment starting to become like several percentage points of of of GDP [00:17:11] of the US and you know, starting to see like interest rates in in America shooting up in the last few weeks. And I I mean, I like at this stage like you know and then you go and look at the neoclass like these are very very sophisticated financial engineering companies and so yeah we we look at that and you know we look at all the options and and it's possible we might have to do similar things ourselves. I wouldn't want to like I would rather focus on being you know like a very lean software company like doing the things we're good at. I don't think it's good in general for software companies to own hard assets you know in the same way as like should you own your office like I I don't think that's necessarily the right uh solution for for most companies. But of course like in order to get the capacity we want like you know everything is on the table. >> Yeah and I guess the supply is going to be constrained for a while so you kind of have to do that. Like do you do you agree with that or do you think this is just like a moment in time? >> I don't necessarily agree with that. I mean like Nvidia's delivering you know several hundred thousand black walls you [00:18:11] know a month right and and I I 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 uh you can get GPU capacity right? Like if you're willing to pay more than everyone else and and of course Anthropic and Open AI is buying up as much as they can and but fundamentally like there's constantly supply coming on the market and and so I don't I don't necessarily think it's like a fully cornered resource. Prices are going up. And and I do think it's worth kind of reflecting on that as like the normal state of hardware is like it's like out of 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 it it's hard to say you know how how long is this going to last you know could be another 6 months could be another 36 months like it's really hard to to to know how how how long and but but I I fundamentally don't know if like it's it's true to say like you know you need to buy capacity in order to to to me that's a little bit the sort of almost like the uh great toilet paper shortage of you [00:19:13] 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 cuz cuz, you know, scarcity 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 effectively like long GPU prices already. Um, so, um, I 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. Uh, but I think that would be a quite bad thing for a lot of the players in the market. >> Why would that be bad? >> I think it would be bad for if for anyone who's locked in a lot of supply. >> Yeah, cuz they paid way too much in >> They paid too much. You know, the so there's a risk of buying at the top, right? And and again, like I I don't worry about that that much. Like I don't I don't think it's going to be like, you know, it's going to happen anytime soon. But like, you know, like I said, like hardware prices in the very long run, [00:20:14] they tend to go down. And so, it's it's worth, you know, reflecting on that, right? Right right like right now, I think I think your your capacity is constrained. And and that leads to sort of, you know, in economic terms, there's a scarcity rent, right? Like, who captured the scarcity rent? I I think it primarily goes to two places. Mostly Nvidia, but of course, but also to some extent, Neo Clouds. Like, Neo Clouds and in general, like, you know, anyone owning capacity has better economics. Like, that's undeniably true today with capacity being constrained. Long term, you know, in the very long run, I don't think that's necessarily entirely true, right? Like, there's always going to be a little bit of a premium you can get from owning GPUs. But is it worth the risk, you know, is it worth the risk like holding these like very expensive assets on your balance sheet? I'm not so sure. But, you know, we're going to see. Like, it's going to take a a years to get through this. And and and in the long run, you know, we might own some, we might rent some, maybe a combination, maybe do some other crazy stuff, joint ventures, who knows. [00:21:14] Maybe some complex SPVs or whatever. Um everything is on the table. >> How do you compete with OpenAI and Anthropic in buying GPU capacity? I'm assuming they'd want to like buy up literally everything that's available. >> They are and and I and I think what you're seeing is um you know, a lot of the the the price is 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's like kind of prefer a little bit of a diversification. Like they don't want like, you know, small amount of large buyers. In much in the same way as you see, you know, hyperscalers getting a little bit of a constrained allocation. Uh I I think, you know, also Nvidia prefers to see, you know, a lot of new clouds and a lot of alternative clouds like getting more allocation. And I think a little bit what we see in the market is also this is more speculation, but I 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 is all right, Anthropic and [00:22:14] OpenAI my feeling is like they prefer to buy from like the the tried and true Uh and part of it is also because they had to do a lot of training runs, so they need to have like very large blocks. But there's plenty of capacity out there, you know, you can get 32 nodes in Armenia, you can get 128 nodes in Indonesia. Like, you know, like we love that kind of Like, you know, so so we can go out there and like get a lot of, you know, smaller capacity all over the world pretty good prices. >> Is it pretty much just like if someone offers you like a certain number of GPUs, like that's all you care about? Is there nothing like, oh, you care about like how reliable it is, you know, is it just some like janky warehouse that someone has these in? >> I mean, sometimes it's kind of a janky warehouse. It feels like, you know, but but like we do have like a spec sheet basically. Like when we talk to vendors, the first thing we do is we send them a spec sheet. We say, "We need this number of IOPS. We need, you know, like the CPU needs to be at least at least, you know, have have this many, you know, whatever the pass mark scores or whatever it is. We need to see like, you know, minimum, you know, north north [00:23:15] south bandwidth egress and and ingress." Uh so so we do have a list of requirements. Often we try to do like an acceptance testing period where we get like one node and then run some tests. Over time, you know, like to some extent it also involves a little bit of a leap of faith. You know, sometimes we sign very big commitments, you know, tens or hundreds of millions of dollars, and we start working with the vendor that we still don't fully know. And, you know, over time like you know, we'll see. You know, and and and I think over like a little bit of like is there's a little bit of sort of just, you know, our own sort of like, you know, how like semi-analysis has like a cluster max. I kind of feel like we almost like have our own like modal max score internally. Like I know which vendors I like more than others, you know, like and and 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 it's not just reliability, it's also are they very responsive when when we have, you know, support issues, do they fix the issues we ask for, the turnaround time. If [00:24:15] things go down, you know, how quickly do they fix it, like things like that. >> Do you agree that the demand for intelligence is basically infinite? And if so, like why wouldn't you just buy up as much as you as much capacity as possible? And then like if you 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 use utilize a lot of it, right? But like >> We 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, price is going down and utilization going down and the market for reselling capacity goes vanishes at the kind of the same time. I'm sitting here and like I'm I'm extremely aggressive like buying capacity every day and like raising money to buy, you