← Latent Space: The AI Engineer Podcast
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Foreign.
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Studio with Ivan Burazin, CEO of Daytona. Welcome.
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A
Thanks for having me, man.
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B
Ivan, you and I go back, way back. I don't even know how you found like we. Did you reach out or for Shift or.
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A
I reach out to you. The reason was you. We were just, we were thinking about. I was one of the co founders of Code Anywhere, the first browser based idea. And so we were thinking a long time of like local hosts should die.
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B
Oh yeah.
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A
And you had this article and a local host. And then I reached out to you because of that. And then we talked and I was actually at a different job and learning about. I was ahead of like developer experience and you were quite well versed in that. And I actually reached out to you among other people, like how do we, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call and I remember I was late on your call with you.
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B
I don't remember.
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A
I remember because I was with my. Then I was thinking of a girlfriend or wife at that point in time. I'm not sure it's the same person. So that's great. And I was late because we were in, you know, Italy on vacation and then I was late for something. I felt so bad. And you were so nice to be good about that.
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B
The reason I'm nice is because I'm also late to other people. So it's like, you know, who's without sin here? Yeah. So I have to, you know, for those who don't know infobip Shift, there's this whole thing that you did in the past and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, oh, you can build and sell conferences.
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A
Yeah. And I remember you asked me at the beginning to give me advisory shares. I was so focused on what we're doing, I said no and I should have took the advisory share. So I'm sorry I didn't. But anyway, we're not venture backed, you
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B
know, anyway, so I think what's interest impressive about you is that Code Anywhere is the thing that you've been trying to build and you know, you kind of put it on hold and came back after infobip. Just give us the story. The story and the origin story going into Daytona.
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A
Sure. Like really way back. Me and my co founder have been together. I've said this multiple times. It's like we were married and divorced and married. Some people Actually asked me is is my co founder, my partner. Like they thought it literally. It's not literally, but we have done multiple companies together. And to your point, we had this shift where we went from the Code Anywhere to the conference called Shift and then back to Daytona. We originally started stacking, stacking servers, doing like virtualization in the early 2000s and you know, routers and doing basically all these things at a foundational level. And that was a services company which we sold to focus on what my co founder actually invented, which was the very first browser based ide. Right. I say the first before us was actually Heroku. They did it for a very, very short time until they became Heroku. But outside of them we were the only one. And it was. There was Cloud nine, there was Cloud nine that came out slightly after us. There was Replit, which came out when we stopped doing it. Replit came out and they have been successful since then, which is great. There was Nitrous IO, there was Qu. Quite a few that existed in time, but it was like too early. But the interesting part is that we at that point in time, because there was no VS code for those that still remember VS code, there was no Kubernetes and Docker had just started when we, or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves. And that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used code anywhere. It was slightly. It was angel backed more than venture backed. We ended up paying everyone back because it didn't have that sort of scale. But you know, three years ago we started something similar with Daytona, which is not what we were what we are today. But it was automating dev environments for human engineers. The basically the underlining stack of code anywhere. And then we did a hard pivot last January to sandboxes.
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B
And so here we are, historic pivot. And you know, it's one of those things where like I had independently invested in code anywhere, but also in to be. And then both of you pivoted into the same thing. And I'm like, you invested.
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Invested in Daytona, invested in Daytona, but. And you were the first. If we had knock out your check, we wouldn't have done it.
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B
No way.
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No, it was like we have to get him on board first. And you were that kicker that we. That got us on the.
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B
No, because you were putting me on your pitch deck. Man, I was like, man, this is like a good trip if I don't invest, like.
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A
Well, that's because it was your quote. It's like we did a bunch of research about end of local host and who was interested in that. So.
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Yeah, no, it's like I put. I wrote that blog post and every single company in that field reached out to me and then every VC who was receiving those pitches then also had to call me and talk through it with me.
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A
It's finally happening. It's finally happening.
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B
It's finally happening.
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It's finally happening.
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With maybe sort of non human users.
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Yeah.
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B
So what is Daytona today? Let's get like a quick description. I'm wearing the shirt.
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You're wearing a shirt? Yes.
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B
I think your branding is very good. It's very consistent. It runs AI code like it cannot be simpler.
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A
Exactly. But we're going to probably have to change that because it's also a subset of what we do, unfortunately. We really love this. A run at code is super simple. People interpret it different ways. I think we've given out 5,6000 of these shirts. People wear them with pride because it doesn't really market to.
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About the person. Yeah, they send us on the back.
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In the back. It markets to the person about the person itself. So I think we did a really good job on that one. But it is also a subset of what we do because people, when they think about running AI code, they just think about these small, let's call it isolates code execution boxes that, you know, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is. The market calls them sandboxes. Yeah.
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B
Which can be misleading, all these things on the.
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A
Yeah, exactly. Because it can be misleading because people usually think about sandboxes as a demo or a test environment versus a production grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, you know, my wife is an architect, so she has like a Windows with 3D graphics card inside to do 3D rendering. Like, computers are different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we, we offer that basically through an API.
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B
Yeah. To give people. I'm trying to sort of front load all the aha moments or the wow moments so that people can stay engaged and click like, and subscribe. The market is exploding. Right. Like you have been reporting 74% month on month growth. And also it's just been going for a while, like it's been going like this. And every single. It's not just you guys, it's every single sort of compute provider. I don't know if you agree with me saying compute provider or not, but. Yeah, so it's like organically PLG driven growth, but also enterprise is doing super well. I think I want to rewind to January last year when you did the pivot. So you obviously called this market early and you were positioned for it and you are now one of the market leaders. But what was the insight that made you do the pivot?
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A
The insight that made us do us pivot is the quarter before that. So end of 2024, when we had. Basically we did a demo with. I don't. I think we discussed this as well. Devin was not public. You actually gave me access to Devin at that time, so. Devin, I did, yeah. You.
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B
I don't think I was supposed to.
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Yeah, exactly. So it doesn't matter.
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I gave like three. Three friends access.
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Yeah. Or it was a cold call and you showed it to me. It doesn't matter. But OpenDevin was available, which is now called Open Hands. And so we're like, oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take Open Devin and launch that as a SaaS. And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents and they were like, hey, my agent needs a compute sandbox, runtime, whatever you want to call it. I forgot what it was called at that point. And then we were like, oh, amazing. This is a new market. Here is our infrastructure, here's our product and go. And what we found really, really fast soon was that people did not like what he had built. It didn't work. And I remember talking to people at the beginning when we're doing this, you know, the sandbo we're building for agents, people are like, oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things. But we saw that everyone we gave it to was like 20, 30 people. They all said, no, like this is not what we need. This, this sort of breaks. And basically me and my co founder not knowing a lot about. Because we're infra people, we're not AI people. So I basically took upon myself to like watch every single podcast that exists, including all of all of these and all that is sort of get up to date, read all the blogs like get. Understand what's going on.
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B
Do you want to shout out who else was useful? Just. Just in case people are also looking.
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So generally we. I looked at there. There's a few of podcast different segments in different types. So there's you guys. No priors. Bill Gurley's was great while. Yeah while. While it was around. So there's a few. 20 VC is interesting from a different dynamic and some are different dynamics. But there was.
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But we're not really about the compute market.
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It was also ready.
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Sorry, I guess you're. You want. You're looking at the agent infra market.
