Indian-origin AI startup Emergent has vaulted into the global spotlight after raising $70 million in a Series B funding round led by Khosla Ventures and SoftBank Vision Fund 2—a deal that signals both renewed investor confidence in Indian AI startups and the rapid rise of a new category of software creation known as vibe-coding.

The round also saw participation from Prosus, Lightspeed Venture Partners, Together Fund, and Y Combinator, pushing Emergent’s total funding to nearly $100 million within just seven months of launching its platform. The company did not disclose its valuation.

What makes this funding particularly notable is SoftBank’s involvement. The Japanese conglomerate has not led a fresh investment in an Indian startup in over three years, largely limiting itself to follow-on rounds. Its return—alongside Khosla Ventures, one of Silicon Valley’s most influential early-stage backers—underscores the belief that Emergent could become foundational infrastructure for AI-native software development.

Within months of launch, Emergent claims it has crossed 5 million users across more than 190 countries, scaled to $50 million in annual recurring revenue (ARR) in just seven months, and is on track to surpass $100 million ARR by April 2026—one of the fastest revenue ramps seen in the global AI applications space.

The Founders Behind Emergent: Twin Brothers With Deep Tech Roots

Emergent was founded in July 2024 by Mukund Jha (CEO) and his twin brother Madhav Jha (CTO), Indian-origin entrepreneurs based in the San Francisco Bay Area. Together, they represent a modern startup archetype: US-based global ambition, deep research credentials, and engineering scale built across continents.

Mukund Jha’s journey spans both India’s high-pressure startup ecosystem and Silicon Valley’s venture-backed scale culture. He studied engineering at Motilal Nehru National Institute of Technology (MNNIT) before attending Columbia University’s engineering school. He is also widely known as the co-founder of Dunzo, the Indian hyperlocal delivery and quick-commerce startup that later shut down—an experience that exposed him to the realities of rapid scaling, cost structures, and execution under pressure.

Madhav Jha, by contrast, brings a research-first pedigree. According to Y Combinator’s company profile, he earned a PhD in Theoretical Computer Science from Penn State University and later worked as a John von Neumann postdoctoral fellow at Sandia National Laboratories. His background in autonomous systems and deep learning underpins Emergent’s technical architecture.

The brothers had been experimenting with automating software engineering workflows years before “AI agents” became a mainstream buzzword. Their shared frustration was simple: while ideas were abundant, turning them into production-grade software remained slow, expensive, and gated by technical expertise.

What Is Vibe-Coding—and Why Investors Are Paying Attention

At the heart of Emergent’s rise is vibe-coding, a term increasingly used to describe an AI-native approach to software creation.

Instead of writing code line by line, users describe what they want in natural language, and AI systems generate, refine, and deploy the application. Builders guide outcomes with instructions such as:

• “Make it cleaner”

• “Add a dashboard”

• “Connect payments”

• “Change the design”

• “Ship a deployable version”

The shift is profound: people move from writing syntax to directing systems that build software autonomously.

Emergent positions itself as an “idea-to-app” platform, using specialised AI agents to handle design, backend development, APIs, testing, debugging, deployment, authentication, databases, and payments. The goal is to drastically compress the time between concept and a monetisable product.

This has made the platform attractive to a wide range of users, including:

• Founders building MVPs

• Solo creators launching side projects

• Small teams trying to outpace traditional development cycles

• Businesses creating internal tools without heavy engineering overhead

The company operates in one of the most competitive AI categories today: software that creates software. But Emergent’s emphasis on production readiness—rather than demos or prototypes—has helped it stand out.

How Emergent Works: AI Agents as a Full Development Team

Emergent’s platform behaves like an entire software development team, orchestrated through conversation.

Its system deploys multiple specialised AI agents, each responsible for distinct engineering tasks—system design, backend logic, frontend interfaces, testing, debugging, iteration, and deployment. Users interact with the platform conversationally, refining their product in real time.

The platform runs on a hybrid AI stack, combining:

• Proprietary, fine-tuned in-house models for specialised engineering tasks

• Integrations with leading large language models from OpenAI, Anthropic, and Google Gemini

This approach allows Emergent to balance speed, reliability, and scale, while maintaining control over mission-critical engineering workflows.

