Ep 95: Modal CEO on the Future of Inference, the Chaos of the GPU Market & Origins of the Sandbox Bet

0:00How much of like an organized marketplaces compute these days versus like the wild west? I went to GTC last year. It was like an open air drug market, but like GPUs. Walking around, like talking to people, trying to get GPUs and meeting people in hotel lobbies, exchanging numbers. It's very far from an efficient market. I mean, some people are working on this. Compute comes in many different shapes or forms. I also feel like structurally, it will be a little bit like sort of unwieldy for a long time. Like it's just not something you can like sort of buy and sell in an exchange because it's not like a fungible contract.

0:33Welcome to Unsupervised Learning. As you can probably tell from my voice, I am not Jacob. I am Patrick Chase, GP at Redpoint. So awesome to have Eric on Unsupervised Learning for the second time. He is the founder and CEO at Modal. I have learned so much from working with him over the last three years. He is one of my favorite people to jam on AI ideas with. He has an incredible ability to move seamlessly from the lowest altitude of developer shipping code to the highest altitude of where the market is going and building what is missing. We talk about the mobile journey over the last three years, the crazy technology they have built to make it all work, inference, sandboxes, and what's coming next.

1:13Hope you enjoy the episode. For you guys, there's obviously a ton of AI compute demand and lots of people going out and trying to reserve, you know, GPUs and CPUs. What's it like for you guys finding compute in the market today? It's pretty rough. I mean, like, you know, not rough in the sense that, like, we can actually find compute. I think there's a little bit of a misconception that, like, we're sort of, we are certainly scarce, you know, in terms of compute in certain ways. But, like, there is capacity you can get. You just have to look a little bit further, accept higher prices, accept longer terms, et cetera.

1:49And we do that a lot. I mean, we probably do like two compute deals every week at this point, if not three. And so we keep, you know, stacking up, you know, more and more computing. And I really think that's the beauty of modal is we can aggregate all this demand and manage the capacity in aggregate much more efficiently. And the value prop of modal has always been like, don't think about capacity. Like we handle all of that, like annoying stuff, the sort of the day-to-day knife fighting of getting capacity. We work with 25 different clouds at this point and adding more all the time. How much of like an organized marketplaces compute these days versus like the Wild West, like random people that somehow have capacity and, you know, late night text messages?

2:27It's a lot of late night WhatsApp groups and like it's pretty unorganized. Like, I mean, I went to GTC last year or so earlier this year in San Jose, the NVIDIA big conference they were doing. And it was like an open air drug market, but like GPUs, like you're just like walking around, like talking to people, trying to get GPUs. and meeting people in hotel lobbies and exchanging numbers. I mean, at some point, I think we got connected with someone who had GPUs because I think our VP of sales cousin was golfing with someone, and we got connected. There's many such cases. It's very far from an efficient market.

3:08I mean, some people are working on this, and I think over time they get a little bit more efficient. But it's very heterogeneous. Compute comes in many sort of different shapes or forms. So I also feel like structurally, like it will not like it will be a little bit like sort of unwieldy for a long time. Like it's just not something you can like sort of buy and sell in an exchange because it's not like a fungible contract. Yeah. But the key difference is being like, I guess, the kind of actual powered shell or size of the cluster and like the chips themselves. Or to your point, there's a lot of people that have been trying to build like, hey, you know, compute as this fungible single asset that you could build a marketplace around.

3:42What are some of the kind of the key differences you observe? I think a lot of this actually boils down to reliability. I mean, first of all, it's like, you know, differences in like disk and networking and of course, CPU and memory and things like that. Like all these things vary and region or like location, like they're all over the world. Like we have GPUs in the US, of course, like all over the US, but we also have GPUs in like Indonesia, like all over Europe, etc. So location is one of those big factors. and then but also reliability uh and and that's another thing we do exceptionally well is like we aggregate all this capacity and then when neoclods die or like when they have failures which they often have because some of these are like former crypto miners who haven't really thought about you know running a 24 7 operation uh we can just take it out of rotation or like when gpus fall off the bus or whatever like in many cases we just file a ticket automatically someone goes and looks at it have you found the data providers the companies that provide pricing for gpu and that sort of things?

4:43Have you found them to be helpful? I think moderately helpful. I think the pricing less so than just their ability to connect with a lot of providers. I don't fully trust the pricing today. But I think some of these providers are actually very good at knowing all the neoclods. And we work with them sometimes to get connections with cloud providers. The pricing, I think, over time will get more accurate. Our internal pricing does not match what we see publicly when we buy capacity. But I think over time, hopefully, like we will have more efficient, like external indices. I think that would actually be very useful.

5:16You've been on this like rocket ship journey these past years. And so lots of things we'd love to dig into for our listeners on the business side. Maybe starting, you know, I think one of the interesting things about the history of modal is, you know, for a while, it felt like you, you know, publicly said, I think on your first unsupervised learning episode that you were less focused, you know, on these LLM inference calls, specifically like a simple API call for a model. And you kind of recently released a product there. Maybe talk a little bit about the journey and kind of that initial reticence and what made you decide that like now is the right time to really double down on this.

5:53Yeah, so we started building this in late 2021, basically, or early, somewhere around that time. And the initial product was this serverless function. And we thought it would be really useful for a wide range of stuff around like data, AI, machine learning, broadly speaking, kind of looked at a lot of different use cases. But then ChatGPT came late 2022, and StableDefusion also came around at the same time. It turned out we were super well-positioned to host a number of StableDefusion applications. So that was the first year of growth, was all StableDefusion. And then we started expanding into adjacent use cases, like audio and video and stuff like that.

6:33And it was like early teams that, you know, they were deploying JNI inference. They didn't want to deal with the underlying infrastructure. They wanted to move fast. Like we started aggregating a lot of different GPUs across a lot of different clouds even back then. We always knew we wanted to be multi-product, but we started with the serverless, like the sort of compute primitives. We felt like it was so general purpose. And we focused a lot on like kind of what I think was like high code use cases, like focus on like machine learning engineers and researchers who want to write code and focus a lot on being kind of lower in the stack.

7:02And pretty early, we built also a storage layer, a file system to store data, in particular, container images. Then we launched a sandbox product in 2023. And the reason why we launched it back then, we started seeing people messing around with LMs to generate code. And we figured actually a lot of stuff we built on the infrastructure side is really well suited to run that code. And they're going to need a place to run that in an isolated way. didn't immediately, it wasn't like an overnight success, but like basically 2025, it kind of blew up because I think models got really good at writing code back then.

