Building A Global AI Startup From India

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/apply Chapters: 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

• 0:00Speaker 00

  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 blocked by, you know, technology barrier to sort of really express themselves are using emergent to sort of build these things out.

• 0:12Speaker 01

  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 over your life, like you are empowering that at scale.

• 0:38Speaker 01

  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 Madhav Ja. They're both twin brothers and founders of Emergent, which went through Y C in summer two thousand twenty four. Emergent 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 Y C'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. Walk us through this like incredible growth you're seeing, actually. When did that hit a real inflection point and how did that that feel for you guys?

• 1:18Speaker 00

  So we both are twin brothers. We actually, you know, started programming when we were age twelve. Both of us came to US to do our PhDs. I dropped out of the PhD program, joined Google and Mary went on to, was in Zenefis, 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 hyper local quick commerce company.

• 1:42Speaker 01

  And Dunzo was a big company, actually, right?

• 1:43Speaker 00

  It was it was really big and and we we are almost a verb in India. So when people ship thing, they say Dunzo it. And and I was managing a really large team of three hundred engineers when, you know, and we had 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, you know, what we want to build in AI, that was the first idea we actually.

• 2:08Speaker 01

  What year was this?

• 2:10Speaker 00

  This was twenty three end.

• 2:12Speaker 01

  Okay.

• 2:12Speaker 00

  Yeah. And and so when we applied to Y C, like we applied with this idea of automating software testing. Uh. That was the first idea. In fact, we went to a lot of V C's with this idea. They thought it was too crazy. Uh, you know, and and now looking back, it it almost looks funny. And so we applied to Y C with this idea. And um. And when we were building this testing agents, we uh realized that if you can solve for verification, which is essentially, you know, you can solve the the testing part, uh, 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, longer period of time. And that's when we pivoted to looking at general coding agent as a space and we uh started building uh general coding agent.

• 2:48Speaker 01

  And this takes us into two thousand twenty four.

• 2:50Speaker 00

  This two twenty twenty four, summer twenty four.

• 2:52Speaker 01

  Yeah. Tell us what the landscape looked like, like how big was lovable at this point and just.

• 2:56Speaker 00

  I mean, nobody had started. Love will had not started. I think Karsa was just just getting getting getting started um and very, very early. Uh, I think Devon had just come out uh so so really, really early and and we looked at this benchmark called Sweet Bench, which is essentially a benchmark. Now it's saturated, but at that point of time, like that was the benchmark where all the coding agents were getting measured on. And we took on this challenge of becoming number one on that benchmark and like we sort of packed ourselves in a room, uh, 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 uh, you know, sort of coding agents which became world number one on Sweet Bench, uh, you know, in two months of time. And that was the time when we sort of discovered a lot of the fundamental truths about building with L and building with agents.

• 3:36Speaker 01

  Your intended users at this point were presumably engineers.

• 3:39Speaker 00

  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 how do we do agent-to-agent communication? How do you scale up test time compute? Uh, a lot of those things which like were sort of coming out, like we would, we would discover something and we'll see three months later, something come out in a paper. Uh, you know, uh, and that sort of set the foundation for for us to.

• 4:01Speaker 02

  So we were like Claude Code before Claude Code was a thing.

• 4:03Speaker 01

  Okay.

• 4:03Speaker 00

  A bunch of the paradigms like multi-agent orchestration, how do you use like different, different outings? A lot of those things we we sort of discovered.

• 4:10Speaker 01

  I definitely want to come back to that.

• 4:11Speaker 00

  Yeah.

• 4:11Speaker 01

  Um. I'm curious at this point in the story, though, when did you sort of? Pivot into becoming a tool for non-technical users.

• 4:18Speaker 00

  Yeah, so we actually like, once we had this coding agent, we actually went the enterprise route. 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 within enterprise. We found that it was too slow. And at the same time, we were internally started using Emergent's platform to build internal tools, internal software. And at that point, you know, we saw like lovable is 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, and bring it out in the world? And we launched a very like small beta pilot almost in June last year, twenty, twenty five. And that really took off. And, and since then, you know, like, we have been just focused on solving a problem for non-consumers. We in fact thought a lot of technical people would use us.

