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 Emergent to sort of build these things out.
>> There's just so much focus on AI's 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 Latent Unfortunately, Gary got called to jury duty and can't be with 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 YC in summer 2024.
Emergent's a platform that lets anyone build and ship production-ready software using AI agents.
You guys are actually one of the fastest-growing companies I believe YC's ever funded.
I mean the statistics you were telling us were mind-blowing.
You have In 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 Well, that twin brothers, we actually you know, started programming when we were age 12.
Both of us came to the US to do our PhDs.
I dropped out of the PhD program, joined Google, and Maddy went on to was in Zenefits and 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.
Um >> Dunzo was a big company actually, right?
>> it was it was really big and and we we are almost a verb in India.
So, when people ship something they say Dunzo it.
And and I was managing a really large team of 300 engineers 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.
So when we started looking at, you know, what we want to build in AI, that was the first idea we had. >> 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. That was the first idea.
In fact, we went to a lot of VCs with this idea.
They thought it was too crazy, you know, and and now looking back at it it almost looks funny.
And 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 know, you can solve the the testing part, you can actually automate all of software engineering.
That was sort of our key insight that like, you know, verification is the loop which sort of keeps agent running for a longer longer period of time.
And that's when we pivoted to looking at general coding agent as a space and we started building general coding agent.
>> And this takes us into 2024. >> This is 2024. 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 Cursor was just just getting getting getting started and very very early.
I think Devin had just come out.
So so really really early and and we looked at this benchmark called SweetBench, 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, four of us, and said, "Okay, let's just look at this benchmark. How do we crack it?"
That sort of set the foundation for Emergent and we built, you know, Soda coding agents, which became world number one on SweetBench, 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 LLMs, building with agents.
>> intended user at this point were presumably engineers.
>> Yeah, at that point we were like purely just a research company, just building coding agents.
We were not thinking about a product.
That was the time when we sort of invented the multi-agent system. We invented memory.
We invented like how how do we do agent-to-agent communication?
How do you scale up test time compute?
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.
>> Bunch of the paradigms like multi-agent orchestration, how do you use like different different routings?
A lot of those things we we sort of discovered >> I definitely want to come back to that.
I'm curious at this point in the story that when did you sort of pivot into becoming a tool for non-technical uses? >> 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 2-3 months trying to you know, make our agents work within enterprise.
We found that it was too slow.
And at the same time we were internally started using Emergence's platform to build internal tools and internal software.
And at that point, you know, we saw like Level 5 was growing like crazy, Bolt was growing like crazy.
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 build a pro pilot almost 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-consumers.
We in fact thought a lot of technical people would use 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 that run real businesses on top of today.
So it's almost been >> And they're based all around the world, right? Like how many countries?
>> they're all global audience.
80% 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 company like Legora entered 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 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 Lava Bowl and Bolt are growing really, really quickly, how did you think about that?
>> There are like two two three different different threads that I would want to pull.
One essentially is that I think the the model every new model generation actually is presenting a new opportunity of looking at the world.
Like for example, when we started GPT-4 was the first model that we sort of started looking at and at the end of the biggest problem that everybody was 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.
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 tasks.
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 for the current uh competition, right?
And also I think you fundamentally start from a different starting point, right?
Like where like your approach to the world is like very different.
Like you your imagination is really big, right?
And I think and and when we we were starting Emergent, we realized that like a lot of the users that were going to, you know, some of 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 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 not just the front-end prototyping.
I think second thing is like how do you sort of get the distribution because you're coming from behind, right?
So even if your product is really, really strong, and fundamentally I think you'll have to enter the market with a really really strong product, which is you know, head and shoulder above what 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 is like in the 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 and partnered with a bunch of influencers to really really spread the word out and and that's sort of you know, kick-started the whole thing for us.
>> So, to me so building the influencer marketing engine is like um it's like tactics to land crab.
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 All or or others or or Emergence was a better fit for them?
>> I mean our our thesis was that like there were 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 broad-based like marketing and and but users that um 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?
>> I it was in the messaging, yeah.
So so we would say, "Hey, come and build real apps."
We would also use the common errors that you would see on other platform, you know, like hey, don't don't see don't face this error on Emergence.
>> It seems like a key insight for you.