7:37So about a year ago. So that was sort of our venture into like kind of becoming like a multi-product vendor, like starting about 12 months ago, like sandboxes has been in a tier and grown like, I don't know thousand X almost or something like that. It's about a third of our revenue right now. Standalone is a 300 million run rate business, sandboxes. So that's been incredible in terms of becoming a multi-product vendor. We just launched also a training product. Sort of trying to lay out all the different building blocks and what does that suite of tool look like and what are the toolboxes in this new world of AI applications.

8:13They're all going to need a bunch of new primitives and we want to offer all those primitives and what does that look like? So LM inference was something we were a little bit on the sidelines for a while until actually I would say the deep seek moment So that was about a year and a half ago, like early 2025, when DeepSea came out, which was like the first Chinese open source model that was like actually really good. And then I was like, fuck it, like we got to get in here. And like we started building up a team to do a lot of inference optimization. Models has always been really good at LM inference, like, but we didn't necessarily have a product for it.

8:45In fact, we didn't even have an inference product until very recently. We're just like really good at hosting inference, including LM inference. But it was kind of custom. And a lot of the teams that want to run LLM inference a year or two ago, they had to deploy it themselves or we would work with them. But we started seeing a lot of demand with the DeepSeek moment over the last 18 months. Started seeing a lot of growth. LLM inference is now about a third of our revenue, too. So we've seen incredible growth with that. And a few months ago, we shipped a number of LLM-specific features. Because we think, you know, we kind of laid this like foundational layers, like the storage, the compute, the serverless functions, the file system, etc.

9:25I think there's a lot of opportunity to move up in the stack. So we started moving up in the stack now and launched a dedicated inference product. So you can get like a single tenant models, like all the state-of-the-art Chinese models or a bunch of US ones too. Like one single click or CLI command, you can just deploy on modal as well as a bunch of shared endpoints. You can just call models off the shelf without having to deploy them, fully token-based pricing. And so I think those are the first out of many more products to come, slightly higher up in the layer. We think there's a lot of opportunity because we're sitting on this very solid foundation in the compute and the storage.

10:02Maybe you guys will start at the bottom of the stack and work our way up. But going back in time, you touched on it, but you guys invested in some really hardcore technology in the early days. you built your own file system, container runtime, those types of things. How does that show up in the product today when the experience developers can have? Yeah, I think a massive source of alpha in this market is just like not being afraid to do hard things. So we went all in when we realized like in order to deliver on the developer experience we wanted to have in the very early days, we realized like kind of have to build our own file system to do container images really fast.

10:43So we started building that in the very early days. And that become, like, over the next few years, you know, it still is, like, a very massive source of alpha. It's been a longer journey, for sure. Like, we've had to go a much longer route, like building our own file system, building our own container runtime, our own schedule. Like, we don't use Kubernetes and Docker. Like, we use, like, very little off-the-shelf software. We use GVisor, which is a fantastic Google product for isolation. and recently started using also KVM for certain tasks. But decided pretty early on that a lot of the stuff we're going to have to build ourselves.

11:19And now let's just do some really cool stuff. I think one of the best examples where that shows up is we can do cold starts extremely fast. Turn out like the initial developer experience that we wanted, like very fast feedback loop when you're launching stuff in the cloud. That is also very helpful when you're building autoscaling applications that need to scale up and down very fast. So first of all, loading the containers very fast. But over time, we also launched a couple of really cool features around CPU snapshotting and GPU snapshotting. So we can very fast load data from the disk or from network straight into the GPU and fire up even these large models in a few seconds, which is really cool.

12:00And over the last six months, we built this incredibly complex system that basically uses RDMA over peer-to-peer between the nodes to copy data very fast. So I think a lot of this stuff is kind of accumulating and really starting to pay off. We built some crazy stuff. And now that's like we're really kind of reaping the dividends of those sort of investments that we made for many, many years. It was a longer route, but now I think we have some incredibly unique technology that's very hard to replicate. And then kind of moving up to inference, what do you view as you guys have made tons of investments there as well and optimizations?

12:41What do you view as the unsolved problems in the inference world or in the inference stack? I think we're still pretty early with inference. I think a lot of stuff is like it's like one of those very empirical sciences. I think people are trying different things and seeing what works. I mean, over time, people have kind of figured out a lot of stuff. Take something like disaggregated pre-fill, right? It's a very non-obvious idea. You sort of do the pre-fill on a set of nodes, and then you copy the data over RDMA and do the decode and kind of swap. There's some very non-obvious things, but we've invested a lot in those types of things.

13:14The KV caching, there's a lot of complexity around KV caching. Of course, running more efficient kernels, that's something people do a lot. But there's a lot of these things that, you know, we have a team of, I think, 20, 30 people right now who are extremely focused on just like LM performance optimization. And, you know, it takes a lot of work. You have to stay up with research. You have to read papers. You have to look at all the software coming out all the time and try and see what works, what doesn't work. It's still kind of an empirical science. Over time, I think, you know, patterns will emerge.

13:47I think also the models themselves will change very quickly. Now we have a mixture of experts and more complex things. Over time, who knows? Maybe we'll swap out the Transformers for something else. And that would change how KV caching works. There's all these things that could change overnight if someone figures out a very different paradigm for all these things. And maybe they will, maybe they won't. I don't know. There's also obviously a lot of stuff around quantization and other things. So yeah, it's a wild world out there. But yeah, we're playing around with all these things. But it feels like there's so many folks that are working on, you know, different parts of improving inference.

14:24And obviously what's, I think, been really interesting in the last two, three years is a lot of those optimizations and improvements have been in open source, right? And like, you know, for the broader community, maybe shared via research. As a CEO, how do you think about like the level of what the kinds of problems you want your team to go solve versus, you know, it's like, hey, we're going to benefit from just the entire community, like pushing this space forward? Hi, I'm Anne Maris, and I'm the producer of Unsupervised Learning. Thank you so much for listening to the show. And if you've enjoyed the conversation, please subscribe.

14:54Subscribing helps us grow, which helps us continue to lock down some amazing guests. Thank you so much for your listenership and see you next episode. As a CEO, how do you think about the level of the kinds of problems you want your team to go solve versus, you know, it's like, hey, we're going to benefit from just the entire community, like pushing this space forward? We actually love open source because I think a lot of our alpha is in the proprietary side, in the infrastructure layer, like all this cool stuff around autoscaling, for instance. So I think on other inference providers, we've actually leaned very heavily into open source.