• 5:03Speaker 00

  But today, eighty percent of users who are on the platform are non-technical users with zero programming knowledge. And they're building like apps that, that run real businesses on top of today. So it's almost been.

• 5:13Speaker 01

  And they're based all around the world, right? Like, how many countries?

• 5:15Speaker 00

  Yeah, so they're all global audience, eighty percent, seventy, eighty percent are in US, Europe, over one ninety countries right now.

• 5:22Speaker 01

  Something that we have talked a bunch about at Y C internally is just, how does first mover advantage versus second mover advantage play out in the AI world? Certainly something that we've noticed, like, if we look at some of our company, like Lagora, entered the legal AI space after Harvey, but it's like growing incredibly fast. So there was clearly, it wasn't maybe as big of a mode 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 lovable and Bolt are growing really, really quickly, how did you think about that?

• 5:57Speaker 00

  There are like two, three different threads that I would want to pull. One essentially is that I think the, the model, every new model generation actually is presenting a new opportunity of looking at the world. Like, for example, when we started, G P T four was the, the first one that we sort of started looking at. And 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, 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 is gonna enable like multiple agents coordinating together.

• 6:36Speaker 00

  And so I think like one of the advantages of starting second, right, is that you can actually one like learn from what is what is not working uh for the uh current competition. Right. 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 you your imagination is is really big, right? And I think and and when we we were starting um emergent, we realized that like a lot of the users that were going to, you know, um some of these these 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?

• 7:15Speaker 00

  And our key insight was that to automate all of software engineering, you will have to build a platform that replicates what 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 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, uh 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 what what exists in the market today for people to take notice. Um we are very confident about the product and 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?

• 7:56Speaker 00

  Um we built out a a large influencer network and that was our initial sort of, you know, um starting point for us, like we use TikTok, Instagram. And part of this bunch of influencers to really, really spread the word out and and that's all, you know, kickstart the whole thing for us.

• 8:09Speaker 01

  To me, so building the influencer marketing engine is like, um it's like tactics to land grab. Like, were you also thinking about just focusing on personas and specific subtypes of users you wanted to go after that weren't? Like either weren't being targeted by level ball or or others or or emergent was a better fit for them?

• 8:27Speaker 00

  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, a lot of our initial messaging was around that, like, hey, come and ship 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 we will convert were users who actually wanted to ship a real, real app on the platform.

• 8:52Speaker 01

  And was that in the messaging then?

• 8:54Speaker 00

  It, it was in the messaging. Yeah. So, 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, don't see, don't face this error on emergent.

• 9:04Speaker 03

  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 Lovable or Bolt is just that it's, 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, you did more than that, the last 20% of that engineering to be production was a lot of work and that's a lot.

• 9:46Speaker 02

  Yeah. And I think like the, the last mile that you mentioned, right, is, is always what people neglect, that, hey, you need to make sure that not, 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 like running. So like we provide like, you know, cloud sandboxes. We don't outsource it to like some third party sandbox provider, which was also pretty popular at that time, right? So we, we built our own Kubernetes text tag from ground up, the container text tag. And one of the insights here is that if you give your agents the same infra during the build time and the same infra 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

• 10:30Speaker 02

  you provide. So we build this like sort of infra and agent like sort of co-build it together and from the, from, from day one. And to your point, right, like, because we, we focused on, you know, building like ship ready apps, which, which are production ready, which has, which comes with backend and, and frontend and everything. The text tag we chose was also pretty unique to us. We have a Python backend server. We have a React front-end server, like most people would like typically go with like a much more like, you know, node, node focus, node-heavy tech stack, right? And, and this like server-client architecture where you can have like background jobs if you want to have background queues. So we knew that, you know, users who would, who would use this app, their ambitions are gonna 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 gonna prompt it. And we wanted to support it from day one, right?