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I was looking at the agent market, the AI market in general and sort of understanding who are the players, what the perception and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first mc, first mineral viable product of what Daytona is today. And I went to sleep at like 3am or something like that I was doing. I just put my like baby daughter and wife to sleep and you know, Happy New Year's and go back to just doing this. And I sent it to my co founder, my cto and he saw it in the morning. He's like, this is absolute garbage. Not show this to anybody at all. But the idea is good. And so he took two weeks and
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B
he did it like look like that.
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And they not even like it was my. It was way worse. But it was like a very. It was a simplistic view of what it should be like. It worked, but it was not ideal. And so he went, we went down the hole, which is his job as CTO to go and he came back with this version. We then called all the people that said like this is garbage, you know, a quarter ago. And we set up these calls and we gave it to. We just demoed it to everyone. And all the calls went long. Every single one. They were 15 minute calls. And they all went like 25, 30 minutes or whatnot. And. And everyone said, we need, we want access. There was no login, just an API key because this was just a beta or an alpha. And they said, oh, we want access. And we're like, sure yeah, okay, thank you very much. But after like the next day, if we did not send it, every single one, like every call that we did, everyone came back, where's my IP I key? Like everyone wanted it. We're like, shit. Like, this is it. Like I've never felt so 1. The the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago, it's not. We just didn't know what was the right primitive. And then when we came and we can talk about what that is and we gave it to these people. I've never experienced, I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. The thing that they want doesn't seem to exist or they have not found it and they really, really want what we want. And then when we understood that we're onto something. And then when you think about the size of the market, like the market for human engineers and enterprise is a very large market. So think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, we are all in on this. And so that is where we made sort of the cut between the old products and the new one.
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B
Yeah, but it wasn't composable at the time.
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A
So it was very. It was basically just a Linux box that you could change that you could define number of CPUs, disk and RAM like that. That is what you could do. But you couldn't have multiple operating systems. You couldn't resize it on the fly. You couldn't add a gpu. You couldn't do like all things. It's just the first sort of variation of that. Yeah.
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B
And was it bare metal from the start?
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It was bare metal from the start. And so the interesting thing that we thought about right away, so rl, which
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you know, give people the background. What is the normal path?
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Yeah, so basically most providers run this on top of VMs.
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Yeah. And also Firecracker.
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Yeah, they run a firecracker on vm as we also we can get. We have multiple isolation layers and we can do that. But the, the common way to do it is that they. One, that the state of the machine or the. The hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible. Like, they can There. There's a time that they can live. And so our thought was, when we're going into this is, agents will be like humans in the sense of, you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid. It's the same state. So agents would want that, like, pause and come back. They want those two things, but also agents really, really want speed. Right. Can they get it? So when we thought about it, like, we need something insanely fast. How to make it fast, how to make it long running and stateful. And so those two things, it's like combining a lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it because we didn't know too much that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't good enough for that. We looked at Nomad. It didn't enable that. And so our history and rewriting our own scheduler at Code anywhere is basically what my CTO came up with. Like, he's like, oh, the learning's from there. He brought it. And the funny thing is, our third co founder, when he saw it, he's like, dude, what is this? This is like 2008. Like, we went back in time and he's like, exactly. And so the reason why Daytona is, like, super, super fast, and you see this on benchmarks, is we essentially. We run on bare metal. We have our own scheduler. We use the underlining disk, CPU and RAM of the underlining machine, which means your Iops are insanely fast because there's no, you know, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates are also preloaded on the bar metal machines. So when you fire off a sandbox from a template or a snapshot, you are essentially directed to the bare metal machine, where that snapshot is based on that NVMe drive. And then it literally just turns on that machine. And it's local. There's no network latency anything on there. And so that is sort of the specificities that we. When we're thinking from first principles what a computer would look like for an agent, that is what we came up with, and that's what we created.
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B
Yeah, I should maybe. I don't know if you endorse this, but there's someone does compute SDK. You guys do very well on there with like the tti, right? I guess. Is this a relevant benchmark for you guys?
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I don't know. And it changes every day. So today, Arkhill, I don't know what.
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B
Arkhill has never heard of it.
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Yeah, yeah, so it is.
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But you are at least a third of the next tier of performance. And then there's a lot of other better known names that are very slow to speed.
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A
Yeah, we've been the number one by far for a long time. And now there's difference. There's different definitions also of sandboxes, different isolation patterns, different other things. So Archilld runs it literally on the S3, the data. So it's very different. And they spin up a container for that. So it's a different type of thing. The definition of a sandbox is something that we can all need to get along with. But yeah, we're insanely fast on getting these things up and running. And so you can see even there that it's a.0, 0.10, 0.11. So, like, close enough.
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B
Yeah, yeah, yeah. I mean, what else do you need? Right.
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A
So the benchmarks itself. So in this, in. I don't think the benchmarks equate to market ownership or revenue or anything like that. And I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.
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It's table stakes. It's just.
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Exactly. But it doesn't. You definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things. I'm like, how many can you split up consecutively? There's a feature set, there's support. There's like all different things that people look at, but you definitely have to be there on the benchmarks.
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B
How many people do people spin up consecutively? Concurrently, I guess, is the concurrency area.
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So there's three metrics that we look at. And so one is like time to spin up one. And so our time to spin up one is 60 milliseconds with network latency. So request spin up, reply the whole thing. 60 milliseconds, that is one. But if you want to spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently. 50,000 some others. There's public data around this, like, take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the. So it is speed of one, speed of like multiple. And then how many can you consistently have up and running? And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they, where they're just shy of a million every single day that they're running. We do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.
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B
And they pay by like VCPU seconds, whatever. Yeah, yeah. The other thing is I guess the sleeping and the resuming because it's all the stateful resumption of all these things. What kind of workload are people putting through this? Right, like do we measure by gigabytes in memory, gigabytes in storage? I don't. In like, you know, network attack storage. You know, what are the costly ones? Out of all these features, the most
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expensive thing are cpu, then it's ram, then it's disk.
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B
We actually don't charge, which is snapshotting. Right.
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So no, it's actually the snapshot is part of it, but basically the size of your hard disk of your machine. So do you have 10 gigabytes? Do you have 20? Do you have 50? Do you have whatever? And then the transference of that. Right now currently we don't charge for network at all at boson.
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Yeah, you got to fix that.
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Yeah, it is very much a large. It's a larger and larger part of our bill. So we're working around that part there. Obviously that is the least expensive. So the hard disk is the least expensive. So it's basically CPU RAM for us network because we don't charge the customer. And then hard disk is how it's spun up. But there's also different types of workloads. So we basically split it up into two types of workloads in Daytona. One is what we call background agents or long running agents. And the other is basically rls and evals, which I put sort of together. And so they have very different patterns of usage. And if you look at the usage of a background. And I'll just name names of companies, not specifically.
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B
So open all hands.
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A
Yeah, so like a background agent, a cognition, a lovable. Like all these things are Harvey. These are all long running background agents. And so if you look at their usage patterns, their usage patterns are similar. To human, which is like follow the sun. Basically the usage patterns of that is like noon is probably the highest and the midnight is the lowest and then weekends are lower, you know, weekdays.
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B
That's a fun question. How global is it? You know, is it very US centric or.
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So the US is a large part, but we have. Currently we have Asia, Europe and the us.
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B
Quite global.
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Yeah, it's quite global. We have it all. It's interesting that our number. I talked to you about this. Our number one city by user is Singapore.
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B
Oh, wow.
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Which is an interesting one. Right. Not by revenue, just by. Just like by individual headcount. Just like an interesting, interesting.