15:27We work very closely with both the VLM team and the SGLang team. We contribute very actively to both those repos and do a lot of optimizations with them. And so I think this may be a little bit of a different approach than maybe other inference providers. Of course, there's going to be certain things that we keep internal, but almost everything we push to open source. And I think it's because we have so much alpha in the infrastructure anyway. We might as well just work with the amazing open source engines that are out there that maybe weren't state-of-the-art a few years ago, but now they are.

16:03And that's an incredible thing that we'd love to leverage, frankly. You know, you were talking about heterogeneous compute and the different places where you're getting compute from and also how the, you know, kind of inference stack is changing. How important are, you know, the long tail of chips to you guys, the non-NVIDIA chips and things like that, as you think about your supply or kind of what your serving stack looks like over time? I mean, I think NVIDIA is still like the king here. Like they have the best chips. There's way more demand for NVIDIA versus anything else. Like, you know, we're curious about some of the other chips.

16:39Like, you know, we made some experiments, like taking a look at it. But NVIDIA definitely has the best chips. But, you know, like looking at AMD, Terranium, TPUs, et cetera, like starting to be like pretty good. So we're always curious about that. And then, of course, there's a lot of like older chips too. And I think the challenge has been like for a lot of the LMs, like you kind of constantly pushing like, you know, how many parameters can you fit? So typically the latest and greatest LMs, they will always require a full node, maybe in the future even multiple nodes, who knows. So like eight B300s, they will require the latest chip and like eight of them because that's how many you can fit on a node.

17:18But there's plenty of models outside of that, like some older LMs, smaller ones that have, you know, they're optimized for performance, might be fewer parameters. and then outside language, there's a lot of stuff around like voice and audio transcription or speech synthesis or like, you know, pharma, like bio stuff. Like a lot of that stuff actually, they don't require like the latest and greatest GPUs. Like they actually get more cost efficient serving on hoppers or even M-Pairs or like, you know, different types of like L40X or RTX Pro 6000 stuff like that. So we actually, I mean, we are ramping up like very, very aggressively on Black quals, primarily GBs now.

17:59But we see actually like plenty of demand also in the older chips, which I think is quite exciting. There's just so much stuff going on. People forget about the hoppers, but like they are our most common type right now still at modal H100s. We run some very like large production use cases based on hoppers. How efficient like is model to chip matching today? Like, you know, in the industry at large, do you feel like most people are using the like optimal chip or from a price perspective for what they're working? Or is there still a lot of room for improvement there? There's a little bit of room to improve.

18:32I mean, one of the things that we want to do over time is kind of automate that for people. I think in practice, it's less of a problem than it might seem like because often you're kind of memory constrained. So knowing how much memory you need for a model, you basically pick the cheapest GPU that fits the model. That's usually the heuristic that works in almost all cases so if you have you know if your model is 80 gigs like you can either fit it on a hopper or an older a100 80 gig i guess in that case it depends a little bit on the flops um but but in many cases it's like whatever whatever gpu like is the smallest that fits your model i think over time like there's a bunch of stuff we've been thinking about that where like basically you just deploy something a model and then we manage like different ships and maybe automatically give you discounts because like we can you know the more options we can have to run it on a lot of different chips.

19:21That lets us aggregate capacity in a better way and run things at higher utilization because then we can switch between different GPU types as there's underutilization on those. So that's something actually we're quite interested in doing over the next six months is giving people an option to deploy things in a way where we automatically switch types underneath in a way that's still consistent with your dollar per request or dollar per token or whatever. I think there's some incredible cool stuff you can do there. Oh, it's super interesting. And one thing I imagine is so interesting in your role, too, is like you're obviously predicting for what your compute fleet should be.

19:56And obviously you see what the workloads are today and you probably have some sense of what models run well on what chips. And you're also trying to predict like, hey, a year or two from now, what kind of workloads will we have? I think there's lots of different debates about whether most tokens running will be really large models or actually we've saturated most tasks and these models will get smaller and smaller. or like, you know, as you think about kind of the evolution of your fleet in the next year or two, like what, you know, what's your perspective on, on like where you'll see relatively more different kinds of workloads and less?

20:26I think there's been kind of like basically over a trend of like, like larger and larger models over the last three, four years. I kind of, you know, I definitely feel like that trend is going to continue. Like we're going to need, like if B300s had more memory, I'm sure, you know, a Kimi K3 would be bigger. People are kind of like, you know, training the models in a way that they fit on these chips. So I think on the, at least on the LM side, like on the, you know, LM's are in a way like kind of a crude type of world model. Like you're trying to like encode as much information as you can in these LMs.

21:00Like you need like very big, like almost like lookup tables of facts, you know, anyway, it's kind of a conception I think about it. And that sort of pushes like the memory requirements a lot. But I mean, at some point, like it might taper off, like who knows, but like for now, like people will make use of every possible memory, like the more memory, like they'll just train more parameters for sure. You obviously sit in like the, in some ways, I'm like the eye of the hurricane of like a very exciting, large and competitive market. And so I'm curious as you think about like, you know, what ultimately will kind of differentiate players in this space and like, you know, and win out.

21:37I think there's so many true believers in the size of the overall inference market. And I think lots of folks are asking what actually wins out in the end of the day. And I know you have a good thesis on this. So I would love it for you to share with our listeners. I mean, I think a lot of the inference market will be quite rough, right? Like I think the sort of straight down the middle, just like, you know, serving tokens, you already see sort of open router basically commoditizing the underlying providers. I tend to think you know like the real alpha or like the real like point of differentiation like there will be a lot of differentiation just in like more efficient serving but it is a little bit of a race to the bottom and I think and I think there's going to be a lot of opportunities to sort of stay ahead of competition and you're going to have to like put a lot of effort into that but I think a more sort of durable differentiation will definitely be in like you know providing the entire platform of a lot of different things which is like I think something we are quite well positioned to do.

22:30We have the sandboxes, we have reinforcement learning, a lot of customers doing also normal fine tuning. A lot of customers, obviously, you know, deploying other types of models. We have batch processing. I think my view in general is like customers, they don't want to buy like single trick ponies. They want to buy like a multi-product set of tools. So that to me is like ultimately like probably the biggest source of differentiation is like, you know, how do you provide that entire set of tools? How do you provide this like very general purpose thing that lets engineers like iterate very fast, deliver value.

