What happens when AI agents — not humans — become your primary customer? That’s not a hypothetical. It’s already happening, and the founders who recognize it earliest are rebuilding their entire infrastructure stacks from scratch.
In this live episode of Founded & Funded from our IA Summit in Seattle, Madrona Venture Partner Jon Turow sits down with Parag Agrawal, former CEO of Twitter and founder of Parallel Web Systems, and Nikita Shamgunov, who led Neon through a rapid AI pivot before its acquisition by Databricks.
What they cover:
- Why Parag is building a new search index from the ground up — and why existing ones weren’t designed for AI agents
- How to pivot an established company in weeks, not months, when your customer base suddenly changes
- The “pagers vs. iPhones” framework for knowing when to lean into disruption vs. protect what you have
- Parag’s two-person hiring rubric for teams operating in deep uncertainty
- Why Nikita added the head of product for ChatGPT to Neon’s board — and what that signaled to the market
- The “two-way door” model for giving agents real autonomy without catastrophic downside
Whether you’re building infrastructure, running an AI-native startup, or trying to figure out where your product fits in an agent-first world, this conversation will sharpen your thinking.
Listen on Spotify, Apple, and Amazon | Watch on YouTube.
You can also read Jon’s takeaways for founders here.
This transcript was automatically generated and edited for clarity.
Jon: You guys have made major bet the company’s strategic calls, not incremental tweaks on how to change your infrastructure-level products based on what you were seeing at the application level. And maybe you could just take us through what it is that you did and why you were so convinced that you should bet the company on doing that.
Parag: Well, thanks for having me here, Jon. It’s good to be here. I’m building this company called Parallel, started about two years ago, and our product is essentially a bunch of infrastructure that agents can use to use information on the web. And I think when Jon says we bet the company on some vision of the future, I think what he’s implying is that we decided not to focus on use cases that were around when we started the company, and instead decided entirely to focus on AI as being our primary customers. And we did that because I was playing around building agents myself two years ago, and I could see all the reasons that it was impossible to do so.
And it became super obvious that things had to change, having built a past generation of infrastructure on the web where you were obsessed about imagining a human on a mobile device and how they browse or navigate or search or act, and what drives them. Every decision you make in building products or technology is about the end customer. And what you could see happening was that your end customer was about to change completely because an AI was going to sit between you and the end customer. And that is what creates the biggest conviction and opportunity.
Jon: One of the things that you’ve had to do is you’ve had to build a search index from scratch, which not a lot of companies have done until now. And how did you connect that requirement all the way up to these use cases that are coming that are so early?
Parag: People have built search indexes. They’ve been doing so for 30 years. There’s two or three really good big ones out there that have been 20, 30 years in the making. I think when the customer changes, everything changes. So I think before starting the company, we went into a multi-month design prototype, idea maze, reimagine the future. Once you go do that, you realize that how and what we crawl must change because a bunch of the… Our index is a complement to models.
Now models, as much as they feel like humans when we use them, they aren’t very human-like in how they work, how they behave, and what’s good for them. The data, they already have a lot of data in their parametric memory. So the index must be a complement to the model. What you rank is different, so you now need to own the index and the ranking function. We believe over time, the agents will move from a pull-based system, as we live in today, wherein people pull information from the web for the most part. Agents will be triggered by changes in the world as they manifest in the web. And once you decide that everything’s going to look so different, you kind of have to go own all of the parts of the stack where what we’ve built to date is no longer right.
Jon: Let’s go to Nikita now, because I think this kind of dovetails. Here you are with an established business that you were already running, and a moment where use cases and customers and history told you that something had to change. And in a matter of months and even weeks, you pivoted the company. Can you talk about that?
Nikita: It was about a year ago when Replit agent launched. And every time I’m going to say agent now, I’m going to be referring to coding agents, agents that generate code. So think about Replit, Lovable, Bolt, Cursor, Windsurf, Claude Code. So when I say agents, it’s one of those. Right before that, we were telling ourselves what we were building at Postgres tier for the internet. Make Postgres serverless, make it super easy to use. And the user, speaking of when the user changes, the user is a human developer. So we divided the world into infrastructure and dev platforms. We were saying dev platforms are different from infrastructure platforms for these reasons: developer experience, taste, workflow. And then, we were building that out. We were on that trajectory, and things were working. Every day, more and more people were signing up into the platform. We were very happy because we were a bunch of nerds, twisting knobs on the backend, and then the world would respond to us with more usage and better retention.