• 11:14Speaker 02

  And so it's the same tech stack on which emergent is built is what we expose to our end users, is what we expose to our 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, 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, 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 in a long-term memory for the agent,

• 11:57Speaker 02

  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, like 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 generated based on previous trajectories. And we run it through a C I C D process and then add it to the long-term memory. So all of that like compounds for us, right?

• 12:42Speaker 02

  So if you, if, if your agent was struggling to do a calendar integration three weeks ago, today it is no longer struggling, thanks to the, the previous session where it was able to make it happen.

• 12:51Speaker 03

  So fascinating. So it learns on its own because I think one of the challenges of all these vibe coding app platforms is At some point, the applications 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 built a lot of what the state of the art is now, but way back a year before.

• 13:16Speaker 02

  Our coding agent is so powerful that we basically internally use it as a replacement for Claude Code as developers, right? So we are so proud of that. And but yet we don't want to expose that sort of, you know, powered tool to our end non-technical user. And so we, even though we have this VS Code editor, we kind of hide it. Because what we have noticed is that non-technical users, they even get panicked as soon as they see a diff, you know? We had a like a fairly technical PM in our team. And like, he doesn't like, like JSON, you know, he's like, no, 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?

• 13:59Speaker 00

  I mean, internally, I have a term called agent experience, right? That we measure that how, like, how is agents experience on the platform?

• 14:04Speaker 01

  Actually, a really important point, I think people don't realize is you guys actually, you actually started out essentially as sort of Devon cursor in like 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, they start with the user experience and they're going to have to develop the power over time.

• 14:33Speaker 00

  Right. And I think fundamentally, it's like, unless you start from, you know, a starting point which 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.

• 14:49Speaker 01

  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 parsing, but was there anything else where you're like? Let's not invest time in that because like Opus four point five will solve it.

• 15:05Speaker 00

  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 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 the models, the the better they perform. Like initially, like our harness was very strict and, you know, like we would we would tighten it up. And and slowly, like what we were observing is that as these models are getting larger and larger, more more more efficient, like, you know, like the more control you give to the model, this is making the better the harness gets.

• 15:47Speaker 01

  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 mod like the models themselves and the models get more powerful?

• 15:57Speaker 00

  Yeah, I think there is this underlying current right now, right? In the industry that that hey, like, is is, you know, like, Anthropic gonna eat everybody up? Yeah, I mean, our view is that I think the the coding aspect is only twenty percent of the job, right? I think like taking an app to production is like really, really hard. And and I think what what matters is how closely are you working with the user? How how well do you understand their needs? And I think as the models are gonna get more and more sort of capable, I think the the human desire is also continuously growing at the same rate. So I think people are gonna want to build more complex apps on the platform. The other thing is that it is that harness, we're able to extract twenty, thirty percent more on top of these models. And and essentially, like, we can use multiple foundation 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.

• 16:41Speaker 00

  For example, now we're thinking about like, a lot of our users who have built the app now want to help with distribution, now want help with growth, now want to help with like, how do you sort of, you know, manage users? And and things like that. And I think for us, the spectrum sort of keeps growing on that side.

• 16:54Speaker 01

  I agree with it. I mean, there's there's another graph that I was sure 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 Y C, we'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.

• 17:19Speaker 02

  That's right. Yes. A hedonistic adaptation to, you know, like, hey, oh, this is more powerful. Now I can do more work. Yeah.

• 17:25Speaker 03

  It is really a Jevon's paradox at play. And I think there's a lot of concerns like, 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 experiencing.

• 17:37Speaker 02

  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 P M, a designer engineer, like a single person is doing, you know, like work of all three together, right? So like we have a P M 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 team can now be just done by like a single engineer or a single P M.

• 18:07Speaker 04

  Y C's next batch is now taking applications. Got a startup in you? Apply at Y Combinator dot com slash apply. It's never too early and filling out the app will level up your idea. Okay, back to the video.