23:01And then like this sort of day-to-day, like we're still going to have to like go and do the knife fighting of like, you know, staying ahead of your competitors in terms of performance and use the latest kernels and the much more efficient KVCache. And there's going to be a lot of that stuff for a long time because there's just so much money in it. But I'm kind of pointing out, I think that's sort of, that's like less durable as an advantage. I think that, you know, because over time, like people, these tricks will sort of spread and people will figure out how to copy them. So the more durable thing, I think, is like being that sort of portfolio of a lot of stuff around inference, not just the inference in itself.

23:34You know, having all the models and a lot of, you know, stuff like around reinforcement learning, maybe, or other things. You know, the biggest modal customers are on multiple products. Maybe can you touch on a few of the more interesting ones? So, yeah, we have some incredibly cool customers like Cognition is using us at large scale both for inference and sandboxes to do a lot of reinforcement learning on model. They use sandboxes for all the rollouts. We have customers like Ramp using this for background agents, for the internal coding agent that they actually launched as a standalone product today that they're selling.

24:08We have customers like Suno and Runway that uses regenerative video and audio. Decagon is another customer that uses this for a lot of customer support stuff, voice and text. DoorDash uses this for a bunch of internal applications, mostly LMs, but also all kinds of other stuff. We actually have a lot of customers in computational biotech using us for various types of drug discovery, using molecular simulations and sequence alignment and all kinds of stuff. We have a bunch of customers in robotics, 1X, and a few other ones using us to train robot models, like doing a lot of training, but also batch processing of video and things like that.

24:43A lot of customers in the data generation space, so customers who are building environments and running benchmarks in modal, often using sandboxes. One thing, you kind of mentioned this portfolio of products, and I think that's one of the most interesting parts of the business you're building. And I imagine, honestly, one of the hardest parts must be just prioritization of all the different things you could build. There's probably so many new things that come up of really exciting tools you can build for developers. And I guess I'm curious, obviously, with sandboxes, you have incredible products around coding agents, around reinforcement learning.

25:22How do you think about kind of building down that vector? There's also kind of like the fine tuning or all as a service stuff that some of your competitors seem like they prioritize. Like, how do you think about, I guess, where to prioritize within that portfolio? I think it's an incredibly hard problem. I mean, most startups, but especially multi-product companies, right? Like the sort of balance of like, you know, first of all, like when do you go multi-product and then where do you put your bets? Like we're 150 engineers or something like that today. And I struggle with it every time, like every day.

25:53Like, you know, I wish we could, you know, do that and, you know, whatever. But I kind of know that even if we had a thousand engineers or 10 ,000 engineers, like it's all, you know, there's lots of dollars. Like, you know, I wish we could build a rocket and go to the moon and whatever. Like, it just never goes away. So like, you kind of have to be just like ruthless with prioritization and really think about, you know, what's the highest ROI and like, where do you draw the line? And you have to be sometimes like pretty aggressive also saying like, actually, like, I know we started on this project.

26:22I know you're really excited about it, but like this other thing kind of came up. So like, we're going to have to pause this for a second. We'll get back to it later. We've had to do that many times, too. It sucks when it happens. But I really think, you know, that sort of prioritization and like, how do you how do you allocate resources? I mean, resource allocation is like basically like, you know, the number one thing that I think a CEO has to really, you know, be ruthless about it. the company and like so much value is being created, like allocating the work to the right resources. The modal roadmap in each board meeting is extensive and, you know, very daunting.

26:56And I'm always impressed by the amount of software that the team can ship and hardcore software while keeping the system running. How much has, you know, you guys were building the company pre-Chat chat GPT and then post chat GPT moment. How much has that changed, maybe on the product side, what you guys were doing, but then also when you were thinking about building and the velocity that you can ship software at? Yeah, I mean, we've always benefited from being quite well positioned at a time where AI kind of blew up. And that's been incredible to see. And we're powering so many different AI applications today that I had no idea would exist four years ago, five years ago when we started Modal.

27:39So that's been kind of incredible. And like you said, like software in itself is also, like how we build software has changed a lot. People write, like, I mean, like, I think probably like at this point, like 95 % of the lines of code that gets merged at Modal is probably written by agents to some extent. Infrastructure still, I feel like relies a lot on like, agents are like kind of good at it. I've actually been on a tier like the last few days, like building or last few months actually, like writing like an insane amount of code using agents. And it's kind of incredible what they can build, but you kind of still need humans to supervise them quite a lot.

28:12And infrastructure has a lot of tail risks around the reliability and making sure that things scale and stuff like that. So at least for now, I think software engineers are pretty safe. And I think we still benefit from having incredibly good engineers that can supervise these agents, I think probably for the next few years. Another thing I think has been kind of interesting to see is like, actually like, I think the adoption pattern is actually the opposite of what I would have expected. Like I thought it would be the junior people that would adopt agents the most. I've actually seen almost the other way around.

28:41It's like the very senior people. Cause for them, it's like giving them like 10 interns. Like they've been kind of constrained by like typing speed for a long time, but they have all these ideas in their heads. And I think using agents now they can get it, like they can get it out like much faster. They're really good at building a test suite. Like, you know, give the agents like, here's an extensive text test suite. it implements all of you know the post-six standard for file systems now go write a new file system and it just goes on like just writes a file system and takes like you know three days to do it uh so so we've seen you know that some of the very senior engineers like go like complete agent maxing and i don't know i mean it's kind of incredible like what you can do with agents you can move very fast with them at the end of the day it seems like humans are still safe as software engineers for the next few years i don't know we'll see how have you found the velocity of shipping software has changed what you guys need to do with code review or CICB or deployment?

29:32Have those pieces of your development lifecycle changed at all? I think a little bit. I think we're coming up against some of those constraints. Like we've had to spend a lot more time on CICB. Like I always like, you know, even long before agents, I was like, you know, I was a CTO for many years. Like I was always like obsessed with CICB as like, you know, very, very important to figure out. So like in that sense, like, I don't know if it's changed my view as you really need to have automated deployments, automated tasks, all these things. Now, by the way, agents are pretty good at writing tests, too.