And one of those strategies was to go and embed ourselves into other developer platforms that are broader than the database platform. So we went after Vercel, Retool, Replit, were successful with this and they would like wrap us because they wanted to offer Postgres on their platforms and we’re like, “We’re easy. If you want to wrap us, wrap us.” And so we got wrapped into Replit and we were expecting a lot of usage and there wasn’t a lot of usage. So we were like, “Ah, Replit.” And then in September last year, they launched Replit agent and Replit agents, for those who don’t know, allows you to prompt and then generate an app based on a prompt. And then we saw that our usage basically skyrocketed on that particular channel to a point that there were more databases spun up by Replit than the rest of the world.
Now, they were very different in consumption because turns out that Replit generates lots of apps and a lot of the apps is throw away. That’s where serverless is useful. And then we realized that the retention of Replit user on the Replit platform is high because the user gets a lot of value. Retention of an individual app super low because people create apps so cheap and then they throw them away. And I remember being on a Zoom call with a bunch and then we have this guy, Arjun on the team, he’s like jumping, he’s jumping on the Zoom. He’s like, “We are having a moment. We’re having a moment.” Arjun runs go-to market. But now what do you do? You’ve built a product for humans and then suddenly you got to take off with this new user. And when the user changes, everything changes.
So now when you take a pause and you think about it, you’re like, okay, somebody, if it’s not you, then somebody will build a dedicated product for the new user. Someone like Parag is probably already working on this, and then you will be left in the dust. I spent some time at Khosla Ventures and I saw some examples when a disruption happens, you either lean in into this or you don’t. And when you don’t, suddenly you’re selling pagers and the whole world moved to iPhones. I think that’s the analogy, but now what do you do? And what we did was A, we said, “Is Postgres here to stay or Postgres is going to go?” Because of that transition. And luckily, I think Postgres is here to stay, lucky for us. Because if it wasn’t the case, then we wouldn’t have a company.
A visceral example in my head was a company called Navisphere. When Kubernetes came in, they had a pretty big dilemma. Are they going to keep betting on Mesas or they need to lean into Kubernetes? Mesos was in the name. Thank God we didn’t have that in the name, a company called Neon, so Neon can be anything. But so again, what do you do? So first of all, you say the user is the new user, which is tricky because you have traffic from Replit, but you don’t have traffic for anything else. But the challenge you’re also having is an internal and external. How does the world perceive your company? So is your company this modern company that leans into disruption or they perceive your company as an older company. And so this whole dilemma of infrastructure versus dev platform changed. Are you now like we successfully doing a dev platform, but are we building an AI platform, AI infrastructure platform or not?
So then what you do is like you look at all your peers. I think Eric is somewhere either in the room or around for modal. It’s like, oh, they’re building an AI platform, AI infrastructure platform. Guillermo from Vercel, who just now raised a 10 billion, is an angel in the company. And I’m calling Guillermo and he’s telling me that they are going to lean in super hard into AI and then you blink and they launch v0. So now probably a lot of people in this room launch v0. So first of all, you identify peers that are leaning into this, as well as companies that don’t. So don’t be the other category, be that category. Then you need to play, right? So September. In December, MCP server, MCP comes out. Nobody knows what MCP was. Anthropic just launches it. Is it exciting? Is it not exciting? It doesn’t matter if it’s exciting or not. You have to play.
So they launch it in December, a week later, we launched our MCP server. That’s not a lot of technology, to be honest, but the important thing that as the world moves and changes, you launch your thing as well. The thing that I learned from Vinod Khosla at Khosla Ventures is the team you build is the company you build. And so if you want to be part of that new world, then you need to have that on your team. So we stood up an AI team, and I remember at re:Invent, that was November, I think, or early December. We were talking about this problem, and that Replit thing happened in September. In November, we’re like, “We need to have an AI team.”
Jon: And it was two weeks later you came back and said, “Done, we’ve got an AI team.”
