How AI Is Unlocking Millions Of New Builders

So I think now we are just truly seeing this unlock where 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 using emerging to sort of build these things out. There's just so much focus on AI is going to replace jobs, knowledge work is going away, like what's that going to mean for employment and civil unrest, but like no one's really talking about the fact that actually like if you have like some agency of interest, 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 Lite Cone. Unfortunately, Gary got called to jury duty and can't be here with us today. Uh, but we are really excited to be joined by Makund and Madav Jar. Uh, they're both twin brothers and founders of Emergent, which went through YC in summer 2024. 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 YC's ever funded. Um, I mean, the statistics you were telling us were mind-blowing. you have in 8 months since launch 7 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? So we both are twin brothers. We actually uh you know started programming when we were age 12. Both of us came to us to do our PhDs. I dropped out of the PhD program joined Google and Maddie went on to uh was in Zenz then went on uh to start the deep learning team at Amazon and uh we've been meaning to do a startup together for a long time and um before this I was running a startup in India called Danzo which was a hyperlocal quick commerce company. Um and Dunano was a big company actually right? Yeah it was it was really big uh and and we we were almost a verb in India. So when people ship thing they say done so it uh and uh and I was managing a really large team of 300 engineers uh when you know and we have been sort of watching the deep learning field for a while and we knew an inflection point is coming. One of the things that I observed when I was running this large engineering team was that software testing was the biggest bottleneck in shipping fast. Um so when we started looking at you know what we want to build in AI uh that was the first idea we actually what year was this? This was 23 end. Yeah. And and so when we applied to YC like we applied with this idea of automating software testing. Uh that was the first idea. In fact we went to a lot of VCs with this idea. They thought it was too crazy. Uh you know and and now looking back it it it almost looks uh funny. And so we applied to YC 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 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 long 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 and this takes us into 2024 this 2024 yeah tell us what the landscape looked like like how big was lovable at this point and just I mean nobody had started lovable had not started I think kurs was just just 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 of 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 emergence and we built uh you know soda coding agents which became world number one on sweet bench 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 LM building with agents your intended users At this point we're presuming engineers. Yeah. At that point we were like purely just a research company just building coding agents. We were not thinking about a product. There was a time when we sort of invented the multi-agent system. We invented memory. We invented like how do we do agent to agent communication? How do you scale up test time compute? Uh a lot of those things which like were sort of coming out like we would we would discover something and we'll see 3 months later something come out in a paper. Uh you know and that sort of set the foundation for for us to so we were like cloud code before cloud code was a thing. Yeah. bunch of the paradigms like multi- aent orchestration, how do you use like different different routings, a lot of those things we sort of discovered. I definitely want to come back to that. Um I'm curious at this point in the story though, when did you sort of pivot into becoming a tool for nontechnical users? Yeah. So we actually like once we had this coding agent uh 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 2 three months trying to uh 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 emergence platform to build internal tools and internal software and at that point you know we saw like lovable was growing like crazy bolt was growing like crazy uh so we thought hey why don't we have this you know 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 pro uh pilot uh almost uh in June last year 20 25 and that really took off and and since then you know like we've been just focused on solving problem for non-conumer We in fact thought a lot of technical people use us but today 80% of users who are on the platform are nontechnical users with zero programming knowledge. Uh and they're building like apps that that run real businesses on top of today. So it's almost and they're based all around the world right like how many countries? Yeah. So they're they're all global audience 80% um 70 80% are in US Europe over 190 countries right now. Something that we have talked a bunch about at YC internally is just um 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 companies like Lora enter the legal AI space after Harvey but is like growing incredibly fast. So there was clearly wasn't maybe as big of a moat around being a first mover um as you traditionally think there is in software when you guys made that sort of the pivot or the slight change in direction into nontechnical users at a time where lovable ball and bolt are growing really really quickly. How did you think about that? There are like two two three different different threads I would want to pull. One essentially is that I think the the model uh every new model generation actually is presenting a new opportunity of looking at the world. Like for example when we started GP4 was the the first model that we sort of started looking at and at the end of the biggest problem that everybody's trying to solve was JSON parsing like hey structured output format and we thought okay like the next model is going to solve for it um 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 it's going to enable like multiple agents coordinating together and so I think like one of The advantages of starting second, right, is that you can actually one like learn from what is what is not working uh for the 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 your imagination 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. 