How Emergent is making app building more accessible with Claude

1. MJMukund Jha0:00

   We are building for like small businesses today, right? Small businesses globally account for seventy percent of employment, accounts for like fifty percent of GDP of the world, right? But they've never had the tools to sort of really express themselves because, like, there are a million niches in small businesses. But for the first time with AI, you can actually serve all of these million niches at zero marginal cost, and that's what we're trying to enable.

2. CRCarly Ryan0:21

   Hi, my name is Carly, and I'm on the Applied AI team here at Anthropic, specifically working with startups. Today, Mukund has joined me. He is the CEO and co-founder of Emergent. So Mukund, let's bring it back to the beginning. Emergent's growth has been one of the standout stories of this year. Why don't you walk me through your journey with YC, some of your pivots, and how you landed on the product that you have today?

3. MJMukund Jha0:42

   Thank you for having me here. Super excited to be here. So I'm doing this startup with my twin brother. Madhav and I actually started programming at age twelve.

4. CRCarly Ryan0:49

   Love it.

5. MJMukund Jha0:49

   And we have been-

6. CRCarly Ryan0:49

   We love, we love sibling founders here.

7. MJMukund Jha0:51

   [laughs] Totally. And we actually, like, have been obsessed with this problem of automating software engineering from the beginning. Before this, I was running a startup called Dunzo where I had a five hundred people engineering team. And when we entered YC, our first sort of insight was to automate software testing, because I'd seen in, in at Dunzo that how software testing was a bot-bottleneck for shipping software fast. And we started with this problem of like how, hey, how do we automate all of the software testing, uh, including mobile apps, web apps? And when we sort of started building that out, we realized that to automate testing, we had to build, you know, our own container technology, our own coding agents.

8. CRCarly Ryan1:25

   Yeah.

9. MJMukund Jha1:25

   And we stumbled upon this insight that if you can actually solve for the verification loop, you can actually make agents run longer. On the day one, we told our YC partners that, "Hey, like testing is great, but you know, we want to now build general coding agents." We were like almost like a applied research lab working, just building, you know, uh, high production grade quality agents. Invented a bunch of things like test time compute, how do we sort of scale the memory across agents, how do we solve our agent-to-agent communication. We were one of the first team to productionize multi-agent systems. When we launched, our first approach was to go to enterprise.

10. CRCarly Ryan1:56

    Yeah.

11. MJMukund Jha1:56

    We had an enterprise customer, started working with them, but realized that the adoption in enterprise is gonna be slow. At the same time, we started using these internal coding agents to build everything internally. In fact, like there were like few non-technical people on the team who were using it more than developers were at that point.

12. CRCarly Ryan2:09

    Hmm.

13. MJMukund Jha2:10

    And we truly realized that the power is actually to democratize software engineering for everyone.

14. CRCarly Ryan2:14

    Yeah.

15. MJMukund Jha2:14

    And that's when we, in June, launched this almost like a research preview, and that time agentic coding was not that popular. And when we launched, it just took off and, you know, we were actually surprised. You know, everybody else at that time was building for front-end heavy applications.

16. CRCarly Ryan2:28

    Hmm.

17. MJMukund Jha2:28

    They were building for more demo kind of things. And we had fundamentally approached the problem thinking that what would it take for actually anybody to ship production-ready software that will actually have real use cases, will have business-critical apps running. And our insight was to sort of automate all of this testing, linting, deployment, security, um, how do you solve for like code reviews. And that actually like really, really resonated with users because most of the users that who are coming to Emergent today are serious builders. These are small businesses, entrepreneurs who actually want to see an economic value from the software they're getting built.

18. CRCarly Ryan3:01

    Yeah. That's awesome. So it's like basically democratizing, it's taking natural language, it's for non-software engineers, but it's also building durable products.

19. MJMukund Jha3:10

    Right.

20. CRCarly Ryan3:10

    It's not just for prototyping. It's really cool. So you've been building on Claude since the really early days.

21. MJMukund Jha3:15

    Right.

22. CRCarly Ryan3:15

    We met about a year ago. What made that the default choice, and what's kept that all the way through? Why have you stayed on Claude?

23. MJMukund Jha3:21

    Yeah, I mean, I think we were almost lucky to get started when Sonnet came out.

24. CRCarly Ryan3:24

    Yeah.

25. MJMukund Jha3:25

    Uh, and, um, and I think in AI, I think every- everybody needs to sort of imagine the world six months ahead and build for that versus building for what's available today. Imagine the world from the lens of what is available and what, what's gonna come next, right? So, uh, people who would have started with the previous generation of models-

26. CRCarly Ryan3:39

    Yep

27. MJMukund Jha3:40

    ... would be solving a different problem versus us, because for the first time, like, I think Sonnet had really good instruction following so that you could actually really imagine what long-running agents would look like. It was really good at programming. It was really good at front end as well. Um, and so that actually like really gave us, you know, a little bit of a futuristic view of what the world of programming could look like and what could agentic programming look like. And our users are actually like comparing us against a dev shop, right?

28. CRCarly Ryan4:03

    Yeah.

29. MJMukund Jha4:03

    So they're not comparing us against like an IDE or a tool. And to them, outcome matters a lot, right?

30. CRCarly Ryan4:09

    Yeah.

Episode duration: 16:38