According to Mukund Jha, this focus on production-grade reliability is what differentiates Emergent from many other vibe-coding tools.

“To build real businesses, you need reliability, testing, debugging, and infrastructure—not just pretty frontends,” he explains.

From Research to Real-World Validation

The brothers’ early research reached a major milestone in November 2024, when their system topped SWE-bench, a globally recognised benchmark for evaluating autonomous coding agents. The achievement gave them the confidence to move from experimentation to product—and to launch Emergent publicly.

Since then, the platform’s adoption has spanned industries and geographies. Emergent reports users ranging from factory owners building custom ERPs and construction firms digitising procurement, to entrepreneurs launching new digital products and agencies delivering client projects at unprecedented speed.

Mukund cites one striking example: a user-built gift card trading platform that crossed half a million views in a single month, signalling that users are not merely experimenting but building revenue-generating businesses.

Why the $70 Million Series B Matters

The size of the funding round is only part of the story. The identities of the investors carry equal weight.

A Khosla Ventures–led round is often interpreted in Silicon Valley as a strong endorsement of category-defining technology. Vinod Khosla, founder of Khosla Ventures, said Emergent is “early in shaping how software gets created and monetized over the next decade—not just the next product cycle,” adding that users are already quick to share their success stories.

SoftBank’s participation, meanwhile, reflects confidence in Emergent’s ability to scale globally. According to Sarthak Misra, Partner at SoftBank Investment Advisers, Emergent is unlocking “a massive wave of entrepreneurship” by removing the technical and capital barriers that have historically limited who could build software.

The round comes just four months after Emergent’s $23 million Series A, which included investments from Lightspeed Venture Partners, Prosus, and others, as well as a strategic investment from the Google AI Futures Fund in December. The rapid progression from Series A to Series B marks one of the fastest A-to-B funding timelines in the AI category.

Expansion Plans: San Francisco, Bengaluru, and Beyond

Emergent plans to deploy the fresh capital across multiple fronts.

A significant portion will go toward expanding research and engineering teams in both San Francisco and Bengaluru, a dual-hub structure that combines US market access with India’s deep engineering talent pool.

“A lot of the investment will go into building and further advancing our products and also advancing our research into coding agents,” Mukund Jha told Reuters.

The company is also strengthening its mobile app builder, developing category-specific workflows such as CRMs, and improving its AI agents to handle increasingly complex, mission-critical software.

Global expansion is another priority, as demand for AI-powered software creation accelerates among entrepreneurs, small businesses, and creators worldwide.

Democratizing Software Creation

At the core of Emergent’s vision is the belief that software creation is undergoing a structural shift.

“Earlier, only people with technical training or capital could turn ideas into real products,” Mukund Jha says. “Emergent flips that model.”

He argues that the hardest part of building AI software today is no longer the technology itself—but keeping up with how fast people want to build.

Emergent reports seeing millions of users building and shipping real businesses, workflows, and products in days, many of whom are generating new sources of income. By lowering barriers to entry, the company believes it is powering one of the most crucial segments of the economy: small businesses and entrepreneurs.

Emergent in the Broader AI Investment Landscape

Emergent’s rise comes amid heightened investor interest in AI startups, particularly those operating at the application layer.

According to Inc42’s Annual Indian Startup Trends Report 2025, Indian AI startups raised nearly $500 million across 63 deals in 2025 alone. Between 2020 and 2025, Indian-origin AI startups raised over $1.8 billion, with nearly 80%—around $1.6 billion—flowing into application-layer ventures.

Investors are increasingly backing companies where AI is embedded deeply into operations and workflows, rather than deployed as a general-purpose add-on. Emergent fits squarely into this trend, positioning itself not just as a tool, but as infrastructure for a new generation of AI-native businesses.

The Road Ahead

For Emergent, the challenge now is turning explosive early momentum into long-term durability. As the vibe-coding category becomes more crowded, expectations around security, reliability, and real-world deployments will rise rapidly.

If Emergent can continue scaling while supporting production-grade applications at global scale, it may not only remain an early leader—but become one of the defining platforms of the AI-native software era.

“It’s still early,” Mukund Jha says. “But if we get this right, building software will stop being a bottleneck—and start being a superpower for anyone with an idea.”

With inputs from agencies

Image Source: Multiple agencies

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