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- [00:00]
Mukund Jha
So I think now we are just truly seeing this unlock where people who are like really close to problem domain expert 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.
- [00:13]
YC Interviewer (possibly Peter)
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. Welcome back to another episode of the Lightcone. 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 Jha. They're both twin brothers and founders of Emergent which went through ice in summer 2024. Emergence, a platform that lets air 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, 7 million apps have been built with Emergent. Walk us through this incredible growth you're seeing. Actually when did that hit a real inflection point and how did that feel for you guys?
- [01:18]
Mukund Jha
So we both are twin brothers. We actually 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 Zenefits, 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.
- [01:42]
YC Interviewer (possibly Peter)
Dunzo was a big company actually, right?
- [01:44]
Mukund Jha
It was really big. And we are 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 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 what we want to build in AI, that was the first idea we actually had.
- [02:09]
YC Interviewer (possibly Peter)
What year was this?
- [02:10]
Mukund Jha
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. So we applied to YC with this idea and, and when we were building this testing agents, we realized that if you can solve for verification, which is essentially you can solve the testing part, you can actually automate all the software engineering. That was sort of our key insight, that 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.
- [02:49]
YC Interviewer (possibly Peter)
And this takes us into 2024.
- [02:51]
Mukund Jha
This is 24, Summer 24.
- [02:53]
YC Interviewer (possibly Peter)
Tell us what the landscape looked like. Like how big was lovable at this point?
- [02:57]
Mukund Jha
I mean nobody had started lovable, had not started. I think Cursor was just getting started and very, very early. I think Devin had just come out so really, really early. And we looked at this benchmark called Sweepbench, 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 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 Soda coding agents which became world number one on sweep in two months of time. And that was the time when we sort of discovered a lot of the fundamental truths about building with LLMs. Building with agents.
- [03:36]
YC Interviewer (possibly Peter)
Your intended users at this point were presumably engineers.
- [03:39]
Mukund Jha
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 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, you know, and that sort of set the foundation for us to.
- [04:01]
Madhav Jha
So we were like cloud code before cloud code was a thing.
- [04:04]
Mukund Jha
Bunch of the paradigms like multi agent orchestration. How do you use like different, different routings? All of those things we sort of discovered.
- [04:10]
YC Interviewer (possibly Peter)
I definitely want to come back to that. I'm curious at this point in the story though, when did you sort of pivot into becoming a Tool for non technical users.
- [04:18]
Mukund Jha
Yeah, 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 the enterprise. We found that it was too slow. And at the same time we were internally started using emergent 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 bring it out in the world? And we launched a very like small beta pilot almost in June last year, 2025. 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 who are 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.
- [05:14]
YC Interviewer (possibly Peter)
And they're based all around the world, right?
- [05:15]
Mukund Jha
Like how many countries they're all global audience. 80%. 70. 80% are in US, Europe, over 190 countries right now.
- [05:22]
YC Interviewer (possibly Peter)
Something that we have talked a bunch about at YC 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 Ligora, enter the legal AI space after Harvey, but is growing incredibly fast. So there was clearly 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 when Lovable and Bolt are growing really, really quickly. How did you think about that?
- [05:58]
Mukund Jha
There are like two, three 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. For example, when we started, GPT4 was the first model that we sort of started looking at. And at that end the biggest problem that everybody was trying to solve was JSON parsing like a structured output format. And we thought, okay, the next model is going to solve for it, 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, Opus is a different class of model right now it's going to enable extremely long horizon tasks. It's going to enable multiple agents coordinating together. And so I think one of the advantages of starting second is that you can actually one, learn from what is not working for the current competition. And also I think you fundamentally start from a different starting point where your aperture of the world is very different. Your imagination is really big. Right. And when we were starting Emergent, we realized that a lot of the users that were going to some of these apps, they wanted to actually really build an app that works. And most of these were actually 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 insight 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 reimagined 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 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 head and shoulder above what exists in the market today for people to take notice. We were very confident about the product. And so a lot of our focus like in 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 partner with a bunch of influencers to really, really spread the word out. And that sort of, you know, kickstarted the whole thing for us.
- [08:09]
YC Interviewer (possibly Peter)
To me, sort of 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 Levelball or others or Emergent was a better fit for them.
- [08:28]
Mukund Jha
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. But users that were coming to the platform that we will convert were users who actually wanted to ship a real app on the platform.
