Topic: Artificial intelligence

I’ll be commenting more as a strategist than a technical expert, so my opinions may stem from limited technical depth in the field.
Context:
I really appreciated your take on the steps the Centre is taking to build their own LLMs. You suggested that instead of building from scratch, the government should leverage existing open-source models. You also cited Ola as an example of a company building its own LLMs. Trust me, their vision is far broader than most people realize. Ola is no longer just a cab-sharing company. it’s evolving into a Data and AI company. Let me explain why.

Let’s draw a comparison using western analogies. We often think Ola is analogous to Uber. But a more accurate analogy would be between Ola and Tesla. Why? Around 2021, Ola entered the EV market and is now working in both the EV and AI sectors seeking to build a data network effect similar to what Tesla has achieved.

But here’s the twist: unlike Tesla, Ola already operates a massive ride-hailing network, giving it access to real-time motion data at scale. This puts them in a unique position to build and optimize electric vehicle infrastructure while training AI models using fleet-level data leading to personalization, optimization, and predictive capabilities.
India’s population creates an “unfair advantage”, more users, more two-wheelers, and hence more running data to train models on. This explains why they entered two wheeler and not Four Wheeler market. If data is the new oil, why not also build the right engine (model architecture) that aligns with that oil?
If Uber doesn’t adapt to such a data-first model, it risks becoming obsolete. Ola, in contrast, is creating a flywheel: cab-sharing → EV adoption → more data → India-specific model training.

Now to your main point, why not just fine-tune open-source models instead of building new ones?

That’s where architecture comes in.
Fine-tuning open-source models like Mistral or LLaMA gives you some control but not full autonomy. You’re still bound to architectures and training pipelines built for Western contexts. For example:
Most open-source tokenizers are heavily biased toward Latin scripts.

India needs tokenizers that are optimized for Indic languages and its dialects.

Many “open” models (like LLaMA) are not licensed for commercial use, limiting redistribution, monetization, and modification rights.

If we aim to build a long-term AI ecosystem, especially one that supports India’s linguistic and cultural diversity, we need to control the foundation model’s architecture, not just fine-tune someone else’s and remain with scalability limitations.
Full control could be:
Monetizing APIs on your own terms.

Licensing the model to governments, schools, and startups.

Creating a sovereign tech stack for AI.

If a private Indian sector company like Ola recognizes this and is investing in building such infrastructure, shouldn’t the Indian government consider doing the same?