Basically, you went very hardcore in terms of being maximalist in engineering from your experience having run large engineering teams at 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 cons of all these other big products like Level All or Bolt is just that it's it's 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 Pareto principle, but you you did more than that the last 20% of that engineering to be production was a lot of work, and that's a lot.
>> 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 tech stack from ground up.
Uh the container tech stack.
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 build 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 tech stack we chose was also pretty unique to us.
We have a Python back end 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-focused node-heavy uh tech stack, right?
And and this like server-client architecture where you can have like background jobs if you want to have background queues.
So, we knew that, you know, users who would who would use this app, their ambitions are going to go bigger and bigger, right?
Hey, I want to run a job which can like do this uh asynchronous video processing, you know, and they're going to prompt it.
And we wanted to support it from day one, right?
And so, it's the same tech stack on which Emergent is built is what we expose to our end users, is what we expose to our agents, right?
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, and 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 all 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.
Uh 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 you know, sort of uh they are generated based on previous trajectories and we run it through a CICD process and then add it to the long-term memory.
Uh so, all of that like compounds for us, right?
So, if you if 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, it's fascinating.
So, it learns on its own because I think one of the challenges of all these uh live coding app platforms is at some point the application will 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 uh sort of uh you know, power tool to our end non-technical 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 non-technical 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, 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 What is agent feeling like, right?
>> have a term called agent experience, right?
That we measure that how like how how is agent's experience on the platform.
>> Actually a really important point I think people don't realize is you guys actually you actually started out essentially as sort of Devin cursor in like the actual like coding agent world for engineers.
You just made the choice to package it up for non-technical users.
So you're sort of like moving almost in the opposite direction from like lovable.
Like you have like the power you have all of the actual like power, you just need to simplify the user experience.
Whereas they like sort of have like start with the user experience and they're going to have to develop the power over time. >> Right.
And I think fundamentally it's like unless you start from you know, a starting point which which sort of solves all of these problems along the line, the whole software development life cycle is 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 as you're engineering the system you just sort of trust in the model?
Like you mentioned passing, but was there anything else where you're like let's not invest time in that because like Opus 4. 5 will will solve it.
>> I mean some of them has has been for example, you know, like library definition, some of the integrations that we have sort of built like you know, we think that next sort of model should solve for us.
Similarly like how do you generate unit tests?
Some of those things that we we actually like would have heavily prompted before.
And the other thing that we are very conscious of is that how do we give more and more autonomy to the models as they the next generation 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 and and slowly like what we were observing is that as these models are getting larger and larger more more more efficient like you know like the more control you give to the model this is making the better the the hardness gets.
>> If we extrapolate that out also like really far out, are you worried about where that sort of leaves you as a company versus the mod like the models themselves and the models get more powerful?
>> Yeah, I think there is this underlying current like not right in the industry that that hey like is is you know like Entropic going to eat everybody up.
Yeah, I mean that our view is that I think 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 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 on the platform.
The other thing is that it is with our hardness we're able to extract 23% more on top of these models and and essentially like we can use multiple foundation models together to sort of extract more.
And I think we'll have to keep continuing you know like delivering more and more things to our users.
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 I want to help with growth.
Now I want to help with like how do you sort of you know manage users and things like that.
And I think for us the spectrum sort of keeps growing that side. >> I agree with that.
I mean there's there's another graph that I showed 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 and more stuff.
And it's just like the rate of like software that you're expected to ship per week just keeps going up and up and up. >> Absolutely, yes.
So, hedonistic adaptation to, you know, like, "Hey, oh, this is more powerful. Now I can do more work." >> Yeah.
>> It is really a Jevons paradox at play.
And I I think there's a lot of uh 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've experienced.
>> I mean, I think we're we're in an expanding market, right?
Like, we are like letting non-developers now be developers, right?
I think, you know, that market is expanding.
We also are internally seeing like the roles sort of combining.
So, like a PM, a designer, engineer, like a single person is doing you know, like work of all all three to together, right?
So, like we have a PM who's like coding internally things.
And recently, like we So, we are seeing this internally right now where a lot of the work that was done by like five, six people team can now be just done by like single engineer or single PM.
>> YC's next batch is now taking applications. Got a startup in you? Apply at ycombinator. 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 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, uh 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, 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 had like selected the wrong tab, it's just like, "Yeah, yeah." >> Yeah, yeah.