- [08:52]
YC Interviewer (possibly Peter)
And was that in the messaging then?
- [08:55]
Mukund Jha
It was in the messaging, yeah. So we would say, hey, come and build real apps. We would also use the common errors that you would see on other platform, like hey, don't face this error on emergent.
- [09:04]
YC Interviewer 2
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 at 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 Level 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 0 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 be production was a lot of work. And that's a lot.
- [09:46]
Mukund Jha
Yeah.
- [09:46]
Madhav Jha
And I think like the last mile that you mentioned.
- [09:48]
Mukund Jha
Right.
- [09:48]
Madhav Jha
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 like running. So we provide like cloud sandboxes, we don't outsource it to some third party sandbox provider which was also pretty popular at that time. So we built our own Kubernetes tech stack from ground up 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 infra during the deploy time, then during this deployment phase you don't encounter those many problems. The fact that we have our own infra also allows us to give rapid feedback to the agent. So your agent is only as good as the feedback that you provide. So we build this infra and agent co build it together. And from day one and to your point, right, because we focused on building ship ready apps which are production ready, which comes with backend 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 node focused, node heavy tech stack. And this server Client architecture where you can have background jobs if you want to have background queues. So we knew that users who would use this app, their ambitions are going to go bigger and bigger. Hey, I want to run a job which can do this asynchronous video processing and they're going to prompt it. And we wanted to support it from day one. 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. 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 tasks 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, 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 skills. And there's a new benchmark called skills bench which shows agent with skills outperform agent without skills. And interestingly, those skills cannot be generated by agent themselves. If you generate those skills by agents, they don't match up to the performance. So we were able to do it in a way where the skills get auto sort of generated based on previous trajectories. And we run it through a CI CD process and then add it to the long term memory. So all of that compounds for us.
- [12:43]
Mukund Jha
Right?
- [12:44]
Madhav Jha
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.
- [12:52]
YC Interviewer 2
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
- [13:16]
Madhav Jha
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 powered tool to our end. Non technical user. And so 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. We had a fairly technical PM in our team and like he doesn't like Jason, he's like don't show me, 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 internally?
- [14:00]
Mukund Jha
I have a term called agent experience that we measure that. How is agents experience on the platform?
- [14:04]
YC Interviewer (possibly Peter)
Actually a really important point I think people don't realize is you actually started out essentially as sort of devon cursor in the actual coding agent world for engineers. You just made the choice to package it up for non technical users. So you're sort of moving almost in the opposite direction from a loverboard. You have all of the actual power, you just need to simplify the user experience. Whereas they start with the user experience and they're going to have to develop the power over time.
- [14:34]
Mukund Jha
Right. And I think fundamentally unless you start from a starting point which sort of solves all of these problems along the line, the whole software development, 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.
- [14:49]
YC Interviewer (possibly Peter)
Do you have any more, I'm really curious, like any more examples of where sort of as you were 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 4.5 will solve it.
- [15:05]
Mukund Jha
I mean some of them has been, for example, 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 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 hardness was very strict and, you know, like, we would tighten it up and slowly. What we were observing is that as these models are getting larger and larger, more efficient, the more control you give to the model decision making, the better the hardness gets.
- [15:47]
YC Interviewer (possibly Peter)
If we extrapolate that out or sort of really far out, are you worried about where that sort of leaves you as a company versus the models themselves and the models get more powerful?
- [15:57]
Mukund Jha
Yeah, I think there is this underlying current right now, right in the industry that, hey, is entropy going to eat everybody up? Yeah, I mean, our view is that I think the coding aspect is only 20% of the job. I think taking an app to production is 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 at least with our harness, we're able to extract 20, 30% more on top of these models and essentially we can use multiple foundation models together to sort of extract more. And I think we'll have to keep continuing 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 to 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.
- [16:55]
YC Interviewer (possibly Peter)
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 more hours, doing more stuff and it's just like the rate of software that you're expected to ship per week just keeps going up and up and up.
- [17:21]
Madhav Jha
Hedonistic adaptation to like, hey, oh, this is more powerful, now I can do more work.
- [17:25]
YC Interviewer 2
Yeah, it is really a Javan's paradox at play. And I think there's a lot of concerns. It's 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 experience, I
- [17:38]
Mukund Jha
mean, I think we are an expanding market, right? Like we are like letting non developers not be developers, right? I think that market is expanding. We also are internally seeing the roles sort of combining. So like a PM designer, engineer, like a single person is doing work of all three together, right? So we have a PM who's bytecoding 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 5, 6 people team can now be just done by like single engineer or a single PM.
