00:18:19 · Anurag Goel, Jonathan Brebner
[00:18:19] S1: Of course. [00:18:20] S0: Yeah. Right? Because you're constantly having to come up with a new strategy, new product, serve a new... Like, go after a new opportunity, and you're of, back to minus one again. What do we do? You know? Yeah. We have to build a conviction in some sort of way forward. Now seems like a time where that's probably actually happening more and more often. [00:18:36] S1: It's more true than [00:18:37] S0: ever. Right? [00:18:37] S1: Like, you hear about all these refounding of companies that's been around forever. [00:18:42] S0: Yeah. And just like the pace of technology Exactly. Picks up. Everyone's like like, okay. What is now possible with the latest version of, you know, this foundation model suddenly, like, all these things are now... Right? So how do you think about that, like as you have been growing the company? Has your approach to it changed? Are there specific things that you've looked back on and like, Oh, actually that thing that I did really early on is really useful as a way of thinking about doing it now? [00:19:09] S1: Yeah. Yeah. So, it's something that I think about all the time. It's something that our executive team thinks about all the time. And it goes back to, okay, well, every milestone you hit for revenue, it's 1,000,000, 50,000,000, 100,000,000, the next doubling, the next tripling, it's not going to be as easy because it's just the standard law of large numbers. [00:19:34] S0: So
00:19:34 · Jonathan Brebner, Anurag Goel
[00:19:34] S0: So [00:19:36] S1: how are you going to think about things differently? And that's where I think the minus one mentality makes a lot of sense because what got you there is not gonna get you to the next stage. This is true for startups just across the board and even for public companies, just any business because of competition, because of how markets change. And so this is why it's so important to be thinking about competitive strategy in terms of how you're different and where you want to be different. And for us at Render, it starts from ultimately who we're trying to serve. And you could change that, but then you're a very different company. I think Render has always wanted to serve application developers, people who are building these applications, not the people who are building AWS infrastructure, not the platform engineers, although they also benefit from Render. But at the end of the day, it's it's the folks who are building applications and these days AI apps and agents. And when we think about them, it's all a question of how are their needs changing. And we realized recently, for example, that of our top 30 customers by revenue, more than half are building some sort of LLM functionality into their app or building It's a [00:20:56] S0: thing nowadays. Yeah. [00:20:57] S1: Yeah. It's a thing nowadays. And this wasn't true even six months ago, by the way. [00:21:02] S0: Really? It's kind
00:21:02 · Jonathan Brebner, Anurag Goel
[00:21:02] S0: Really? It's kind [00:21:03] S1: of reached a tipping point. Yeah. And because of that, as we talk to these customers, we find that new problems are emerging that Render is actually uniquely positioned to solve. So for example, something that we excel at is running long running compute workloads incredibly reliably, scalably for weeks, months at a time. And this is as opposed to some of the serverless architectures that you hear about where you can run something for five minutes, but that's very different from keeping something running for months. And Render has built this incredibly reliable platform that runs applications for as long as people want to run them for. And that has allowed us to attract a lot of AI workloads that are coming to us from places like Cloudflare, places like Vercel, and even AWS Lambda, because all of these places are built for ephemeral execution. Whereas when you call out to an agent, it could take a while. When you want to run some sort of processing using an open source model, that could take a while. So people are realizing that these platforms, while they might be flashy, they're not the right fit to run AI workloads, backends. And so we're seeing a big demand for that on Render. [00:22:29] S0: And in fact, even it's sort of a trend line, right? What is one of the benchmarks for, you know, agents and new models? Yeah, well, how long-
00:22:29 · Jonathan Brebner, Anurag Goel
[00:22:29] S0: And in fact, even it's sort of a trend line, right? What is one of the benchmarks for, you know, agents and new models? Yeah, well, how long- [00:22:38] S1: Right. Yeah. And so you're not going to be able to really run an agent and have it do what you want to do if cutting it off at five minutes, which is maybe the limit for your infrastructure provider. So because that's happening, now the folks on Render are saying, the customers, they're saying, Okay, it isn't just the long running stuff that we need. We actually need everything else around it. There is a need for a suite of products that form together an agentic runtime. And by that, I mean, okay, these long range workloads, but also this notion of workflows because agents, they have a series of steps that need to be executed in sequence and reliably, and each step could take really long and you need observability and you need concurrency limits and you need retries. And so you need an infrastructure primitive that allows agents to operate that way. But that's just one part of what people want us to build. They want us to build an AI gateway. They want us to build sandboxes. They want us to build a shared file system. And they want us to build these things because they need them as they build agents. And right now, because vendor doesn't have them, they have to go out to a bunch of point solutions, but they really don't want that. They want everything to be part of the same systems so you can get end to end observability, security, compliance, all of that, and it's obviously is one bill.
00:24:09 · Jonathan Brebner
[00:24:09] S0: Yeah. I I mean, is this a case where it's like just the demand, like, happened so quickly and was so clear that you're... That it's like, alright. Now you have to kinda prioritize which you wanna do. I mean, we we were talking a little bit earlier about, like, part of the difficulty here, this is... I mean, this is also true, you know, with the the, data science product that you created is, like, you have customers paying you for something else. And then suddenly there's this other thing where there's a bunch of demand for it. Like, maybe they fit together or maybe it's like they're complementary, but like maybe it's... You have to think about, okay, where do we wanna focus the company? Right? Are there ways that you've gone about sort of managing that like decision tree?
00:24:45 · Anurag Goel, Jonathan Brebner
[00:24:45] S1: Yeah, absolutely. So, I still think that building this is a bet. It's not a clear cut thing that will definitely succeed. We don't know how the demand is gonna change. And in fact, there are people at Render who are like, well, if you wanna make Render the best place for AI apps and agents, what about the AI bubble? What happens when it bursts? And my point when that comes up is, look, yeah, there might be a valuations bubble. There probably is, but real value is being created. We're all using AI every day and people are building genuinely useful apps using LLMs. So it's not like the crypto bubble, forgive me for saying anything about that, but certainly there is much more value that I see here that developers see, that application developers see. And so you could either, you know, put your head in the sand and say, Oh, we're just going to be the naysayers and we know that the bubble is going to burst. And at this point, that's actually just everyone's opinion. And we'll just be right at some point. Or you could say, you know what? The bubble, yeah, maybe there is one, maybe there isn't. But the rational thing to do right now is to assume that things will work out in some form. And people had these problems today, so why don't we solve them? And we'll see what happens when the bubble bursts. Yeah. And so that's, for us, a minus one to zero step. Yeah. For, okay, let's jump into this with our customers. Let's figure it out together. [00:26:19] S0: It's ultimately... It is still a bet.