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 toend 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 uh 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 were very confident about the product and 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 um we built out a a large influencer network and that was our initial sort of you know starting point for us like we used Tik Tok Instagram Instagram and part of this bunch of influencers to really really spread the word out and and that sort of you know kickstarted the whole thing for us. 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 or or others or or emergent was a better fit for them. I mean our 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 uh real software what we did was like a little bit broad-based like marketing and and but users that u you know were coming to the platform that we would convert were users who actually wanted to ship a real real app uh on the platform and was that in the messaging then it it was in the messaging yeah so so we would say 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 emergence. It seems like a key insight for you. Basically, you went very hardcore in terms of being maximalists 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 uh share a bit how you built it? One of the uh cons of all these other big products like Loal or Bolt is just that is difficult to get those into a fully usable. you can get to a prototype 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 parto principle, but you you did more than that. The last 20% of that engineering to production was a lot of work. And that's a lot. 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 uh you know cloud sandboxes uh 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 uh text tag from ground up uh the container text tag and one of the insights here is that if you give your uh 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 uh encounter those many problems right and the fact that we have our own infra also allows us to give like rapid feedback to the agent so your agent is only as good as the feedback that you provide. Uh so we built this like sort of infra and agent like sort of co-build it together and from the uh from from day one and and to your point right like uh because we we focused on you know building like uh ship ready apps which which are production ready which has which comes with back end and and front end and everything. The text stack we chose was also pretty unique to us. We have a python backend uh 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 text stack right and and this like server client architecture where you can have like background jobs if you want to have background cues so we knew that you know users who would who would use this app their ambitions are going to go bigger and bigger right hey I want to run a job which can like do this asynchronous video processing you know and they're going to prompt it and we wanted to support it from day one right and so it's the same text on which emergent is built is what we expose to our end users is what we expose to our agents Okay. Uh on the agent side, we were very early on the multi- agent architecture. Uh 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 uh search or I want to do like you know integration search like how do I integrate this unique API. Um 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 uh 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 uh that people are like uh interested in now you would have noticed that people are interested in skills uh like people create like skills and uh the uh there's a new benchmark called skills bench which shows like agent with skills outperform agent without skills. Uh and interestingly like those skills cannot be generated by agent themselves like if you generate those skills by agents they don't like uh match up to the performance. So we were able to do it in a way where the skills get auto uh you know sort of uh generated based on previous trajectories and we run it through a CI/CD process and then add it to the long-term memory. Uh so all of that like compounds for us right so if you if your agent was struggling to do a calendar integration 3 weeks ago uh today it is no longer struggling thanks to the uh the previous session where it was able to make it happen. So fascinating. So it learns on its own because I think one of the challenges of all these uh 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 uh the context window for all the models because that seemed to be the the bottleneck and I think you guys architected your way out. So you kind of built a lot of uh what the state-of-the-art is now but way back a year before. our coding agent is so powerful that we basically internally use it uh as a replacement for cloud code as developers right so we uh we are so proud of that and uh but yet we don't want to expose that sort of you know power tool to our end nontechnical user and so we even though we have this VS code editor we kind of hide it uh because what we have noticed is that nontechnical users they even get panicked as soon as they see a diff you know uh we we we had a like a fairly technical PM in our team and uh like he doesn't like like JSON on you know he's like no don't show me you know I 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 what is agent what is agent feeling like right internally have a term called agent experience right that we measure that how like how how is agents experience on the platform actually a really important point I think people don't realize is you guys actually you actually started out essentially as sort of devon cursor in like the actual like coding agent world for engineers you just made the choice to package it up for nontechnical users. So you're sort of like moving almost in the opposite direction from like a lover board. Like you have like the power, you have all of the actual like power. You just need to simplify the user experience whereas they like sort of have like start with the user experience and they're going to have to develop the power over time, right? Right. And I think fundamentally it's it's like unless you start from you know a starting point which which uh sort of solves all of these problems along the line the whole software development life cycle it's actually really hard to come from the other side and solve these problems because you you'll make some architectural choices which are very hard to reverse. Do you have any more I'm really curious like any more examples of where sort of as you were engineering the system you s just trust in the model like you mentioned JSON passing but was there anything else where you're like let's not invest time in that um because like Opus 4.5 will solve it I mean some of them has has been for example um you know like library definition some of the integrations