know, so overall I'm I'm very bullish, but like I do want to point out and like, you know, like there [00:25:15] are tail risks here and 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 was actually had my first job in 1999 so I saw the dot com crisis and I've seen the great financial crisis and things like that and you know, like I want to build a company that that is around for like 20 to 30 50 more years, right? Like and and so, you know, I I think we can certainly go out and like buy an insane amount of capacity right now and try to just corner the market. But you know, do you know about the Hunt brothers? It's like a legendary story in trading. They try to corner the silver market. It fell spectacularly. Um, you know, so it's a cornering the market I I think in general like it can be um not not so good idea. Um, and but but we are in general very very aggressive. But but we're we're ramping up, you know, like at an at an aggressive plan that basically matches our demand growth and matches where we want to try to get to at the end of next year in terms of run runway or run rate I mean. >> Are you only buying up Nvidia stuff or [00:26:16] also like, you know, maybe a capacity from other types of companies like are you guys looking at Cerebrium and >> We're we're looking at it. I I I think the alternative accelerators are starting to get very interesting. It's you know, ironically like every every year in the last five maybe even 10 years I've been saying, you know, like next year it's going to be the year of like non Nvidia accelerators and and then I'm always wrong. But but I do think 2027 might be the year of non Nvidia accelerators finally. So we'll we'll see if I'm wrong this year, too, but so I I you know, AMDs are starting to get like really good scores on certain things. I I think, you know, TPUs are are obviously used very, you know, widely internally at Google. They seem to work really well. I I don't know much about Tranium. I I don't feel like I see quite as But, you know, the the team is very good. I know they're working on a lot of the software stuff. You know, hardware is hard in a in a certain way. Like, software is like easier. And that 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 And I think fundamentally, you know, with Nvidia being such a bottleneck, like we'll probably see, you know, more alternative accelerators. [00:27:16] 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. Uh but, you know, long term, I think monopolies don't always last. And so, I I'm I think it'd be healthy for the market to have more alternative. As much as I love Nvidia, like to make that clear. Like, I mean, they're obviously an amazing company and with an amazing product. Um I I do expect to see a little bit more divers- diversity in in the market. I also also thinks, you know, like Cerebras and Groq and these things. >> Yeah, what are your thoughts on like some of those uh providers? >> I think they're pretty exciting. And, you know, and and you see them doing really well on on certain workloads. And, you know, there there's some interesting, you know, people doing these like hybrid, you know, disaggregated inference where they run, you know, Nvidia for the prefill and and Cerebras for the decode and and things like that. >> heard of that, actually. >> Yeah, and, you know, Groq, too. Like, cuz 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. Uh and I think that's quite exciting. It It starts to get very complex when you're deploying these things in [00:28:16] practice. Uh but but I I I do think there's a lot of There's a lot of, you know, opportunity in in in in these sort of, you know, more complex hardware setups. >> Are there any learnings that you get from uh some of these neo clouds, 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 with neo clouds is like how can they expand so quickly? I 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 in the pipeline despite barely having any capacity. How can neo clouds grow so fast? I mean, there's like an you know, and and and basically the answer is like, you know, and and this might be obvious, but like what they do is like they go out to like, you know, Anthropic or or or or OpenAI or some some big buyer or whatever and they basically say like, "Hey, do you want to buy like 5 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 [00:29:17] know, they they committed to $10 billion worth of chips. Uh we just need the money right now." And then they go on to 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. And then, you know, now they have like, you know, deliver it to the customer, right? So 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 and that means, you know, you can scale up extremely fast. And so that's what we've seen with these neo clouds is is is they can scale very, very well. I think Modal, because we've always had a little bit more of a PLG focus and and and not the sort of, you know, ability to lock in long-term contracts in the same way, it it's a little bit more challenging from a financial perspective to scale that fast. Uh 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 neo clouds and especially like CoreWeave and and those big ones, um they've been very, very savvy with, you know, how they raise money and you know, how to, you know, think about GPU as collateral for loans and all these things. [00:30:18] >> What's the relevance of like I I forget who it was, but like some like financial company was like, "Oh, we're going to make" Oh, I think it was Nvidia, actually. They were they were like, "We are going to turn GPUs into an asset class." Is that just cuz 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 Computer Exchange and like Orin and and and a few other ones that are like basically like trying to get to like you know, creating price indices and indexes and and and trading derivatives and things like that. I think it's interesting. Like I I think with compute like it does there are a number of challenges with compute as an asset class. Like it it's definitely not as homogeneous as other asset classes. Uh you have different network and different locations and and and different disks and and CPUs and all kinds of stuff and reliability. I mean that being said like you can say the same thing about oil and like we kind of created like you know, relatively commoditized, you know, tradeable assets out of oil. It remains to be seen. Like [00:31:19] I do think, you know, at the scale where we are with the sort of AI like data center GPU deployments like we do need to be a little bit smarter with like risk hedging and and and and how we share risks and and and you know, having more like sort of insurance and and things like that. So, I I I I I I'm I'm very happy that people are thinking a lot about this. That being said like I'm I'm I'm not entirely sure if we're going to see, you know, GPUs becoming sort of, you know, whatever the sort of Henry Hub like, you know, Brent crude type like, you know, super commoditized like tradeable asset. >> Yeah. I want to shift the conversation to Modal Sandboxes. >> Yeah. >> I guess like before we do that, what are the different products that Modal has? It's I guess like the main product obviously is like the the GPUs. Then you have Modal Sandboxes. Is there anything else? >> I think of the like the two big products that we have is basically number one is Modal Functions and number two is Modal Sandboxes. So, so with Modal Functions we we launched that, you know, back in 2021. [00:32:21] It was the idea of like serverless functions that are very 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 and running on things like that. But we actually never had like, you know, dedicated like inference product or like a GPU product per se. Like we we we never thought of Modal as like a Neo Cloud and and I think there's like a risk of like getting sucked in that direction and becoming a Neo Cloud. Like just selling, you know, sort of what I think was like rectangular. Like here's like a 3-year, you get this many GPUs. Modal's always