• 18:21Speaker 05

  Could we see a demo of emergent?

• 18:23Speaker 02

  Oh, yeah, sure. Yeah. So this is how what emergent interface looks like. And I'm gonna like put a prompt where like, because we are coming for this podcast, we 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 the right agent to use is a mobile app builder, right?

• 18:49Speaker 05

  So even though you like selected the wrong tab, it's just like, ah.

• 18:53Speaker 02

  Yeah, yeah. The behind the scenes auto. Yeah, I got you, right? So while 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 a 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 built this out without any coding background knowledge, right? Like, hey, this is the kind of. AV setup I want. So you you you go and you build your room and then you you get it's a lead gen sort of a form. But this is a fairly full stack app.

• 19:28Speaker 01

  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.

• 19:33Speaker 00

  So we have actually spent a lot of time on like maybe the design is actually good.

• 19:37Speaker 01

  Yeah.

• 19:38Speaker 00

  Are are like so earlier, there used to be a big trade-off between design and functionality. Like if you're optimizing for design, like your functionality would not be that strong. Uh, 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.

• 19:50Speaker 02

  There's another sort of person based out of Norway. He he sold his previous business to a P E and and realized how much lawyers have to struggle with spreadsheets and other things. So he built a CRM for lawyers. He he describes himself as like business developer. I I like the word he used, like I'm a business developer and he 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 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 L L M key. So you don't have to worry about getting API key from third party.

• 20:36Speaker 01

  This feels like a good example of what you were saying.

• 20:38Speaker 02

  Mm hmm.

• 20:38Speaker 01

  Um, because this is sort of like the ask user question skill in code, but you just like abstract that away. But it's like built into the experience for someone who had no idea about.

• 20:46Speaker 02

  Absolutely. I can be very like casual here. I can say, hey, uh, for the first one, use emergent API key. Rest as you go 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 if agent requires additional uh thing. Once it's done, uh, you see a preview of your app. So here, for example, in this case, I can practice what is my origin story. Uh, I can record uh what my origin story is and I can keep going to, you know, various questions. Eventually.

• 21:19Speaker 00

  This is a podcast preparation app.

• 21:20Speaker 02

  Yeah, and then you can go ahead and revisit what answers you gave uh to your uh 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 and build a web app, right? So uh, that's generally the trend we are seeing. The only other thing I wanted to show was.

• 21:39Speaker 00

  Uh. This is, this is an actual Asana clone that our team built, like one of our QA engineers built internally. And uh, so this is actual real emergent data.

• 21:49Speaker 01

  I I'm curious what prompted that, like, was there some, 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?

• 21:58Speaker 00

  Yeah, it kind of like started off as a QA engineer's curiosity. He, he like his first prompt, I looked at his all jobs, the first prompt was clone Jira, okay? And then like he just kept going with that. And uh, 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 uh, uh, involvement in, in, in many, many ways. Uh, and definitely like we, when we were using Asana, it was very uh, like even to customize it to, to make it to your uh, work style was not easy. And, and we, we are, we are also saving like, you know, like three thousand, four thousand dollars a month in subscription.

• 22:36Speaker 02

  This is really the world of personal software.

• 22:38Speaker 05

  Yeah. Has anybody actually edited the code for this or is it just a hundred percent built, built with emergent?

• 22:42Speaker 00

  Hundred percent. Hundred percent built with emergent and, and the good thing is that like if I want to add a feature, I have to just go to that uh, you know, project and just add a feature and it just starts building.

• 22:51Speaker 05

  It's probably useful for you guys to dog food the platform this way, because this is probably at the edge of the, of the, 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 like this.

• 23:02Speaker 00

  In, in fact, like a lot of the teams internally are now building, um, 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, uh, our customer support team is building customer support software, uh, 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 well and are able to now build uh, these apps. And the speed at which we are able to ship, you know, these internal apps is like crazy.

• 23:27Speaker 01

  How far down does it go though? I'm curious, like even within the company, do you have people who want their like separate versions of like your internal Asana?