30:01They weren't like a year ago. So I don't know if we've necessarily yet run up against the foundational limits, but I mean, Anthropic, a couple of days ago, they announced they were moving off Git, I believe, or at least GitHub. We might run up against those limits at some point when we have 10x the amount of commits every day. It's interesting to think about the how different the world is with these really powerful coding agents. And obviously, I think part of what I believe your co-founders talked about is the shift even in who the consumer of modal is over time, from a developer to agents themselves.

30:36And I'm curious how you think about what that means for modal as a business and some of the decisions you might make differently as more and more of folks interacting with modal are either doing it via an agent or purely end-to-end an agent. It's actually changed a lot less than I think I would have expected. It turns out agents, I mean, in the early days, we built modal in order to give engineers a better developer experience and the ability to move faster. It turns out agents kind of want the same thing. They want fast feedback loop. They want good error messages, good documentation, a lot of the stuff that engineers want too.

31:12So most of the stuff is actually quite similar. I do think certain things are a little bit harder. One of the things we found, for instance, is deprecations is much more annoying because agents are trained often on a lot of old code that exists online. So removing features is much harder for us. We can't just rely on remove it from the documentation and wait three months because it's in the training data. People will use LMs to generate modal code, and that will be using deprecated features. And that's quite hard to change. So we've had to change our deprecation window for quite a lot. And another thing we've started doing quite a lot is also setting up a lot of automated pipelines to basically have like LMs continuously write modal code and making sure it compiles and or not compiles, but it builds and runs.

32:01Trying to just like catch like things that agents struggle with. And then maybe another thing we spend a lot of time on is more like kind of like agent optimization stuff, which is actually more like similar to like search engine optimization. like if you you can go and ask you know cloud code like something like hey like i want to do something with inference i want to deploy this model and not say anything about modal and just like see like which tool does it recommend and and and that obviously is something we we wanted to recommend modal as much as possible uh so that's something we track internally make sure that you know over time like and actually i think that's like something that that um will change software engineering a little bit maybe almost like to do worse like i like i feel like in a way Like because of the training data and the training loops, like there's more like, you know, like incumbency power in a way.

32:51Like there's more staying power in software today. Like if you get inside the training data and you get the models to recommend, you know, a certain framework, I've heard people say like, oh, I'm just going to use React for everything because agents are really good at it. I'm not going to try these like new fancy front end frameworks, right? Like I think there's like something similar with like a lot of the frameworks today. It's like that staying power is actually very, very valuable now. So we obviously want to make sure that agents recommend modal as much as possible because that creates like a little bit of a flywheel.

33:19And one thing I'm struck by is you obviously had this like very prescient bet in 2023 on sandboxes, right? It sounds like you guys had kind of this instinct of like, hey, this is going to be something that's required for coding agents. And then, you know, it took a few years for it to really take off. And then, you know, it became this really important primitive. I'm curious, is there anything else that you guys are working on today that you feel like you're a little early on, but like, you know, you're equally as excited? And how much of your product portfolio is like those kind of bets versus like, okay, we know this is a here on fire, like here and now problem.

33:48Jacob, are you fishing for the next thing we should be investing in? A hundred percent. A hundred percent. I'm nothing if not shameless. I feel like sandboxes, they did create a little bit of like a high bar for us. Like everyone's like, what's the next thing? You know, what's the secret sauce you're working on? And like, what's cooking in the lab? You know, and I wish I had like an easy answer. Like, yeah, like a hundred percent confidence. You know, this thing is. would you have answered sandboxes in 23 or was it just like part of a bunch of things you were doing no because what sandboxes represented back then was like a little bit of like an asymmetric bat like sandboxes was not like a very hard thing for us to build back then like to getting out like a prototype of sandboxes took like a shot like a few days and then i spent a little bit of time you know like improving the sdk and some other people like started building and then we started seeing some customers we wrote it and then in 2025 when it started exploding then we had to rewrite the system like five times just to handle like more and more scale and make it faster and and more reliable and things like that so but but the initial investment was actually pretty pretty basic so in that sense like it was a little bit like a sort of asymmetric bat like a kind of a low downside high upside bat i don't know if there's like anything similar today that we're working on i am i mean like just just kind of call that like a couple of like you know things that i'm quite bullish on and these probably represent like way larger investments uh like you know larger risks and and you know in terms of like that size low latency like i think you know there's going to be a lot of voice to voice a lot of real-time video type stuff that requires us to rethink a lot of how we manage to compute like globally how do we get down the number of hops like how do we scale up and down in different regions like almost like building a cdn for inference uh so i'm pretty excited about that um i think training is actually kind of an underrated thing.

35:31I think there's a lot of opportunity in training like mid-sized models and reinforcement learning. That's been a big bet for the last couple of years, getting all the RMA, InfiniBand type stuff working. I think a lot of VM stuff is quite interesting. I think supporting full-blown operating systems like Mac and Windows and Android and iPhone could be kind of cool. I think there's a lot of upside there. I have a lot of those ideas in my head. And we're like building a lot of those things. I don't know if there's anything that represents the same like sort of explosive potentials like sandboxes. I mean, maybe like if we have this podcast again in like three years, like we'll kind of look back and...

36:11We're going to make it recurring at this point. So we'll look forward to revisiting that. You know, I guess like obviously, you know, sandboxes have become this fundamental primitive for, you know, coding agents. And it feels like that then brings you into this world where there's like, you know, I imagine you look at the current state of coding agents, you're like, God, there's so many more tools that would be helpful for these agents to make them more effective for people that use them. How do you think about whether you further build into that world or the sets of things that are interesting for you there?

36:38We do think about it, but I think we think about it more from an infrastructure perspective. I think it's very easy to kind of get lost in whatever is the latest today. When I look at the architecture of agent harnesses and how we're building agents and stuff like that, I still kind of feel like we're so early and these architectures like evolving so fast and like what our job to some extent is like trying to figure out is like what are like the sort of core building blocks that we can put out today that like will last you know like people will need them for the next five years and and that to me is like less obvious like we are interested in like sort of agent orchestration as a category i think there's a lot of interesting stuff there um we're interested in like you know evals and model gateways and all kinds of other stuff around like, you know, like the whole, like, you know, life cycle of learning from agents and innovating on agents.

37:27But I think it's like less clear exactly like how the architecture, my view is like, it will be more cloud centric and it will be probably more, you know, like, like durable objects, like kind of workflow engine type driven. And we have some ideas of like, you know, stuff to build in that space. But I find it like harder to like, you know, lay out exactly what agentic development will look like in five years. Part of it because I also think to some extent all software will basically be agentic. I kind of hate the word agentic in certain ways because I feel like it's like, okay, so it's using AI to make decisions.