been very good at like you deploy something, you can kind of scale it up, you have this like very cyclical or like bursty stuff. Like we manage all the capacity. And the beautiful thing is like cuz 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 the 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 2/3 of our revenue and and and empowering a lot of, you know, in practice it's mostly inference, but [00:33:21] but you can use it for all kinds of other stuff. Modal Sandboxes, we launched that in 2023. And it's actually kind of, you know, took a while to see product market fit, similar to functions. Why did we launch it? We realized a lot of people we talked to were using LLMs to output code. This is like me and Akshat talking over lunch one day. We're like, maybe they need a place to run that code. And and and we realized like actually we kind of have like a lot of the infrastructure already to just take code and run it cuz we've already built the primitives for like managing containers and isolation. We already had, you know, gVisor as an isolation primitive and and and things like that. So we got kind of excited about that idea and and and Akshat wrote a prototype and launched it in basically 1 day or 2 days or something like that. Zero like we 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, growth. >> so. >> Yeah, it was 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 for certain things. But but like overall like it was like a [00:34:22] pretty insignificant source of revenue into going into the summer of 2023. So 2 years later, we even discussed like Do we even need this product? Like should we shut it down? Then it just started exploding. So, 2023 I And And I think what what really happened in 2023 was like LLMs got really good at generating code. >> In 2023? 2023? >> I mean, it was like around that time. Like, I I feel like that was like when they went from like being able to like refactor functions and like write snippets to like generating like full apps, right? And and And so, we started seeing a lot of growth coming from like vibe coding, you know, companies like Lava Ball and and other ones. Uh and and then, you know, companies like Ramp started using Modal for background agents. And then we see started seeing tremendous growth coming from a number of Neo Labs doing things like reinforcement learning on Modal Sandboxes. So, Sandboxes is now about a third of our revenue. You know, we we consider it like basically like our our second product. And And to me, it's like really exciting cuz like even back in the early days of Modal, I always knew [00:35:22] 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, working on a bunch of other stuff. So, so to me, that sort of evolution into becoming like a multi-product company, I think it's been amazing cuz that was that was always so core to the vision, even in the very early decks. I I had a bunch of slides on this. Uh but but it wasn't necessarily true to the external world uh until recently. >> Do you have anything in the works that, you know, you think it's going to be like that next product? Any predictions maybe about the market? Anything like that? >> I mean, I'm I'm very bullish on training. I think training is like kind of an underserved market. I Obviously, there's like a lot of focus on like very large, you know, training runs. Uh but we actually see a lot of demand in the sort of smaller and and mid-size [00:36:22] training segment. So, I'll I'll be talking a little bit about that later this week. >> Mhm. >> We're organizing a conference. Batch is I think like a really interesting market. I 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 and we we had a lot of capacity. So, we we let them do that, you know, and took a few days. I I think there's some really interesting use cases there. Very bullish on like low latency stuff. I think there's going to be a lot of, you know, once we sort of get the audio like the speech to speech models working like the voice models. I sort of expect that to be very interesting market. Then of course like all this like agentic stuff, right? And in a way like I I have like mixed feelings about like the word agentic cuz I feel like in in certain ways like I think all software will become agentic. So, it's almost like to me like what does it mean to be agentic? Like I think I think all software in a certain way like will become like AI-powered over over time. But but like clearly there's like a set of like building blocks of [00:37:22] like, you know, doing more sort of LM-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 we want to give you like amazing, generalizable, battle-tested, you know, general-purpose uh composable building blocks. And and I think there's some really interesting things stuff also like, you know, with the with the agent use cases. >> That's interesting you bring up training cuz I think in another podcast, maybe like a year ago, maybe like 2 years ago, you said that like training you were not focused on like specifically. Like what changed in the market? >> I mean we we've been building a training product for multiple years. So, you know, on and off. So, so we we I I wouldn't say we're like we've never been focused on it. But it's like it's like one of those things where like I mean now we're like 150 engineers, but you know, a year or two ago we were 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 it was our sole focus for [00:38:22] many years, but we're bigger now. Like we we we do have opportunities to also place bets in in other markets. I'm also really interested in, you know, kind of continuing that that that the list of things post-training reinforcement learning. I think there's some really exciting stuff around that. Um I 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 and all kinds of like I don't know. I I I think the long-term view of of Modal is like basically like you want to build some cool AI 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. It's That that's very much the sort of company and the product that we want to build. >> When you say training, are you focused on more of like the bigger enterprises doing huge training runs? Are you Okay. >> They're going to go and like, you know, do these like, you know, big hyperscale or or rather neo neo cloud mega deals and get, you know, 100,000 [00:39:24] GPUs or whatever. But but but there is 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 like you don't need a, you know, 100,000 GPUs. That market I think is like we're we're not going to like focus on that for a while. But but I think there's a lot of stuff in the, you know, slightly smaller markets that that 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 can you say three or maybe like can you 2.5? Uh that, you know, a A of people are using those in 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 [00:40:25] models, they're not quite as good, but you know, if you take, you know, a small model and you fine-tune it on your specific domain, you can actually reach the frontier in terms of, you know, quality and cost." Uh today, I would say like the open-source models are so good that like you can basically just like swap them out and just run, you know, instead of, you know, the the frontier ones, you can run, you know, Kimiko 3 or or or or whatever, DeepSeek 4.1 or GLM 5.3. And they're like basically almost as good as like the frontier model at a much lower cost. So, we still see a lot of demand of uh for for post-training, and I do think it's a really interesting space. We're working on some some products in that space, you know, both sort of SFT and 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 it's it's very optional. Like you 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 [00:41:26] 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 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 in my opinion, I don't have numbers, but like I think we have by far the largest market share in the space. Like we're just very good at running, you know, hundreds of thousands