Why Sovereign LLMs Are India's Big AI Test

Use cases abound in public service delivery but enterprises are on a wait and watch mode

- Leslie D'Monte & Shouvik Das

For years, the world's most powerful artificial intelligence (AI) models have spoken in English. Trained on sprawling datasets like Wikipedia, Reddit, and Common Crawl, models such as OpenAI's GPT-4, Google's Gemini 2.5, Meta's Llama, Microsoft's Bing AI, and Anthropic's Claude have mastered the dominant global internet dialect. But they all falter when faced with the linguistic diversity of countries like India.

English-dominated AI models can hallucinate (fabricate facts), mistranslate key phrases, or miss the cultural context when prompted in Indian languages.

The concern is also over inclusion. With over 1.4 billion people and 22 official languages, alongside thousands of dialects, India can ill afford to be an afterthought in the AI revolution. The country is expected to total over 500 million non-English internet users by 2030. If AI models can't understand them, the digital divide will only widen.

To address this, the Indian government launched a $1.2 billion IndiaAI Mission in February 2024. One of its central goals: to fund and foster the development of sovereign local language models and small language models (SLMs)—AI systems that are built, trained, and deployed entirely within India, on Indian data.

While large language models (LLMs), such as GPT-4, handle broad tasks, having been trained on copious amounts of data, SLMs are smaller, typically built for specific uses.

In January, the government opened a nationwide call for proposals to develop foundational AI models rooted in Indian languages and datasets. By April, more than 550 pitches had poured in from startups, researchers, and labs eager to build either SLMs or general-purpose LLMs.