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If you look at like last 30 years, like most of the economic gain in the world has come from software companies.
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If you remove all the software companies from you know, NASDAQ and and S&P, you'll see it's been just a flat line.
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And and we started thinking, okay, what if we could bring this power to almost everybody in the world? >> Welcome.
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Super excited to be here. What a crowd.
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So, Mulchand, um maybe not everybody knows what a merchant is and also like what a big deal it is.
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[clears throat] For those who don't know, a merchant is one of the fastest AI growing is the fastest growing AI companies in the world and really I would say one of the first truly AI native companies in India to get to real scale. >> Right. Right.
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>> Um and so you're going to get to hear from I really see you as like a pioneer of a next generation of startups coming out of India and we you're going to get to hear how how he's done it.
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Um to start with, maybe you can just tell everybody what a merchant is.
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>> Yeah, so I mean thanks for inviting me.
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I'm super excited to be here.
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I can't imagine like so many people coming to the school and the whole energy in India about the whole YC trip has been amazing.
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I was at IIT Delhi a couple of days back, same energy.
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So, super excited to be here.
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Emergent is a platform that allows anybody without any programming knowledge to be able to build software that you can actually ship.
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Uh that your users can use, that you can monetize.
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Uh essentially we're riding on this whole wave of coding becoming easier with AI.
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Uh and when we started our journey, we actually started off as a research lab building coding agents.
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Um became world number one on SweetBench, which is the benchmark for all of the coding agent.
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Uh it was just a four people team which actually got us there and then we started thinking about like, hey, what would happen in the world if we can democratize coding for everybody?
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Um and me being a programmer, Madhav who's my co-founder, who's my twin brother, um he both of us have been programming since age 12 um and super super passionate about uh you know, programming and um, of the things that we realized that if you look at like last 30 years, like most of the economic gain in the world has come from software companies.
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If you remove all the software companies from, um, you know, Nasdaq and and S&P, you'll see it's been just a flat line.
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And and beside thinking, okay, what if we can bring this power to almost everybody in the world.
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Like there are billion people with so many ideas.
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So many ideas just die because you do not have an access to sort of bring them to life.
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And and that was the mission that we started with.
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Uh, today we have more than 8 and a half million people, uh, who are using the platform.
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More than 10 million apps have been built.
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Uh, we recently crossed $100 million in annualized run rate.
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Uh, today one of the fastest growing startups in the world and and the reason is that we are able to allow people to actually really ship what they dream.
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And it's as easy as just chatting with your agent and we take care of everything from hosting, deployment, maintenance of the product, um, and truly unlocking the power of, you know, bringing an idea to life with just chatting with your agent.
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>> How long since you launched the current version of the product?
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>> Yeah, we launched about 9 months back. >> 9 months, okay.
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So, keep in mind, this is basically a 9-month-old company.
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Tell us like about the scale that you're operating at just 9 months in.
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>> Yeah, so we have, uh, close to about 8 and a half million users on the platform, um, and um, we are we are well over 100 million in annualized, uh, revenue run rate.
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And again, like I think the latent demand in the market is is really really high.
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People There are a lot of people who want to, uh, build software and and so far have not been able to have the access to these tools.
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And platform like ours truly enables them to ship, you know, an idea that they have had in mind.
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A lot of our users are actually entrepreneurs who do not have a, uh, tech team and have been sort of handicapped by access to technology and and now are able to build. >> Who are your users?
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And also, where are your users?
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>> Yeah, so we, uh, have users all across the globe in 190 countries.
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Uh, when I started when I actually, like just to give you a little bit of background, right?
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When I came to India in 2014, I was before that I was in Google in in US.
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And I've always had this thought that hey, why is there no Google from India?
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Why is there no Facebook from India?
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We have so much talent, so much engineering talent.
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In fact, you look at the top leadership of all of these companies, you know, like Microsoft, Google, you know, there are sort of Indian folks who have sort of gone there, right?
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And I've always wondered why is there no sort of technology first global company from India, right?
