It’s been a week since the Mumbai Edge AI Summit, I’m still processing all the insightful conversations. This remarkable gathering of founders, investors, and builders gave me a clearer understanding of India’s unique position in the global AI race. My most striking takeaway was seeing how traditional business rules are being completely reimagined. Traditional GTM channels are no longer the priority, AI startups are now finding their greatest traction in communities like Reddit and Discord. These platforms offer an unparalleled concentration of early adopters, and direct engagement with these communities has become the most effective way to validate ideas and generate authentic growth.
This reality of the market directly shapes the funding landscape, which is incredibly lopsided right now. There’s a ton of capital chasing AI opportunities, but it’s almost all focused on applications and products. If you’re building a tool that solves a real customer problem, investors are listening. But if you’re trying to build a foundational AI model from the ground up in India, you’ll find the ecosystem is still very capital-constrained for that kind of deep tech venture. So, the resounding advice for founders was crystal clear: focus on building products, not models. The path is simply easier and more fundable. It’s about chasing customers and ensuring your solution solves a real-world need, especially in the application or memory layer of the AI stack.
This is where the idea of an “ecosystem play” becomes so critical. Success in this environment isn’t just about building the smartest model; it’s about connecting your product to the right users, partners, and communities. The companies that are truly taking off are the ones that understand this and are building networks, not just software. This reinforces the idea that in India, ecosystems will be the ultimate winners. Beyond how to build, there was a lot of talk about what to build. Everyone and their cousin is creating a “chat-with-data” solution, but the real, lasting value comes from embedding deep, domain-specific context. If you’re building for the manufacturing industry, your AI can’t just answer questions about a dataset; it needs to understand supply chain nuances, production bottlenecks, and machine-level efficiency. That’s what transforms a generic chatbot into a genuine decision-making tool.
This naturally leads to the question of where India can truly carve out its niche. While building large-scale models is tough, there’s a massive opportunity in the layers that support them. We can win in AI infrastructure. This means creating the tools and platforms that the whole ecosystem can use. This could involve developing hyperspecific search capabilities to help developers find the perfect AI models and agents for their needs, or even building out proprietary Model Context Protocol (MCP) solutions to handle sensitive data or function calling securely. The AI landscape here is nuanced and exciting. Key players to watch out for are Supermemory, Exa and Composio.