or even millions of sandboxes. >> Yeah. >> I will say we'll have to We had to rewrite our systems, you know, a bunch of times in order to handle that scale. So, so but we were always like very good at it. There are other things like over time. I mean, the sandbox space in in general like, you know, like it's like not the most like differentiated space, but you know, we're working on some really cool stuff. Like over time like we've had to add support for VMs, like virtual machines. So we we we previously built our our sandbox product on on G Visor, but now we offer full virtualized machines as well. Turns out a lot of for for certain use cases you kind of need full Linux [00:42:27] compatibility. There are other things we've had to build, you know, better primitives for like more interactive, you know, piping data back and forth between sandboxes, working with file system. We've had to spend a lot of time on snapshotting. I think a lot of the things that over in the long run will differentiate will be like, you know, really cool things like, you know, forking sandboxes, snapshotting, resuming snap like live migrations. In general like I I think sandboxes the novelty of sandboxes was always, you know, this sort of ephemerality. Like you can spin up sandboxes for a few seconds and and and that's something I like that's a string I I want to pull more in. I I think there's a lot of really cool stuff you can do, you know, kind of decoupling the agent and 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 may maybe one per command, like things like that. So I I think there's a lot of cool stuff there. We've already been spending a lot of time doing that, you know, around snapshots. But But I think there's a lot [00:43:27] 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 Like what would that give us that we don't get right now? >> I think the the big thing for me is like today a lot of sandbox workloads are kind of like lift and shift. Like people think a lot about sandboxes almost like, you know, I have my dev box. Now I'm going to shove it in the cloud. I'm going to have an agent control it. Maybe I'll run the harness inside the sandbox. I don't know if like long-term that's like the right way to think about, you know, on like compute for agents. I I think a much better model would be if 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, 24/7. You know, every once in a while they're going to run commands and and you know, having the ability to like spin up a sandbox in, you know, 100 ms, 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, [00:44:27] you know, go to sleep cycle, the more you can sort of create sandboxes that only run for like a very brief period. And and that will make compute much more efficient. And it will also enable you to to to to do some really cool things, you know, like forking and and moving things around and and and you know, having like imagine, you know, like cloud code instead of you know, having sort of a very linear flow, like sequential flow, like kind of branching and you know, running hundreds of agents at the same time, right? I think a lot of like CI is like another like illustrative example. Like for most of the the history of CI, uh you know, I talk about like GitHub actions and things like that. Like you had a very sequential flow. Like you you you know, you slowly install all your stuff, then you run the test suite, and then you know, generate some reports or whatever. And it's like you can break it up into shards, but it's kind of annoying. Like what I would love to do is like take your entire test suite and just run every single test in its own sandbox, right? Like and just parallelize the out of that. Run like a thousand sandboxes. Why can't that be done? I mean, the problem is like there's too much [00:45:27] 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 think want in parallel. So so I you know, I'm an infra geek like I said. Like I I 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 we previously could not do with with software. Why hasn't that already been built? I would assume even previously people would would want to parallelize as much of of their CI as possible. >> You can, but the overhead is like Amdahl's law whatever, right? Like the overhead of parallelization right now is very high cuz like right now like you can get up actions for instance. Like we we parallelize in in the modal test suite, right? But like what that basically means is like now if you take your test suite and you split it up in two, like now each of those shards both need to do all the installs and and the test setup. And that takes a long time. Like [00:46:28] it 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 a lot of the the benefits cannot be uh you can't materialize those benefits on on until you get the the friction and and the overhead down. >> Mhm. >> 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 the managed agents with with Anthropic this is sort of a bring your own sandbox integration. So you can you can use modal for those, yeah. >> Oh, cool. How did that come up? Like come to be? >> I think Anthropic is like trying to create, you know, like a thriving ecosystem and community and and so I think, you know, they make money from inference, right? Like so I I I think it's very much in their interest to promote, you know, like a for everything else, you know, as much of, you know, of an ecosystem as possible. They want to have the largest possible surface area as long as you use Anthropic for inference. So I I think it makes like very rational sense for them to to to partner with with us as a sandbox [00:47:29] provider. >> Also, I feel like we have to bring up the Hugging Face incident. Are you okay talking about that? >> Yeah. Yeah, I mean it's all there. >> Yeah, yeah. So yeah, could you just briefly describe like what happened and I think they were using the modal sandbox, but it wasn't your, you know, it wasn't like a fault of the sandbox. >> Yeah, exactly. I I I don't I forget the all the details of 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 the the agents running inside, you know, that that that exploited hugging face they discovered a a vulnerability in an app that was deployed on Modal. >> Mhm. >> And they used that to sort of as almost like a springboard for for getting access, getting full internet access is my understanding. I don't remember the full details. To be clear this is an app running on Modal that was hacked. Like it was not Modal that was hacked. >> Yeah. >> So it's like AWS a lot of people run stuff on AWS, occasionally those app get hacked. Does that mean AWS got hacked? Like no, of [00:48:30] course not, right? And and so there's a lot of people running apps on Modal and you know, one of those apps got hacked. And I I say but but like there are some lessons we can learn. And and 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 in the as just just as another analogy you know, S3 used to be this like, you know, it used to be very easy to to accidentally make an S3 bucket public and AWS realized like, you know, it doesn't necessarily cause that you know, pose a security problem for AWS per se but a lot of their customers accidentally would, you know, like leak data. And so S3 it's much harder now to make S3 buckets public. And so in a similar way it's like we're exploring like, you know, can't we make security a little bit more restricted by default without necessarily, you know, destroying the magic that make that makes Modal good. And I think this also, you know, it's a kind of a separate side track is like agents are getting really good at cyber security. So we've actually also as a [00:49:30] sort of, you know, separate sort of learning from this incident. Like we started using agents internally quite a lot to to to try to find vulnerabilities. So far hasn't found anything, but like they're very good at like probing into like very deep parts of the system and exploiting trying to exploit things and trying to escalate and get out of sandboxes. Um so, you know, it's it's