Y Combinator Startup Podcast - Building A Global AI Startup From India

Episode Date: March 16, 2026

In this episode of The Lightcone, we talk with Mukund and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building in India for a global audience.Apply to Y Combinator: https://www.ycombinator.com/applyChapters:00:00 - Intro01:06 - What Is Emergent?01:18 - Founder Backstory02:09 - From AI Testing to General Coding Agents02:52 - Getting Ahead of the Market04:18 - The Pivot to Non-Technical Users05:22 - Why Second Movers Can Win in AI09:04 - Building for Production, Not Just Prototypes18:21 - Live Demo: Building Apps with Emergent24:40 - How Emergent Hires and Runs a Lean Team29:04 - Is SaaS Dead? The Rise of Personalized Software34:04 - The Future: Niche Apps, Solo Builders and AI Agency

So I think now we are just truly seeing this unlock where people who are like really close to problem domain expert and but have been blocked by, you know, technology barrier to sort of really express themselves or using emergence to sort of build these things out. There's just so much focus on AI is going to replace jobs, knowledge work is going away, like what's that going to mean for employment and civil unrest? But like no one's really talking about the fact that actually like if you have like some agency of interest and you want to start your own business and have autonomy over your life, like you are empowered. that at scale. Welcome back to another episode of the Lycone. Unfortunately, Gary got called to jury duty and can't be here with us today, but we are really excited to be joined by Mukund and Madav Jha. They're both twin brothers and founders of Emergent, which went through YC in summer 2024. Emergence is a platform that lets anyone build and ship production-ready software using AI agents. You guys are actually one of the fastest growing companies. I believe YC's ever funded.

I mean, the statistics you were telling us were mind-blowing. You have in eight months since launch, seven million apps have been built with Emergent. What goes through this incredible growth you're seeing, actually? When did that hit a real inflection point? And how do that feel for you guys? So we both are twin brothers. We actually, you know, started programming when we were age 12. Both of us came to us to do our PhDs.

I dropped out of the PhD program, joined Google. And Maddie went on to, was in Xenifers, then went on to start the deep learning team at Amazon. And we've been meaning to do a startup together for a long time. and before this I was running a startup in India called Dunzo which was a hyperlocal quick commerce company and Dunso was a big company actually right it was really big and we were almost a verb in India

so when people ship things they say Dunzo it and I was managing a really large team of 300 engineers and we have been sort of watching the deep learning field for a while and we knew an inflection point is coming one of the things that I observed when I was running this large engineering team was that software testing was the biggest bottleneck in shipping fast So when we started looking at what we want to build in AI, that was the first idea.

What year was this? This was 23 end. And so when we applied to YC, we applied with this idea of automating software testing. That was the first idea. In fact, we went to a lot of VCs with this idea. They thought it was too crazy. And now looking back, it almost looks funny.

And so we applied to YC with this idea. And when we were building this testing agents, we realized that if you can solve for verification, which is essentially, you know, you can solve the testing part. You can actually automate all the software engineering. That was sort of our key insight that, like, you know, verification is the loop which sort of keeps agent running for a longer period of time. And that's when we pivoted to looking at general coding agent as a space.

And we started building general coding agent. And this takes us into 2024. There's two, 2020. Yeah. Tell us what the landscape looked like. Like how big was lovable at this point and just... I mean, nobody had started. Lovell had not started.

I think Kursa was just getting, getting started. and very, very early. I think Devin had just come out, so really, really early. And we looked at this benchmark called SweetBench, which is essentially a benchmark. Now it's saturated, but at that point of time, that was the benchmark where all of the coding agents were getting measured on.

And we took on this challenge of becoming number one on that benchmark. And we sort of packed ourselves in a room, four of us, and said, okay, let's just look at this benchmark. How do we crack it? That sort of set the foundation for emergent. And we built, you know, soda coding agents, which became world number one on sweet bench, you know, in two months of time.

And that was time when we sort of discovered all of the fundamental truths about building with LLAM, building with agents. And you're intended to use at this point, I'm presuming the engineers. Yeah, at that point, we were like purely just a research company, just building coding agents. We were not thinking about a product. There was a time when we sort of invented the multi-agent system. We invented memory. We invented like how do we do agent-to-agent communication? How do you scale up test time compute?

A lot of those things which were sort of coming out. Like we would discover something and we'll see three months later something come out in a paper. And that sort of set the foundation for us to. So we were like cloud code before cloud code was a thing. A bunch of the paradigms like multi-agent orchestration. How do you use like different different routings? A lot of those things we sort of discovered. I definitely want to come back to that.