Nikita: Yeah. And of course it’s hard to hire an AI team for a non-AI company. So modernizing your company and changing that perception is important. Then you can afford to tell a story, then you come up with a story, you stand up the AI team. The other thing that we did was kind of unusual. I was at the Menlo event and then Kevin Weil was there, chief product officer from OpenAI. I was obnoxious enough to invite him on the board. He said, “No.” But he said, “But you should talk to Nick Turley, who is the head of product for ChatGPT.” And then I went to Nick and invited him to the board. He agreed. So now we’re bringing talent into the company, we’re bringing talent to the board. What you also could do, what we also could do, what we didn’t, is to bring AI talent on the staff team.
But for that, we thought we don’t have a product yet quite to do that. But so then we kind of stopped because first you figure out what to do and then you figure out who and then you bring the who and then your team you build is the company you build and the company steers into the direction with a new set of people.
Finally, I want to say when you lean into something that’s new and uncertain, you will fire a bunch of bullets and you’re not going to score on each one of them. And so a bunch of things that we did didn’t work, but that comes to your batting average. So compare and contrast it with either being very, very, very careful choosing those bullets and then you hit every bullet, but you fire too few or not doing that at all, and then you missed the whole wave. So that’s what we did. Probably like four out of eight, nine bullets turned out to be really transformational. Two, three just didn’t work at all. And then the rest was kind of like a shrug.
Jon: There’s three points that stick out for me. And then I want to bring it back to the experience of Parallel. One is storytelling. And there’s a story that you told to me, and you were pretty loud about that hasn’t come up here, that at one point you were seeing agents create new databases at 4X the rate of human beings, which is striking and captures the imagination, and probably got Arjun excited and illustrated who is our real customer, one. Second is what I can only describe as a ferocious pace of execution. You were one of the very first companies that we saw actually have an MCB production server and that’s useful for…
Nikita: Yeah. And that was December, right? Then it blew up on Twitter in February. And now it’s like, I’m CP, whatever. And so again, that’s to your point is you have to play. Sometimes it’s expensive, by the way, because it distracts you from your other “core roadmap”, but you have to do it because if the user changes, then the user demands what the new features were.
Jon: Yeah. Parag, can you talk about how you’re building the team that maps to the customers, the end customers, and the intermediate technology you want to build?
Parag: Yeah, I have perhaps a couple things that were core to building the team. One, gently when you’re building in a time of extreme change, and for the future, we were going to… One of the harder things was that there wasn’t a market of AIs when we started working on this. We started building technology and infrastructure, but our customers hadn’t yet shown up in a very real way. And the customers that were around looking for the kinds of technologies we were building and the words we were using, we didn’t want to serve them at the time. And I think that guides the team you build. Number one, the team has to believe in this future and be persistent on it and be willing to not go take what’s available now and build toward this future. That’s number one.
Number two, the team has to be agile and adaptable. That’s why we built a very in-person team so that as soon as the opportunity presents itself, you are really, really fast in moving. Number three, the team must be… My philosophy is that you h-ave to… I agree with the idea that the team you build is the company you build. And if you want to take risk… We used to talk about go big or go home. If you want to take risk, you have to take risk on the team. So there’s basically two types of people on the team. One, people who add a bunch of risk and the corresponding upside to the company. And two, people who are great at working with people who add that kind of risk. So you kind of need both types to have a large team. And if everyone is only a risk adder without people who can actually channel the risk into a concentrated bet on the future, then you don’t actually get concentrated risk, you get diffused risk. So those are the only two rubrics of how we hire.
Jon: Got it. There are human beings at the end of this chain, and I’ll make a concrete example of why this matters in the case of Parallel. I have human beings who want to do searches on a new index, and Parallel can decide how much compute to burn, a little or a lot, based on how important that is. But Jon the human is not talking to Parallel. Jon’s talking to some app that rides on Parallel. So how do you actually get the UX right and the experience right end-to-end, such that Jon gets the information that he wants with the value and the level of compute that he wishes to burn?
Parag: It’s a really hard question to get the UX right, and that is why we leave it to the great customers we have to solve this problem, to interact and build. So we built a horizontal platform for agents to use. So we have customers who will do anything from sales to finance to recruiting to consulting to any kind of knowledge work effectively, where the web might be useful. And our customers have to do a lot of work around getting the UX right for getting work done. The thing we’ve actually… Our main prioritization rubric on what we work on is how much work or how many searches happen on the web for any single user action. And we believe that agents will use the web a lot more than humans ever have. Then you have to believe that the amount of work that happens is not limited by the number of humans.