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Singapore is. Singapore is weirdly high in the adoption charts of AI for the population. It's like a 7,8 million population and it keeps showing up.
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A
No, it's quite interesting. We were quite shocked and I was like, oh, this is interesting. And also one that's.
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B
There's a reason I'm doing AI in Singapore.
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A
Exactly. We're there, we're going to be there. And it's interesting that. That Japan is in the top or like Tokyo's in the top, which is in all the tech cycles. It has never been. Yeah, it has never been. So it's quite interesting that I think
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B
the Japanese just love AI. Yeah, yeah, it's. It's that and then it's Brazil.
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A
Yeah, but Brazil has always been in the. Even when I look, if you look at like GitHub's data and us historically with code anywhere, it was always like us, Western Europe and then we have like India, Brazil, China, like that would be there but like Singapore was not in specifically Japan was never in this sort of pockets. Yeah, so it's.
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B
So actually that. But that helps you to distribute your load through all time, you know.
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A
Yeah. So the interesting thing is like we have those kinds of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of ten thousand or fifty thousand or a hundred thousand CPUs whatever it may be, when they fire off a run, it's just a hundred percent and then just runs, runs, runs and then it stops. It's very. The usage pattern is squares basically. Right. And it's also not follow the sun because people will fire it off at midnight before they go to sleep, but then wake up. And so it's very unpredictable. So you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun. Even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that because it's sort of. It grows in a, in a way you can project. When you have companies doing sort of like evals and rl, they're super spiky. So they're going to come in. It's like we're going to use nothing. Then can we have a hundred thousand. Right. And then go back down and then have a thousand back down. So it's very, very different. Right. And so.
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B
So do you want to lock them into commits?
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A
So yeah, yeah we do. So we have to lock them into some sort of commits to have that capacity. Because we have to have. Basically we have to have the capacity for peak. Yeah, yeah. Right. And so right now Daytona's mean utilization is 151 5.
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B
Oh my God.
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A
So it's very low because it's very spiky. But it's very spiky. But we get up to 90%.
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B
Yeah. Y.
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A
So we have these things and so what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around. But that works really well for basically the background agent where it's follow the sun. But this it's not like it's a very different shape. Obviously with scale you figure these things out. But that's an interesting new problem that we have as a compute provider in the agent space.
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B
Yeah.
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A
And when we were doing the conference recently and so we talked to like Nikita from Neon and the I should bring it up parag from parallel and whatnot. Everyone has the same problem, whereas the usage is super spiky and this is something that has not happened before that you have these types of like it was always the amplitudes were not this high. Right. So it's quite interesting. Use case and problem solve.
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B
Yeah. I don't know if we're going to bring this up again, but let's just talk about the conference. You had like a thousand something people at the warriors game at the. Sorry, where is it?
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A
What's the Chase Center?
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B
Chase Center, Chase Center. I went. It was very impressive. Obviously you know how to throw a conference. What did you learn? You know, you pulled together all these impressive names.
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A
Yeah.
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B
What were you looking for?
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A
My thesis behind the Compute conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user. What are the Ergonomics and usage patterns of agents and so can do that. And what I found this was a theory wasn't proven is that we all have these problems. As I touched on too and I was, as I was talking on stage, it was like we all have the same underlining infra problems which is this spiky workloads, unpredictable workloads that we've never had before in human compute or human infrastructure. And it's again it's the same when I was talking to Parag or when I was talking to Lynn, Nikita, Lynn especially I was talking to her the other day as well. Like the, the it is a very interesting type of, of problem to solve because I can touch on cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos and basically as users work in different places and depending on your tier they can move you around the geos and so that that's how they get to the higher utilization. But you can sort of predict these. And if it's something and you'll rarely get a spike that is 10 orders of magnitude, like you'll get a spike like you say one of your customers has some like an exponential curve. What is that to. I'm using cloud, for example, 10%, 20%. Whatever reason I don't have this data, I'm just assessing. It's surely not 10x right. It's truly not something there. And so how do you go out and solve this problem? And we're all solving this in different ways.
- [24:31]
B
So she also has the same thing.
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A
So yeah, I know specifically that like Neon had that issue as well, like how are we solving these spiky loads and things like that? Because we talked about. And so the interesting thing for me to actually internalize was yes, everyone that's building for agents first is going through this and we're all solving similar problems.
- [24:49]
B
Let me double click on this. Okay, so for example, Neon, I happen to know that they're very sort of S3 oriented, right. So they're just like fully bet on S3 and you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And for listeners, we did an episode with her one and a half years ago and. And you have to care.
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A
Yeah, but like right, so Parag cares for sure and Nietzsche.
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B
And Parag is CEO of Parallel.
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A
Yeah.
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B
Former CTO Of Twitter.
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A
Twitter, yeah.
- [25:22]
B
They are the search. Yeah, you and I know. Yeah, the listeners don't know.
- [25:28]
A
We can put it down in the screen. And so because we were putting up
- [25:32]
B
on this on the screen so people can look it up if they need to.
- [25:35]
A
And. Yes, but they still have CPU and RAM allocation that you have to have and running. And so CPU ram, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over provision and you can handle the bursts, or two, you basically have, I don't know if this is a term just in time compute, which is like as your usage comes in, you can fire off requests for vms or bare metals at other cloud providers and then get them up and running.
- [26:02]
B
So this is if you go above 100%, right?
- [26:03]
A
Yeah.
- [26:04]
B
Your overflow.
- [26:05]
A
If your overflow, like spillage or whatever,
- [26:06]
B
you probably lose money on it, but doesn't matter, right?
- [26:09]
A
Not. Well, you might, you might not. That is a more cost effective way to do it, but it's a slower way to do it because basically what you have to do is you have to like queue your requests, spin up these just in time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine and you can do that. But if your customer, and especially for, let's say the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs. Right. So you want your GPU running at, at what, a hundred percent the entire time? And so when you're running runs on CPUs, when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down.
- [26:50]
B
Right.
- [26:50]
A
And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait and that's incurring our cost. So there's things that you have to try to solve for there.
- [27:01]
B
Yeah, let's talk about the different workload, Right. You said that was it a few months ago, you had zero RL workload and now it's 50%.
- [27:09]
A
It will be this one. 50%. Yeah.
- [27:11]
B
Let's talk about how different it is. Right. Like I imagine for example, a lot less dynamic code generation of like arbitrary code like here is probably all the same code. You're just doing parallel runs or something.
- [27:22]
A
Yeah. So you'll have multiple Depends on the like. For each run you'll have a snapshot. And for the most part, they actually do use our declarative image builder, which is like, oh, the agent wants these dependencies, these N bars. Yeah. The clever engine builder, which is a
- [27:35]
B
very modal like thing.
- [27:37]
A
Yeah. And so we build it on the fly and then we propagate that snapshot. And then you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can or like eqb, be automated. It's like, oh, now for the next run, we need to install these things or remove these things or whatever to get a task done. And then it goes off and runs that. So, yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. Yeah. So eks, gks, whatever that is what the vast majority run on. And anyone that has tried Daytona versus gks, EKS is like, I'm never going back. That has always been. There's a few reasons. One is the ergonomics. So if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that Daytona. Although it's a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is in aws. Like you have an API and SDK. It's quite like easy and seamless to get these things up and running. That's one. The other is the speed to which we spin up, which we mentioned earlier, which is much, much faster, and the scale to which we can go to. We haven't gotten to features, but an interesting feature is that it's very hard to oom or out of memory our sandboxes because we can dynamically on the flight resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when the Terminal Revenge team brought us, actually. So thank you, Alex. And the team that brought us into this whole space.