I'm curious at this point in the story. When did you sort of pivot into becoming a tool for non-technical users? Yeah. So we actually like once we had this coding agent, we actually went to the enterprise that was the common wisdom at that point that, hey, like go to enterprise, build for enterprise. And we spent like two, three months trying to, you know, make our agents work with an enterprise, we found that it was too slow. And at the same time, we were internally started using

emergence platform to build internal tools and internal software. And at that point, you know, we saw like, like, Loveable was growing like crazy. Bolt was growing like crazy. So we thought, hey, why don't, we have this really strong coding agent. How do we sort of package it and, and bring it out in the world? And we launched a very like small beta pilot almost in June last year, 25. And that really took off. And since then, you know, like, we have been just focused on solving problem for non-consumers. We in fact thought a lot of technical people use us. But today, 80% of users were on the platform are non-technical users with zero programming knowledge. And they're building like apps that run real businesses on top of today. So it's almost

been... And they're based all around the world, right? Like how many countries... Yeah, so they're global audience, 80%, 70% are in US, Europe, over 190 countries right now. Something that we have talked a bunch about at YC internally is just how does first mover advantage versus second move advantage play out in the AI world. Certainly something that we've noticed, like if we look at some of our company, Lagora, enter the legal AI space after Harvey, but is like growing incredibly fast. So there was clearly, it wasn't maybe as big of a moat around being a first mover as you traditionally think there is in software. When you guys made that sort of the pivot or the slight change in direction into non-technical users at a time where level ball and bolt are

growing really, really quickly, how did you think about that? like two three different different threads I would want to pull. One essentially is that I think the model, every new model generation actually is presenting a new opportunity of looking at the world. Like for example, when we started, GPD4 was the first model that we sort of started looking at. And at then, the biggest problem that everybody was trying to solve was JSON parsing, like a structured output format. And we thought, okay, like the next model is going to solve for it. You know, like let's not spend time on that. And I think with every new model, what's happening is that you need to start reimagining the world.

For example, like Opus is a different class of model right now. It's going to enable extremely long horizon task. It's going to enable multiple agents coordinating together. And so I think, like, one of the advantages of starting second, right, is that you can actually, one, like, learn from what is not working for the current competition. And also, I think you fundamentally start from a different starting point, right? Like where, like, your aperture of the world is, like, very different. Like, your imagination is really big, right?

And I think, and when we were starting emergent, we realized that like a lot of the users that were going to, you know, some of these apps, they wanted to actually really build an app that works, right? And most of these were actually like really, really optimized for front-end prototyping at that point. So we started fundamentally reimagining that, okay, what would world look like if you could actually ship things to production? And our key inside was that to automate all of software engineering, you will have to build a platform that replicates what best engineering team do, like code reviews, automated testing, debugging. deployment, security, hosting. So we reimagine the entire platform from ground up saying what would an end-to-end platform look like. And the real user need was actually to ship the product, not just the front-end prototyping.

I think second thing is like, how do you sort of get the distribution? Because you're coming from behind, right? So even if your product is really, really strong. And fundamentally, I think you'll have to enter the market with a really, really strong product, which is, you know, head and shoulder above what exists in the market today for people to take notice. We are very confident about the product. And so a lot of our focus, like, in the early days, once we sort of launched, was on

how do we sort of rapidly scale up distribution? We built out a large influencer network, and that was our initial sort of, you know, starting point for us. Like we used TikTok, Instagram, and part of this bunch of influencers to really, really spread the word out, and that's sort of, you know, kickstart the whole thing for us. To me, so building the influencer marketing engine is like, it's like tactics to land grab.

Like, were you also thinking about just focusing on personas and specific sub-types of users you wanted to go after that weren't, like either weren't being targeted by level of all, or others or Emergent was a better fit for them. I mean, our thesis was that, like, there are a lot of users who would want to build serious applications, right? And that was our sort of target audience. And a lot of our marketing, lot of our initial messaging was around that.

Like, hey, come and ship a real software. What we did was, like, a little bit broad, broad-based, like, marketing. And, and, but users that, you know, were coming to the platform that you will convert, were users who actually wanted to ship a real, real app on the platform. And was that in the messaging then? It was in the messaging. So we would say, hey, come and build real apps. We would also use the common errors that you would see on other platform.