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And so when I was after Dunzo when I was thinking what what to do next, like one of the things that that I had in back of my mind was that I truly want to build a global company from India, like just like Facebook and Google.
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And today we have people have been using us over 190 countries.
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And most of the like revenue comes from US and Europe.
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India accounts for about 10% of our revenue.
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And but yeah, audience is fully global.
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>> And I'm not sure the people know, but before you started a merchant, you started another company that I'm sure they all know called Dunzo, which is like a really big deal.
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You raised what, like a half a billion dollars?
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And it was like a this is a huge company. Yeah.
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>> Yeah, I'm sure in Bangalore I think a lot of people would know us.
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You know, we were pretty popular in Bangalore.
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We like um you know, at our peak we were one of the most loved consumer brands in the country.
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Even today when people ship something they say hey, Dunzo it and and almost became a verb in the country.
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At peak we were doing about 10 million monthly orders.
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We were one of the first people to start the trend of quick commerce in the country.
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The 10-minute delivery trend.
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You know, it was it was a pretty different journey.
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Like I was solving problems which were very operational nature.
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Also like last mile logistics, you know, how do you sort of set the dark dark store network.
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Um and the lesson that you know, like I I would say was applicable there and is applicable here as well is be picked up to solve the hard problems.
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When we started Dunzo, there were about um 87 companies which were doing exactly the same thing, right?
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Because we had It was very simple.
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You could just WhatsApp us and and we would you know, we were kind of like a concierge on WhatsApp.
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So, it was super easy to get started, but I think the hard part was the last mile.
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Like, how do you sort of really make sure the the end consumer actually gets the product, the product is delivered in the right state, um and and we chose to sort of do that.
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You know, we were actually doing deliveries ourselves early on.
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Like, I had you know, a bike and a car and I would just um in the night get an order, I would I would jump on a bike myself and and go and deliver.
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And I think early days just doing things yourself and and this is one of the YC mantra, doing things that don't scale, right?
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Really really helps you get close to the customer, understand the real pain point, whether there's a value or not.
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Um and I think like uh just just you know, being a customer yourself or doing things for the customer really really helps.
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>> Can can we actually go back in time a bit and talk a little bit about your personal background?
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I learned just a couple of days ago that a merchant is actually not just your second company, but your fifth startup.
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This guy's actually started five startups. Um >> Yeah. >> Tell us like >> Yeah.
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>> Um Maybe tell us a bit about your early career, where you grew up and went to school, coming to the US, and just sort of like getting started.
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>> Yeah, so I actually like grew up in a very uh I would say middle class upper middle class family. My dad is an engineer.
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Um and um obviously like uh got into engineering college, um did my engineering.
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Um always had this uh um idea that I want to do something of my own.
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I actually very early on saw a lot of videos of Steve Jobs and was like really really inspired.
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Um I mean, I I saw him him launching the first iPhone in 2007, and that was the moment like you know, I thought oh, I want to bring something to the world uh you know, in in similar fashion.
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And in fact, I went to uh Spain for an internship in 2008, bought an iPhone.
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I mean, it didn't work in India, but I just bought it because I liked it so much.
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Just bought it as a souvenir for myself.
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Try to hack it to make it work.
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And then 2009 is when I went to US to do my PhD.
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Um and then did an internship at Google.
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I liked it so much and whatever research I was going to do Google had actually done that research already 2 years back.
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So I thought there was no point.
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So I dropped out of the PhD program, joined Google, was in the search ranking team.
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There was a 50-people team that controlled all of Google search ranking.
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I was the youngest person in that team.
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So I got a lot of liberty to sort of, you know, question a lot of things because I was a young person who could just just challenge the system.
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And at that time Google was very anti-machine learning.
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Like they didn't want to like have machine learning in search.
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And I was a machine learning engineer.
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So so I I got a lot of, you know, leeway in terms of asking a lot of questions saying, "Hey, like why are we not using machine learning here?"
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Eventually like got to push some of the biggest changes in search ranking when I was there for for a couple of years.