- [18:08]
Madhav Jha
YC's next batch is now taking applications. Got a startup in you apply@y combinator.com apply. It's never too early and filling out the app will level up your idea. Okay, back to the video.
- [18:21]
YC Interviewer 3
Could we see a demo of Emergent?
- [18:23]
Madhav Jha
Oh yeah, sure.
- [18:24]
Mukund Jha
Yeah.
- [18:24]
Madhav Jha
So this is how what Emergent interface looks like. And I'm going to like 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 are 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?
- [18:50]
YC Interviewer 3
So even though you like selected the wrong tab, it's just like yeah, behind the.
- [18:55]
Madhav Jha
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 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.
- [19:28]
YC Interviewer 3
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.
- [19:34]
Mukund Jha
So we have actually spent a lot of time on making sure the design is actually good. 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. 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.
- [19:51]
Madhav Jha
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. And so one of the unique things to Emergent is that before the 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 OpenAI 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.
- [20:36]
YC Interviewer (possibly Peter)
This feels like a good example, what you were saying, because this is sort of like the ask user question skill, include code. But you just like abscrap that away. But it's like built into the experience. For someone who had no idea about.
- [20:47]
Madhav Jha
Absolutely. I can be very 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 at this point I can just close my laptop. We also have a mobile app, so you can 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. Eventually.
- [21:19]
Mukund Jha
This is a podcast preparation app.
- [21:21]
Madhav Jha
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 and 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.
- [21:50]
YC Interviewer (possibly Peter)
I'm curious what prompted that? 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?
- [21:59]
Madhav Jha
Yeah, it kind of like, started off as a QA engineer's curiosity. His first prompt, I looked at his all jobs. The first prompt was clone Jira.
- [22:06]
Mukund Jha
Okay.
- [22:07]
Madhav Jha
And then, like, he just kept going with that. And. 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 build it very customized to the way we do things. Like, we have a QA involvement in many, many ways. And definitely, like, we. When we were using Asana, it was very like, even to customize it to. To make it to your work style was not easy. And we are. We are also saving like around like 3,000, $4,000 a month in subscription personal software.
- [22:38]
Mukund Jha
Yeah.
- [22:39]
YC Interviewer 3
Has anybody actually edited the code for this or is just 100% built with the merchant?
- [22:43]
Mukund Jha
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 project and just add a feature and it just starts building.
- [22:52]
YC Interviewer 3
It's probably useful for you guys to dog food the platform this way because this is probably at the edge of the most complex apps people have built with emergence. So it allows you to test what happens when people get to a very complex app like this.
- [23:03]
Mukund Jha
In fact, like, a lot of the teams internally are now building you 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 understand the problem really well and are able to now build these apps. And the speed at which we are able to ship these internal apps is, like, crazy.
- [23:28]
YC Interviewer (possibly Peter)
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?
- [23:35]
Mukund Jha
So currently, like, everybody in the company is using this, this one tool right now, and it is collaboratively being built. Collaboratively. Right. 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.
- [23:49]
YC Interviewer (possibly Peter)
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.
- [23:59]
Mukund Jha
Yeah. So there is a testing phase, this 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 as the agent will build it out and then like once it's accepted, then it'll go to the release it's not
- [24:20]
YC Interviewer 3
managed to get though. It's like your own workflow thing.
- [24:22]
Madhav Jha
So you can connect GitHub if you want to. Like we internally connect GitHub for our projects.
- [24:26]
YC Interviewer 2
Right.
- [24:27]
Madhav Jha
And like non technical developers outside of emergent, like they actually call GitHub GitHub. Right. So they, they have very like limited knowledge of GitHub and so we take care of like versioning on site even if they don't connect GitHub.
- [24:40]
YC Interviewer 2
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?
- [24:48]
Mukund Jha
Yeah, so we actually from day one have been very conscious of the kind of team that we want to build. And essentially we index on two things. One is problem solving, like how good are you at problem solving? And second is ownership. We think that people who can really take ownership, we index on that. And a lot of our early sort of hires were people like we were really obsessed with top hundred IT rankers. So we had this program going on where I told our team that hey, we must hire top hundred IT rankers. Right now I think we have IT rank one, IT rank 12. All of those people working with us and a lot of the initials that also came from Dunzo. Because I was able to build a really good team, we were able to get some initial folks from that. The focus that we have is essentially one or two people doing work of what a company would be doing. For example, our deployment, which almost mirrors what Vercell would look like, is done by two people. Like our memory, like where you have like multiple startups solving for memory, it's 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.
