Why build Applied AI in India?

The Global AI Race

The AI race isn't just about building bigger models. It's about creating solutions that work for the world, not just Silicon Valley. And if you want to build for the world, there's no better place to start than India.

India: A World in Miniature

India is like a miniature version of the developing world. Hundreds of languages, diverse cultures, and economic conditions ranging from high-tech hubs to rural villages. If you can make AI work here, you can make it work anywhere.

But here's the key: diversity in AI isn't just nice to have, it's essential. Monoculture in AI is as vulnerable as it is in agriculture. India's diversity is its superpower - the antidote to the bias plaguing many AI systems.

Think about it. Most AI models are trained on data from a narrow slice of humanity. They understand Silicon Valley engineers, not farmers in Bihar. By building AI in India, we're creating more robust, adaptable AI that can truly serve global needs.

Problems as Opportunities

India's real strength isn't just its diversity - it's its problems. India faces challenges the developed world solved decades ago, alongside issues that stump even the most advanced nations. Healthcare, education, agriculture, urban planning - these are battlegrounds where good tech can make a real difference.

Tech giants are in India, sure. But they're mostly treating it as a market to sell to, not a laboratory to learn from. They're adapting Western solutions. We're doing the opposite: building for India, then scaling globally.

The Application Layer: Where the Magic Happens

The real opportunity isn't in training bigger language models. It's in the application layer - taking open-source models and applying them to real-world problems in novel ways. In doing so, we generate proprietary data to fine-tune and advance these models further.

This approach is capital-efficient. We don't need billions for compute. We can move fast, experiment rapidly, and iterate based on real-world feedback. As AI capabilities commoditize, our edge comes from understanding the problem space better than anyone else.

Turning Challenges into Advantages

Challenges? Of course. Infrastructure can be spotty. Talent sometimes flows outward. Data privacy laws are still evolving. But these aren't roadblocks; they're opportunities to innovate. Constrained environments breed creative solutions valuable not just in India, but in every emerging market.

Creating Markets, Not Just Serving Them

Some argue India isn't ready for advanced AI. That misses the point. We're not just serving existing demand; we're creating new markets. As we solve real problems, we'll create our own demand. Building Applied AI in India isn't just a business opportunity. It's a chance to ensure AI serves the many, not the few. It's creating solutions that are robust, scalable, and globally relevant from day one.

The Real Question

The question isn't "Why build Applied AI in India?" It's "Why wouldn't you?" To lead in AI, solve hard problems. Serve underserved markets. Think globally from the start.

This is how revolutions happen. Not by building slightly better versions of existing things, but by reimagining what's possible. And there's no better place to do that than India.