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Then got bitten by the startup bug.
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Left that Google, started a company which was trying to build a group education platform where you can actually, you know, bring a group group class together. Raised a bunch of money.
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Eventually like we pivoted into a P2P software company and realized that my passion was not that.
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I really wanted to sort of solve education, wanted to build something consumer first.
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So returned the money, shut down that startup.
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Started another company into sort of habit creation.
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How do you sort of help people form better habits? Same time got married.
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My wife didn't want to move to US.
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So I moved back to India.
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And I thought I could do startup from anywhere.
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And I had an engineering team in New York.
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I was I was working from India.
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But realized it the hard way.
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It's really hard to coordinate, you know, without at that time. So gave that up.
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And a lot of things that sort of has stuck with me like since the beginning has been that and which is sort of, you know, like over time I have sort of realized to, you know, like do more of is just trust my intuition more.
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And and So So, even with Dunzo, like I started with this personal problem that when I moved to Bangalore, like there were too many things to be done.
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Like I had a car to be serviced, I had, you know, electricity uh to be set up, gas, all of those things.
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And I thought there must be an easier to do this.
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And I just, you know, um started a WhatsApp chat group and gave that number to a lot of my friends saying that, "Hey, if you need anything, just ping me on this group.
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We'll we'll help you uh get that done."
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So, started with a personal pain pain point that, "Hey, like we wanted to sort of, you know, make life more convenient in urban cities."
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Um and I think that has sort of stuck with me throughout, you know, that where whenever I'm I've been able to sort of, you know, um solve a personal pain point, like the feedback always went stronger.
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Um you relate with the problem more deeply, you relate with the customer more deeply.
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And and even with the version, same thing happened, like, you know, me and Maddy, both of us are like idea guys.
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Like we have like thousands of ideas all the time.
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And we wanted to sort of, you know, like automate and get more of these ideas out in the life.
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And and and that's why we sort of started automating uh programming and and got started on the journey.
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>> Dunzo was a huge deal.
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I mean, you scaled a massive company.
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Maybe you can remember some of the some of the stats about how big it got. How many Yeah.
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>> Yeah, so Dunzo, like we had almost a million riders on the ground.
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Uh and we were doing 10 million monthly orders, almost like 5,000 store stores overall.
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Uh yeah, so pretty uh large scale, yeah.
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>> Do you have like lessons that you took away from that experience?
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Either things you think, you know, you did right in order to scale something so large or you mean maybe even things that you would do differently? At the same time.
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>> Yeah, I mean, I think Dunzo, like even though, like, you know, like it was a bitter sweet ending like for us, like the um takeaway was was was like for me was like two two three things.
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One was like solving the hard problem, right?
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We actually, as I said, there were like 87 companies doing the same thing.
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And we really, really uh cared about the consumer a lot.
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Like I remember, you know, earlier back then there was no AI.
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So, so all the chatting had to be manual and every evening there would be a spike in traffic and every single engineer would drop what they were working on, get back on you know on the chat screen, talk to our customers and very early on
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we had this you know like culture where we really deeply cared about the customer like there was there was a customer who wanted to ship something to a different city and we actually put a driver the one of the riders on a plane to send that packet. So we would go that
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So we would go that extra mile for every single customer and that's how sort of we we created this genuine love from all the customers.
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Uh second thing I think like one of the things that I learned from like us not being able to sort of scale eventually was I think like focus is really important.
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Like I think for us like dark store wasn't really working and working really well, but at that point we were doing like 10 other things.
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Like we were doing a marketplace model, we were doing pick up and drop, we were doing you know like bunch of those things.
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Um so I think like us like knowing that hey this is working, let's double down on this model would have really really helped.
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Um but eventually I think like I I just see this as a series of you know like me being a builder, you know you know just just as a stepping stone to to do something bigger. Yeah.
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>> Okay, so you worked on Dunzo for a bunch of years, you scaled it to this really huge company.