- [25:50]
YC Interviewer 3
Where is your team located?
- [25:51]
Mukund Jha
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.
- [25:58]
YC Interviewer 3
And you guys yourselves, you're kind of like split across both countries. Can you maybe just explain how the setup works?
- [26:05]
Madhav Jha
Yeah, so I mean I live here in sf. I've been in like, you know, Bay Area for like last 10 years.
- [26:10]
Mukund Jha
I split half my time in SF, half my time in Bangalore. Constantly jet lagged.
- [26:14]
YC Interviewer (possibly Peter)
I think you guys are probably the most successful AI company. That's, it's not fair to say you came from like it's an Indian company, but that's got like significant presence in India. Why is that?
- [26:25]
Mukund Jha
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? India, right. So like from day zero I was thinking, you know, even though I started Dunzo, 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, 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, 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.
- [27:16]
YC Interviewer 3
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 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?
- [27:39]
Mukund Jha
Internally we have really set really high standards as a global product, Both in hiring, both in the way we develop product. And I think us spending time here also helps. One of the things that we do really religiously is everybody talks to a customer once a week, twice a week.
- [27:55]
YC Interviewer 3
Everyone in the entire company.
- [27:56]
Mukund Jha
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 12 people team, and one person was always on call for customer support. It was a 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 engines 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 use, really help us bridge the gap in terms of what our users want today. And it's funny because when we launched my first 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 French, German, because a lot of the users are global. And thanks to AI, we were able to understand that reply to that. And I think that is also helping us bridge the gap there.
- [28:52]
Madhav Jha
And we are hiring Kirin Asad. So if anybody's interested in joining in various positions, like be it research across the board, like backend engineers, front end engineers, we are hiring here in SF and in Bangalore.
- [29:05]
YC Interviewer (possibly Peter)
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, is SaaS dead now? I mean, you guys essentially killed Asana for yourselves. Is that bad for asana and other SaaS companies?
- [29:20]
Mukund Jha
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, 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 our own do IT project management tool. And we are seeing a lot of these people building these internal tools, these software on platform like ours. And I feel the nature of software itself is changing. I think a lot more software will become agentic in nature. A lot of people 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 bunch of the workflows.
- [30:15]
YC Interviewer (possibly Peter)
You have some interesting. That sounds really cool. Any interesting examples of people.
- [30:18]
Mukund Jha
Yeah, app that Mari 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, 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, lot more of the work on its own.
- [30:38]
YC Interviewer 2
Where do you think this goes as agents horizon for task gets longer and longer? I mean one of the meter chart. Yeah, chart is one of the ones that was very shocking recently.
- [30:48]
Mukund Jha
Yeah, I think that's the chart of the year, I would say. Right. Like the meters exponential growth and 4.5 was at like I think 4 hours and 4.6 is at 10 hours. And we are internally sort of now experimenting with agent swarms where agents can actually work for a much longer horizon and multiple agents can sort of coordinate on a single task. Early results are pretty exciting. We'll see. I think by end of the year you'll have have agents which are running 24 hours and maybe hundreds of agents collaborating on just single task. And that's where we sort of see the future going right now.
- [31:22]
YC Interviewer 2
How are you building for that?
- [31:23]
Madhav Jha
People's ambitions are increasing.
- [31:24]
Mukund Jha
Right.
- [31:24]
Madhav Jha
And so we want to give agents more autonomy. Right. And so the main thing is to make sure that the trajectory doesn't get derailed. So you always want to have an overseeing agent.
- [31:35]
Mukund Jha
Right.
- [31:35]
Madhav Jha
So it's like let's say a few agents are collaborating, then there is an overseeing agent as well, which is like parallelly monitoring the overall task. So we are experimenting with many different architectures. Something even as simple as just you would have heard of this Ralph Wiggum loop kind of phenomena. So the idea that hey, 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. So it comes back to hey, are you able to give autonomous verification feedback to the agent? 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 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.
- [32:29]
YC Interviewer (possibly Peter)
How do you think about some movement in the opposite direction? We talked about 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 Claude code for non technical users is a really valuable application to build, what implications does that have for you?