38:03Well, shouldn't all software do that? So I almost feel like if you look at it on a five-year horizon, I think all software will become agentic. And then what does software look like then? I don't know, but I hope we can build a lot of the building blocks. to support all kinds of aspects of, in general, just how software uses AI. Yeah, I mean, it still is one of the hardest parts of building an infrastructure company, right? Across the board, for many people, building AI infrastructure companies, what actually persists versus is a pattern, is like RAG from two years ago or whatever it is. RAG is a good example, exactly.

38:36If we had made a bet on RAG two years ago, it would have been kind of a bad thing to do. So it's hard, but I would also say one of the benefits to building an infrastructure is like, you can take a longer view and you don't have to like, listen to all the noise. I mean, you have to listen to the noise, but like, you kind of have to like, ignore a lot of it and trying to figure out, okay, what are the patterns here? You kind of alluded to some of these other, you know, other workloads that like low latency workloads like voice to voice and real-time video. I feel there's a lot of chatter in the air about like multimodal inference, robotics inference, these other categories.

39:10I'd love to hear you just riff a little bit on like, to what extent are those problems, you know, similar to the set of problems the inference markets had before? To what extent are they like different and new? I think they're all quite similar, but all, I mean, they're like snowflakes, unique in their own way. Robotics is going to be a lot of it on device, of course. So that's a big difference. And, you know, I don't sort of envision like we're going to support, you know, most of what robotics looks like because a lot of it is going to be on the devices themselves. But I think we're going to be able to support a lot of the testing and development and training of robotics and a lot of the simulation environments.

39:44So that's something I'm very excited about. on the multimodal stuff. I mean, there's audio and music and image and stuff like that. Like people don't talk about it as much, but that keeps growing like crazy too. I feel like LMS have kind of dominated the airspace in the last few years, but I'm just incredibly, you know, excited about all the different applications. Like why are like voice assistants so bad in 2026? Like, I don't get it. Like they're just horrible. Like I have a Google home and like every question, it just bungles. It does not understand what I'm talking about. Like, you know, so I just go to ChatGPT.

40:16Like, why can't you just connect all of it, like, end-to-end and train, like, a system that can, you know, do, like, speech-to-speech? I think there's a lot of, we're going to see a lot of sort of incredibly cool applications in that space. I always think about, you know, you look back, like, you know, four years ago, three years ago, whatever, you know, could you have a guess that, like, things like Instinct or Suvino or, like, Cognition or, like, none of those things, like, are very easy to guess. like, you know, like three years ago. So I kind of feel like also like if you look down the road, like three years, like most people don't have an idea of like what the world is going to look like in three years.

40:53It's so hard to know. But like what I do know is like kind of judging by the past three years, it's going to be like, there's going to be some really cool applications. I just don't know what it's going to be, but they're going to be very cool. And hopefully they'll use modal. I was looking back at an email you sent me with the modal series A pitch deck. And this is what you said. This was after we had signed a term sheet. By the way, we never actually reviewed a modal deck. But here's something we made earlier that I just updated with the latest numbers. Could be good to review at some point.

41:23When you sent this, you were at roughly 2 million in run rate. You now, you know, you've crossed a billion run rate, which is insane in three years. What's been the biggest kind of change to supporting that kind of scale in just, you know, a couple of years? So first of all, I actually realized like the other day, I said on another podcast that I've never made a pitch deck. And then I was actually thinking about it. I was like, maybe I did make a pitch deck after this series A because we had a couple of angels that came in and they were asking about it. So I was like, oh, fine. I guess I'll put something together.

41:55So I realized I did lie. Like I did make a pitch deck, but it was actually really cool. You never showed it to us. So I don't know that it counts. Yeah, so I don't know if it counts. Like I never like sent a pitch deck to an investor. I sent it to a few angels actually. uh but that and that's the only push deck i've ever made actually um but uh in terms of the scale yeah i mean we're grown you know 500x i guess in terms of revenue something like that since then uh over the last three years um so a lot of growth um i think i think in a way like i don't know maybe i'm like just you know my experience has mostly been at hyper scale startups like i was a spotify for seven years uh and some other places so so for me like this like fast growth is kind of all I've seen.

42:35Like, you know, there's like, you know, every, every six months, you have to rewrite all the systems, you have to kind of rethink everything you're doing, you have to kind of change all the processes, like you always feel like you're behind. Like, I don't know, I kind of like the mass, I guess, we'd love to, you know, since we have your mind on the podcast, we'd love to kind of zoom out, maybe talk a little bit about the broader inframarket and some things that have been in the news of late. You know, maybe most top of mind these last few weeks, I feel like there's been tons of discourse about neoclouds, right?

43:04They like, you know, how the debt's trading, they've traded down, they're kind of back up a little bit now. And I'm curious, like, what do you think happens to this landscape over time? I think the neoclods are like, fine business models. Like, I think people have been like, confusing it like a little bit. They're like, wow, there's so much demand, like revenue is going to grow like so fast. And then, you know, there's a little bit of sort of a, almost like a VC mindset of like, oh, like thinking in like revenue multiples. but I think all of these like these neoclouts like growth is actually quite expensive for them because they have to go and raise a lot of debt and so I don't know if necessarily like you know just because like they grow a lot like you know like it's very like capital inefficient like these companies are at their core like you know financing vehicles like they're very like they're doing leasing like of hard assets and that's you know and that's like inherently you know harder to scale the capital now that that market is kind of saying whoa we deployed a lot of capital into neoclouts like maybe maybe hold on for a second like you know we don't have that much more capital to lend and so the interest is starting to go up um but also by the way like these companies have incredibly high leverage so of course they're gonna have more volatility like the the residual equity value is going to go up and down much faster on the stock market with gpu prices and stuff like that um but they're like fine business models like you know they kind of locked in customer contracts uh and raised that against it and like you know now they're paying off that debt and and And in many cases, it turns out the residual value has been higher than people expected.

44:30Lifetime of GPUs has been longer than people expected. So they can resell once they paid it off at a higher price than people thought. So they're going to make a bunch of money from that. It takes a few years to get there. But I think in that sense, NeoClass will do well. They're not particularly differentiated. So long term, I think they're going to struggle with that. Yeah, and you work with a lot of them. Is there any differentiation you see? It's like a little bit. But at its core, it's relatively undifferentiated space. So I mean, my view is like, you know, I mean, Elon was tweeting the other day, like, oh, the new clouds are going to die.