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It must have been a very intense experience running a you know like atoms based business where all kinds of things go wrong every day, I'm sure. >> Very very very hard.
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I mean I mean we had we had a team called watchtower which would watch over every single order and it it was almost like a war room.
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You were in a war room continuously because every day operational things break pretty pretty often. Yeah.
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And lot of that is sort of I borrowed here.
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So the way we sort of run Emugen as well is we monitor all the all the all the all the tasks that are that are getting built, all the software that is getting built and and if something's are breaking we flag that.
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So lot of the operational rigor I've been able to borrow from um you know Dunzo to Emugen as well. Yes.
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>> So in 2023 you've been doing this for a number of years and you left Dunzo.
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Um tell us the story of like leaving Dunzo and then what what Emergent like came out of.
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>> Yeah, I think 2023 like um at one point we thought like Dunzo was too big to fail.
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Um and you know, we had raised $200 million in a recent round.
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And I actually told my co-founder that hey, I think now we are too big to fail. Right?
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And and of course like the story didn't end end that way.
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Um so when I got out in September 2023, I was actually pretty depressed.
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Uh like didn't didn't want to do anything in my life.
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Um and for like first 6 months I was just, you know, um reflecting on hey, what could have we done better?
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Luckily like AI AI was happening at that time.
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So you know, like ChatGPT was just taking off.
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Um GPT-4 had just come out.
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Uh so I think it was it was a little bit easy for us to sort of building and and sort of building and coding became sort of my escape from from all the, you know, the noise that was there.
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So I would actually spend like 10 12 hours just sitting on my computer tinkering with um all of the things that was coming out.
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The new voice models were coming out.
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You know, people were there were new open source models coming out at that point.
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So I actually got this luxury of 6 months of like just pure tinkering on things that I really liked with no sort of objective in mind.
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Um I I built this like an assistant on my Mac where it could actually talk to me and I I could sort of something very similar to Open Club but very early version of that.
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And I I was just following, you know, like whatever was exciting to me at that point.
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And it became very clear to me very early on that like coding as a space is going to be one that's going to get disrupted very quickly.
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Um and I spent a lot of time in US with my friends with with people at the labs.
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Um but I think it was just pure joy of tinkering, pure joy of just building something without any pressure um that sort of led us to sort of think of this idea, led led us to sort of you know, um build Emergent in some way because all of the insights that we got while tinkering, we were able to apply while we were building the product.
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Um and and uh you know, that really helped.
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And I think just having this um the sense of curiosity and sense of um you know, like when you're you're building things just for the pure joy of it, just for the um you know, because because you want to solve a problem, right?
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I think I think that allows you to go really deep into the problem and bring insights that is otherwise very hard to get.
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>> I I like I kind of love this picture of you.
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You like you just have the super intense experience.
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You build one of the top companies in India. You're burnt out.
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You're basically just like recuperating. >> Yeah.
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>> And in your spare time, cuz you have some time then, you're just like tinkering with the latest model.
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You're just seeing, oh, maybe we could get like ChatGPT to write some code. I don't know.
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>> Yeah, I mean it it was practically like just, you know, like um me I mean, just going back to like in the old times when I was a kid, you know, like I would just pick something new and and play with it.
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And it just felt like the same thing that I was just playing with this new technology.
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And and uh the pace at which uh you know, models were sort of accelerating, that was it was really really fascinating for us to see that.
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And for us to build a lot of deep insight into like how elements are going to progress.
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But for example, like uh when we started Emergent, most of the companies were building uh copilots.
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That That was the fashion.
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That was That was what what every VC uh wanted to hear.
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We in fact went and pitched to like 10 12 VCs, got rejected from most of them.
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Uh and this is, you know, tons of founder who was just at a big company coming out, getting rejected from most VCs.
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Because we told them, "Hey, we're going to automate software engineering."
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And they thought it was crazy.
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Like that, you know, it would the AI is not there yet.
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And but we could see we could see the model are capable, like you know, if you if you just project it out a little bit, um you know, that the steps that they are feeling like could be easily trained back.
