Building A Global AI Startup From India

**Mukund Jha** (0:00)
So I think now we are just truly seeing this unlock where people who were really close to the problem, domain expert, but have been blocked by technology barrier to really express themselves, are using Emergent to build these things out.

**SPEAKER_2** (0:12)
There's just so much focus on, AI is gonna replace jobs, knowledge work is going away, what's that gonna mean for employment and civil unrest? But no one's really talking about the fact that actually, if you have some agency of interest, you wanna start your own business and have autonomy over your life, obviously you are empowering that at scale.
Welcome back to another episode of The Lightcone. Unfortunately, Gary got called to jury duty and can't be here with us today, but we are really excited to be joined by Mukund and Madhav Jha. They're both twin brothers and founders of Emergent, which went through YC in summer 2024 Emergent's a platform that lets anyone build and ship production-ready software using AI agents. You guys are actually one of the fastest-growing companies I believe YC's ever funded. I mean, the statistics you were telling us were mind-blowing. You have in eight months since launch, seven million apps have been built with Emergent. Walk us through this incredible growth you're seeing actually. When did that hit a real inflection point and how did that feel for you guys?

**Mukund Jha** (1:19)
Both are twin brothers. We actually started programming when we were age 12 Both of us came to the US to do our PhDs. I dropped out of the PhD program, joined Google, and Maddy was in Zenifest, then went on to start the deep learning team at Amazon. We've been meaning to do a startup together for a long time, and before this, I was running a startup in India called Dunzo, which was a hyperlocal quick commerce company.

**SPEAKER_2** (1:42)
Dunzo was a big company actually, right?

**Mukund Jha** (1:43)
It was really big, and we are almost a world in India. When people ship things, they say, Dunzo it. I was managing a really large team of 300 engineers, and we have been 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.
When we started looking at what we want to build in AI, that was the first idea we had.

**SPEAKER_2** (2:08)
What year was this?

**Mukund Jha** (2:09)
This was 23 and. When we applied to YC, we applied with this idea of automating software testing. That was the first idea. In fact, we went to a lot of VCs with this idea. They thought it was too crazy. Now looking back, it almost looks funny. We applied to YC with this idea, and when we were building this testing agents, we realized that if you can solve for verification, which is essentially, you can solve the testing part, you can actually automate all the software engineering. That was our key insight, that verification is the loop which keeps agent running for a longer period of time. That's when we pivoted to looking at general coding agent as a space, and we started building general coding agent.

**SPEAKER_2** (2:49)
This takes us into 2024

**Mukund Jha** (2:50)
This would be 2024

**SPEAKER_2** (2:52)
Tell us what the landscape looked like. How big was Lovable at this point?

**Mukund Jha** (2:57)
Nobody had started. Lovable had not started. I think Cursor was just getting started. And very, very early. I think Devon had just come out. So really, really early.
And we looked at this benchmark called Sweet Bench, which is essentially a benchmark. Now it's saturated, but at that point of time, that was the benchmark where all of the coding agents were getting measured on. And we took on this challenge of becoming number one on that benchmark. And we sort of packed ourselves in a room, four of us, and said, OK, let's just look at this benchmark. How do we crack it? That sort of set the foundation for Emergent. And we built Soda Coding Agents, which became world number one on Swybench in two months of time. And that was the time when we discovered all of the fundamental truths about building with LN and building with agents.

**SPEAKER_2** (3:36)
Your intended users at this point were presumably engineers.

**Mukund Jha** (3:39)
Yeah, at that point, we were purely just a research company, just building coding agents. We were not thinking about a product. There was a time when we invented the multi-agent system, we invented memory, we invented how do we do agent-to-agent communication, how do you scale up test-time compute. A lot of those things which were coming out, we would discover something and we'll see three months later something come out in a paper. That set the foundation for us to do this.