- [29:18]
B
It's just very, very, very rare that. But, you know, a framework would just say, guys, just use Daytona.
- [29:24]
A
Yeah, I think it says it similar. Yeah, yeah.
- [29:26]
B
I was like, what is this?
- [29:28]
A
There's all. There's multiple there. But they also mentioned a few other places.
- [29:31]
B
Yeah.
- [29:32]
A
And so Daytona specifically, we have. Just jumping on themes here. We. I don't know where it says Daytona.
- [29:39]
B
Sorry, I don't know. There's a very, very strong recommendation, which is, like, very unusual, which is. It's.
- [29:45]
A
We do not pay them for this. I know.
- [29:47]
B
They just like you.
- [29:48]
A
Yeah, they like us. Yeah. And also a thing. So Dazon has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container that's hardened with Sysbox. So it's Docker's isolation that is security equivalent to a vm, but it's still a container and that is the default. And they, especially in these trading workloads, really like that as an interface to be able to use just a basic Docker container. And we enable Docker and Docker, which, for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3s inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that through that. We showed them that we could do that and they enjoyed that quite a bit. They being the harbor people.
- [30:39]
B
The harbor people. Do you know, are they a company yet?
- [30:42]
A
As far as. I do not know. All right.
- [30:45]
B
It's like super obvious that, like, you know, there's a lot of excitement and success around these things. Tell us more. Right. Like, this is an exploding workload. Harbor adopted you, which helps speed things along. But what are you learning as this new workload comes online? Sure.
- [31:00]
A
There's a couple of things that we learned which we chat about in the beginning, and this has led our story. As we mentioned, we talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And I think it's that the ecosystem is so small and. Or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap. If, like, three to five customers come with the same request in that week, it's like, very bizarre. And it happens so many times, which is like.
- [31:30]
B
Because they're all friends. Sorry, they're all friends. They're all in the same group chat.
- [31:33]
A
Yeah, probably. Yeah, because. And they're like, oh, can you do this? Okay, this is interesting. We'll put it on a feature request. And then the next one's like, oh, can you do this? Okay. It's all the same. Right? It's always the same. And so what we try to do, and I personally try to do, I try to be on, on as many quote unquote sales calls I can. I'm in every Slack channel. We literally have about a thousand Slack Connect channels, something like that. It's an interesting. There's so many interesting things you find out when you have old Slack channels. You can also see where people transfer between companies. You see, leave Slack channel and Slack channel. It's an interesting thing also just. I digress. I feel that Slack connect is literally LinkedIn what it should be.
- [32:08]
B
Yeah.
- [32:09]
A
You have a list.
- [32:09]
B
LinkedIn charges you to, you know, use your own connections. But Slack doesn't. Right. Slack is like do it for free. It's more lock in. It's great.
- [32:16]
A
Yeah, it's amazing.
- [32:18]
B
You're going to pay Slack for life.
- [32:19]
A
Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we talked about earlier is we made a big bet and put a lot of investment on computer use that is not seen publicly the light of day. We haven't ga'd that yet, but we have.
- [32:33]
B
Is there a thing I can pull up there?
- [32:34]
A
Is computer use there? It's right up a bit.
- [32:36]
B
Oh yeah, okay.
- [32:37]
A
Yeah, cool. And what we have, we talked about and what we've seen publicly is there's this theme now about like the human emulator where. And Elon from Xai has talked about this publicly. And if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, you know, terminal or whatnot. But if we, if we look at knowledge work in general, there's about a hundred million knowledge workers in the us about a billion in the world. And knowledge workers and the salaries of them aggregate to 10 trillion in the US, 50 trillion worldwide, something like that. And if we look at the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about about 25 trillion. And most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very, very long time. Like people just won't invest in that. How much of it? Our assumption is the following. If like in the RPA market, which is similar market, not the same. 25% of like these white collar workers work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's like 40%, right. And so if you take 40% of that, you get to essentially like $10 trillion a year.
- [34:12]
B
That's your TAM.
- [34:14]
A
That is a TAM. So that's a TAM of the models, right. That's not essentially ours, but you get to that size and to be able to do that you essentially have to give agents these computers with the legacy. So computer use either Mac or Windows or Linux. Linux we also obviously have and others have, but Windows specifically, something very, very new. And the only option right now is an EC2 with Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox. So it's a second instead of milliseconds. But you have like point in time snapshots. You have like forking. You have all the things that you have from a sandbox but essentially enables you to hopefully unlock all this value. And so that's been our big push and what we've sort of like kept our ear to the ground. What is sort of the next things in the market?
- [35:01]
B
Yeah, knowledge, work and building and sort of RPA. The next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPOing and it was like a very hard. Isn't it Eastern European?
- [35:13]
A
It is Romanian.
- [35:15]
B
Romanian, yeah. It might be the only Romanian big unicorn. Okay. Yeah. I don't have like a. I think there's a stage being set for the resurgence of RPA because everyone understands that. Yeah, no one wants to deal with these shitty apps and no one's going to rewrite them. You just have to do like a remote operation and programmatic operation of them.
- [35:37]
A
But if you want to unlock it, like my own setup was. Was basically the following. So I was doing a board deck. Yeah. Recently, last month, whatever. And I'm like okay, let's just, let's just do automate it. So like all our data's in like Clickhouse and Post Hog and Quickbooks like where everyone else's is. And I'm basically like connected that all to like my cloud code. Like go off and go cloud code, whatever. Go off and like here's integrations. Go do that. It pulled out the first report which was great. It connects to Brex and all these things pulled out which was great. And I say okay, now pull out this, this, this, this. And I kept getting like really well McKinsey style design reports. But the data said partial data, all data, missing data, partial data, like it can't access all the things. And I got so frustrated and so I got, I got, you know, my Mac mini virtual sandbox with OpenClaw. I gave it its own account in our company and then I went to all these services and created a read only account. So literally like an intern in your company. And so I would say now go and do this report and it'll get the same. Or like I can't via the MCB or the API or whatever. I can't get all the information. I'm like go log in.
- [36:41]
B
Yeah.
- [36:41]
A
And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed and I to get value, like I get immense value right now but it has to be a computer usage unfortunately. And so I spend a bunch of tokens just on that. But I get the job done. And so if even a startup like ours and using all the hottest tools still needs a computer agent, what hope does you know Goldman have to have a headless. Right? Yeah, yeah.
- [37:13]
B
Why isn't Microsoft doing this?
- [37:15]
A
Well, I'm pretty sure like Satya had a post yesterday. Every agent needs a computer.
- [37:19]
B
I see, I see.
- [37:20]
A
So they have launched something.
- [37:22]
B
Yeah, they have Microsoft Power Automate. I'm sure like you know they're going to have their version version of that and you're going to try to do yours. And I always know there's always demand for Mac, but I know it's like tricky to host macOS sandboxes so we
- [37:37]
A
will have macOS sandboxes fairly soon. The problem with macOS OS sandboxes is I'm deep in this. I don't know how much interesting is this. MacOS has this problem.
- [37:44]
B
It's a licensing thing.
- [37:45]
A
Licensing thing. So one, you're allowed to run only two parallel VMs per machine. So that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically if I want to charge you per second and I charge you one second, I have to have it idle for the rest of the day. Like I can't have anyone else doing that. So the pricing will be different in the sense that we would have to charge for 24 hours. And that's not even, that's not even the most difficult Thing but the thing above that is from a security perspective they enable you to do memory snapshots, pause, resume, but only on the same physical drive, physical machine. And so what you can do in like Windows World or Linux World is that I can move in the background your snapshot from one to the other and manage load right here. If you want to do that you essentially have to have your. Yeah, snapshots physical machine.