- [32:54]
Mukund Jha
Eventually I think. Do you understand your customers requirement really well? Are you building closer to them? I think all of those fundamentals of startup building remains the same. And I think for us like as long as we are focused on really understanding our users need really, really best, I think we'll compete on the process.
- [33:10]
YC Interviewer (possibly Peter)
Do you think about all the model companies as the same or the differences between them?
- [33:14]
Mukund Jha
If you look at the models themselves, they're very different. For example, Opus is obviously a workhorse. Codex is really good in backend debugging. Gemini is really good in front end. So I think all of these models have their own behaviors. A good thing for us is that we can actually utilize these spikes that models have to provide the best experience to the user. And I think eventually at least my worldview is that most of these models are going to get really commoditized where all of these models will have similar behaviors, they'll have price competitiveness between them and you can already see open source is maybe three to six months behind. And there's enough optionality for us to really build the layer on top where we really meet the user, where they are and support them in their journey. Who understands the customer needs, needs really, really well and is able to build for that is going to sort of win the space.
- [34:04]
YC Interviewer 3
Users have built 7 million apps with Emergent. What are all these apps, who are the users and what surprised you seeing what people do with it?
- [34:11]
Mukund Jha
The users who are coming to platform for us are generally people who want to build a serious app. 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 spreadsheets 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 we are bringing down, it would have costed 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 unlock that we are sort of bringing to the world right now. Second, for example this morning I was talking to a user, Christy, she's based out of Alaska and she built this, she's a clinical psychologist. She's also a sports school 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 and she couldn't find one, so she wanted to build one. She actually went to a dev shop,
- [35:14]
YC Interviewer (possibly Peter)
definitely the intersection of Bunny. She is.
- [35:16]
Mukund Jha
Yeah. 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, you know, discovered Emergent, started building out and she, she just launched her app like a couple of weeks back. It's called Equimind, an app store. And it actually marries her insights in psychology and into this sports coaching. She has hundreds of users right now using the platform and I think that is the unlock that we're trying to build. People who have had an idea for a long time, people who are really domain expert, very close to a problem, can now go and build things up. We also have a lot of solopreneurs building on platform 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 app that was built on Emergent.
- [36:05]
YC Interviewer 3
Really?
- [36:05]
Mukund Jha
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 are like really close to problem domain expert, but have been blocked by technology barrier to sort of really express themselves are using Emergent to sort of build these things out.
- [36:23]
Madhav Jha
And also like one thing these people tell us that it's not just about money. Like, hey, I can give money to the dev shop, but a lot get lost in the transl 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, 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 others focus on the business aspects of it. So this single solopreneur sort of attitude of 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.
- [37:01]
YC Interviewer (possibly Peter)
Yeah, I think it's a really important story that doesn't get told enough actually is 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, what's that going to mean for employment and civil unrest? But no one's really talking about the fact that actually if you have 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.
- [37:26]
YC Interviewer 3
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 7 million other apps that like nobody would have ever gotten to build.
- [37:45]
Mukund Jha
We're getting to the niche of niches today, Pete.
- [37:48]
YC Interviewer (possibly Peter)
This is like just an extension of the trend PG we wrote about a while ago, right? And so maybe coming out of the second World War you had sort of a few big companies and people built whole careers, hopefully staying at 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 are 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 runs their own business that's at the intersection of clinical psychology and horse rid and finds an audience and livelihood that way.
- [38:27]
Mukund Jha
Yeah, I mean we are excited about so many ideas coming to life. We really want to reduce this gap between idea and reality and truly enable people to express themselves and really, really have this Cambrian explosion of ideas, which is great for yc, I would argue
- [38:42]
YC Interviewer (possibly Peter)
it doesn't have to be actually. I think it's just really interesting, the whole explosion of being able to start businesses that aren't venture funded, that aren't trying to raise lots of capital, that is just one person following their passions and having control over their life. I think it's like, it's really uplifting message.
- [38:59]
Mukund Jha
I think we're just in the early innings of this right now. I think this expansion is going to grow and we'll see larger and larger projects being built on emergent. Yes.
- [39:08]
YC Interviewer (possibly Peter)
Okay, well, that's all we have time for today. Mukunda Madhav, thank you so much for joining us. It was a really fascinating conversation and congratulations on all the growth and we're excited to see where things go from here.
- [39:18]
Mukund Jha
Thank you. Thank you so much for having us.