it's something we're exploring very actively right now. >> How exactly do you get the agents to like run through the code base? You're just like, "Hey, like find security vulner- vulnerabilities?" >> Well, no, you just like you just put them in a sandbox and and tell them like, "Can you try to escape?" >> Just run through the >> 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 a few months ago. And it's pretty exciting. >> Like when stable, I guess, came out. >> Yeah, roughly around the same time. >> Okay. >> I mean, my my view on like cybersecurity is that, you know, I actually think they're very good at finding vulnerabilities, but I think you can also use them defensively well very well. So, I I I think, you know, companies like Model and many other companies right now, like I think, you [00:50:31] know, if if you go to talk to like pretty much every CISO, I'm sure they're all thinking about the same thing. Like, how do we use agents defensively to to to, you know, fix all our vulnerabilities? So, over time, actually, in a way, like I'm not that nervous. I think if if anything, like agents and 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 like core expertise. But when I think about like cybersecurity, a lot of what you're selling is effectively fear, and that's almost like a branding and like marketing strategy, as much as, you know, obviously, the core products. But I think, you know, brand and things like that are actually like arguably like more valuable in the era of AI. So, I 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 [00:51:31] know, big sales teams who are very good at telling, you know, spooky stories and and things like that. And And I think they're going to continue to be very good at that. I just asked cuz 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. >> true. Yeah, yeah, you kind of want to hire in many cases just like a established vendor uh in order to blame them. I I I mean, to some extent, it it does reduce a little bit the need for us to do to bring in pen testers like human pen testers, which most startups like do on a regular cycle. So, maybe previously, you know, we would do it I actually don't know what the cycle was, but but I think we used to do it like once every 6 months roughly. Maybe now we only need to do it once every year. I 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 I I don't know. I'm I'm kind of a believer also that like humans with AI is even more powerful now. So, like, maybe there's an argument that like we can bring in pen testers [00:52:31] who are really good at using agents and, you know, find vulnerability even faster. I I don't know. I I'm not that, you know, I'm an optimist. I started a company, so like, you know, I'm I'm generally like optimistic about everything, but like I I certainly don't think humans are cooked or companies are cooked. Like, I 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. >> Mhm. >> I want to transition a little bit to like developer experience cuz like early on I mean, even now, like you guys focus a lot on that. And you've talked a lot about that. Does developer experience even matter anymore? >> I think it still matters. And and then I think you're you're sort of, you know, like the context I think you're fish 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. Is kind of what we've seen in practice, which is like things like having good docs, having descriptive error messages, [00:53:31] having fast feedback loops, right? Like I do think there's certain things have changed. The other day I was like setting something up with Cloudflare, and it's like I find Cloudflare like notoriously annoying to work with sometimes. and and I throw an agent at it and it was able to like just configure a bunch of like you know things and and balloon me for me. And you know and and so I will say like agents does they do make it a little bit easier to work with things that are like harder to work with in the past, you know, like the sort of human toll of dealing with the animal has arguably gone down a little bit cuz you can just like you know tell your agent to deal with it. But but for the most part I think you know the things that make developer experience good also make agent experience good. I think to some extent also like infrastructure over time anyway becomes and needs to be more programmatic. I think that's always been the thesis about sandboxes is that you know like things that you previously had to configure using YAML that should just be an SDK. That should be more like imperative like launch a sandbox like do stuff with it. Tear the sandbox down, [00:54:31] right? Like rather than sort of declarative using using you know some sort of DSL. It also pushes like a little bit of a different interface that maybe previously didn't exist. I think in either way like SDKs are as relevant as they were as they always were if not more. >> Is there anything you changed in your product just because like you know most people are just running agents on on it? >> We do. I mean we we also we actually do run a lot of internal tests where we have agents. We tell agents to like build a modal app and run it and I mean a very concrete thing that is kind of funny is that we actually seen that like because of agent training data it's very hard to deprecate things now. We see that like agents often like love using you know old versions of modal syntax and and it's kind of hard to nudge them away from that. So so we've actually had to be a little bit more conservative going forward like you know over the last year or so with with how we deprecate and remove features. It takes we have to be very careful to update every single [00:55:31] example that's out there online. >> Yeah. >> And and and you know >> Yeah, skills, right? >> Yeah, there's like stuff like that. But but like there's also an insane amount of like modal code that's now like online too that goes into the training data. I've seen like that to be like kind of an 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 where like ask a an agent like hey like can you deploy stable diffusion for us? Use whatever tool you want and and we see if it recommends modal or not and those things are very useful for us as it attract over time or like hey spin up some sandboxes or like hey like rank all the sandbox vendors. So so we we spend a lot of time like doing doing those things to to to make sure. I mean what do you do about that? Yeah you say hey like you know build this stable diffusion thing does it recommend modal or not? Like what yeah what do you do about that? Yeah I I think that's harder as I think you know it's like search engine optimization I think it's primarily [00:56:31] through content is like you just have to put out a lot of content. You can also to some extent like sort of see the perception I think a little bit more like you can ask an agent like you know how does you know rank all the sandbox vendors by reliability or rank all the sandbox vendors by price and and sort of see and it does feel like agents are a little bit more prone to like mis understanding pricing and and you know things like that so we also spend a lot of time like just making content more clear and more like you know understandable for agents cuz we know they're all indexing like everything that's out there and 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 [00:57:32] 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 they 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 kind of scaled it up a lot. I think we started this year having like four FDEs now we I think we have like 24 or something like that. So we've grown a lot and they do a you know it's more it's not exactly the pounder 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 LLMs. We we see it out [00:58:32] there's so much benefit from like you know sitting down with customers and tuning draft models and tweaking uh you know the parameters in DLM and SULang. Um they often you know fix bugs or or not fix bugs but like you know when customers run into issues like