• 23:35Speaker 00

  So currently like, uh, everybody in the company is using this, this one tool right now and, and, and it is collaboratively, being built collaboratively, right? So like, you know, a PM can give a feature, a QA can give a feature, uh, somebody from our HR team can give a feature to, to sort of build that out right now.

• 23:48Speaker 01

  How do you think this version control, like feature flagging, 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?

• 23:59Speaker 00

  Yeah, so there is a testing phase, there's a deployment phase, right? So we have different versions maintained, right? 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, the agent will build it out. And then like, once it's accepted, then it'll go to the release.

• 24:19Speaker 05

  It's not managed through Git though, it's like your own workflow thing.

• 24:22Speaker 02

  So you can connect GitHub if you want to. Like, we internally connect GitHub for our projects, right? And like, non-technical developers outside of emergent, like, they actually call GitHub GitHub, right? So they have very, like, limited knowledge of GitHub. And so we take care of like versioning on our side, even if they don't connect GitHub.

• 24:40Speaker 03

  Talking 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?

• 24:48Speaker 00

  Yes, 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 are you at 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 hundred 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 hundred IT 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 Dunzo, so because I was able to build like a really, really good team, we were able to get some initial folks from there.

• 25:28Speaker 00

  The focus 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 Vercel 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 give way more responsibility to people. And I think people are generally attracted towards harder problems that they want to solve.

• 25:50Speaker 05

  Where is your team located?

• 25:51Speaker 00

  So most of the team right now is in Bangalore in India office. We have a very small office in SF, like three to five people here.

• 25:57Speaker 05

  And you guys yourselves, you're kind of like split across both countries. Can you maybe just explain how the setup works?

• 26:05Speaker 02

  Yeah, so I mean, I I live here in SF. I've been in like, you know, Bay area for like last 10 years.

• 26:09Speaker 00

  I split half my time in SF, half my time in Bangalore, constantly jet lagged.

• 26:14Speaker 01

  I think you guys are probably the most successful AI company. That's what it's obviously came from like it's an Indian company, but that's got like significant presence in India. Why is that?

• 26:25Speaker 00

  I mean, I think it's like when I went back to India, you know, after Google and I always had this thought that why is there no Google or Facebook from India, right? So like from day zero, I was thinking, you know, even though I started Anzo, it was an India, 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, a lot of capital available, everything is available in India. Like, why are people not building globally, truly global tech first companies from India? And and that was the ambition that 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 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 is sort of fully penetrated, people, people can actually get understanding knowledge from everywhere. I think every single, you know, country has an opportunity to build for global audience.

• 27:09Speaker 00

  And if you have that sort of mindset, that ambition, I I think, I think a lot, we'll see a lot more companies coming out of India doing the same.

• 27:16Speaker 05

  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 US and Western Europe. It's not a product 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?

• 27:39Speaker 00

  Internally, we have like really set really high standards like as a, as a, as a global sort of product. I mean, both in hiring, both in like the baby sort of developer product. And I think us spending sort of time here also also helps. Like one of the things that we do really religiously is everybody talks to a customer once a week, twice a week.

• 27:54Speaker 05

  Like everyone in the entire company?

• 27:56Speaker 00

  Everyone in the company, right? They talk to a customer, everybody does customer support. So like. We were like a really, really small engineering team, like twelve people team. And one person was always on call for customer support. It was really hard decision 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 like what our 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.

• 28:39Speaker 00

  And most of the customer requests were coming in a different language, like French, German, because a lot of users are global. And thanks to AI, like we were able to understand that, reply to that. And I think that, that, that, you know, like is also helping, you know, us bridge the gap there, yeah.

• 28:51Speaker 02

  And we are hiring here in SF. So if anybody is, you know, interested in, you know, joining in various positions like be it research across the board, like backend engineers, frontend engineers. We are hiring here in SF and in Bangalore.