We go initially working towards use cases where a single human action will trigger a large amount of work. Now, how might that happen? A single user action goes and triggers and fills up an entire CRM or an entire database with information collected, recent, overstructured, and insights extracted from the web. So you get a million rows in a database from one human action, or you have one human action that triggers this extremely deep research thing where an agent does what humans would take four, six, eight hours to do.
A single human action creates this always-on system that is now doing work for you repeatedly every day for a year. And so we go and focus on those use cases. Now, we’ve been lucky that there are a bunch of fast-moving companies. Some of them were even on a slide earlier today that are our customers who are innovating on the end user experiences. I know Jon is smiling because I was telling him that their lift is already still, we should be on there.
Jon: You got to come back next year.
Parag: Next year.
Jon: Well, that sounds like a good segue to a couple of minutes for questions from the group. The folks who would like to hear from Nikita and Parag, now’s the time.
Audience question: Great. Thanks for running this, Jon. So since you mentioned two calling a few times, of course, you had agents creating a bunch of database 4X -the rate of humans. I’m curious how you guys are thinking about setting the right guardrails and lives of trust on tools that can take actions that are irreversible. How are you thinking about the confidence there to invoke humans as little as possible, but yet have a level of confidence that you need to build robust systems that your customers will come back for, right?
Parag: I can start. So two things. I think when you think of confidence and guardrails, there’s two important things. One is how do customers trust that the agent’s end-to-end quality is very high? To do that, we’ve done basically two or three things, and that’s been our core focus. Number one, we build a lot of evals, which are general-purpose, but also customer-specific. In order to qualify use cases that’s going from this no longer works with agents to now it works reliably enough with agents. That’s a lot of the work. Second, we do a lot of work to… We have built models that actually evaluate the confidence of all the outputs we produce and produce evidence to them, and they’re actually calibrated against many, many evals. What that means is we are able to somewhat reliably know when we don’t get answers right. And that’s really important for enterprise use cases that we try to serve.
Now, in terms of guardrails, we’ve built a system that is read-only. There’s a reason that we’ve focused initially on being entirely read only and we do not… The words we use are we leave the web just as we found it. And that helps protect from some of the big downsides.
Audience question: What is the most trust you’d give an agent to do something for you? What is your personal level of trust to set an agent loose to manage finances or manage things in your family? I’m just curious, you guys are brilliant, right, so I want to get your own personal sense of how far you would go to be most courageous here to let an agent go into it?
Parag: I personally take a decent amount of… Get agents to do a decent amount of work. So anyone who signs up to our website, we know we run agents to figure out who they are, what they do using our own APIs. When I personally will let an agent to do all of my data analytics on our company for me, I don’t have a static dashboard. We let it fix bugs, let it even take the initial crack at coalescing and prioritize a bunch of things we should work on as a result of all the customer calls I might have done in the last two weeks. And of course, then we refine from there, but we burn a lot of GPUs experimentally.
Nikita: I have an interesting take on this reliability thing. I think Basis one time famously talked about two-way doors and one-way doors. So if your infrastructure is such that it’s a two-way door, meaning actions can be undone and verified, then I want to give agents an opportunity to just rip. From the infrastructure standpoint, when we change our user, we build a snapshot to restore functionality for the database. Now the agents can change the state of the database, but if it is not to their liking, they can go back very, very quickly. And the other thing that I think was going to happen in the infrastructure is a parallel execution. So whatever the notion of an environment is, so data and analytics, database is kind of one, but I think you can apply it for almost anything, for like CRM. So if you can fork your environment, M times, and then run agents in parallel, that will allow you to burn a lot more GPUs.
TK was talking yesterday in a different event that Google optimized the cost by 33X in a span of six months. So I think the cost of those actions are going to go down, and then the infrastructure around them will change such that you can try and go back functionality as well as try multiple things in parallel and choose the winner.
Now, there’s also one-way doors because maybe a customer interaction when you actually present results to a human could be a one-way door if it’s like the final result, or it could be a two-way door when you use a ,human for verification. So the more two-way doors we have and the better ways to, with evals or whichever other way to validate a human in the loop at the end of the day to validate that the results are good, the better it’s going to be. And now we can burn off our compute.
Jon: We could talk about this a lot longer, but I think we’re going to have to leave it there. Nikita Shamgunov and Parag Agrawal, thank you so much for joining. Thanks everybody.