You know, like, hey, don't face this error on emerging. It seems like a key insight for you. Basically, you went very hardcore in terms of being maximalist in engineering from your experience, having run large engineering teams of 300 engineers, having worked on deep learning teams at Amazon. You really knew how to architect the systems. Can you maybe share a bit how you built it? One of the cons of all these other big products, like Lovell or Bolt, is just that it's difficult to get those into a fully usable.

You can get to a product type very quickly. But yours, you went zero to 100% very quickly. And that takes finesse. It's almost like that. 20% gets 80% effort, like the Pareto principle, but you did more than that, the last 20% of that engineering to big production was a lot of work. And that's a lot.

Yeah. And I think like the last mile that you mentioned, right, is always what people neglect, that, hey, you need to make sure that not only app gets built, it also gets deployed. And this is one of the conscious reasons why we chose to build our own infra on which the agent is running.

So we provide, like, cloud sandboxes. We don't outsource it to, like, some third-party sandbox provider, which was also pretty popular at that time. So we built our own Kubernetes tech stack from Groundup, the container tech stack. And one of the insights here is that if you give your agents the same infra

during the build time and the same infrared during the deploy time, then the sort of like, during this like deployment phase, you don't encounter those many problems, right? And the fact that we have our own infra also allows us to give like rapid feedback to the agent. So your agent is only as good as the feedback that you provide.

So we build this like sort of infra and agent like sort of co-build it together. And from the, from day one. And to your point, right, like because we, we focused on, you know, building like ship ready apps, which are production ready, which comes with back end and front end and everything.

The tech stack we chose was also pretty unique to us. We have a Python backend. server, we have a React front-end server. Most people would typically go with a much more like, you know, node focus, node-heavy tech stack, right? And this like server-client architecture where you can have like background jobs if you want to have background cues. So we knew that, you know, users who would use this app,

their ambitions are going to go bigger and bigger, right? Hey, I want to run a job which can like do this asynchronous video processing, you know, and they're going to prompt it. And we wanted to support it from day one, right? And so it's the same tech stack on which emergent is built is what we exposed to our end users, is what we expose to our end users, is what we expose to agents, right? On the agent side, we were very early on the multi-agent architecture. So we knew that you want to be very frugal about your context management. So what you do is, hey, let the main

agent, the driving agent handle the main routine. But any delegated task that you want to delegate, you delegate to a sub-agent, be it like testing, be it like, hey, I want to do a design search or I want to do like, you know, integration search. Like, how do I integrate this unique API? And along the way, when we were like finding or doing all of this, we were able to figure out, okay, all the trajectories that we are generating, we can kind of aggregate over time and like sort of build a long-term memory for the agent, which is very unique in the sense that your agent learns not just from your own session, it learns across the sessions. This is something I would say is one variant of continual learning that people are like

interested in now. You would have noticed that people are interested in skills, like people create like skills and the, there's a new benchmark called Skills Bench, which shows like agent with skills, outperform agent without skills. And interestingly, those skills cannot be generated by agent themselves. Like, if you generate those skills by agents, they don't, like, match up to the performance. So we were able to do it in a way where the skills get auto,

you know, sort of, you are generated based on previous trajectories. And we run it through a CICD process and then add it to the long-term memory. So all of that, like, compounds for us, right? So if your agent was struggling to do a calendar integration three weeks ago, Today, it is no longer struggling thanks to the previous session where it was able to make it happen. So it's fascinating. So it learned on its own because I think one of the challenges of all these vibe coding app platforms is at some point, the application would get so complex that if you build it very simply, you would run out of the context window for all the models because that seemed to be the bottleneck. And I think you guys architected your way out.

So you kind of build a lot of what the state of the art is now, but way back a year before. Our coding agent is so powerful that we basically internally use it as a replacement for cloud code as developers, right? So we are so proud of that. But yet we don't want to expose that sort of, you know, powered tool to our end non-technical user. And so even though we have this BS code editor, we kind of hide it. Because what we have noticed is that non-technical users, they even get panic as soon as they see a diff, you know. We had a fairly technical PM in our team. and like he doesn't like JSON, you know, he's like,

don't show me, you know, I get intimidated. So building that user empathy, where you have that user empathy and building that agent empathy, you also have to empathize with your agents. What is agent feeling like? I mean, internally have a term called agent experience, like that we measure that how, like, how is agents experience on the platform? Actually, a really important point I think people don't realize is you guys actually, you actually started out essentially as sort of Devin, cursor,

in the actual like coding agent world for engineers, you just made the choice to package it up for non-technical users. So you're sort of like moving almost in the opposite direction from like a lover board. Like you have like the power. You have all of the actual like power. You just need to simplify the user experience. Whereas they like sort of have like start with the user experience and they're going to have to develop the power over time.