• 29:04Speaker 01

  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? You know, I guess the provocative question is, is SaaS dead now? I mean, you guys essentially killed Asana for yourselves.

• 29:17Speaker 00

  Yeah.

• 29:17Speaker 01

  Like, is that bad for Asana and other SaaS companies?

• 29:20Speaker 00

  I mean, I definitely think that like the current way SaaS is existing today needs to change, right? I think like, I feel there are two like sort of massive headwinds. One is more and more of these SaaS workflows are going to get consumed by an agent, right? Like, 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, our own do it project management tool. And we are seeing a lot of these people, you know, building these internal tools, the software on a platform like ours. And like, I feel the nature of software itself is changing. I think a lot more software will become agentic in nature.

• 30:04Speaker 00

  A lot of people are building on emergent today, like roughly twenty percent 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 much of the workflows.

• 30:14Speaker 01

  Do you have some interesting, that sounds really cool. Any interesting examples that people do?

• 30:18Speaker 00

  Yeah, I mean, like the app that Manny was just showing, you know, the CRM for lawyers, that is an agentic app where, you know, an agent can take a workflow and run through the process. The software itself is now morphing into, you know, agentic. Like a lot of people would just want to, you know, build agents that can actually just do, you know, a lot more of the work on.

• 30:37Speaker 03

  What do you think this goes as agents' horizon for task gets longer and longer? I mean, one of the the meter

• 30:44Speaker 00

  The meter chart, yeah.

• 30:45Speaker 03

  Chart is one of the ones that was very shocking recently.

• 30:47Speaker 00

  Yeah, I think that's the chart of the year, I would say, right? Like the the meters exponential growth and and like four point five was at like, I think, four hours and four point six is at ten 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. At least those are like pretty, pretty exciting. You know, we'll see. I think I think by the end of the year, you'll have, you know, agents which are running twenty four hours. And like maybe hundreds of agents collaborating on the single task. And that's where that's where we sort of see the future going right now.

• 31:21Speaker 03

  How are you building for that?

• 31:23Speaker 02

  People's ambitions are increasing, right? Like, and so like, we we want to like give agents more autonomy, right? And so like, the the 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 parallelly like monitoring the overall task, right? So 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 a 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?

• 32:05Speaker 02

  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 like build a Opus four point five alternative right away. But we do want to augment it through our custom fine tune verification layers. So so some of the fun stuff on the research side we are doing is on that side.

• 32:29Speaker 01

  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? Uh 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, Claude code for non-technical users is a really valuable application to build, what implications does that have for you?

• 32:53Speaker 00

  I mean, I think eventually, eventually, I think like, uh, do you understand your customers' requirement really, really well? Are you building closer to them? I think, I think all of those fundamentals of like sort of building remains the same. And I think, you know, like for us, like as long as we are focused on like really, really understanding our users need really, really best, I think, you know, we'll compete on the.

• 33:08Speaker 01

  Do you think, I mean, maybe, do you think about all the model companies as like the same or the differences between them?

• 33:14Speaker 00

  If you look at the models themselves, right, like they're very different. Like for example, you know, um, Opus is obviously a workhorse, um, you know, like, um, Codex is really good in backend debugging. Gemini is really good in front, front end. So I think all of these models have their own behaviors and, and, and one of the like, a good thing for us is that we can actually utilize these spikes that models have, like to, 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, get really, really commoditized, like where all of these models will have similar behaviors, uh, they'll have, you know, price, price competitiveness, um, between them. And, and you can already see like, you know, like Open Source is like maybe three to six months behind, right? And, and there's enough optionality for us to sort of really, really build the layer on top where we really meet the user where they are and, and sort of support them in, in sort of their, their journey.

• 33:59Speaker 00

  Who understands the customer needs really, really well and, and is able to build for that is going to sort of win the space.

• 34:04Speaker 05

  Here's about seven million apps with emerging. What are all these apps? Who, who are the users and what surprise do you seeing what people do with it?