- [38:36]
B
You can't break it up, you can't
- [38:37]
A
move things around that and all of that is that part is like from a security standpoint. If it is, I understand the security aspect of that but it disables you from doing these agentic like really scalable agentic workloads.
- [38:51]
B
You need to do a vibe coded clean room implementation on macOS that you can. Then there's like clean OS or something.
- [38:59]
A
I don't know. I guess so it's like Linux was
- [39:01]
B
originally like a clean room rewrite of
- [39:03]
A
Unix or something like that.
- [39:04]
B
Right. Like same thing to macOS. Somebody needs to do it.
- [39:08]
A
Someone will do that. We'll have some long running agents for a few days to figure this stuff out. But yeah, so definitely we're really close to offering something because people do want it. But the pricing will be different and the feature set will be sort of stringent.
- [39:20]
B
Yeah, nobody's going to use this. I mean the labs will because they
- [39:23]
A
want to domain have to go relegate and but the point is with the RL part if you, if you do RL on macOS then the next iteration of the model comes out, it will be able to use these tools significantly then you actually need to run those that somewhere. So you're going to have to have that later on and from if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if it would just enable a concurrency model similar to what you can get on a Windows and a Linux.
- [39:58]
B
Yeah, I'm sure they've heard this before, they just don't care. Yeah, and maybe they will change their mind with the new CEO.
- [40:04]
A
Yeah, we'll see. High hopes. High hopes.
- [40:07]
B
Okay. But I mean it's very clear the market opportunity is huge in Windows and you can go for a long time on just Windows, but your customers are going to want both. It is interesting to me that this is the, the sort of God application of agents. Right? Like how big was openclaw for you guys? Was there like a significant bump or.
- [40:29]
A
Not for us. So we're Kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. It's either B2B or B2B2C. So like in the researcher world it's B2B so you're selling to labs and neolabs and things like that. But on the long running agents it's mostly from a scale revenue perspective it's mostly B2B2C where you have a app layer agent that uses you.
- [41:02]
B
Like a manuscale. Yeah, yeah.
- [41:04]
A
Like a manus lovable type thing.
- [41:06]
B
Yeah. B2B2C is basically to me what I've been calling an agent lab which is kind of like you're not a model lab but you're making a very, very good wrapper. That is a platform that other people can sign up so they don't to code those things. Yeah, that sounds like a much better market than the direct open claw market.
- [41:24]
A
So I've like we. I've done multiple things so the code anywhere part of our career path are on the calendar. Was very much an end user developer product.
- [41:33]
B
Yeah.
- [41:34]
A
And so that is great. You can get a lot of developer love and I feel that we do as a company have a bunch of developer love but it's a different type where it's people building these things. Again it's more akin to a Twilio because you don't as a person you wouldn't run Twilio. I don't know how many people remember it was like ask a developer billboard and whatnot and people really love Twilio but they only used it inside of like oh, I'm building this app or service for thing. And so we're very much directionally to that. And you also know that I used to work for a competitor for Twilio so it's kind of ingrained I guess in my DNA.
- [42:04]
B
People don't know infobip is that big.
- [42:06]
A
Yeah, it's like because they're all American,
- [42:09]
B
they're like whatever's in Europe doesn't matter to me. But it's the same size or bigger?
- [42:14]
A
No, no, it's high. It's about half the size. Half the size. It's like so huge. Multiple billions a year. Yes.
- [42:19]
B
Crazy.
- [42:19]
A
Exactly. These are like really interesting and large revenue generating, very sticky businesses. Whereas when you're selling to the. When your focus is the end developer it is a very hard sell because they're very price sensitive, very price conscious, very you know, around that. And there's very hard, it's very hard to scale. Your cap is the number of people that are willing to spin up for. First I want to spin that up and then spin up multiple of these. Whereas if you're in the enterprise, one, like we know everyone's talking about like how many tokens they're spending. I'm spending. Like a lot of companies today are like, this is our company. Spend as much as you can. Like, basically, that is where we're going. And so if you think about that paradigm where you're selling to companies that say, spend as much as you can to generate, you know, productivity versus, oh, I'm a single person, I have this much budget and I'm doing this thing because it's fun or it's helping me out or whatever. Like, it is a different. It's a different go to market, I think strategy.
- [43:16]
B
Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus cli. Obviously you want cli. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.
- [43:34]
A
I mean, those things work well in our favor to your point, just because.
- [43:37]
B
But they kind of drop in the bucket, right?
- [43:39]
A
I guess I think it's like sort of all the things come together and so there's so many things that, that impact that. To your point, like open cloud wasn't huge for us, but like having the agent SDK from Anthropic, so. Or Claude. Claude code was very interesting. The reason why it was interesting is that a lot of. Let's call them app. I don't know what to call them App layer agent companies, essentially, they are like, oh, I can create this new app, this new agent. All I need, I just use cloud code and I throw it into a sandbox and then I have my interface to the human to that. And so that enabled so many more companies to actually offer this and then they would pull on sandbox. So that was. That was interesting. And to your point, like MCP versus the cli. I mean, the MCP is an interface against an API, whereas the CLI is like you can actually go do things like this. Is it the difference between integrations and actually running scripts or data or analysis against the thing? So being able to use CLI very, very well enables the agent to do more things and because that people will invoke a sandbox, they'll run in the CLI and but it'll do analysis on that data and then give you an actual result versus just, you know, pulling data from an API.
- [44:51]
B
Yeah, it's a layoff indirection basically. It's the same thing as agentic search versus rag, which.
- [44:55]
A
Exactly, exactly.
- [44:56]
B
Yeah, just like you just win whenever people put more agents into the workflow.
- [45:01]
A
Exactly.
- [45:01]
B
So it doesn't really matter. But I'm just kind of teasing out what else have people heard about that? It's sort of, oh yeah, this is another sandbox use case. Oh yeah, that's another one. Am I missing any big ones?
- [45:13]
A
So the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is and to your point, we've talked to so many people over the last year, it's like, oh, why do you need a sandbox? Why do you need this? Why this? And to your point, it's like, oh, I need sandbox for this, I need a sandbox for that. And so you go, oh, I need it for every single thing. And so basically what I, what I. And it sounds like a broken record is like you use a laptop every single day, right? And you are n of one, it's just you. But now imagine how many. And by the way, the, the laptop, the computer PC market, the PC market is about equal to the cloud market. So it's about 150, 180 billion a year, something like that. It's about roughly the, the three cloud hyperscalers is about equal to like Apple, hp, Lenovo, whatever is a little bit less, but sort of like that. And now imagine, and that's just like, so how big is the directional market? How many people are there in the world now? What's the last day?
- [46:06]
B
And it's called 8 billion.
- [46:07]
A
8 billion. And so let's say you can have two computer like you have one personal and one business whatever, like, so it's double that. And so that's 16 billion. How many agents are going to be running in two years and 10 years and 100 years and for every single task they will need one of these. And so how big is that market is essentially quote, unquote infinite. You will get to the point, point. And Dylan Patel was at the conference talking about from simian analysis that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint you won't be able to grow or we won't be able to have enough of these because there won't be enough CPUs to basically do that.