45:03I think that's kind of bullshit. I do think Elon had a point when basically when he implicitly was saying SpaceX has a much lower cost of capital. I don't think there's a coincidence that if you look at the three successful hyperscalers, Amazon, Azure, or AWS, Azure, and GCP, that they're all attached to companies that have very solid cash flow, and they're all public companies that have very low cost of capital. Because that at its core makes the life a lot easier if you want to build something that's very capital-intensive. So I think in general, that is something that Neoclast will struggle with.

45:39They're going to have a high cost of capital, and it's going to be hard for them to grow fast because of that. So we'll see about the market. But they're like decent business models. I think there might be an opportunity to buy the dip. I don't know. You had an interesting tweet that was a lot of the Neo labs could become Neo clouds, you know, if their model doesn't work out, but they have locked in these contracts. I definitely could see the world going that way. If, you know, if you have valuable compute that continues to appreciate because there's so much demand. But yeah, I mean, Mistral kind of did that, right?

46:14And, you know, like it seems to be fine. I think a lot of people are making these like very large buying decisions and they kind of rationalize it. They're like, well, if you don't need the GPUs, we can just resell them, right? That's almost like a form of insurance. And you have to assume with all these Neolabs, there's so many Neolabs out there that have each raised$1 billion. They always raise exactly$1 billion for some reason. I don't know why. I guess they like that number. And then buying GPUs. Very precise financial modeling that points to that. Yeah. So some of them, I think, will probably work out really well.

46:47And, you know, but I imagine in the grand, you know, like some of them will not work out. And if they don't work out, well, now they're sitting on a ton of GPUs. Like, you know, let's resell those GPUs, right? Like, especially if there's ever like a scenario where like VC funding pulls back. because I think what's kind of a scary thing about some of these commitments is like, you know, effectively, like what happens today when you make a GPU commitment is you only have to pay about 30 % upfront, sometimes a little bit more, sometimes a little bit less. But what that means is like you can raise a billion dollars.

47:18You can go out and get GPUs for$3 billion and only pay a billion dollars upfront. I mean, not exact math, but the problem is like, you know, in a year, now you're on the hook for another few hundred million and like every month now you have to pay. So I think that creates a little bit of implicit leverage in a lot of these companies and a dependence on subsequent fundraising. And that works as long as they can go and raise VC dollars to fund the GPU bills. But at some point, it might not work. And in that case, I think you're going to see a little bit more of a secondary market where some of these Neo Labs might pivot to doing a little bit more of the sort of NeoCloud style.

47:55Like we have a bunch of GPUs or just resell it back to the cloud. or I don't know. I'm very bullish on AI as a whole, but I always look for these sort of structural risks. So overall, I think there's going to be incredible application, incredible demand. But is 100 % of the Neolabs going to succeed and be a trillion-dollar company? Probably not. I feel like on the AI M4Sci, one big category that has gotten a lot of buzz is this URL as a service market and kind of a bunch of folks almost forward deploying and helping companies build their own models. And you've had folks like Satya saying, hey, every company is going to have their own model.

48:30Like, do you subscribe to that? And like, what do you kind of think of this, of that overall market? No, I think some companies will have their own models. I think that like weighted by dollar revenue, like I think most models will probably be post-trained, right? Like, but like weighted by company, like in terms of like numbers of companies, like most companies will not have the need to like do their own post-training, right? But like the large ones, right? like cognition is training their own code models and and and cursor date and and all these companies are and like those are the ones also serve a lot of tokens uh so i i think when you look at like reinforcement learning and post training like certain types of you know parts of it is very attractive right like you can work with companies and and and serve you know better models and do it at lower cost and higher throughput and all of this stuff some of it looks like it's kind of like just a service business so i i think a lot of the sort of tension over the next few years when you look at post-training, it's going to be, can you turn this into product?

49:29Like, can you sell that product to these companies? This is something we think a lot about is like, how do we create this like amazing product you can just kind of sell and not turn it into just like a service business? And then, you know, I guess last one before we move to the quickfire round would just be like, you know, I think there's this question about, you know, how many tools live under like a single infra provider in the AI world versus, you know, our separate categories. I've always struck when we talk to you about like just how much you want to go build and all these kind of building blocks.

49:54I'm wondering if there's categories of AI infra that you think will be unique and distinct from the stuff you're building that you're optimistic on and that will kind of live as still large standalone opportunities. Yeah, I mean, we're not going to go into model training, I think. I think TypeSafe with Java is going to be probably going to be a lot of these custom models. I'm sure in other verticals too, other modalities like audio and video and music. Obviously on the application side, there's going to be a lot of incredible companies. And then there's probably going to be a lot of companies like focusing more on like software development and stuff like that.

50:31And, you know, I don't know, like my general view on these things is like there's always like more boxes on the market map than you expect. But I also do think that overall, every company that is successful in the end, to some extent, has to become a multi-product company, too. So I don't know if that's a tension or not. Like, I think, you know, we obviously want to find, like I said, a cluster of like very adjacent things and do all of it in a way that like, you know, maximizes the revenue and the shareholder value. But, you know, by no means, you know, are we going to try to expand into everything?

51:05I think people kind of make this like false assumption. Also, like, you know, you look at like Anthropic or OpenAI and they're like, why don't they move up to stack and launch their own law firm and launch their own whatever? Like, they're very good at like serving these models and doing that at scale. That's how they achieve scale, right? And they're very good at doing that. I don't think it makes sense for every company to do everything. And so similar for us, we're not going to do everything, but we're going to do a very large cluster of adjacent things and do it exceptionally well. And I think there's going to be a lot of room in the market to create a bunch of very large $100 billion or$1 trillion opportunities in the market.

51:40We'll end on our quickfire round where we stuff probably a similar set of questions what we asked you three years ago, maybe a little bit of an evolution. To start, maybe I'd love just one thing you've changed your mind on in AI in the last year. Well, the opportunity size of sandboxes has obviously been kind of crazy, right? Like I didn't think it would be anywhere close to, you know, I mean - I thought you knew that in 23 and that's why you made the big bet. Yeah, so that's been kind of incredible. Coding agents have changed a lot of what it means to be an engineer. How, if at all, has it changed how you think about hiring?