• 34:11Speaker 00

  The users who are coming to platform for us are generally people who want to build us users apps. People who like really, 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, on email, WhatsApp, spreadsheet, uh, and would have gone to a dev shop to sort of build a custom software, um, to run, 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 costed you like five hundred thousand dollars to build the software. Now you can build it for five thousand dollars 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. Uh. Second, for example, this morning I was talking to user Christie, she's based out of Alaska.

• 34:52Speaker 00

  Uh. And she built this, she's a clinical psychologist, uh, she's also a sports coach for equitarian, 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, on uh, horse riding side. And, and she said she looked around everywhere to find an app that does that. And she couldn't find one, she wanted to build one. She actually went to a dev shop. Yeah, definitely the intersection of both issues. Yeah, and, and she went to a dev shop in Nova Scotia and tried to find somebody who can build it. Uh, they were charging her a bomb, so she, you know, discovered emergent, started building uh, out and she, she just launched her app like a couple of weeks back. It's called EP Mine on, uh, on App Store. Uh, and it actually marries, you know, like her insights in psychology and, and, and uh,

• 35:34Speaker 00

  into this, this uh, sports coaching. Um, she has like hundreds of users right now using the, using platform. And I think that is a lock that we're trying to build. Like, you know, people who would have been, um, who have had an idea for a long time, people who are like really, really domain expert, very close to a problem, uh, can now go and bu- build things up. Um, we also have like a lot of solo entrepreneurs building on platform, like who would have had to go and hire a technical CTO, uh, to, 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 four million dollars, uh, on an app that was built on emergent. Uh, really? Yeah, yeah. And, and I need to get their permission to, to share more, but yeah. And so I think now we are just truly seeing this unlock where

• 36:12Speaker 00

  people who, who were like really close to problem domain expert and, but have been blocked by, you know, technology barrier to sort of really express themselves are, are, are, you know, like using emergent to sort of build these things out.

• 36:23Speaker 02

  And, and also like one thing, uh, these people tell us that like, uh, it's not just about money, like, hey, I can give money to the dev shop. But a lot, lot get lost in the translation when you're trying to express your idea to the, through a developer. And they say, hey, I know what I want to build. If I could just say it out my la- out loud myself, I would, I would do a better job. And so, uh, the Norwegian, uh, 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 gonna do it myself. I have the domain expertise.

• 36:55Speaker 00

  Nothing is lost in translation. That kind of agency is what people are looking forward to with these kind of platforms.

• 37:01Speaker 01

  Yeah, I think it's a really important story that doesn't get told enough. Actually, it's like what you're 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 and you want to start your own business and have autonomy over your life, like, you are empowering that at scale.

• 37:26Speaker 05

  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.

• 37:44Speaker 00

  Yeah, we're getting to the niche of niches.

• 37:46Speaker 05

  Yeah.

• 37:47Speaker 01

  I mean, so Pete, this is like just 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 were 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 the world becomes higher resolution. People like, oh, 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 and life livelihood that way.

• 38:27Speaker 00

  Yeah, I mean, we are excited about so many ideas coming to life. Like, we really want to like reduce this gap between idea and reality and and, you know, truly enable people to express themselves and and and really, really like have this camera explosion of ideas like, which is great for VC.

• 38:41Speaker 01

  I would argue it doesn't have to be actually. Like the whole, like, I think it's really interesting, the whole like explosion of being able to start businesses that aren't like venture funded, that aren't going to raise lots of capital, that is just like. One person like following their passions and like having control over their life. I think it's like it's really an uplifting message.

• 38:59Speaker 00

  And I think just in the early innings of this right now, like I think, I think this explanation is going to grow and, and, and we'll see larger and larger, you know, products being built on emergent, yes.

• 39:08Speaker 01

  Okay. Well, that's all we have time for today. Uh, Makundamada, thank you so much for joining us. This is a really fascinating conversation and congratulations on all the growth and we're excited to see where things go from here.

• 39:18Speaker 00

  Thank you. Thank you so much for having us.