that we have sort of built like you know we think that you know next sort of models are solving for us similarly like how do you generate unit tests some of those things that we we 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 hardness was very strict and you know like we would we would tighten it up um and and slowly like what we were observing is that as these models are getting larger and larger more more more uh efficient like you know like the more control you give to the model uh this making the better the the harness gets. If we extrapolate that out or sort of like really far out, are you worried about where that sort of leaves you as a company versus the mo like the models themselves and the models get more powerful? Yeah, I think there is this underlying current right now, right, in the industry that that hey, like is is uh you know like anthropic going to eat everybody up. Yeah, I mean our view is that I think uh the the coding aspect is only 20% 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 going to get more and more sort of uh capable I think the the human desire is also continuously growing at the same rate so I think people are going to want to build more complex apps uh 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 and essentially like we can use multiple foundation models together to sort of extract more uh and I think we'll have to keep continuing you know like delivering more and more things to our users for example now we're thinking about like a lot of our users who have built the app now want to help with distribution now want to help with growth now want to help with like how do you sort of you know manage users uh and things like that and I think for us the spectrum sort of keeps growing on that side I agree with it I mean there's there's another graph that I show 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 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 the rate of like software that you're expected to ship per week just keeps going up and up and up. It's accelerating. Yeah. It's a hedonistic adaptation to you know like hey oh this is more powerful now I can do more work. Yeah it is really a Javon'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 experienced I mean I think we are we're in an expanding market right like we are like letting non-developers not be developers right. I think you know that market is expanding. We also are internally seeing like the roles sort of combining. So like a PM, a designer, engineer like a single person is doing you know like work of all all three together right. So like we have a PM who's white coding uh internally things. Uh and recently like we um so we are seeing this internally right now where um 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 PM. YC's next batch is now taking applications. Got a startup in you? Apply at y combinator.com/apply. It's never too early and filling out the app will level up your idea. Okay, back to the video. Could we see a demo of emergent? Oh yeah, sure. Yeah. So, this is how what emergent interface looks like and uh I'm going to like put a prompt where like because we were 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 are going to a job interview and you want to practice questions, right? So, you can build a full stack app on on emergent you can build a mobile app. Our prompt engine is smart enough that once you give it a prompt u 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 is a mobile app builder. Right. So even though you have like selected the wrong tab it's just like uh yeah the behind the scenes auto. Yeah I got you right. So while while this is running let me quickly also uh show you a few uh user apps. So this is by somebody based out of Illinois. uh he's uh sort of has a business of audio video setup uh that they do like on as manually right so basically whatever this kind of like intake form they would have taken through spreadsheet and and other calls they basically build this out without any uh coding background knowledge right like hey this is the kind of AV setup I want um so you you you go and you build your room and then you you get it's a lead genen sort of a form but this is a fairly full stack app one thing I noticed about that is like the design is really good like the icons like it just like it looks like a well-designed app. So we have actually spent a lot of time on like making sure the design is actually good and 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. There's another sort of person based out of Norway. He he sold his previous business to a PE 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. He has 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 uh requirements properly and uh another thing is that nontechnical users probably don't know the concept of API key. How do I get an open AI API key? So in this particular case I can just say hey use emergent LLM key. So you don't have to worry about getting API key from third party. This feels like a good example what you were saying um because this is sort of like the ask us aer question skill include code but you just like abstract that away but you just like build into the experience for someone who had no idea about absolutely I can be very like casual here. I can say hey uh the for the first one use emergent API key rest assume good defaults and then go. This is the first time I hand off the agent and like at this point I can just like close my laptop. We also have a mobile app. So you can like on the go keep trying to prompt agent if 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 uh eventually. So this is a podcast preparation app. 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 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 emerging data. 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." Yeah, it kind of like started off as a QA engineer's curiosity. He he like his first prompt I looked at his old 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 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 op 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 also saving like around like $3,000 $4,000 a month in subscription. Yeah. This is really the world of personal software. Yeah. Has anybody actually edited the code for this or is this 100% built built with a merchant? 100% 100% builds build the merchant 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. 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 emergence. So it allows you to test what happens when people get to a very complex app like this. 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 a 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 understand 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. 