Right. And I think fundamentally it's like unless you start from, you know, a starting point which sort of solves all of these problems along the line, the whole software development lifecycle, it's actually really hard to come from the other side and solve these problems because you'll make some architectural choices which are very hard to reverse. Do you have any more, I'm really curious, like any more examples of where, sort of as you're engineering the system, you just trusted in the model? Like you mentioned JSON passing, but was there anything else where you're like, let's not

invest time in that because like Opus 4.5 will solve it? I mean, some of them has been, for example, you know, like library definition, some of the integrations that we have sort of built. Like, you know, we think that, you know, the next sort of models are solving for us. Similarly, like, how do you generate unit tests? Some of those things that we actually, like, would have heavily prompted before. And the other thing that we are very conscious of is that how do we give more and more autonomy to the models as they, the next generations come out?

And the more autonomy you're able to give to the models, the better they perform. Like, initially, like, our harness was very strict and, you know, like, we would, tighten it up. And slowly, like, what we were observing is that as these models are getting larger, a larger, more efficient, like, you know, like the more control you give to the model, this is making, the better the hardness gets. If we extrapolate that out or sort of like really far out, are you worried about where that sort of leaves you as a company versus the models, like the models themselves and the models

get more powerful? Yeah, I think there is this underlying current right now, right, in the industry that, that, hey, like, is, you know, like, anthropic going to eat everybody up. Yeah, I mean, our view is that I think the coding aspect is only 20% of the job, right? I think taking an app to production is like really, really hard. And I think what matters is how closely are you working with the user, how well do you understand their needs? And I think as the models are going to get more and more sort of capable,

I think the human desire is also continuously growing at the same rate. So I think people are going to want to build more complex apps on the platform. The other thing is that it is with our harness, we're able to extract 20, 30% more on top of these models. And essentially, like, we can use multiple foundational models together to sort of extract more. And I think we'll have to keep continuing, you know, like delivering more and more things to our users. For example, now we're thinking about, like,

a lot of our users who have built the app now want help with distribution, now want help with growth, now want help with, like, how do you sort of manage users and things like that? And I think for us, the spectrum sort of keeps growing on that side. I agree with it. I mean, there's another graph that I shared recently is just like the number of software engineering positions available is actually going up, right?

And I feel like at least internally at YC you're experiencing this. It's like the more powerful the tools get, the more ideas you get and the more work you want to do. And it just feels like everyone here is working, like, more hours doing more stuff. And it's just like the rate of like software that you're expected to ship per week just keeps going up and up and up. It's a hedonistic adaptation to, you know, like, hey, oh, this is more powerful. I can do more work.

Yeah. It is really at Javan's paradox at play. And I think there's a lot of concerns that go, oh, the software engineering jobs will be gone. I don't think that's the case. I mean, based on everything that you're telling us and what we're experienced. I mean, I think we are in an expanding market, right? Like, we are, like, letting non-developers now be developers, right? I think, you know, that market is expanding.

We also are internally seeing, like, the roles sort of combining. So, like, a PM, a designer, engineer, like, a single person is doing, you know, like, work of all three together, right? So, like, we have a PM who's white coding internally things. And recently, like, we, so we are seeing this internally right now where a lot of the work that was done by, like, five, six people's team can now be just done by, like, single engineer or single PM. YC's next batch is now taking applications.

Got a startup in you. Apply at Ycombinator.com slash apply. It's never too early, and filling out the app will level up your idea. Okay, back to the video. Could we see a demo of a merchant? Oh, yeah, sure. Yeah, so this is how what emergent interface looks like.

And I'm going to put a prompt where, like, because we were coming for this podcast, I thought, like, you know, there should be an app which lets you practice, you know, podcast questions, or maybe you're going to a job interview and you want to practice questions, right? So you can build a full-stack app on Emergent. You can build a mobile app.

Our prompt engine is smart enough that once you give it a prompt, it will figure out that this is talking about a mobile app. So it'll figure out, like, hey, the right agent to use is a mobile app builder, right? So even though you had like selected the wrong tab, it's just like, yeah. Yeah, behind the same is auto. Yeah, I got you, right? So while this is running, let me quickly also show you a few user apps. So this is by somebody based out of Illinois.