The behind the scenes auto Yeah, I got you, right?
So, while while this is running, let me quickly also show you a few user apps.
So, this is by somebody based out of Illinois.
He's a sort of has a business of audio video setup that they do like on a as manually, right?
So, basically, whatever this kind of like intake form they would have taken through spreadsheet and other calls, they basically build this out without any coding background knowledge, right?
Like, "Hey, this is the kind of AV setup I want."
So, you 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 up.
>> One thing I noticed about that is like the design is really good.
Like, the icons like it just like it looks like a well-designed app.
>> So, we have actually spent a lot of time on making sure the design is actually good. >> Yeah.
>> Our like so earlier there used to be a big trade-off between design and functionality.
Like, if you are 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 like the word he used, like, "I'm a business developer."
And he he doesn't have a programming background.
So, a lot of CRM-related apps we are seeing small businesses as your second monetization uh avenue, right?
And so, like one of the unique things with Emergent is that before agent goes off to build things, it asks you for some clarification because agent wants to make sure that it understood your your requirements properly.
And another thing is that non-technical users probably don't know the concept of API key.
How do I get an 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.
>> This shows a good example of what you were saying cuz this is so like the agent uses question skill and code, but you just like abstract that away, but you just like build it into the experience for someone who had no idea about >> Absolutely.
I can be very like casually and I can say, "Hey, 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 thing.
Once it's done, Uh, 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 >> 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 Emergent 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 uh, engineer's curiosity.
He He like his first prompt I looked at his all jobs, the first prompt was clone Jira. Okay.
And then like he just kept going with that and uh, and I think the other thing is we do have do things a little bit differently.
So, for example, we ship like three times a day, morning, evening, night.
So, kind of like built it very customized to the way we do things.
Like, we have a QA uh, involvement in in in many many ways.
Uh, and definitely like we when we were using Asana, it was very like even to customize it to to make it to your uh, work style was not easy.
And and we we are we are also saving like around like $3,000 to $4,000 a month in subscription.
>> Yeah, so we do so well at the personal software.
>> Yeah, has anybody actually edited the code for this or is this 100% built built with Emergent?
>> 100% built with Emergent and and the good thing is that like if I want to add a feature, I have to just go to that uh, you know, project and just add a feature and it just starts building.
>> It's probably useful for you guys to dogfood the platform this way cuz this is probably at the edge of the of the of the most complex apps people have built with Emergent.
So, it allows you to test what happens when people get to a very complex app like this.
>> In fact like lot of the teams internally are now building you know apps using Emergent internally.
So we have like a marketing team built out of complete CRM completely built on Emergent.
We are now like our customer support team is building a customer support software completely built on Emergent.
And the power is that these are people who are close to the problem like who you know who understand the problem really really well and are able to now build these apps and the speed at which we are able to ship you know these internal apps is like crazy.
>> How far down does it go though?
I'm 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 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 to sort of build that out right now.
>> How do you think the sort of 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?
>> Yeah so so there there there is a testing testing phase is deployment phase right?
So we have different versions maintained right and and there is a primary owner of the software like who actually manages this right now and and so it was it was like somebody will make a feature request somebody will sort of build that out as an the agent will build that out and then like once it's accepted then it will it will go to the release.
>> It's not managed through GitHub it's like your own workflow thing.
>> So you can connect GitHub if you want to like we internally connect GitHub for our projects and like if non-technical developers are outside of Emergent like they actually call GitHub GitHub right?
So they they have very like limited knowledge of GitHub and so they we we take care of like versioning on our side even if they don't connect GitHub.
>> It's all about how you run your team.
The way you hire must be very different.
I mean you're 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, uh you know, like we index on that.
And a lot of our early sort of hires were people like, you know, we were we were really obsessed with like top 100 IIT 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 IIT rankers.
Uh right now I think we have like IIT rank one, IIT rank 12, uh all those people working with us.
And a lot of the initials that 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 that.
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 WhatsApp 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 they like we give way more responsibility to people.
And I think people are generally attracted towards harder problems that they want to solve.
>> Where's your team located?
>> 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.
>> And you guys yourselves, you're kind of like split across both countries.
Can you maybe just explain how how the setup works?
