Q: You've gone from zero to $50 million ARR in seven months and raised $100 million total — that's exceptionally rare. What's driving this velocity? Is this primarily product-market fit, timing, or something else entirely?
Essentially, we have been able to address this massive gap in the market where non-technical users, people who do not have any coding background or experience, want to build their own software. So far, most of the other tools are great at building demos but fall short of getting you to a production-ready app that can actually be used and monetised. And that's the gap Emergent is filling today, where people can come in, describe what they want, and get a full working app that they can actually use, deploy, and sell.
Q: With 5 million users across 190 countries, who is actually building on Emergent? Can you paint a picture of your core users — are they technical founders, small business owners, or something unexpected?
We are largely seeing small and medium business owners — people running factories, construction businesses, or who have a need for custom software. For them, it was very expensive to procure. They had to go to a dev shop to get software built for anywhere between fifty thousand and a million dollars. Now, they are able to come to the platform and build that on their own on Emergent. Then there are a lot of entrepreneurs who do not have a technical team today but are able to come to Emergent, build their software, and launch their businesses on top of us. There are also many agencies — development agencies, marketing agencies — coming to accelerate their client work on Emergent. Many of our users are people who have never seen a line of code in their life.
Q: The AI code generation space is crowded — GitHub Copilot, Cursor, Replit, v0, and others. You're calling Emergent a 'software creation platform' rather than a coding assistant. What's the fundamental difference in your approach, and why does that matter for your target user?
Most of the dev tools that exist today, like Cursor or Copilot, are built for technical users. They expect the user to understand technical nuances. We are built for non-developers, people who do not have any coding experience, (but) who just understand the problem really well. For them, we have to fundamentally redesign and rethink the whole system. The way we designed the system is a multi-agent system where different agents come at different points of your software building and do a specific job. For example, we have a testing agent that tests your app. We have a design agent that designs your app. Essentially, it acts like your entire dev team in the cloud, which builds really high-quality software. We also manage the entire lifecycle of software. Not just the build, but also how to deploy it, maintain it, and scale it. It's a fully end-to-end platform for you to develop software and launch your business on top of it.
Q: You've achieved product-market fit faster than almost any Indian tech startup by revenue. What did you learn — or unlearn — from previous ventures that enabled this? What would you tell founders chasing similar growth?
For us, technology is at the heart of it. We've spent a lot of time making sure our coding agents are the world's best coding agents. A lot of our effort has gone into solving deep technical problems, which translates into better output for consumers. In every single field in AI right now, there's a lot of depth that needs to be achieved. People should focus on solving hard technical problems to get an edge.
Q: There's debate about whether AI tools democratise software creation or simply shift who captures value. Five years out, do you see a world with 10x more software products, or do you see consolidation where AI platforms themselves become the gatekeepers?
If you look at what we're trying to replace, it's essentially latent demand in the market — people want to build things and lack the tools. There's a big gap between what models provide today and what end users need. That is the gap application layers like us are trying to bridge. LLMs (large language models) are great at giving code output, but you have to make sure they have the right input and context. Once software is built, it has to run in production and be maintained. That's a huge gap we're filling.
Q: With $70 million from Khosla and SoftBank Vision Fund 2, what's the next inflection point you're building toward? Is it about reaching a billion-dollar ARR milestone, expanding into enterprise, or fundamentally changing what 'software development' means?
With this funding, it's largely going into three buckets. One, expanding our team in the US and India across engineering, research, product, and GTM (go-to market). Two, increasing product depth — making our agents better and more reliable, and making deployment seamless. Three, accelerating growth through GTM activities, building a community, tutorials, and learning — removing blockers for users to succeed.
Q: Are you seeing a trend of AI startups moving to San Francisco (SF) for technology access?
The company is headquartered in San Francisco. We have a small office in SF and most of the team is in India right now. We are actively hiring in both the regions. For us, it was natural because my co-founder Madhav is in the US. In the early days, it's easier to build out of SF, with access to research labs and talent. I'm not sure about the trend overall, though. I would encourage people to join AI-native startups now. It's a great time. You fundamentally rethink how you run teams, build software, and processes. The scale is incredible.
Q: Any anecdotes about building the company with your twin Madhav Jha and how do you both coordinate?
He comes from Amazon, I come from engineering. It's an amalgamation of research and engineering that's brought us to this level in platform accuracy and reliability. When we started, many were building copilots and similar tools. We took a leap of faith that AI would exponentially improve. Soon, you can describe your app in plain English and AI agents will build most of it. It's good to see that coming to life. It's a lot of fun. We've always wanted to do a startup together. There's a high level of trust. We complete each other's sentences and align on thought processes, which helps us move fast. We typically collaborate on harder problems and then divide execution. Having almost a replica of yourself is helpful.
 "Emergent co-founder Mukund and Madhav Jha.")