He's sort of has a business of audio, video setup that they do, like, as manually, right? So basically whatever this kind of like intake form, they would have taken through spreadsheet and other calls. They basically build this out without any coding background knowledge, right? Like, hey, this is the kind of AV setup I want. So you go and you build your room and then you get, it's a lead gen sort of a form. But this is a fairly full stack app. One thing I noticed about that is like the design is really good. Like the icons, like it just like it looks like a well-designed app.

So we have actually spent a lot of time on making the design is actually good. Yeah. Like, so earlier there used to be a big trade-off between design and functionality. Like, if you were optimizing for design, like, your functionality would not be that strong. And so we had to figure out like how do we sort of, you know, share the context in a way where design also gets better. There's another sort of person based out of Norway. He sold his previous business to a PE and realized how much lawyers have to struggle with spreadsheets and other things. So he built a CRM for lawyers.

He describes himself as like business developer. I like the word he used. Like, I'm a business developer. He doesn't have a programming background. So a lot of CRM-related apps we are seeing small businesses. It's your second monetization avenue, right? And so, like, one of the unique things to emergent is that before agent goes off to build things, it asks you for some clarification because agent wants to make sure that it understood your requirements properly.

And another thing is that non-technical users probably don't know the concept of API key. How do I get an open AI API key? So in this particular case, I can just say, hey, use emergent LLM key. so you don't have to worry about getting API key from third party. This feels like a good example for what you were saying. Because this is sort of like the asked user question skill in code, but you just like have scrap that away. But it's like built into the experience for someone who had no idea about.

Absolutely. I can be very like casual here. I can say, hey, for the first one, use emergent API key. Rest assume good defaults and then go. This is the first time I hand off the agent. And like at this point I can just like close my laptop. We also have a mobile app. So you can like on the go keep trying to prompt agent if agent requires

additional thing. Once it's done, you see a preview of your app. So here, for example, in this case, I can practice what is my origin story. I can record what my origin story is, and I can keep going to various questions.

This is a podcast preparation app. Yeah. And then you can go ahead and revisit what answers you gave to your app. And so what we have noticed is that a lot of personal apps, people use, people build mobile apps, but a lot of business apps, they would go build a web app, right? So that's generally the trend we are seeing. The only other thing I wanted

to show was this is an actual Asana clone that our team built, like one of our QA engineers built internally. And so this is actual real emergent data. I'm curious what prompted that. Like was there some feature that Asana was lacking or something it wasn't doing that made them say, hey, we should just build our own? Yeah. It kind of like started off as a QA engineer's curiosity. He, like, his first prompt, I looked at his all jobs. The first prompt was Cloned Jira. Okay. And then, like, he just kept going with that.

And I think the other thing is we do do things a little bit differently. So, for example, we ship like three times a day, morning, evening night. So we kind of, like, built it very customized to the way we do things. Like, we have a QA of involvement in many, many ways. And definitely, like, when we were using Asana, it was very, like, even to customize it to make it to your work style was not easy. And we are also saving like, you know, like $3,000, $4,000 a month in subscription. This is a real world of personal software. Yeah.

Has anybody actually edited the code for this or is just 100% built with a merchant? 100% built with the merchant. And the good thing is that like if I want to add a feature, I have to just go to that, you know, project and just add a feature and it just starts building. It's probably useful for you guys to dog boot the platform this way because this is probably at the edge of the most complex apps people have built with emergent. So it allows you to test what happens when people get to a very complex app. In fact, a lot of the teams internally are now building, you know, apps using emergent internally. So we have, like, a marketing team built out of complete CRM, completely built on emergent. We are now, like, our customer support team is building customer support software,

completely built on emergent. And the power is that these are people who are closest to the problem, like, who, you know, who understands the problem really really well and are able to now build these apps. And the speed at which we are able to ship, you know, these internal apps is like crazy. How far down does it go, though? I'm curiously, even within the company, do you have people who want their, like, separate versions of like your internal Asana. So currently like everybody in the company is using this one tool right now.

And it is collaboratively being built collaboratively. So like you know, a PM can give a feature, a QA can give a feature, somebody from our HR team can give a feature to sort of build that out right now. How do you think there's sort of version control like feature flagging or all this stuff like develops in a world where anyone could just like write a couple of sentences to update the software they're using? Yeah. So there is a testing, testing phase.

This is deployment phase. So we have different versions maintain. And there is a primary owner of the software, like who actually manages this right now. And so it evolves, like, somebody will make a feature request. Somebody will sort of build that out as an, the agent will build it out.