52:15Not much. I think we're still going to need humans for a long time. I think the sort of demand for humans is just going up the more productive they get. I think, you know, we've seen this for the last 30, 40, 50 years of software engineering, you know, whether it's compilers or high level programming languages or CICD. Like every time you make engineers more productive, like the demand just goes up. And so I think we're going to need a lot of different, a lot more engineers. I do think maybe on the short term, it's going to be a little bit harder for new grads, except for the very high side of the spectrum.

52:51But in the long run, I'm very bullish on still computer science. Actually, ironically, I didn't study computer science, but I'm very bullish on studying computer science in general. If my kids wanted to do it, I would definitely encourage them. What's the biggest mistake you've made in building mobile so far? I don't know. I think one mistake I made in the past is that I think sometimes like you kind of want to sequentially do things. You're like, well, first we need to do this. This is the blocker. We'll get to this other thing later because this other thing is blocked on this. But like in a world where like everything is moving fast, like one of the things I've learned is like sometimes you kind of need to do both at the same time.

53:29Like you need to assume that like, oh, we're doing this thing to unblock it. You might as well do this other thing at the same time, just in the interest of moving fast. So that's something I, I'm naturally like not mega aggressive, which is kind of ironic because like we've run 15x in the last 12 months. I sort of had to retrain my brain to like be a little bit more like aggressive and saying like, this is a massive market opportunity. We need to be even more, you know, deployed in like hiring, deploying capital, like whatever it is, like massive is like paralyzing everything we can. Sweden has a ton of interesting startups that have emerged.

53:59what are your thoughts on why that is? And has the Swedish house mafia been replaced by the Swedish tech mafia? Did you know that one of the Swedish house mafia members was my classmate? It's a fun trivia fact. Really? I did not know that. Yeah. Back in the early 90s, growing up in Sweden, when I was eight, he told me my bike was ugly. I was very upset. Yeah, I mean, we've had a lot of tech in Sweden. then Spotify, Klarna, you know, a bunch of gaming studios. More recently, obviously, Lovable and Agora, stuff like that. I think my theory about like a lot of this stuff is like success kind of just creates success.

54:42Like, you know, a lot of like what makes people go into tech is like they know about some cousin's friend who made a lot of money in tech or something like that. So a lot of it is I think it's just like a little bit of a flywheel effect. I do think that Sweden as a European country has always been pretty entrepreneurial and commercial. I do think there's a little bit of that. Like we've always been a little sort of maybe going back to the Vikings, like kind of let's go conquer the world in the past. Maybe it's been through music and furniture and stuff like that, but now increasingly with tech.

55:09So, you know, there's that. Like, I think there's sort of a mentality in Sweden. Like you can actually, Stockholm is like funny because like everyone in Stockholm thinks they're like this like mega global city. That's like part of the world in a way where I actually think they aren't. But like, I think the fact that they think they're so global is like kind of good. They have this vision that everyone cares about Stockholm. But I actually kind of like that because it means they're out there building things and they're saying from day one, we're going to conquer the world. We're going to do this startup.

55:35It's going to be awesome. And I like that, the sort of megalomania. I have this vision of your elementary school reunion and just what a wild group that would be. That's quite the cast in what I imagine was not a particularly large class. Yeah. Yeah, it's kind of a random fact. I guess a shameless question. Do you think GP rental prices 12 months from now will be higher or lower? I think higher. You know, it's funny. Like, I actually changed my mind on this, like, in the last 48 hours. And I'll tell you why. I read the Anthropic S1. And I actually had an agent summarize it for me. But it was kind of incredible how profitable that company is.

56:15To the degree that I hadn't anticipated. I thought, like, in my mind, like, 48 hours ago, my argument was like, Oh, Anthropica, they have really good gross margins, but they're spending so much money on training that as soon as they go public, they're going to have to slow down in how they grow training and post-training. And they're going to maybe grow inference as much, but not the other stuff as much. But then I realized they're basically a profitable company. So they have zero incentive to slow down on capacity acquisition. And I imagine there's a similar dynamic going on in OpenAI. And so OpenAI and Anthropic has obviously been the biggest two buyers of capacity out there.

56:53And so the fact that I don't think they're going to slow down buying because clearly financially they're doing very well, I think it will mean that there's going to be a lot more demand. And I think for that reason, prices will probably go up in the next 12 months. I've heard you say that you've always thought of modal as your 20-year project, your five years in. What do years 10 through 20 look like? Wow. I mean, hopefully I'll hire a bunch of people that can do a lot of those sort of, you know, I'm too in the weeds of like everything right now. But I don't know. Like, I just like love this stuff.

57:33Like, I love to keep working on this. Like, I think, you know, year 10 to 15, we're going to look more like a conglomerate. We're going to have a lot of different products. We're going to have probably some like crazy bets going to moon and Mars or whatever. but I still hope to be quite involved in the sort of the high level, like resource allocation and what are the things we're betting on. And I'll probably be less involved in sort of day-to-day, like, you know, let's get a contract from this GPU vendor, like whatever. But like, I think that like sort of like high level, like resource allocation and stuff like that, to me is, that's the most fun and hope to stay involved in that for another 20 years, hopefully, maybe longer.

58:09Yeah, I mean, if we keep current pacing at least five more unsupervised learning episodes in that timeframe, right? Yeah, exactly. Well, Eric, thank you so much for joining us. We really appreciate it. We'll give you kind of the last call to action for the audience. Where can they go to learn more about modal or anything that you would direct them to? Well, you should obviously go to modal.com. You should also run pip install modal in Terminal and play around with it. You know, there's a lot of people maybe listening to the show who are building startups or thinking about building startups. and, you know, we'd love to see all the cool stuff they're going to build.

58:44And, you know, hopefully at Modal, we can help you with those startups. So let me know. And feel free to reach out, by the way. Like, I always love talking to customers. But feel free to play around with Modal and looking forward to see what everyone's going to build. I'm Jacob Efron, and this has been Unsupervised Learning, a podcast where I get to talk to the smartest people in AI and ask them tons of questions about what's happening with models and what it means for businesses in the world. As I hope is clear, I have a ton of fun doing this. It's a nights and weekends project in addition to my day job as an investor at Redpoint.

59:12but our ability to get these incredible guests on really comes from folks like you subscribing to the podcast, sharing it with friends. It's really what ultimately makes this whole thing work. And so please consider doing that. And thank you so much for your support and listening. We'll see you next episode.