Why India Needs Its Own LLMs
Let’s start with something obvious that somehow no one treats as obvious: language isn’t just communication. It’s compression. It’s the lossy encoding of an entire culture’s worldview into syntax, idioms, metaphors, jokes. When you train a large language model, you’re not just training it to predict the next word — you’re training it to predict reality as seen through a particular civilization’s eyes.
Now think about India. 1.4 billion people, hundreds of languages, thousands of dialects, infinite cultural contexts — all stacked on top of one another like some cosmic recursion of Babel. Every village, every caste, every generation has its own dataset. Yet our AI, our so-called “intelligent” systems, are trained mostly on English internet text, scraped from Reddit, Wikipedia, and Twitter. That means our models speak like Silicon Valley. They reason like Silicon Valley. They hallucinate like Silicon Valley.
But India doesn’t think like Silicon Valley.
Indian reasoning — in its philosophical, linguistic, and even mathematical roots — is nonlinear. Sanskrit grammar was formalized two thousand years ago by Pāṇini in what is effectively a programming language: recursive, compositional, modular. Indian logic, from Nyāya to Vedānta, doesn’t separate emotion from inference the way Western logic does. And the cadence of Indian languages — the way they intertwine context, relationship, and implication — is structurally richer than English tokenization ever captured.
When you realize that, it becomes obvious: training India’s future on Western data is like trying to simulate the Vedas on a typewriter.
India needs its own LLMs — not just translated, but native-born. Models that grew up on Indian text, learned from Indian discourse, and internalized Indian ways of thinking. Models that understand that “acha” can mean yes, no, maybe, or “I heard you, but I’m still deciding.” Models that don’t treat Hindi and Tamil and Bengali as “low-resource” languages, but as primary operating systems of thought.
This isn’t just a matter of linguistic justice. It’s a matter of cognitive sovereignty. Whoever trains the models, trains the minds. The next billion users won’t read philosophy; they’ll chat with it. They won’t learn history; they’ll ask it questions. If the models answering those questions are trained on Western priorities, they’ll inherit Western biases — subtle, invisible, but deeply real.
The danger is quiet. You won’t notice when a model starts preferring certain histories, certain heroes, certain tones of reasoning. You’ll just wake up one day in a digital world that speaks your language but not your soul.
So yes, India needs its own LLMs — not as a nationalist project, but as a cultural survival mechanism. Imagine a model that can code-switch between Sanskrit’s precision and Hinglish’s chaos; one that understands the humor of Mumbai and the poetry of Madurai; one that can translate between languages not just semantically, but philosophically.
The technical challenge is immense. We’ll need curated corpora from local sources, multilingual tokenizers that respect morphology, and training pipelines tuned for code-mixed text. We’ll need to rethink benchmarks — because why should “commonsense reasoning” be defined by American common sense?
But the reward is equally immense: a model that’s not just Indian in data, but Indian in cognition. A system that embodies our way of seeing the world — plural, paradoxical, context-dependent, resilient.
So if we build it right, India’s LLMs won’t just serve India. They’ll expand what AI itself can be. They’ll teach the machines that there are other ways to think — that the human mind isn’t a monolith, but a kaleidoscope.
Because in the end, language isn’t just a tool we use to talk to AI. It’s the substrate through which AI learns what it means to be human. And humanity, in all its accents and contradictions, deserves to be represented.
That’s why India needs its own LLMs. Not to compete with the West. To complete the story of intelligence.