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.