And then like once it's accepted, then it'll go to the release. It's not managed through Git though. It's like your own workflow thing. So you can connect GitHub if you want to. Like we internally connect GitHub for our projects. And like if non-technical developers outside of emergent, like they,

Like, they actually call GitHub JitHub, right? So they have very, like, limited knowledge of GitHub. And so we take care of, like, versioning on our site, even if they don't connect GitHub. To talk about how you run your team, the way you hire must be very different. I mean, you're a very lean and small team. How do you hire for engineering?

Yeah, so we actually from day one have been very conscious of the kind of team that we want to build. And essentially, like, we index on two things. One is problem solving, like how good idea you had problem solving. And second is ownership. Like, we think that people who can, like, really, really, take ownership, you know, like we index on that. And a lot of our early sort of hires were people like, you know, we were really obsessed

with like top 100 IT rankers. So we had this like program going on where like I told, you know, our team that, hey, we must hire like top 100 ID rankers. Right now I think we have like IT rank one, IT rank 12, all of those people working with us. And a lot of the initials that also came from Dunso. So I, because I was able to build like a really good team, we were able to get some initial folks from that.

The focus that that we have is essentially like. one or two people doing work of what a company would be doing. For example, our deployment, which almost mirrors what what cell would look like is done by two people. Like our memory, like where you have like multiple startups solving for memory is just built by one person. So I think like we gave way more responsibility to people. And I think people are generally attracted towards harder problems that they want to solve. Where is your team located? So most of the team right now is in Bangal, in India office.

We have a very small office in SF, like three to five people here. And you guys yourselves, you're kind of like split across. both countries? Can you maybe just explain how the setup works? Yeah, so I mean I live here in SF. I've been in like Bay Area for like last 10 years. I split half my time in SF, half my time in Bangalore, constantly jet lagged.

I think you guys are probably the most successful AI company. It's obviously came from like it's an Indian company but that's got like significant presence in India. Why is that? I mean, I think it's like when I went back to India after Google and I always had this thought that why is there no Google or Facebook from

So like from day zero, I was thinking, you know, even though I started Anzo, it was an India-focused company at that time. And when I was starting the second company, I always thought like, hey, there has to be, you know, like we have so much talent. We have, you know, so a lot of capital available. Everything is available in India. Like, why are people not building truly global tech-first companies from India? And that was the ambition that we started with. And in my opinion, I think a lot of it is with, you know, like just your ambition. Like if you just dream big, if you're able to sort of really, really think global from day zero. I think now, because internet, is sort of fully penetrated, people can actually get understanding knowledge from everywhere.

I think every single country has an opportunity to build for global audience. And if you have that sort of mindset, that ambition, I think we'll see a lot more companies coming out of India doing the same. I'm curious to hear what it's actually like sort of on the ground running this sort of like split country company where the team is mostly in India, but the product is overwhelmingly used in the U.S. and Western Europe. If it's not, probably for the Indian market at all. What is it like running this company?

How would it be different if you had built a normal Silicon Valley style company that was all based here? Internally, we have like really set really high standards, like as a global sort of product. I mean, both in hiring, both in like the way we sort of develop product. And I think us spending sort of time here also helps. Like one of the things that we do really religiously is everybody talks to a customer once a week, twice a week. Like everyone in the entire company. Right. They talk to a customer.

Everybody does customer support. So like we were like a really, really small engineering team, like 12 people team. And one person was always on call for customer support. It was really hard for us because, you know, you're a really small team. You need to ship really fast. And then move like one of your best engineers out to do customer support was really hard. But I think that really, really helped us build the customer empathy from day zero. And I think given that like a lot of our distribution happens online, like, you know, like the teams are able to learn from digital things and build for it.

But I think us building that customer empathy from day zero, like talking to our users, like really really help us bridge the gap, you know, in terms of what I users want today. And it's funny because, like, when we launched my first, like, five days, I was just glued to a desk doing customer service support only. And most of the customer requests were coming in a different language, like, you know, French, German, because a lot of the users are global. And thanks to AI, like, we were able to understand that, reply to that. And I think that, you know, like, is also helping, you know, there's the gap there.

And we are hiring Kieran S. So if anybody's, you know, interested in, you know, joining in various positions, like research across the board, like back in engineers, threatened engineers, we are hiring here in SF and in Bangalore. I'd love to go back to what we were talking about regarding personalized software. And what do you think the implications are for SaaS in general?