they often work with customers trying to figure out like how do we work around that or uh another thing we we've used FDEs a lot for is like just kind of proactively you know if if we see certain customers you know over provisioning with model like customers requesting too much CPU or too much memory for instance like that's kind of expensive and like we we try to catch those things and we try to sit down with those customers like yo like you're spending a lot of money on model right now we can actually help you save money and then of course like with with sort of active deals you know we we help them migrate code from other platforms uh things like that. Um I think you know we we we never have the intention of becoming like a service heavy business. I I think the the goal for FDEs is like, you know, A is to be part of you know, the the sort of sales process and like closing deals. [00:59:34] B is to serve customers and and do some like, you know, customer support. But like I actually think that like the most important thing is like C is like bridging gaps in the product that we later want to turn into like real features. So I 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 FDE for this? >> Yeah. Do you guys hire PMs? >> Product managers? >> Yeah. >> No. >> Would you say these FDEs are kind of like pseudo-ish PMs? >> It's a little bit of a different function. Like I I think certainly on the more like, you know, customer-facing side they are, but there's like, you know, on the more like inward, you know, internal side like there's a sort of separate part of like product management which is more like, what are the next building blocks? What else should I invest in? And like, you know, and then there's like something in in in between like, you know, looking at like maybe 10 20 different customers that are all asking for like slightly different things, but maybe what are the patterns? Like how do we turn this into product? We do have people doing product with us and and it's sort of evolved, you know, [01:00:35] we've had people basically like become product managers after previously being either dev rel or engineers and they're showing strong aptitude on 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 and I think there's a risk that you 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 have just thought of like kind of like widgety things and just shipped them. I think doing product management at an infra company really well is very very hard cuz 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 1 to 2 years? Cuz 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 [01:01:35] little bit slightly different things? That's sort of level of thinking is something that, you know, is very rare to find. And then 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 Gen AI? Cuz I would assume Yeah, I mean, you obviously worked at like Spotify. You had like a certain team structure. Um have your team structures like evolved a lot now that, you know, we have coding agents? >> In a funny way, like yes. I 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 an IC, that I actually think is very, very positive for the space as a whole. I think, you know, if you look back at the space like 10 years ago, 20 years ago, everyone wanted to be in management cuz that was the way you 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 [01:02:35] accelerated with AI. Like, you know, we we we make it very clear to everyone at Model that like manager or IC, 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 cuz 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 I still write a lot of code at Model and and actually too and and pretty much everyone in 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 caused by Gen AI, but I think GenAI has like greatly accelerated it cuz it 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 we think of it as optional. I mean for most [01:03:37] my view is like code reviews in 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 and CI and and and things like that is 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 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 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, testing things. >> Yeah, and exactly like, you know, in for companies like we we need one more nine [01:04:38] than the average startup, right? In terms of uptime. But like, you know, these are investments you just have to make. Like you have to build systems that are very resilient to like, you know, you have to have rate limits everywhere. You have to decouple systems. You have to make sure that like nothing is a single point of failure. You have to have extremely good test coverage. Agents are actually really good at writing tests these days which was like, you know, a few years ago I thought like one of the major things missing in agents. Now they're actually quite good at like cuz 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 open-source ones? >> We experiment with everything. But yeah, I mean, you know, initially it was a lot of cursor then, you know, of course Claude and Codex and and now a lot of people are using, you know, night shift and and all these open-source stuff or or other stocks. So, I don't necessarily have like a strong point of view. Like I want people to experiment and try different things. [01:05:39] Like we, you know, use frontier models, use open-source models. We try to do a little bit more dog fooding internally now that we're actually running inference for a lot of the open-source models like at a very large scale. I think it's also helpful to like use our internal endpoints for a lot of agentic development. Uh so, we try to promote that a little bit. >> Mhm. Something I read maybe like, I don't know, a year ago, maybe like 2 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. Um have you maybe like changed your opinion on that? >> No, I have not changed my opinion. Um I think more or less to me, I'm a cloud maximalist. And maybe to take a step back, like when when the cloud came in 2008, 2009, or whatever, my first reaction was like, "That's crazy. Like why would I want to take my code and run it on someone else's computer? That's like insane." And then like 6 months later I was doing it. And I think, you know, like took me 6 months to change my mind. It took probably enterprises like another 15 to 20 years. But like you look at, you [01:06:39] know, vendors like Snowflake or I mean AWS and like you know, I I think there's many examples of DataDog, etc. Like 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 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 in anymore, right? Like you 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 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 [01:07:40] the hardware, we deploy, we manage all the infrastructure, we deploy and like manage health and observe, you know, all the metrics on all the hosts. It means we can iterate very fast and build a greater product. And I think that's very very valuable versus, you know, customers in the past who had enterprise editions that they had to deploy on customers environment. They had to be very, you know, they had to think very carefully about release management and and having depend like like we don't care like if we want to introduce a new database, like we can introduce a new database cuz we have control over it. Like we don't need to think about what customers have access to. So to me multi-tenancy is is 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, I at the right price I I I would still build it. And and I think there's like maybe separately like another thing which I [01:08:41] think is interesting which is like in theory BYOC might also be a way to work with and and get capacity in certain ways. Like we we sometimes work with Neo Labs and those Neo Labs have already have very large capacity reservations. And, you know, the question is like, you know, how how do we how do we get them to run Modal when they already have capacity? And BYOC might be a solution. We