‘Future of AI in India does not rely on its metros but goes beyond’

You often describe your professional life as unfolding in innings. How does that framing shape the way you understand innovation today?

When you live your career in innings, it creates a kind of longitudinal perspective that most people miss. My early years, the first inning, were driven by necessity. I built because I had to, not because I had the luxury of knowledge or sophistication. It was a period defined by raw execution and survival. The second inning was more expansive. I built a global software testing company, learned how scale truly works, experienced the complexities of operational detail, and eventually exited to a German public firm. The acquisition taught me the discipline of enterprise technology, global mindset, and the hard realities of running a B2B company at scale.

Now I’m in my third inning, and it feels completely different. It is no longer about building only for myself, but about enabling others. Through Ideas to Impacts in Pune and through Pentathlon Ventures, I have the privilege of watching founders build their first innings. That vantage point fundamentally shapes how I see innovation today. It makes me realise that innovation is not a single lightning bolt. It is a long continuum where each inning builds on the last. The tools change, whether it was QA automation years ago or AI today, but the foundational rhythm remains the same: understand a real problem, stay close to the customer, and keep refining your craft over years, not months.

You’ve said many times that innovation doesn’t belong only to metros. Why do you believe the future is distributed?

Because I have seen it first-hand, and not as a theory but as lived reality. When I was running my previous company, a surprising pattern emerged. Nearly half of my workforce came from towns and smaller cities outside Pune. These weren’t places anyone thought of as startup hubs, but the talent that emerged from there was extraordinary. They were sharp, grounded, and deeply aware of the problems around them. What they lacked wasn’t ability, it was exposure, access, confidence, and the ecosystem that metros take for granted. As we built the Ideas to Impacts ecosystem, something remarkable started to happen. We saw entrepreneurial energy surfacing in places that are invisible to the mainstream venture capital narrative. Cities like Surat, Nashik, Kolhapur, places that rarely appear on the startup map, began producing founders with immense clarity. One of our recent portfolio companies is from Surat, which almost no one associates with venture capital. But the founder’s understanding of his domain was sharper than half the pitches we see from the traditional hubs. The future is distributed because talent is distributed. What is not distributed is opportunity, access, and capital. And that is slowly beginning to change. As that shifts, you will see that the next wave of meaningful B2B innovation in India will not come only from Bengaluru or Gurgaon. It will come from the quieter corridors of India where understanding of real problems is far deeper.

Do metro and non-metro founders differ in how they approach problem-solving? Has this shaped how Pentathlon evaluates founders?

There is no single archetype, but there are perceptible patterns. Founders from metro cities tend to be more polished. They understand the vocabulary of fundraising, they know how to package a pitch, and they are often better at communicating in investor-friendly language. Founders from smaller cities, on the other hand, often bring a very different strength to the table. They come with raw clarity. They tend to be closer to the problem, to the customer’s world, and to the underlying economics or operational pain.

At Pentathlon, we orient heavily toward substance rather than style. We look for founders who truly understand the domain they are operating in. Founders who can explain not only what the problem is, but why the problem persists, what the customer feels, and what the real levers of improvements are. This depth cannot be faked, and AI cannot manufacture it. It comes from years of wrestling with reality on the ground. If a founder has that depth, we can help them with everything else, presentation, fundraising, scaling, hiring. But depth has no shortcut.

You’ve been vocal about the danger of building AI for the sake of AI. Why is this so risky in today’s environment?

Because AI has made it dangerously easy to create the illusion of progress. You can spin up a prototype using AI. You can generate a pitch deck with AI. You can even write code faster with AI tools. But none of this gets you closer to actual customers. None of this gives you domain understanding. None of this validates whether the problem even needs solving.

When founders begin with technology, we want to build with AI, they often end up searching for problems after the fact. This is the worst possible direction. The tool becomes the hero instead of the problem, and the startup becomes a hammer looking for a nail. If you remove the AI from the startup and the idea collapses, then the idea never had a reason to exist in the first place. That is the core of what I call AI washing. It looks impressive from the outside, but there is no real value underneath.