The provocative question is SaaS dead now? I mean, you guys essentially killed Asana for yourself. Like, is that bad for Asana and other SaaS companies? I mean, I definitely think that the current way SaaS is existing today needs to change, right? I think, like, I feel there are two sort of massive headwinds. One is more and more of these SaaS workflows are going to get consumed by an agent. So like, you know, unless your SaaS company pivots into like an agent first company, you know,

I think that's going to be hard to sort of survive. And second headwind is obviously like, you know, like people would want more and more customized software, like which they can build on Emergent, just like we built, you know, I don't do it, project management tool. And we are seeing a lot of these people, you know, building these internal tools, the software on platform like arts. And like I feel the nature of software itself is changing. I think a lot more software will become agentic in nature. A lot of people who are building on emergent today, like roughly 20% of them are actually agentic apps.

So people are actually embedding our own emergent agent inside those apps to sort of power, you know, power a bunch of the workflows. Do you just so interesting, that sounds really cool, an interesting example that people do that. Yeah, I mean, like the app that Maddie was just showing, you know, the CRM for lawyers, that is an agentic app where an agent can take a workflow and run through the process. The software itself is now morphing into agentic. A lot of people who just want to build agents that can actually just do a lot more of the work on its own.

What do you think this goes as agents' horizon for task gets longer or longer? I mean, one of the meter chart is one of the ones that was very shocking recently. Yeah, I think that's the chart of the year, I would say, right? Like the meter's exponential growth and like 4.5 was at like I think four hours and 4.6 is at 10 hours. And we are internally sort of now, like, you know, experimenting with agents' forms where agents can actually, like, work for a much longer horizon and multiple agents can sort of coordinate on a single task. Util users are, like, pretty, pretty exciting. You know, we'll see. I think by end of the year, you'll have, you know, agents which are running 24 hours and, like, maybe hundreds of agents collaborating on the single task.

And that's where we sort of see the future going right now. How are you building for that? People's ambitions are increasing, right? And so, like, we want to, like, give agents more autonomy, right? And so, like, the main thing is to make sure that the trajectory doesn't get derailed. So you always want to have, like, an overseeing agent, right? Like, so it's like, let's say a few agents are collaborating. And there is an overseeing agent as well, which is like, parallely, like, monitoring the overall task, right?

So we are experimenting with many different architectures, right? Like, something even as simple as, like, just, you know, you would have heard of this Ralph Wiggum loop kind of phenomenon, right? Like, so the idea that, hey, like, just keep poking the agent, hey, continue until it's done. And all of that is only possible if there is a good verification loop, right? So it comes back to, hey, are you able to give autonomous verification feedback to the agent? Like, was the job done? So a lot of our work internally right now is, in fact, still going on on building best verifiers. There we are actually doing some custom fine-tuning as well.

So we are very careful about, like, not directly competing with the models in the sense that we don't want to build an Opus 4.5 alternative right away, but we do want to augment it through our custom fine-tuned verification layers. So some of the fun stuff on the research side we are doing is on that side. How do you think about some movement in the opposite direction? I mean, we talked about sort of like the models themselves, maybe getting more powerful and what does that mean for everyone building on top of them. But how about at least some of the model companies are explicitly trying to build applications

and own the application layer themselves? If one of those companies decides like, you know, clawed code for non-technical users is a really valuable application to build, what implications does that have for you? I mean, I think eventually, eventually, I think like do you understand your customers requirement really well? Are you building closer to them? I think I think all of those fundamentals of like startup building remains the same. And I think, you know, like for us, like as long as we're focused on like really,

really understanding our users need really best, I think, you know, we'll compete on the process. I mean, maybe do you think about all the model companies is like the same or the differences between them? If you look at the models themselves, right, like they're very different. Like, for example, you know, Opus is obviously a workhorse, you know, like Codex is really good in backend debugging. Gemina is really good and front end. So I think all of these models have their own behaviors

and one of the, like, a good thing for us is that we can actually utilize these spikes that model have, like, to provide the best experience to the user. And I think eventually, like, at least my worldview is that most of these models are going to get really, really commoditized, like where all of these models will have similar behaviors. They'll have, you know, price competitiveness

between them and, you can already see, like, you know, like open source is like maybe three to six months behind. Right. And there's enough option. for us to sort of really build the layer on top where we really meet the user where they are and sort of support them in sort of their journey. Who understands the customer needs really well

and is able to build for that is going to sort of win the space. Users have built 7 million apps with the merchant. What are all these apps? Who are the users and what surprise do you seeing what people do with it? The users who are coming to platform for us are generally people who want to build a serious apps, people who really have a business use case that they want to automate

or they have a business idea that they want to launch. primary users who are coming to us are small medium business owners. They're running their business today on email, WhatsApp, spreadsheet and would have gone to a dev shop to sort of build a custom software to automate their business. They're coming to us.