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? So currently like everybody in the company is using this this one tool right now and and and it is collaborative 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 how do you think the sort of version control like and 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. Yeah. So so there is a testing testing phase there is deployment phase right. So we have different versions maintained uh right and and there is a primary owner of the software like who actually manages this right now and and so you know it evolves involves like somebody will make a feature request uh somebody will sort of build that out as the agent will build that out and then like once it's accepted then it it'll go to the release it's not managed through git though it's like your own workflow thing so you can connect GitHub if you want to like we internally connect GitHub for our projects right and uh like if nontechnical developers outside of emergent um like they actually call GitHub GitHub, right? So they they have very uh like limited uh knowledge of GitHub and so they we we take care of like versioning on our side even if they don't connect GitHub. So 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? Yeah. So we we actually from 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? Uh and second is ownership like we think that people who can like really really take ownership u you know like we index on that and a lot of our early sort of hires were people like you know we were really obsessed with like top 100 IT rankers. So we had this like program going on where like I told you know our team that hey we must hire like top 100 IT rankers. Uh 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 also came from Dunzo. So I because I was able to build like a really really good team. We were able to get some some initial folks from there. The focus that that we have is 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 versel 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 way like we give way more responsibility to people and I think people are generally attracted towards harder problems that they want to solve. Where is your team located? So most of the team right now is in Bangalore. uh in India office uh we have a very small office in SF like three to five people here and you guys yourselves you're kind of like split across both countries can you maybe just explain how the setup works yeah so I mean I I I live here in SF I've been in like uh you know Bay Area for like last 10 years I split half my time in SF half my time in Bangalore uh constantly jetlagged 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 Yeah. Um why is that? I mean I think it's like when I went back to India uh 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 uh the second company I always thought like hey there has to be you know like we have so much talent we have you know lot of now capital available everything is available in India like why are people not building glo 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 um think uh 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 that opportunity to build for a global audience and if you have that sort of mindset that ambition I I think I think lot we'll see a lot more companies coming out of India doing the same. I'm curious to hear what it's actually like sort of on the ground running this sort of like split country where the team is mostly in India but the product is overwhelmingly used in the US and as Europe is 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? Internally we have like really 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 develop product uh 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 everyone in the everyone in the company right uh they talk to a customer everybody does customer support so like we were like a really really small engineering team like 12 people team and one person was always on call for customer support it was really hard 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 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 helped us bridge the gap uh you know uh in terms of like what our users want uh today and it's funny because like when we launched my first like 5 days I was just glued to a desk doing customer service uh support uh only and most of the customer requests were coming in in a different language like you know French, German because a lot lot of the 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. And we are hiring here in S. So uh if anybody's you know interested in uh you know joining uh 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. I'd love to go back to what we were talking about regarding personalized software and what do you think the implications are for SAS in general? Yeah, I guess the provocative question is is SAS dead now? I mean you guys essentially killed Asana for yourselves. Like is that bad for Asana and other SAS companies? I mean I definitely think that like the current um way the SAS is existing today needs to change right I think like I feel there are two like sort of massive headwinds. on is more and more of these SAS workflows are going to get consumed by an agent right like so like um you know unless your SAS company pivots into like an agent first company uh you know I think uh 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 um you know our own do it uh project management tool and we are seeing a lot of these people um you know building these internal tools uh these software on on 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. Um a lot of people are building on emergent today like roughly 20% of them are actually agentic apps. So people are actually you know embedding our own emergent agent inside those apps to sort of you know power bunch of the workflows. Do you have some interesting that sounds really cool any interesting examples that people do? Yeah, I mean I like the uh uh app that M was just showing uh you know the uh CRM for uh lawyers that is an agentic app where you know an agent can take a workflow and and run run through the process. The software itself is now morphing into you know agentic like a lot of a lot of people just want to you know build agents that can actually just do you know lot lot more of the work uh on its own. Where do you think this goes as uh agents uh horizon for task gets longer and longer? I mean one of the the meter meter chart yeah chart is one of the ones that was very shocking recently. Yeah, I think that's the chart of the year I would say right like the the meters exponential growth and and like 4 4.5 was at like I think four hours and 4.6 