- [46:42]
B
Yeah, well, I actually had a really good podcast with Doug o', Laughlin, which was his president at Semi Analysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs
- [46:56]
A
needed.
- [46:57]
B
What's next? Networking. Yeah, yeah, networking actually has been in shortage for a while if you're looking at like just GPU networking. But yeah, I mean, it's really crazy the amount of computer use that's going on. Yeah, cool. I guess other questions are just the one very big part is the open sourceness, which you didn't have to do, your competitors don't do. I guess a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.
- [47:31]
A
Yeah, there's a bunch. So the original product that we did was open source. Yeah, Doing that was actually very good for us. There's basically a saying of. What's the saying? Like companies that are doing really well measure themselves against, you know, free cash flow that are kind of okay, it's ebitda then, you know, it's a worse. Yeah, so you go all the way down GitHub Stars. And so our original one was GitHub Stars. That's what we talked about. We're at the point where we talk about revenue. So we've gone up the stack on that and so we Profit.
- [48:00]
B
Profit.
- [48:01]
A
Yeah, we haven't. We'll get there. But basically at that point we did stars and GitHub and voila, it was useful. And the original variation that we did, we split the core into its own repo and it was Apache 2.0, so very permissive. And then we basically would bundle that, that on the enterprise side with a proprietary repo. So it was like open Core, but it didn't fill out the repository. The repository was very clean. When we did the pivot, we didn't have time to rethink this and we wanted to. We had this open source community. It felt a shame not to do that. And so but we still did want to add some restrictions. So in the new Sandbox product we did add a AGPL3 which is, you know, it's a kind of a shortcut way to do that where you are open source and it is true open source in the sense of that Enterprise can use it if it wants, but you essentially can't make a competitor without open sourcing your stuff.
- [48:53]
B
It's one of like three approaches. Like there's like BSL and some of the other sort of Elastic license.
- [48:58]
A
Yeah, there's some others there. So pure open source believers agree that this is not full open source and I totally respect that. That is absolutely true. But we did leave that and Daytona in its essence, everything outside of what's under a feature flag today, which is like the Windows stuff, GPU stuff and whatever it is in this open source, it is there. So everything is there. Like our own scheduler, everything is there. So we are, I've had some competitors say like you guys are actually open source, open source. Like you, you're really like, you can actually see that. And I mean people do like that and it has helped a bit, but it's actually more helped in the consumption of our cloud products than actually transferring people over. The reason is you can actually, you send the repository to your agent when you're integrating Dato up and just has more context. It's like, oh, okay, this is why this is happening. This is why that's not it.
- [49:50]
B
You could equivalently just have docs that you can, yeah, okay, I agree.
- [49:55]
A
But it's to be fair and so it actually doesn't really help the growth significantly. Today we've had this kind of conversation with like investors and other people. It's like, how do you convert people from open source?
- [50:06]
B
The open source business conversation is so all over the place. Right. Okay. I just like for listeners who maybe they haven't thought this through, a lot of people say, oh, it's all free tier. Right. Like oh, if you run it yourself, but when you get serious, call us.
- [50:19]
A
Right.
- [50:20]
B
And then me personally because of my temporal experience. It actually is the way that it's GTM into some of the largest companies where we wouldn't pass their review process, maybe because we're too young of a company or there's parts of the stack that we haven't, that just doesn't work with them. But because it's open source, then they adopt it and then later on we figure it out there, that's the low end and the high end. I, I, I don't know if it.
- [50:44]
A
No, no, no, absolutely. And that has been historically the thing that we have found in this AI transition is as we haven't talked about this, Daytona's customers are everything from, you know, the single developer, the YC startup, to people say Fortune 500. I'll say Fortune 5, like the biggest companies of the world, like and, and
- [51:03]
B
big neolabs you, you told me about.
- [51:04]
A
Yeah.
- [51:05]
B
Why keep them anonymous?
- [51:07]
A
Enormous companies, right? Yeah. And because the market pull is so strong, we're able to circumvent these processes. I'm not saying we go, we pass security audits, you pass all these things. But as you know, Tier mentioned like temporal way back in the way day in our old version of Daytona, like it took us months and usually at the end they would churn off because just like, oh, you're too small of a company, like we don't trust you enough. Whereas today we've had these large companies push us, like they would push us through. Like usually when you would go through procurement to become a vendor of large companies, it would take you like two, three months. We could have done five days. Now and this is not saying that maybe we're great, but it's more, I think a sign of the market where it is today. And so when you think about that, the open source is something that we from a go to market perspective don't think about that much because everything that we've created right now has been plg through the cloud, product people signing up and just pulling us inwards.
- [52:00]
B
This is a personal interest and I don't know if you have an answer, but do you have problems with getting GitHub?
- [52:07]
A
I did a little bit.
- [52:08]
B
A little bit, yeah. Tell me because you know, I'm thinking about like, okay, what would it take to replace GitHub?
- [52:13]
A
So there's a lot of things. I thought about this and I've talked, I've tweeted about this and I looked at some. I've actually invested personally in some.
- [52:19]
B
Is it entire?
- [52:21]
A
No. No.
- [52:22]
B
Yeah.
- [52:22]
A
So I, and I've met Thomas earlier virtually and we've talked. So I really think, and this was my reason for that because we have a bunch of background, long running agents and for a time most of them were coding agents. Like everyone was building up a competitor to Lollable or Devin or whatnot. What we saw from our customers was that they were all trying to figure out how to do versioning. Everyone is doing it in different ways. There were some really weird ways where people were doing that. And the reason was that GitHub as is was an overhead. Like it wasn't fast enough what they needed. It didn't solve the problem that they needed. And to be fair, like GitHub is for post your. The inner loop, right? It is Post your laptop.
- [53:09]
B
Yeah. GitHub is the point at which the auto loop starts.
- [53:11]
A
Exactly. People started using that for sandboxes, which is inner loop, which is usually, you know, it's on your laptop. Right. And so that is not what it's made for. And then we had everything from people. Actually the most interesting one is we had one customer that will literally take the entire code base inside the sandbox and every. I forgot what the time sequence was. They would just dump it all into a JSON and then push that to S3. Yeah. And that's it.
- [53:37]
B
You make your own git.
- [53:38]
A
There's not even diffs. It's just a whole thing. Every single time. It's just every. Because it was super fast. And then it would go back and search and find, you know, sort of what the file was and right now and whatnot. Because there's text file, there's Jason. Like they're very small so that the network cost is very low. And they didn't care and they just did in that way. And I'm like, if people are doing this, that means there needs to be a new solution to this problem. Right. And so for me, it's quite interesting to look at who is building these types of new things. Agent first, I think Git as is still exists in the Future. Maybe even GitHub exists, but there will be a whole new.
- [54:14]
B
Yeah, exactly. Git is like the deploy artifact to kick off CI cd, but then there's a layer before that that is like the agent collaboration layer.
- [54:21]
A
And so I think something's to be said there. But on the other side there's issues with. With another interesting thing is just like CI right now. So the amount of PRs being created is insane right now, right?
- [54:31]
B
In general, even for you guys, right?
- [54:33]
A
Everyone's creating a bunch of pr, like everyone. And then all that has to go through CI. And then that's the bottleneck. Like everyone is bottling. Like not just action. Like, not just actions, but like go to any CI provider. You will not be able to if you have a high throughput of PRs. There's one company we're talking to, they do a thousand PRs a day, which means like, and they're just waiting. They have just a queue do on that, right?