And if you look at the price point that, you know, we are bringing down, it would have cost to you like $500,000 to build the software. Now you can build it for $5,000 completely on your own. And that is the kind of, you know, like unlock that we are sort of bringing to the world right now. Second, for example, this morning I was talking to user Christy, she's,

out of Alaska. And she built this, she's a clinical psychologist. She's also a sports court for Equestrian, the horse riding. And she wanted to marry these two fields, like, you know, like that. She has a lot of insights on psychology side.

She has a lot of insight on horse riding side. And she said she looked around everywhere to find an app that does that. She couldn't find one. She wanted to build one. She actually went to a dev shop. Yeah, definitely the intersection of the money she is. And she went to a dev shop in Nova Scotia

and tried to find somebody who can build it. They were charging her a bomb, so she discovered emergent, started building out, and she just launched her app a couple of weeks back. It's called Equimine on an app store. And it actually marries, you know, like her insights in psychology and into this sports coaching. She has like hundreds of users right now using the platform.

I think that is a lot that we're trying to build. Like, you know, people who have been, who have had an idea for a long time, people who are like really late domain expert, very close to a problem, can now go and build things up. We also have like a lot of solo preterners. billion platform, like, who would have had to go and hire a technical CTO to build these apps. And the success that we are seeing on the platform is, like, recently somebody pinged me that,

hey, like, this company has raised, like, $4 million on an ad that was built on immersion. Really? Yeah, yeah. And I need to get their permission to share more. But, yeah. And so I think now we are just truly seeing this unlock where people who were, like, really close to problem, domain expert and but have been logged by, you know, technology barrier to sort of really express themselves are, you know, like using immersion to sort of build these things out.

And also, like, one thing, these people tell us that, like, it's not just about money. Like, hey, I can give money to the dev shop, but a lot of get lost in the translation when you're trying to express your idea through a developer. And they say, hey, I know what I want to build. If I could just say it out loud myself, I would do a better job. And so the Norwegian person I was talking about, like, he said that, hey, in my team, I am the only builder. I don't even bring in anybody else because I know exactly what to build.

and like others focus on the business aspects of it. So this like single solo printer sort of attitude of like, I'm going to do it myself. I have the domain expertise. Nothing is lost in translation. That kind of agency is what people are looking forward to with these kind of platforms. Yeah, I think it's a really important story that doesn't get told enough actually. It's like what your building is really necessary for society.

There's just so much focus on AI is going to replace jobs, knowledge work is going away. Like what's that going to mean for employment and civil unrest? But like no one's really talking about the fact that actually like, If you have like some agency of interest, you want to start your own business and have autonomy of your life, like, you are empowering that at scale. It's so cool the, like, amount of human creativity that you're unlocking. Like, who would have thought that the thing that the world needs is an app that marries clinical psychology with horse riding? And in a world of limited software, that app would never have been built.

But in a world of unlimited software, you can build that and seven million other apps that, like, nobody would have ever gotten to build before. Yeah, we're getting to the niche of niches. Yeah. So, Pete, this is like an extension of the trend PG wrote about a while ago, right? And so like maybe coming out of the Second World War, you had sort of like a few big companies and people like built whole careers, hopefully staying at like IBM or whatever for a couple of decades and then retire. Then the startup wave came along and suddenly like the world becomes higher resolution. People like, maybe I should start my own company or at least join a smaller company and work at multiple companies or found multiple companies. And the next extension of that is just everybody like runs their own like business that's at the intersection of like clinical psychology and horse riding.

And finds an audience and life livelihood that way. Yeah, I mean, we are excited about so many ideas coming to life. Like we really want to like we do this gap between an idea and reality and, you know, truly enable people to express themselves and really, really like have this campaign explosion of ideas like, which is great for YC. I would argue it doesn't have to be actually. I think it's really interesting the whole like explosion of being able to start businesses that aren't like venture fund there that aren't going to raise lots of capital

that it's just like one person like following their passions and like having control over their life and it's like it's really an uplifting message I think we're just in the early innings of this right now like I think I think this explanation is going to grow and we'll see larger and larger projects being built on emergent yes

okay well that's all we have time for today Mukundamadav thank you so much for joining us is a really fascinating conversation and congratulations on all the growth and we're excited to see where things go from here. Thank you. Thank you so much for having us.