00:19:23 · Aditya Agarwal, Amjad Masad, Ruchi Sanghvi
[00:19:23] S2: Wow. That's amazing. [00:19:24] S0: Three d scan of your body. And they were working with offshore developers and they created like a mess for them. And now they restarted everything on Replit and it's just working. And it's like a healthcare app, but it's like a delightful user experience. And I didn't know that Replit is good at doing three d models and three d experiences. And these are the kind of things that weren't built pre vibe coding because the market cap for something like that, I don't think it's going to be you know [00:19:58] S3: Yeah, that's the question I have for you. Who is Replit's user? Is it the creator, the individual user? Is it the enterprise? I understand it's both. But I'm kind of curious about, can you build a business model without enterprise? Because every other competitor or so called competitor you might have there, whether it's Claude or Cursor or all of these other people, the main revenue even Cognition, the main revenue source is enterprise. So can you build a revenue model from this creative everyday user?
00:20:30 · Amjad Masad
[00:20:30] S0: Yeah, I think so. Mean, our revenue is now still dominated by individual entrepreneurs and small businesses. Our enterprise revenue is growing fast. I'll tell you some of the enterprise use cases. But by the way, I don't think these personas are that different in that I think what Replit unlocks today is I am a domain expert. I'm not a programmer. I know how to create value in the world. But previously, I didn't have the tools to do that. So outside the enterprise, it is this doctor that has deep domain knowledge in a very niche subject where some subpopulations of world are interested in an app for that. She can create that. That previously wasn't created because if you're tech pro in Silicon Valley, you're not interested in that, right? So now that gets created. In the enterprise, for example, one of our customers, Zillow, there's a product manager that was managing the real estate agent team. They're responsible for lead routing and managing the real estate agents to kind of be able to close deals. And he always had ideas on how to increase conversions, but didn't have the engineering resources to do it. And he was able to do it and he created millions of dollars of value of the company and became super popular in the company. He was sitting on board meetings and hanging out with the founders. That's entrepreneurship too, except it's inside the enterprise. So I think it's really the same type of user. The challenge for AI in general broadly today is that the concept of ICP is actually not very useful because it's like, oh, I'm going after the product manager that went to school to do X, Y, and Z. Instead, we're starting to think about traits. Like, what are the traits of the Replit user? And resourcefulness, So things PG used to write about, the hacker mentality, of that is the kind of user that we're going after.
00:22:30 · Haya Odeh
[00:22:30] S1: So for most part, people like to think about persona and not to think about persona is very difficult because you need to de attach yourself from these personalities and start to see common traits between all of them and see and notice behaviors. And what makes all of these people across all of their domain and expertise, the behaviors are all the same, which is, I wanna be heard, I wanna do something, I notice a problem, and I want to do something about this problem now. And these people are very stubborn. And because of all of that, too, they want to win, and they want to prove that they're right, they're not wrong. So whether they're designers, they're PMs, they are small, medium business owners, they are an enterprise, they all have these traits and qualities. And it's very similar from the consumer to enterprise.
00:23:32 · Aditya Agarwal
[00:23:32] S2: I'm curious, Amjad, I mean, you more than you and Haya have more than almost anyone kind of a front row window into the evolution of the models and especially kind of the accelerating progress and capability of these models over the last three or four months, right? And I think that you're laying out a version of the world, which I subscribe to as well, which is that these models have seen, particularly kind of Claude, has seen a tremendous kind of rate of increase in terms of capabilities starting, whatever, December, January. And right now in some ways we are in peak love with Claude Code because these models, they feel like you want to be raw, you want to be close to them, and it gives you a sense of like, wow, between Claude Code, Claude Code work and so on, there's a tremendous amount of capability. But pretty soon you're like, well, I kind of don't want five terminal windows running. I want something that's a bunch more managed. I want different UI. I want more capabilities, right? I guess the first question is, what do you think changed in December? I'm curious to get your analysis of what changed in the model capability. And number two, how quickly are you seeing users get, in some ways, frustrated with the minimal UI of these raw models and wanting more?
00:24:47 · Amjad Masad, Aditya Agarwal
[00:24:47] S0: I think there are different phases of model improvements. Say 2021 or like 2020 to 2023, it was a pre training era where you just put in a bunch of crap, really any kind of data, it didn't matter. And you just have as much parameters as possible, and you grow this model and it just keeps getting better. We have the scaling laws than we thought we [00:25:17] S2: could In a simple word, more data, more parameters, everything was better, yeah.
00:25:20 · Amjad Masad
[00:25:20] S0: Yeah, but then we a plateau, and the plateau for a bunch of reasons. One is there wasn't a lot of data left in the world. Another is the training infrastructure got a little too complicated for growing these models a lot more. And so we entered the sort of RL and thinking era. So like, say, 2024, 'twenty five, starting with one and deep seq and all that, you had this moment of time where the training regime changed and we entered this moment of you're doing reinforcement learning and these models are getting more autonomous because not only the raw intelligence is improving, but they're staying coherent longer over time. I think the big unlock that happened just in late December, November, December, I think the coherence just became almost indefinite. Interesting. Like, Agent three was kind of the most autonomous agent in September 2025, and we struggled to make it run for an hour. Right now, if you have enough evaluation in the middle, if you have computer use or unit tests or whatever, really the models can run indefinitely. The other thing is models also gained some self consciousness almost. I think that's too much of a loaded word, but they started training on their own context window. So they actually also got really good at compression and really good at taking context and storing it into files and being able to search across these files and recollect the contacts based on the next tasks that you're going to do. And all these things culminating together created these different models. The other big thing, at least this is the rumor, is that it looks like the big labs have figured out how to scale pre training again. So I think we're gonna enter another phase of scaling where we're gonna see bigger and bigger models. So Opus is like a really big model, and I think they're gonna get even bigger. And I think the reason token prices are not going down is because the models are getting bigger. But it's sort of ebbs and flows and easy to understand what stage we're sort of in. So I expect the raw capabilities to continue improving in the near future. That being said, I still feel that there's a lot of white space between the AGI providers and what the users want. And the kind of thing that is actually fun and exciting to use, that doesn't change. 100%. That's human, and humans don't change all that much. We're visual creatures.