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Um so, we we took this very massive view that AI progress is going to be exponential.
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And we will always build in the direction of AI.
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And and that's sort of let us to um sort of think from a problem perspective that, "Hey, let's automate all of software engineering versus piece by piece uh thinking of that."
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So, I I I think having that uh downtime and just that tinkering energy like really really helped us find the way. Yeah.
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>> I I just want to like pull on a thread from this because I think this is really good general advice for everyone in the room.
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Like what what Mukund was doing, we we we have a name for this at Woven by Iiconic.
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We call it living at the edge.
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It's like the models weren't good at writing code yet.
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And when you like pitched to VCs, they were like the models like aren't going to be able to do this.
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And like they weren't quite able to do it yet, but you could tell that they were that like if you if you project it out >> But you could see the sparks, yes. >> Right.
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And like that's where a lot of the best startup ideas come from.
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It's the things that aren't quite possible yet.
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That's maybe a good segue to talk about some of the technical details of Emergent.
17:58
Like if you just go to Emergent, maybe you don't realize the sort of like deep technical foundations that it's built on. Can you talk about that?
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>> Yeah, so I mean we actually you know, like when we started our journey, like most people were building co-pilots.
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We thought we'll build autonomous agents that could do Agents was not even a word then.
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Now it's obviously everywhere, but like we built this multi-agent orchestrated system where you have different agents who which will come in different point of time and and perform different action.
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Like for example, we have an automated testing agent which will test your app.
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We have a design agent that'll design your app.
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All of this is coordinated, you know, through large memory system that we have built which is sort of self-learns.
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Every time a new app gets built on Emergent, like you know, our agents actually extract from that what are the learnable aspects and sort of store it in memory.
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So, every every new app actually getting built on Emergent makes the platform even better.
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And a lot of the energy has gone into us into collecting all of the data that we have now.
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We do a lot of RL on top of that.
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We do some amount of fine-tuning.
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And and but a lot of the things that we have built essentially is all of the infrastructure that we have built ourselves.
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So, we have built all of the coding agent.
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We have built all of the infrastructure.
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For example, we when we started, there was nobody building deep container technology.
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So, we had to invent a lot of the container edge technology ourselves, like for example, we wanted to preserve state so that you could have multiple parallel agents running on the same same snapshot.
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So, we had to invent disk snapshotting, memory snapshotting, all of those things.
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And I think one of the things that you as you said, like you know, living on the edge, you actually discover these problems much early on before, you know, like other others other ecosystem discovers it.
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And often time you'll have to go solve it them yourself.
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Like for example, today like we have multiple different sort of parallel agents that can sort of swarm together and and complete a task.
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Which we think is going to be like like like the future.
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And and what we are observing is that every time a new model comes out comes out, like for example, a new class of model, for example, Opus is a new class of model, like you have to actually delete whatever you have learned so far and sort of reimagine the world from the lens of this new model.
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So, so far like, you know, in 9 months we have already rewritten our system three times.
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And and just just when a when a new model comes out we have to sort of start rethinking that, okay, what are the new possibilities that's going to open up and what what where this model is going to be in 6 months.
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And And one of one of the things I was telling you before that, you know, like that like when we started merging, like one of the biggest challenge was actually that models could not do a good JSON output.
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And like there were like at least 20 or 30 YC companies that were solving the exact same problem, JSON parsing, right?
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And we took this view that, hey, like, you know, like the next model will be able to solve this.
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So, let's we just completely skip that problem, we started building the agent and and sort of, you know, went on the journey.
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So, I think living on the edge and just trying to imagine what is possible in next 6 months is really important as you sort of progress through your startup journey.
20:41
>> Um can you talk about beating the benchmark?
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Is that's that's like a core core part core part of the founding story here? >> Yeah.
20:48
So, so one one of the things that like happened when we went to YC was that and this happens with a lot of YC founders that that, you know, like you you come in with a different idea, you sort of, you know, stumble upon a different idea.