- [54:54]
B
What do they use? Like build Kite or I don't know, Circle. You know, technically your tech can be used for CI.
- [55:01]
A
That was the conversation.
- [55:02]
B
Oh, okay.
- [55:03]
A
That was the conversation.
- [55:04]
B
Is that a serious conversation?
- [55:05]
A
So we'll see how that goes. We've had quite a few conversations around that. We're. We are not A CI provider by any means.
- [55:10]
B
Right, but what, what is, I mean, what's missing?
- [55:13]
A
Essentially you could use Daytona sandbox instead of whatever you use for, you know, your GitHub runners, essentially. Yeah, yeah.
- [55:21]
B
The only thing I would say is like maybe CI machines are supposed to be very cheap. Maybe it's like the low end because it's supposed to be like, you know, non blocking or something, like a background job. Like it's. The urgency is not that important for CI performances though.
- [55:37]
A
Yeah, performances, yeah. Yeah.
- [55:39]
B
Okay, that's interesting. Before we leave Daytona and go into like sort of broader like founder takes and what have you, when startups evaluate you so you have all these names and you have more that you can't even name. They see all your wall of competitors and yeah, you have differentiation versus many of these. But what sells them?
- [56:01]
A
The thing that we found that sells people the most, this is more maybe a day two thing instead of a day one thing. And we've seen this again and again. So we have a bunch of case studies and we have a bunch of them still coming out. They're all done by a third party, so we don't do the case studies. And it's actually interesting to watch those cases. I watch, they're the recorded. And because it's a third party, people are actually more open and they will tell you, oh, we use this competitor or we like this competitor more or this thing or whatever. And the number one thing that people come back to us for is that we have an insane responsiveness.
- [56:31]
B
In terms of your team, in terms
- [56:32]
A
of the team, insane responsiveness has been by far the. Now we can talk about features and breadth of product and concurrency and CPUs and all those things, but I feel that that would probably. So if all other things are equal, that is very much a differentiator I found and I did not.
- [56:49]
B
And is that entirely slack or Slack plus email?
- [56:51]
A
There's email there as well, there's calls, but the vast majority is like on site. So it's slack. Like we have had customers like, hey, we have a problem. Can you get on huddle? Like we will get on that huddle like five minutes literally. I've done this multiple times. So yeah.
- [57:04]
B
Wait, okay, so how big are you?
- [57:06]
A
25 today.
- [57:07]
B
How, how do you do this kind of support? Like this.
- [57:10]
A
We're insane. We don't sleep. 007. Have you heard the new thing?
- [57:13]
B
007? I mean I. They're very impressive, they're very dedicated. But like also how do you get a team to do that? You know that's so there's. I have slack exhaustion, you know.
- [57:24]
A
Yeah, we all have slack exhaustion. We're very, very tired. The thing that's unique, I don't know, unique about us, but unique, I would say unique about any successful serial founder is that you're able to pull in people that you've worked with before and so you can't do that as a first time founder. Like I couldn't have done that or not. But of the 25 people in Daytona, I think about 13 of them, them we have worked with seven years plus.
- [57:50]
B
Yeah.
- [57:50]
A
So it's like high trust, high throughput, high. We know what we're signing off to do and especially these people worked with us when we were starting and we were actually hustling, you know, hungry for food hustling type level. And so those are the people that work with us. The now the, the new segment that has come is almost everyone is sort of of, you know, one, one degree of separation. So it's like someone that someone has known and so they sort of come into this org and we've had people that have like not fit into Org as well. It's just like it's that type of culture where there is a high expectation of like being online replying for these things. And I do that first. You will. If you ask any engineer, they're like, you never sleep like about me. And so then I do that as an example. I don't do it as a sample. That's just how I'm wired. My wife doesn't appreciate that. My wife doesn't appreciate that. I told her about 996.
- [58:41]
B
She said I wish it's like these Chinese people are slacking. Yeah.
- [58:47]
A
So that is something there. And so I think every company has their own culture and that's something very, very deep Mars. And it's something that's come up again and again and every single day we're reminded about that. And I didn't go out thinking that that is how I'm going to build it. It's just how I built these things.
- [59:01]
B
I'll transition a little bit on the founder side. Like I'm very impressed by you in general of like your sort of balance. You have a young family, two kids. Yeah, two kids now.
- [59:10]
A
Yeah, two kids now.
- [59:11]
B
I think a lot of people I meet, they're like, well I'm starting a family, I can't be a founder and all that. What's your advice to those people every single day?
- [59:19]
A
So my family, they're here right now, but they're usually I fly between Croatia and here. Like, a lot of our team is in Croatia. A part of our team and are growing is here now in San Francisco. And so I spend a lot of time away from a family. And that is hard. Like, that is a sacrifice that you have to. But going in, like people say, like on your deathbed, you're gonna miss some of those things. The thing that. And probably might be true, but the thing that going into this, I already said, like, I know that this is gonna hurt and everything has to hurt, by the way. I'm very much of a feeling that everything has to hurt. Going to the gym hurts. Losing weight hurts. Like everything has to hurt, right? It does. Like, no pain, no gain. It is literally. But you actually have to enjoy the pain. And just like, if you don't enjoy the pain, it's not for you. And so you get accustomed to that pain. And so a lot of kids, especially I'm a daughter and a son. Daughters, the eldest, like, love her and do miss her when she's not here. But it's like, that's what I signed up for. And there is a plan and target of what I'm trying to achieve. And now hopefully with my wife, which does support me, we can get ourselves together more. So it doesn't there. But she takes a large part. Portion of that. And so if you have a partner on the other side that is okay with that, then you can do that. But even if they do, you have to be okay with not being there. Right?
- [60:34]
B
Yeah. This is my vision for you, this meme.
- [60:38]
A
Yeah.
- [60:40]
B
So that's your kids in the future.
- [60:41]
A
Yeah, I think so. Yeah. But we have to teach them that
- [60:43]
B
because dad, you know, built the compute. Sandboxes.
- [60:47]
A
Dad made sandboxes.
- [60:48]
B
Dad made sandboxes and built the spiritual successor to serverless and Kubernetes and for agents, any other sort of hot topics, trends. You have a lot of hot takes. Actually, you are best known for. You were sort of in sort of hustle culture mode, right. And someone quoted you and said, I haven't even heard of you, bro. Just log off and take the Christmas off. And then your response was.
- [61:15]
A
My response was like, that's why I can't. Yeah, yeah.
- [61:18]
B
So I mean, I think that's like, very typical of you. I don't have it here. I can't bring it up. But I think that's very typical of that culture. But I think you have a lot of interesting hot takes like that. Any. Any other sort of takes on the startup ecosystem.
- [61:32]
A
Oh yeah, startup ecosystem. And this was the, the recent one which is, I think that, and this is general like business. I, I feel that the. It didn't come off. I think well on Twitter something misread it which is the market is adding premium to SaaS vendors that are reselling tokens.
- [61:51]
B
Yes.
- [61:52]
A
And I think that's incorrect. Why, why I think that's incorrect is that if you look at what one, your pricing depends on what the price is, if it's a public market or if it's private or whatever you're saying, the person that's reading that, that the re acceleration of revenue is equal to the old revenue, which it's not. Not even close. Because one you had on SaaS, you had typical SaaS margins, whatever it was, right. Say stickiness and all these things. Now what you're doing is you are saying here is my agent and I have whatever the margin is, it's way worse. Right. And now you're using, using Anthropic or you know, or OpenAI or whatever through me the, the SaaS product. And then we as a community are saying now that is re acceleration. And so one, I think that's wrong because they first it's not the same. The mara. The makeup is not the same. The other thing is, and go back to like what, what I mentioned earlier is like the, the Kua and how I set up OpenCloud, whatever. I don't want your agent. Agent essentially because what happens right now we have a problem that and this historically been. You have data siloed in again. Clickhouse, QuickBooks, it's all siloed. And now you're giving me an agent. They'll give me the data, but it's still siloed. Right. And so now I have to like take that data and then get another agent.