Real innovation starts from the other side. You begin with the customer, the problem, and the pain point. If AI happens to be the best way to solve it, that’s wonderful. If not, that’s equally fine. AI is simply a tool. A powerful tool, yes, but still just a tool.

You distinguish between AI-first and AI-native startups. Can you expand on this distinction?

This difference is crucial but often misunderstood. An AI-first startup takes an existing workflow or use case and reimagines it using AI. The problem already existed. The workflow already existed. But AI allows you to rethink the process, automate parts of it, or turn a traditionally manual task into an intelligent one. The opportunity lies in redesigning what people already do. AI-native startups are fundamentally different.

These are products or experiences that simply could not exist without AI. Think of something like ChatGPT, without language models, this category doesn’t exist. AI-native startups tend to be more horizontal, more open-ended, and often more B2C in nature. Most Indian founders do not need to pursue AI-native ideas, nor should they feel pressure to. The real opportunity in India lies in AI-first innovation, taking deeply entrenched B2B pain points and using AI to solve them ten times better than before. That’s where India’s strengths truly align.

From your vantage point as an investor and mentor, what defines a truly strong AI startup?

A strong AI startup is not defined by the sophistication of its model or the cleverness of its technical architecture. It is defined by its relationship to the problem it aims to solve. The first and most essential ingredient is deep domain expertise. Without this, AI becomes ornamental. AI systems depend completely on context, and context comes from understanding the domain intimately.

The second ingredient is access to relevant and high-quality enterprise data. This is the real moat in AI. Anyone can use an LLM, but not everyone has access to the proprietary datasets that drive domain-specific intelligence. The third ingredient is the presence of real, validated use cases, not hypothetical scenarios. A startup built on AI must be solving a problem that exists today, not one imagined in a deck.

Finally, the startup must be anchored in a niche where depth is possible. Trying to be everything to everyone rarely works. The most effective AI companies focus on a clearly defined vertical and go unusually deep into it. This depth becomes their competitive edge and their defensibility.

Where do you believe India’s real AI opportunity lies? Hardware? LLMs? Applications?

India’s opportunity is not in competing directly with the global giants in hardware or LLM development. These are capital-intensive arenas requiring billions of dollars and massive talent density. Instead, India’s strength lies in the sheer scale and diversity of its enterprise landscape. Indian companies operate in complex, fragmented environments. They generate unique datasets, deal with multilingual interfaces, and struggle with compliance, logistics, manufacturing, and operations in ways that global companies cannot fully understand. This is where Indian AI startups can build something truly defensible. They can train models on enterprise data that the rest of the world simply does not have access to. They can solve problems that global players don’t even recognise because those problems are peculiar to our markets. This is where India’s AI differentiation will come from, the application layer, the domain layer, and the messy middle of enterprise workflows.

You say India is not a DIY market. How does that shape AI adoption?

India is a mediated economy. In many sectors, people do not adopt products directly. They rely on trusted intermediaries, ASHA workers in healthcare, banking correspondents in finance, VLEs in rural governance, teachers in education, and shopkeepers in commerce. These individuals act as bridges between the system and the citizens. AI in India will scale only when it augments these intermediaries, not bypasses them. Imagine a banking correspondent with an AI assistant helping a rural customer understand loan terms in their local language, or a teacher with an AI-powered tool that gives personalised learning paths. These intermediaries carry trust, and trust is the most valuable currency in India. AI must empower them, not replace them.

What should India’s policy posture be as AI becomes more central to the economy?

India should not attempt to build the next OpenAI. That is a distraction. Instead, India should take inspiration from what worked with India Stack. We need to create an enabling ecosystem, public infrastructure that private companies can build on top of. That means making more datasets accessible in safe and anonymised forms, accelerating the development of Indian- language models, expanding access to compute resources, and creating regulatory sandboxes where startups can experiment without fear. Policy should be about enabling innovation, not dictating its direction. Our strength lies in our ability to create large-scale digital infrastructure that dramatically lowers the cost of innovation. The same philosophy should guide our AI strategy.