>> Yeah, so I mean I I I live here in SF.
I've been in like, you know, Bay Area for like last 10 years.
>> I split half my time in SF, half my time in Bangalore, constantly jet lagged.
>> I think you guys are probably the most successful AI company that it's obvious it came from like that it's an Indian company but that's got like significant presence in India. Um why is that?
>> I mean I think it's like when I went back to India, you know, after Google and I always had this thought that why is there no Google or Facebook from India, right?
So like from day zero I was thinking, you know, even though I started Dunzo, it was an India India focused company at that time.
And when I was starting the second company, I always thought like hey, there has to be, you know, like we have so much talent, we have, you know, so a lot of now capital available, everything is available in India.
Like why are people not building globally 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 sort of fully penetrated, people people can actually get understanding knowledge from everywhere.
I think every single, you know, country has an opportunity to build for global audience.
And if you have that sort of mindset, that ambition, I I I think I think lot I we'll see a lot more companies coming out of India doing the same.
>> I'm curious to hear what it's actually like sort of on the ground running this sort of like split country company where the team is mostly in India, but the product is overwhelmingly used in the US and after in Europe it's not a product for the Indian market at all.
What is it like running this company?
How would it be different if you had built a normal Silicon Valley style company that was all based here?
>> 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 way we 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.
>> Like everyone in the entire company?
>> 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 to do this for us because, you know, you're a really small team and you have to ship really fast and then move like one of your best engineers out to do customer support was really hard, but I think that really really helped us build the customer empathy from day zero.
And I think given that like a lot of our distribution happens online, like you know, like the team is able to learn uh 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, in terms of like what our users want uh today.
And it's funny because like when we launched my first like five days I was just glued to a desk doing customer service uh support only.
And most of the customer requests were coming in in in different language, like you know, French, German, because a lot of lot of these users are global.
And thanks to AI like we were able to understand that, reply to that, and I think that that that uh you know, like is also helping you know us bridge the gap there, yeah.
>> And we are hiring here in SF.
So, if anybody is, you know, interested in, you know, joining in various positions, like be it research across the board, like back-end engineers, front-end engineers, we are hiring here in SF and in Bangalore.
>> I love Sked actually, but what were you talking about?
So, regarding personalized software, and what do you think the implications are for SaaS in general?
You know, like I have to provoke the question, is this SaaS dead now?
I mean, you guys have essentially killed Asana for yourselves.
Like, is that bad for Asana and other SaaS companies?
>> I definitely think that like the current way SaaS is existing today needs to change, right?
I think like I feel there are two like sort of massive headwinds.
One is more and more of these SaaS workflows are going to get consumed by an agent, right?
Like so, like you know, unless your SaaS company pivots into like an agent-first company, you know, I I think that's going to be hard to sort of survive.
And second headwind is obviously like, you know, like people would want more and more customized software, like which they can build on Emergent, just like we built you know, our own Do It project management tool.
And we are seeing a lot of these people you know, building these internal tools, 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 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, you know, embedding our own Emergent agent inside those apps to sort of you know, power a bunch of the workflows.
>> So, you have some interesting that sounds really cool.
Any interesting examples of people doing that?
>> Yeah, I mean, I have like the app that Manny was just showing, you know, the CRM for lawyers, that is an agentic app where, you know, an agent can take a workflow and and run run through the process.
The software itself is now morphing into, you know, agentic.
Like a lot of lot of people would just want to, you know, build agents that can actually just do, you know, a lot lot lot more of the work on their own.
>> Where do you think this goes as agents' horizon for task gets longer and longer?
I mean, one of the the meter >> that? 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.
5 was at like I think 4 hours and 4. 6 is at 10 hours.
Uh, and we are internally sort of now like, you know, experimenting with agents forms where agents can actually like work for much longer horizon and multiple agents can sort of coordinate on a single task.
I think this is also like pretty pretty exciting. You know, we'll see.
I think I think by the end of the year you'll have, you know, agents which are running 24 hours.
Uh, and like maybe hundreds of agents collaborating on the single task.
And that's where that's where we sort of see the future going right now.
>> How are you building for that?
>> People's submissions are increasing, right?
Like and so like we we want to like give agents more autonomy, right?
And so like the the the main thing is to make sure that the trajectory doesn't get derailed.