- [63:09]
B
Just expose the data to my agent.
- [63:11]
A
Yes, just expose it. And one thing I have to say and so I'm like just expose everything and charge me for that. That. So charge me for consumption of API. So you'll have your old seed based pricing for humans. Yeah, yeah, charge me for this. The number of agents will skyrocket and essentially you'll have more usage and charge for more if your product has value. So like there's arguments. Some of them do have value. It's a database, not database. We can get into that. But some of them really do. And I was actually shocked that the first person to do this was Benioff.
- [63:40]
B
Mm. Salesforce. Yeah, Agent Force.
- [63:42]
A
There was a tweet I Think three days ago where she said, every product in Salesforce has been exposed via API. Everything. And I'm like, now I understand why this person is built. This is insane. Kudos to him. Amazing. It's like, thank you. I don't know if you listen to me or someone else, but thank you for someone. This is the direction of the world. And so if you can get real acceleration against that, against consumption of API, I.e. actual revenue and that is actual real acceleration. And that is where value come from. And I think that, that there will be a cold shower when people understand like, no one's actually going to use and pay for these agents and tokens. And that wasn't actually really re acceleration, but it'll drop back down.
- [64:21]
B
Yeah, yeah, yeah. I mean, look, like, obviously I think generally correct. And I agree, I think. But people are going to try to become an AI company.
- [64:29]
A
No, no, absolutely. And nothing against that. And this is. No, to be very clear, this is not a downer on anyone that's building this thing. Everyone has to get to like, get to the revenues, gets the multiples, get the valuations, do what you have to get to the next step. Absolutely agree. But we as community are now like saying, oh, this is like the magical way to get out. This is not like. That is not what is happening. Right.
- [64:50]
B
No. I think there was like this kitchen appliance company that put out some AI nonsense recently, but it was also the sneaker as well.
- [64:57]
A
It's called the Allbirds.
- [64:57]
B
Allbirds, yeah. Now Allbirds is pivoting to gpu. That's fine. It's like, you know, I have some money left, I'm just going to do some lottery tickets. Would you go into offering GPUs?
- [65:09]
A
Oh, yeah, we will, but not for inference. Like, essentially what we think about is like the GPU sandbox. So if you think about of like if you have a GPU in your computer, that is what you have a GPU in the sandbox. So there are workloads that do need GPUs. Again, I always go back to 3D rendering because it's the easiest one to comprehend. But like, if you want to do any type of RL on like CAD or something like that, you will need a GPU in the sandbox. And so that's coming now as well.
- [65:32]
B
How about own data centers?
- [65:33]
A
Own data centers. So we run on colocation providers, bare metal machines, data centers. We technically can run on that or our own data center. Like that's how we architected it today from a gross profit margin. Perspective doesn't make sense for us to get in that you have to raise a large amount of capital, large amount of risk for like single digit percentage points. So today that doesn't make sense. But we are fundamentally architected so that we can do that if we want. Yeah.
- [66:01]
B
I mean you're a large customer of these guys now. Do you see any opportunity?
- [66:05]
A
We will see, yeah, we will see. Yeah. Yeah.
- [66:07]
B
I see a lot of people like trying to do the bare metal thing. We talked to Railway the other day and they're also doing a very similar strategy.
- [66:16]
A
I think they're building out something or they have their own sort of data centers now.
- [66:19]
B
Yeah, they have majority their own data centers. But I do think like, I mean they still use Equinix and all those things. So I think it's just interesting that you know this model basically hasn't changed. It's basically a real estate model. They manage the facilities and then you do everything else else. I wonder how it can be changed for the future. Because the AI wave is the opportunity to reinvent everything.
- [66:41]
A
Yeah.
- [66:42]
B
Anything else cool. I think that's about it. I didn't have any other topics. I think this is as best and comprehensive. If you have any questions about the compute market and sandboxing and Daytona, this is the best place to start. Where does this go? Man? We're here in April. Things are going 75% month to month. Like what we. What are we going to be by end of year?
- [67:06]
A
It's an insane number. I'm sort of scared to say it out loud. So like it is, it's very big. Just the sandbox market on and we there, we talked about this. In general the entire infrastructure Market is growing 40 plus or minus month over month. Everyone is growing 40% month over month. And that's also a hot take is like if you're not growing 40% ish. It's not that it's just the market. You might as well. You don't have to come to work. You'll grow that amount. Basically. I'm half kidding. But you know that that's where it's going. And so where does it end? We will see. The thing that I think about from at least a CPU perspective, GPU is even crazier. From a CPU perspective it is like there is a high probability that actually owning the CPUs beforehand will be a go to market tactic. And it will probably because as you do probably talk to a lot of GPU providers, their growth is hindered by the amount of GPUs that you have. Right?
- [67:54]
B
Yeah. Right. It's whatever Nvidia decides to bless that day.
- [67:57]
A
Yeah. Is that how much. That's how much they're going to grow. Right. And so we're the CPU market in general. Be it like something like Railway for example or Vercel or whatnot or Deployment or like the sandboxes, there's still CPUs. So like each is. Is growing at the pace of the market of their, the market and what they're, you know, plus or minus of that market. But it's still not constrained by that. And so my thought is like for every, for all of us in this market and databases fall into that as well because database also runs CPUs and it's like we all have to grow as fast as we can so we can get enough of CPUs tomorrow from intel or from Nvidia because they have now CPUs and everyone else later on. So it'll be interesting when we get to that.
- [68:36]
B
Maybe one version I'll phrase this is like is the potential new Heroku, new AWS or new Stripe. What's the analogy that is most appropriate?
- [68:50]
A
There's interesting. There's like analogies of like so the, you know, there's new Cloudflare, but new Cloudfare is new Cloudflare. Like they're actually doing a really good job about like.
- [68:58]
B
And Cloudflare owns networking. No one can fight. Come on.
- [69:01]
A
They're doing. No, no, they're doing really well. No, what I say is in the sense of their whole agent portfolio is actually really good. And I should say there are some technical limit I think personally around like everything's under constrained under workers. Like workers is their thing. But from a go to market vision perspective, I think they're actually really, really good. I think they actually get it, unlike some and to your question is like what is going to be. There will be an equivalent. Everyone says like an AWS for AI agents. But your answer like it might look more like Stripe than AWS in a sense. So there will be a cloud built out specifically for agents. And so that cloud will have sandboxes and it will have web search and it'll have databases like SQLite or Neon or whatever specifically for agent and other things. We are not at the end of the new infrastructure primitives for agents. There are more coming. So people think like oh, there's nothing else to sit. There are more like we have some ideas about the next ones. We don't have time to do them. But there are definitely more primitives that are being built out for AI agents. And there will be, I think, a cloud that runs all that.
- [70:07]
B
Yeah. OpenAI has said AI cloud. Vercel has said AI cloud. And you are potentially also one of the other the prospective AI clouds. I think it's a very big prize to win. Well, thanks for coming. Coming on.
- [70:17]
A
Thank you for having me. It's been amazing.