can still keep the control plane in 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 so there's some like kind of fun stuff you can 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 I should explain the acronyms. >> Um let's say hypothetically you are going to raise a a ton 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 [01:09:41] say that's required is is capacity prepayments, right? And and so that to me represents the best use of of of cash today. Uh we're ramping up capacity the 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 is the the greatest use of cash right now. There's of course other things like you know salaries and office spaces and stuff like that. But that actually in the grand scheme of like our our financials like it's is basically a rounding error, right? >> Yeah, interesting. Do you think that's going to change? >> No. I I think you know the 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 the GPUs cost a lot more money. >> 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? [01:10:41] >> Capacity and the sort of financial complexity and you know, in general like how do we how do we think about that space? How do we think about pricing and packaging? How do we think about the sort of tension between ownership and renting? Should we be going in a more neo cloud direction? Should we go in the opposite direction? This sort of counter precision? Like that's probably the biggest thing that I think a lot about. Uh the second biggest thing I think a lot about is um probably around like more like product strategy. Like I think a lot about like what is the next building blocks? Like if if we you know do more like agentic stuff, like what are the things we need? You know, are there certain storage primitives or compute primitives or in general like what is the thing that the world is going to wake up and realize they need over the next few years? And and ideally we have that at that point. We've been pretty good in general at like you know anticipating demand and building products in advance of it with the serverless functions in the sandboxes. So what's the next thing? Then I would say like actually the third thing I think a lot about is like more around like culture and company building and stuff like that. I mean we recently crossed 200 people. We're we're growing [01:11:41] 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 cuz they get to build something incredibly cool and they know you know what makes Modal successful, what they can do, aligning the incentives and and making sure that you know we we we people have like a very positive culture where they have this 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 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 when you're like 10, 20, 30 people, you can all just like kind of sit in a room and like it's all kind of implicit and you kind of just like feel it. People can sort of read read me. Like they can tell me when I get a little bit annoyed. They can tell when I'm happy. But like now we're 250 [01:12:41] people and like people don't necessarily see it and you know of course there's like a lot of new people. They're like maybe a little bit nervous to like talk to the CEO or 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 know you can double the team every year and that's not super hard to do but 4x what you start to see is you know cultural drift you start to see islands of different working styles. You start to see people doing like very speculative things like that that's something I always watch out for in the engineering team. You go and talk to the engine an engineer and I'm like what are you working on and and it turns out they're like building something like [01:13:41] kind of like fringe and like is this really what you should spend time on? So growing very fast has that cost and you know has the cost of ramping people up. So I I don't know. I I think by the end of the year maybe 250 by the end of the next year you know but but like you know we're going to keep growing like maybe two and a half 3x so 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. >> Most of the engineers. We're ramping 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 and cloud and and stuff like that. So so that's that's that's the core of what we're looking for like people are really good at those things. We're also looking a lot at like you know more like cuz we moved up in the stack a little bit like we're doing a lot of work in the LM infra space. So now we're dealing with a separate set of concerns around you know CUDA and like GPUs and and and and MFU you know like maxing out the tokens that you can get per GPU and [01:14:43] and and a lot of that things and you know even though I actually like will say a lot of the work in that space, in my opinion, turns out it's like more systems-driven still cuz it turns out like KV caching and routing is actually like is still like very like systems-like, like like these are very like systems-heavy problems. But yeah, a little bit more like sort of GPU performance, that kind of stuff. We're starting to build up a research team. We're very excited about, you know, moving into stuff around like, you know, post training and and distillation and and better methods for for quantization and things like that. >> or for internally? >> Both. I I think it's like a little bit like with the FTEs, like I I I think we initially will do a lot of stuff with our customers to optimize their models, but over time figure out how do we turn those into products. I think we'll sort of arguably fail in a certain way if every time a customer needs to optimize a model, we have to go and work with them and, you know, long-term it has to be turned into products. And and so with the FTEs and [01:15:44] with the research team, initially you're going to have to do a lot of work with the customers, and we are at that. But over time we're also building a lot of internal tooling and a lot of overtime internal products that are fully self-serve. Uh so we're working a lot of stuff right now in in the post training space, but also in in the GPU performance space. So a lot of cool stuff, a lot of cool stuff. >> Back to the product strategy stuff that you were talking about. Um yeah, like what are your thoughts? Like what do we need over the next year, 2 years? >> I think it's incredibly hard to say. And and if I said like I know exactly what we need to build, like I I think I would would be like delusional. I think what what I've what we've seen so far, you know, and and what I kind of believe in on a more general level is like we need better building blocks. Like this is a new era of of software, and we're unlocking a whole new type of applications that could not be built a few years ago. You know, doing, you know, all the GenAI stuff, code that, you know, LLMs that output code that, you know, it needs to run like in an isolated way. Like, there's [01:16:45] so many like new primitives that are needed to to work with GPUs and work with LLMs. And, you know, we built some building blocks so far and and, you know, that's unlocked, you know, a bunch of applications that that that the world, you know, has seen over the last few years, not just us, of course. But, I think over the next few years, like, there's going to be so many more cool things. And so, I I I mentioned some stuff. Like, I'm very bullish on training. I'm very bullish on fast jobs. I'm very bullish on, you know, certain things around like agents and and low latency and and things like that. But but like, in the end, we consider ourselves like an infrastructure shop, right? Like, we we're not trying to build, you know, very narrow things. Like, we try to build like very general-purpose building blocks and put them in front of customers. And then, those customers will combine those building blocks and build some incredible applications that we could have never anticipated. >> I want to go through some of your tweets. This will be the last part of it. >> Okay. >> and we can just like maybe you want to