21:00
When we actually went to YC, we were building testing agents initially, right?
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And and and and when when sort of we were coming coming from India like we drew drew this on a whiteboard that hey like very soon you'll be able to build build web apps mobile apps you know through AI.
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And we had this diagram that hey like we'll be able to build web app mobile apps on on on this thing.
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We day one we went to our YC partner told them that hey we want to build a consumer app building company and they said okay this this you know like maybe you should think about enterprise this this seems too ambitious.
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Um and for the first like it's a three-month program so for for for three months every week we would have a new idea on the board okay idea of the week is you know let's say AI Zapier and and we'll spend a week sort of you know building that or or tinkering with that.
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Um and eventually like you know every every week we'll have a new idea we would pivoting like crazy.
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And and team was getting frustrated hey like you have a new idea every week what are we going to do?
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Uh so almost just to distract them I actually picked this benchmark sweet bench which is the hardest benchmark at that time and I told them hey like while I figure out what what we're going to build let's just attack this benchmark because you know it allows to solve harder problems and then solve almost send them in that direction.
22:05
It took us three months to sort of crack that benchmark became world number one on that benchmark but that's really set us the foundation for emergent where we were able to build world's best coding agent.
22:13
world's best coding agent. Uh all of the innovation that we sort of have in emergent right now whether it's it's paralyzed test time compute um all of the memory agent to agent communication a lot of those things we were able to discover when when we were on this benchmark and I think like like even
22:25
today I think like um at attaching yourself to a number which which can sort of show you progress is really really good way to sort of you know attack a goal or or go go towards uh building a company because that sort of focuses you into right direction it gives you like a really good feedback in terms of what's happening. Yeah. Yeah.
22:41
>> Yeah it's super super impressive what you guys did beating beating that benchmark before you even really had a startup idea like for what to do around it.
22:50
Um a recurring theme of the talks today has been um this concept of second mover advantage.
22:58
Um you know like Zepto wasn't the first grocery delivery company and Giga wasn't the first AI customer support thing.
23:05
Emergent was also not the first AI website builder to launch.
23:07
When you launched Emergent, there were already a couple of like pretty big players and probably a whole bunch of small ones.
23:13
Um much like I guess your story of starting Dunzo when there were already 80 80 80 similar companies.
23:20
What What What gave you the confidence to launch this anyway even though you weren't the first to the market and how have you been able to carve out such a such a big space for yourself?
23:34
>> Yeah, I mean for us um when we looked at uh the problem space, we realized that like most of the other platforms that were out there like they were mostly focused on front end and building demo ware, right?
23:47
And and that's what like where a lot of these were finding product market fit, right?
23:51
But what we realized was that like users are actually going to want real software to be shipped.
23:56
Um and you know, the problem is far from solved, right?
23:58
Like and we saw that the the expectation that users have versus you know, and I'm sure the same thing is with Giga as well, right?
24:05
Like the expectation that user has like hey, my queries get solved.
24:07
Same expectation our users have that my software should actually work, right?
24:11
When I when I'm prompting something.
24:11
And most of the solutions out there like even though they they were like good at getting started, right?
24:16
They were like really bad at finishing.
24:17
Like you you will not get a working software out of that.
24:20
You will not get a You will not have a real back end.
24:21
You will not have a real databases attached and and so we came to this from from a very different angle saying that hey, if you were to automate all of software engineering, how would you approach the problem?
24:30
We almost built everything ground up.
24:31
Um >> [snorts] >> and we could see like in practice when we sort of ran prompts on all all the platform and asked like we were massively outperforming everybody else in the market, right?
24:40
So that allowed us to sort of really really attack attack the market in a big way.
24:43
But I think again like we we came to this from a very sort of consumer insight that consumers are actually going to want real software that is working and not just prototype and demos and and nobody in the market was solving that.
24:54
Um and there were no good solutions that actually could could take you to the finish line, and that's why sort of we we attacked that.
25:00
And once we had the product like we had to think through GTM, how do we market it?