So you always want to have like an overseeing agent, right?
Like so it's like let's say a few agents are collaborating and there is an overseeing agent as well, which is like parallelly like monitoring the overall task, right?
So so we are experimenting with many different architectures, right?
Like something even as simple as like just, you know, you would have heard of this Ralph Bingham 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 autonomous verification feedback to the agent? Like was the job done?"
So a lot of our work internally right now is in fact still going on on building best verifiers.
There we are actually doing some custom fine-tuning as well.
So we are very careful about like not directly competing with the models in the sense that we don't want to like build a Opus 4.
5 alternative right away, but we do want to augment it through our custom fine-tune verification layers.
So so some of the fun stuff we on the research side we're doing is on that side.
>> How do you think about sort of moving in the opposite direction?
I mean, we talked about sort of like the models themselves maybe getting more powerful and what does that mean for everyone building on top of them?
But how about at least some of the model companies are explicitly trying to build applications and own the application layer themselves.
If one of those companies decides that, you know, code code for non-technical users is really valuable application to build, what implications does that have for you?
>> I mean, I think eventually eventually I think like you understand the customer's requirement really really well.
I will be linked 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 are focused on like really really understanding our users need really really best, I think, you know, we'll we'll compete on the product side.
>> I mean, maybe do you think about all the model companies is like the same or are there differences between them?
>> If you look at the models themselves, right?
Like they're very different.
Like for example, you know, Opus is obviously a workhorse.
You know, like Codex is really good in back-end debugging.
Gemini is really good in front front-end.
So, I think all of these models have their own behaviors.
And and and one of the like a good thing for us is that we can actually utilize these spikes that model have like to to provide the best experience to the user.
And I think eventually like at least my worldview is that most of these models are going to get get really really commoditized like where all of these models will have similar behaviors.
They'll have, you know, price price competitiveness between them and and you can already see like, you know, like open source is like maybe 3 to 6 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.
>> Users have built >> 7 million apps with a merchant. What are all these apps?
Who are the users and what surprise do you see 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 have a business use case that they want to automate or they have a business idea that they want to launch.
Primary users who are coming to us are small medium business owners.
They're running their business today on on email, WhatsApp, spreadsheet, and would have gone to a dev shop to sort of build a custom software um, run automate their business, they're coming to us.
And if you look at the price point that, you know, we're bringing down, it would have cost you like $500,000 to build the software.
Now you can build it for $5,000 completely on your own, um, and, uh, that is the kind of, you know, like unlock that we are sort of uh, bringing to the world right now.
Uh, second, for example, this morning I was talking to user Christie.
She is 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, uh, horse riding side.
And and she said she looked around everywhere to find an app that does that.
And she couldn't find one.
So she wanted to build one.
She actually went to a dev shop >> Yeah, that's definitely the intersection where she is.
>> And she went to a dev shop in Nova Scotia and tried to find somebody who can build it.
Uh, they were charging a lot of money, 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 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 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 experts, very close to a problem, uh, can now go and build build things up.
Um, we also have like a lot of solopreneurs 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, uh, we saw somebody ping me that, "Hey, like this company has raised like $4 million uh, on an app 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 are like really close to problem, domain expert, and but have been blocked by, you know, technology barrier to sort of really express themselves are are are, you know, like using Emergent to sort of build these things out.
>> 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 of 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, the 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 solopreneur 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.
It's like what you're building is really necessary for society.
Like there's just so much focus on AI's 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're empowering that at scale.
>> It's so cool that 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. >> Yeah.
>> 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 before.
>> to the niche of niches. >> Yeah. >> Yeah.
>> I mean so Pete, this is like an just an extension of the trend PG wrote about a while ago, right?
And so, like maybe coming out of the Second World War, you had some like a few big 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 it suddenly like the world becomes higher resolution.
People are like, "Oh, well, 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 and horse riding um and finds an audience and and life 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 we truly enable people 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 tied to capital, that is just like one person like following their passions and like having control over their life.
I think that's really uplifting message.
>> I think we are just in the early innings of this right now.
Like I think I think this explosion is going to grow and and and we'll see larger and larger, you know, projects being built on emergent ideas.
>> Okay, well that's all we have time for today.
Uh Mukunda Madhava, thank you so much for joining us.
It's 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.