I heard you say that not knowing how to code can give you an advantage.
I think people who have been coding with AI for a while are also at a disadvantage because most recent generations of AI models, like the frontier, are actually better with very simple prompts.
What do you think was that pivotal moment where you felt like you already hit that product-market fit?
The group of people, about like 30 of us, that were working on Replit Agent, which is really the first coding agent on the market. There's no examples of coding agents at the time. But the moment we started seeing it work internally, we just had tremendous conviction in it. Now, still, we didn't know that we were going to 100x revenue in a year.
How far do you think we are from having mainstream adoption where companies can confidently say, "Okay, we have some automated feedback loops here"?
Hey, this is Carlos, CEO at Product School and your host on the Product Podcast. My guest today is Amjad Masad, CEO of Replit. 10 years in, and he says the first eight were just finding product-market fit. Then they shipped the agent, 100x revenue in a year, and now they're at 50 million users and a $9 billion valuation, chasing a billion in revenue. Over 75% of those users are not engineers, and he corrects me early on: nobody in AI has recurring revenue; it's run-rate revenue. Here are the things we'll cover: why he says chat was never the thing, code is; not knowing how to code is now an advantage; his prompts have gotten lazier on purpose; the self-driving company and why he thinks it's mid next year; frontier performance at 40% of the cost through model routing. Let's get into it.
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And yet, welcome.
To ProdCon and to the Product Podcast. My 9-year-old son couldn't be here, but he asked me to say thank you.
Because he's been busy coding some video games on Replit.
That's awesome. Video games have gotten so good with AI, it's crazy. I was just actually at a booth. One of our sales engineers is like, he's always making video games with Replit. He just showed me FIFA that he made. I'm like, this is better than the FIFA I played with growing up, and it was like one prompt.
Wow. Well, but not everything was rainbows and sunshine. You've been grinding for 10 years, right? And the first eight kind of were the beginning of finding product-market fit.
So, what's been happening? What do you think was that pivotal moment where you felt like you already hit that product-market fit?
In the history of startups, we always had this idea that like your company's like you spend two years in seed, two years in A, you get product-market fit, and you scale from there. But I think that sort of was only like a SaaS-era phenomenon. If you look at old businesses, like it takes about five, six, seven, eight years to build something meaningful. And even recently, when you look at businesses like Notion or Figma or some of our peers from that era, it took them a while to get there. So I think like I've had the patience because I just felt like we are riding on certain trends that are going to become more and more important in the future. One of them was cloud, two was AI, three was like mobile, and four was like collaboration and kind of the future of work. So always had conviction. Obviously, there are times when there's a lot of self-doubt. But when we were working on Replit Agent, we kind of knew it was the future. Like it was a rough time for the company. We had just done a layoff because we were spending a lot of money and not making any. But the group of people, about like 30 of us, that were working on Replit Agent, which is really the first coding agent on the market, there's no examples of coding agents at the time, but the moment we started seeing it work internally, we just had tremendous conviction in it. Now, still, we didn't know that we were going to 100x revenue in a year. That was like no one predicts that. It's really hard to predict. But we knew it was the future.
So a few years forward, now you are with over 50 million users, $9 billion valuation. You're aiming for a billion dollars in revenue, in annual recurring revenue, by the end of this year. How is that going?
By the way, no one in AI has annual recurring revenue. So we need to get used to saying ARR, run-rate revenue. [laughter] But yeah, things are going well. I think there are a few things that are interesting. I think that enterprise adoption of AI has really advanced this year. So if you look at the last like, you know, since GPT-3, enterprises were not really using AI. ChatGPT sort of started to change that, and then like Microsoft Copilot. But if you look at most enterprises today, they just use chat. Most people here who work at large companies, if you're not an engineer, engineers, you know, are typically on the cutting edge, but if you're not an engineer, you just get access to chat and maybe some RAG system or whatever. But now this year we're starting to see like big banks sign up to Replit, big financial institutions, Visa, Amex, some of our customers, governments, the US government is a customer of Replit right now. It's a we're we're finally starting to see how AI could transform work and how we do work. Because I don't think chat really has a big impact. And there was like this big question, oh, we're investing so much in AI, we're not seeing impact. I don't think chat is the thing. I really think ultimately code is the thing. The idea that everyone can make software, can create code, and can like work with agents, and most agents are coding agents essentially, to get their work done. That's quite transformative on the economy, and we're in the early innings of that, but the compounding nature of it is quite astounding.
I wanted to learn more about it because it seems like over 75% of your users are non-engineers. And I heard you say that actually not knowing how to code can give you an advantage.
I think people who have been coding with AI for a while are also at a disadvantage. Because most recent generations of AI models, like the frontier, are actually better with very simple prompts. My prompts have gotten lazier over time. I use sort of like less skill files, less... So models are getting more and more capable. So the more you try to micromanage them, the worse they are. You know, there's like this wisdom in Silicon Valley that micromanagement is not great, which is true, right? Like if you have a talented engineer, you shouldn't be micromanaging them. But like most engineers, their instinct is to micromanage AI. Whereas someone who doesn't have that instinct might actually get better results.
And you're starting to see that.
I saw that with Replit Agent, you're able to spawn multiple sub-agents, right? And the next thought that comes to that is how do you actually call it orchestrate or oversee? I don't want to say micromanage, but to make sure that at least you have a good sense of what's going on.
So the UI obviously needs to give you... So, you know, with parallel agents, we have like a thread for each one of them. You can sort of like dig into each thread of work and see what's going on. Also, agents come back with questions, and we have the mobile app. If you install it, it'll like send you a message. It'll be like, "Hey, Mike, I'm going to go over budget, or I'm going to use this new package, or I'm going to do this refactor." And so you have that sort of oversight. And also, the agent kind of learns your pattern of usage. And so if you're someone who is totally hands-off, the agent kind of learns that, but if you're an engineer who is micromanaging the AI and that's what you prefer, you know, more power to you, and like Replit will sort of adapt to that.
So you recently coined this term of the self-driving company. How can companies learn to learn?
So I think there is a compounding nature of learning to learn. Very much like everything we generate can go back into context. The problem now with most companies is most people are using Claude or ChatGPT on the desktop, and that is not actually feeding back into context. At Replit, most of our AI usage happens in Slack, and a lot of it in public channels. And so everything is self-referential. We write documents, they kind of go back to the context. And so collaboration becomes very important. I mean, we spend a lot of time figuring out how to do collaboration with AI on Replit, like having multiple people on the same project. None of the big labs are focused on that, but I think that's like a mistake. Like you would want like, I think it's more important right now to approach, you know, building companies more collaboratively and doing work more collaboratively, because we're going to have less work on our plate to go do as individual contributors. And where new ideas sort of emerge happens from brainstorming, happens from building on each other's ideas. You almost want to approach building a company like an open-source community, where in the open-source community things might be a little bit chaotic and there's no centralized leader, but you know, everyone's building on top of each other's things, everyone's learning from one another. And I think the future of how companies work, as companies become more autonomous, when a lot of the engineering is autonomous, when the support is autonomous, when a lot of the operations is autonomous, you have to ask: what is the role of the human? And ultimately, I think it is knowing what the customer wants, I think figuring out what's the best thing to build, figure out the major decisions, kind of setting the destination, and then trusting the self-driving autonomy of the company to do that.
