This is an interview with Mukund Jha – CEO and co-founder of Emergent
Can you tell us more about your background and what led you to build Emergent?
I grew up in India, deeply curious about technology from a young age. My brother Madhav and I have been builders our whole lives, literally since our dad handed us a C++ CD instead of the game we asked for and told us to make our own. That spirit never left us. When I was scaling engineering at Dunzo, one of India’s largest hyperlocal commerce companies, I saw firsthand that 40–50% of engineering time was consumed by testing. When AI started accelerating, we initially wanted to automate that bottleneck. But during YC, on day one, we told our partner Nico: we want to go further. We want to build a coding agent, the hardest, most exciting problem in the room. That determination became Emergent.
Can you tell us more about Emergent and the problem the company is solving today?
Emergent is a vibe coding platform that lets anyone build full-stack, production-ready apps through natural language with no coding skills or knowledge required. Think about how many people have a brilliant business idea but no way to build it or operate.
A psychologist who also rides horses built a mobile app combining her two passions to help people learn riding using psychological principles. A musician in Denmark launched a gig-booking marketplace. Filmmakers in Barcelona shipped a fundraising platform, and none of them wrote a single line of code. That’s what Emergent does: it removes the barrier between having an idea and making it real.
Why did you believe the market needed a platform supporting non-technical founders and small businesses?
What has tended to happen in software is that companies move upmarket towards enterprise to justify the cost of acquiring each customer, leaving small businesses buying generic, off-the-shelf software and adapting their processes to fit it, whereas with AI we can serve all of those niches at almost zero marginal cost.Our own research found that a third of the operators we interviewed had built something that replaced nothing at all, not a spreadsheet, not a subscription, not an agency, because the tool simply never existed at a price they could pay.
We are taking that beyond app building and into becoming the operating system a business runs on, which is what our agent product, Wingman, is about: a business can build its software with us, keep it running on our infrastructure, and use always-on agents to automate finance, scheduling, lead generation, or managing a social media account. Emergent isn’t just a place to build custom software; it’s where that software lives after launch, supporting the end-to-end creation of a business from idea to launch to daily operation.
We’re seeing major AI companies increasingly focus on small businesses — including Anthropic recently launching Claude for Small Business.
Why is SMB adoption becoming such a critical battleground in AI?
With small businesses accounting for 70% of global employment and 50% of GDP, they are truly the backbone of the economy, but historically, they have been left out of the conversation around AI transformation.
Major AI companies are targeting small businesses because the enterprise market is rapidly saturating and SMBs represent a massive, untapped economic engine. The SMB sector thrives on self-serve, product-led growth where AI can be embedded directly into the everyday software stacks they already use.
By shifting the narrative in which AI moves from a chat window into an operational co-pilot, tech giants are unlocking high-volume subscription revenue while giving teams the enterprise-grade bandwidth they need to survive and scale.
For years, custom software has been out of reach for smaller operators.
How is AI changing the economics of software creation for SMBs specifically?
Custom software used to mean hiring engineers, months of build time, and enterprise-level budgets. That made it a non-starter for most small operators, who were left choosing between generic tools that didn’t quite fit and custom builds they couldn’t afford.
Operators who went out and got a quote from a development agency reported a median price of $20,000, with some quoted as high as $100,000 and timelines stretching past a year. Cost was the single most cited reason they walked away. AI is changing that; a founder can now describe a workflow in plain language and get something working back the same day, no developer required. The expensive part of custom software was always the translation from “what I need” to “working code,” and that’s exactly what AI has gotten good at automating.
That shift matters most for small teams, who don’t need polished systems so much as tools that bend to how they actually work. What used to require a dev team on retainer now just requires asking.
What are the key trends you are seeing with Emergent customer case studies? And what industries are really accelerating their AI adoption?
Most people are building serious apps, either supporting existing businesses or digitising operations. We’re seeing ERP systems, CRMs, inventory management, and logistics apps. Many people are building SaaS applications and AI agents on the platform. We also see entrepreneurs building marketplaces, e-commerce sites, and AI applications.
Our sample of more than 50,000 live, deployed apps spans 28 industries, from healthcare and retail to construction and poultry farming, with roughly eight million people using those apps day to day.
Our deployment success rate has improved, so this is translating into genuine user success. Roughly one in four deployed apps take payments directly, and one in three runs on its own custom domain. We’re seeing numerous success stories, users generating revenue, securing funding, and achieving productivity improvements within organisations. People building internal apps are seeing operational efficiency gains across the board.
What should every non-technical founder understand about AI in 2026 and beyond? What’s next for Emergent?
The biggest shift founders need to understand is that AI stopped being a tool you prompt and started being a teammate you delegate to. A year ago, the value was in faster drafts and quicker answers. Now these systems can hold a queue of work, make decisions, and carry a task through to completion with barely any supervision. Judging AI on how clever its output sounds is the wrong bar in 2026.
The real question is whether it follows through reliably enough to actually own a piece of the business. The second thing worth understanding: technical skill is no longer the gate. You don’t need to know how to code to build something custom for your business; you need to know your workflow well enough to describe it. That’s a real advantage for founders who’ve been running the messy, improvised parts of their business by hand for years. They already know exactly what needs fixing.
Our vision is very deliberately anchored in the small and medium business and the new entrepreneur, and that is a choice we have made. What has tended to happen in software is that companies move upmarket towards enterprise to justify the cost of acquiring each customer, leaving small businesses buying generic, off-the-shelf software and adapting their processes to fit it, whereas with AI we can serve all of those niches at almost zero marginal cost.