25:02
Uh, we looked at like which companies are growing really fast, what what what they have done.
25:07
Um, and uh, sort of almost converted our growth into a math problem saying that, hey, how many social views do we need?
25:14
How many like, you know, um, impression do we need?
25:16
How many clicks will we get?
25:18
How many users will we get?
25:20
And at that point we knew, okay, like influencer is a good strategy for us to sort of really really launch because we knew the product is really good, working really well.
25:26
We just need to get in front of as many users as possible, and that's sort of been the growth engine for us.
25:32
>> Where is the Emergent team based, and how do you think about building an AI-native company that targets a global audience, um, here?
25:41
>> Yeah, so most of the team is actually in Bangalore.
25:42
Uh, we have 95% of our team in Bangalore.
25:44
Uh, pretty much built out of India completely.
25:46
We have a very small team in SF.
25:48
We have recently opened a new office in SF, so small team is there.
25:52
And by the way, we are hiring, so if people want to, you know, uh, apply and and work at a strong AI-native company, please write to me mukund@emergent. sh.
26:00
Uh, happy to take a look at that.
26:00
And I think like one of the things that I've I've realized, uh, you know, like and we generally like hire for like learning slope.
26:07
People who are like really really passionate about, you know, uh, solving a problem.
26:10
People who get excited about, uh, you know, solving some of these problems.
26:13
And what we have seen is is that I think like one of the things that separates us right now from from other companies that everybody in the company generally enjoys solving and working with AI, right?
26:22
I think there's this added Of course, the growth is great and and you know, we we get to solve real user problems.
26:26
But I think just the, um, you know, the the complexity of the problem and the possibilities are so much that we generally enjoy, like day-to-day problem solving with AI right now, so that's amazing.
26:37
>> You've had a chance to build two very different companies.
26:41
You built Dunzo in the sort of like first wave of great Indian startups that were building things like Zepto, a lot of like local local local stuff.
26:46
Um, and now you're building Emergent, which is like the part of this like second wave of AI creative companies post chat GPT.
26:56
I'm curious first, what are what are some of your your takeaways from building those two kinds of companies?
27:02
And then second, what would your advice be to folks in the audience who are thinking about where to look for startup ideas and what kind of things to build?
27:11
>> Yeah, I mean I think I mean my realization after building the two companies is that like building a company for for India, a local company versus building a global company is actually exactly same effort.
27:23
You know, it's it's equally hard to build a company in India versus building a global company.
27:26
And so my advice to a lot of people right now is just think global from day one because I mean it's going to be equally hard to build both the startups.
27:32
I mean and and it's kind of like a like a prevalent wisdom that actually starting a harder idea is easier because you can inspire a lot more people to go after a harder problem, right?
27:40
And you can sort of, you know, inspire yourself to to go after these.
27:44
So So I would I would I would recommend that like think global from day one because like now you have the reach, the access, internet is with everyone.
27:50
Technology is a big leveling, you know, for for everyone.
27:53
Everybody has the same access, same technology.
27:56
And and you can actually just reach global customer from day zero from from India today.
28:02
The other thing I would say is that one I think um just following your intuition is really really I mean you'll get a lot of advice, but I think following as a founder following your intuition is actually much better because, you know, like you probably have a better sense of general, you know, what your customer wants, what your customer needs.
28:21
And also I think just thinking big and ambitious, I think whatever you're thinking right now just 10x that, 100x that because I think the next you know, with AI I think a lot of lot of things are changing and and it's it's not a time to sort of attack the floor, it's the time to attack the ceiling. And think really big.
28:36
And the bigger you think the the the I would say the higher probability that you'll get to success.
28:43
>> That's an amazing piece of advice for us to end on.
28:45
Mukund, you're an inspiration to us. Thank you so much. >> Thank you.
28:49
Thank you so much for having me here and and the energy is electric here and I'm looking forward to lot [applause] more of gigas and emergent coming out of India over the next year or so and looking forward to this people here. >> [applause] >> Cheers.