double-click on anything. A lot of long-running persistent sandbox stuff right now. Uh today feels lift and shift. I suspect sandboxes will evolve from dev boxes in the cloud to something more ephemeral. [01:17:45] Uh 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 I I'm fully I I think it's I think the median sandbox in Modal today is like 8 seconds long or something like that. Like, which is kind of incredible when you think about it. And and over time, I think it might get even shorter. We want to support long-running sandboxes, too. Like, I think there are some like very valid use cases around long-running sandboxes. But like, my vision is like, you know, the world needs also we need to figure out this. Like, how can how can we just massively parallelize 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. >> [laughter] >> The biggest downside of GPU scarcity is not that it's hard to get GPUs, but that almost all capacity is sold as 3 years as a 3-year rectangle starting 3 to 6 months in the future. That's not what demand looks like for most users. I think we talked about that a little bit as well. >> Yeah, and you know, maybe just uh go a [01:18:46] little bit deeper now. Like one thing I think Modal has always been very, very good at is fully on-demand capacity, right? Like if a customer comes to us and needs 100 GPUs, like we can typically give them that like immediately. You don't even have to talk to sales person. And a lot of workloads in in 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 the challenge with a lot of customers today. Like the And And I do think it's a legit problem for for the space. It's like the only the prime way to get capacity is to to buy these rectangular blocks. It's not great. I think the world needs to evolve. And And that's fundamentally, I mean, I'm I'm I'm very biased, but like that's why I think Modal can play a very large role in in that sort of, you know, whatever you want to call it, sort of duration transformation of offering more flexible capacity. And we can do that because 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? Like >> I think so. [01:19:46] >> Oh, really? Okay. >> Yeah, massive differentiator. I actually, in fact, like I think Modal's like, if you want to be like very sort of reductive, like you can break down Modal's value prop into two things. One is a developer experience, the ability like iterate and like build things and not have to think about infrastructure. The other thing is the capacity stuff. >> Yeah. >> The more and more I think about Modal and like where we're going in 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 cuz we built all this work around, you know, scaling up and down extremely fast, you know, all the stuff around GPU snapshotting. We're working on some some ways to make it much, much faster. And that model works at very large scale, like really, really well cuz you can aggregate lots of customers. >> Every month I keep saying our same max revenue might apply to it, but it keeps going up 30% every month. >> It's kind of nuts. Like I don't under you know, like every month I'm like, "What well, you know, Sound boxes grew a lot, but like clearly they kind of plateau. And then they keep growing even more. So so I I I I don't know. And a lot of VCs like they're like how did you know Sound boxes was going to be big? Like and and the truth is [01:20:47] like I didn't. Like like [laughter] I I thought it was going to be bigger than it was in 2023 for sure, but like the fact that Sound boxes 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 Sound boxes. 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 Sound boxes for what it is today a little bit back in even 2023. But I I I think you know, if like I probably would have never, you know, taken a bet that revenue would be this large for Sound boxes and the market demand would be so much. >> There is an interesting structural reason why inference providers should exist separately from model builders. The best model keeps changing a lot. Inference providers keep can easily reallocate resources between models since overall demand is quite smooth. >> So I mean, if you think about all the Chinese labs and you know, in the future hopefully more US labs too, every time a new lab releases, you know, the latest and greatest open-source models. Like you see big shifts in demand, you know, for for these models. And and so I think [01:21:47] 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, you know, but but of course like in the background they're working on the next version. So like I think you know, there's like a structural reason like I said like why you're going to start you're seeing demand kind of shifting to like the latest and greatest Chinese lab model. I mean, not like it's not like you know, this is like everything just moves overnight cuz cuz a lot of models and prompts and stuff like like a lot of things that get sort of there's like a certain stickiness to it. But I I do think you know, there's an argument that it's very easy for model to like, you know, right? Today we're running, you know, X number of thousand GPUs for one model, tomorrow we need a shift like, you know, 50% of that to another model. Sure, that's just like that's what the system was built to handle. No one even needs to tell us to do that. It just magically does that. >> So, you agree that basically the LLMs are commodities? And if you agree, then where do you [01:22:47] 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 should we release this a big album and goes in a tour and makes a lot of money and then, you know, then there's like a down year when she goes and like records, I don't know if this is best analogy. And then she releases a new album, you know. So, I I I don't know. Like she still has like a lot of brand and there's a lot of, you know, power and like makes a lot of margin. So, like as long as the the labs like repeatedly can launch new models and as long as the margin that they have on those models are such that they they can sort of pay off amortize like that the cost of training those models, it could still be like a decent business, I think. >> So, you don't think that the intelligence is like plateauing? >> No, I don't I don't think so. I mean, I I think it remains to be seen, right? Like models that today are like very good at like doing things that can be trained very efficiently, you know, using reinforcement learning. So, so we've seen that initially with, you know, coding and writing simple snippets. Over time it's gotten really good at also writing production line [01:23:47] level code. It's gotten really good at math. It's gotten really good at cybersecurity. Like these are all like very quite verifiable things. I I still think it remains to be seen, you know, are models going to be really good at whatever, cooking food or I I I don't know. Like things that have like more like diffuse targets that maybe longer feedback loops or whatever. I think humans probably like my feeling is like we'll be quite good and quite competitive with with models for a long time in those domains. But, you know, certainly you know, AI models they're they're getting there. They're very good. As 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 I don't know, but I think anything that has like kind of a verifiable >> A computational bio I think you mentioned >> Yeah, computational bio as I think those are like not exactly verifiable, but there's like certain ways you can sort of run simu like you can use an LLM to output [01:24:48] predictions, then you can do like a computational like like methods to like simulate the molecular dynamics and get like kind of feedback loops. I mean like I think like material science is like maybe another area where like you have a little bit of that loop where you can sort of, you know, synthetically manufacture certain materials and measure the properties and you just kind of put an agent in the loop and like, you know, do things. So 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. >> [music] [music]