is at 10 hours uh and we are internally sort of now like you know experimenting with agent swarms where agents can actually like work uh for a much longer horizon and multiple agents can sort of coordinate on a single task. Um early results are like pretty pretty exciting. um you know we'll see I think I think by end of the year you'll have you know agents which are running 24 hours uh and like maybe hundreds of agents collaborating on just single task um and that's where that's where we sort of see the future going right now. How are you building for that? People's missions 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 then there's 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 uh you know you would have heard of this Ralph Wiggum loop kind of a phenomena 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 autonom verification feedback to the agent like was the job done. So a lot of our work internally right now is in fact still going on on building best verifiers there we are actually uh doing some custom fine tuning as well. So uh we are very careful about like not directly competing with the models in the sense that we don't want to like build a 4.5 alternative right away but we do want to augment it through our custom fine-tuned verification layers. Uh so so some of the fun stuff we on the research side we are doing is on on that side. How do you think about some movement in the opposite direction? 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 clawed code for nontechnical users is a really valuable application to build what implications does that have for you 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 startup building remains the same and I think you know like for us like as long as we're focused on like really really understanding our users need really really best I think you know we'll compete on the product do you think I mean maybe do you think about all the model companies as like the same or the differences between them if you look at the models themselves right like they're very different like for example you know um opus is obviously a workhorse um you know like um Codex is really good in backend debugging uh 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 is these uh spikes that model have like to to provide the best experience to the user. Um 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 who understands the customer needs really really well and and is able to build for that is going to sort of win the space has built 7 million apps with emergent what are all these apps who who are the users and what surprised you seeing what people do with it the users who are coming to platform for us are generally people who want to build a serious apps. People who like really really have a business use case that they want to automate or they have a business idea that they want to launch. Um primary users who are coming to us are smallmedium 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 $500,000 to build the software. and now you can build it for $5,000 completely on your own. Um and uh that is a 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 a user Christy she's based out of Alaska uh and she built this she's a clinical psychologist uh she's also uh a sports coach 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 on horse riding side and 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 that's definitely the intersection of learning she is yeah and 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 bomb. So she, you know, discovered Emergent, started building out and she she just launched her app like a couple weeks back. It's called Equine on an app store. Uh and it actually marries, you know, like her insights in psychology and and and uh into this this uh sports coaching. Um she has like hundreds of users right now using the using the platform and I think that is the unlock 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 build build things up. Um we also have like lot of soloreneurs 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 $4 million uh on an ad that was built on emergent uh really yeah yeah 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 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 immersion to sort of build these things out 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 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'm 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 soloreneur sort of attitude of like I'm going to do it myself. I have the domain expertise nothing is lost in translation. Uh that kind of agency is what people are looking forward to with these kind of platforms. Yeah, I think it's a really important story that doesn't get told enough actually is like what you're building is really necessary for society that 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. 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. Um 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. You're getting to the niche of niches. Yeah. So this is like just an extension that trend PG wrote about a while ago, right? into like maybe coming out of the second world war you had sort of like a few big companies and people like built whole careers hopefully staying at like IBM or whatever for a couple of decades and then retire then the startup wave came along and suddenly like the world becomes higher resolution people like maybe I should start my own company or at least join a smaller company and work at multiple companies or found multiple companies and like the next extension of that is just everybody like runs their own like business that's at the intersection of like clinical psychology techology and horse riding um and finds an audience and and life uh livelihood that way. 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 uh to express themselves and and and really really like have this Cambrian explosion of ideas like which is great for YC. I would argue it doesn't have to be actually like the whole like I think it's just really interesting the whole like explosion of being able to start businesses that aren't like venture funded that aren't trying to raise lots of capital that it's just like one person like following their passions and like having control over their life. I think it's like it's really um uplifting message, right? And I think we're just in the early innings of this right now. Like I think I think this this exponential is going to grow and and and we'll see larger and larger, you know, projects being built on uh emergent. Yes. Okay. Well, that's all we have time for today. Uh Makunda Madav, 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. Thank you. Thank you so much for having us.