India’s Principal Scientific Advisor, Ajay Sood, has emphasized the critical need for developing indigenous foundational AI models, highlighting that the country’s unique demographics, linguistic diversity, and socio-economic factors require AI solutions tailored to its specific needs. Speaking at a recent technology summit, Sood stressed the importance of AI independence in ensuring that India remains at the forefront of technological innovation while addressing local challenges effectively.

Why India Needs Its Own AI Models

Sood’s statement comes at a time when AI is rapidly transforming industries worldwide. However, most large AI models are developed by global tech giants based in Western countries, primarily trained on datasets that may not accurately represent India’s cultural, linguistic, and economic diversity. This creates gaps in performance when these models are deployed in an Indian context.

Key reasons why India needs its own AI models include:

• Linguistic and Cultural Diversity: With 22 official languages and hundreds of dialects, India requires AI systems that support and understand regional languages beyond just English or Hindi.

• Sector-Specific Needs: AI applications in agriculture, healthcare, and education in India differ significantly from those in Western economies.

• Data Sovereignty: Ensuring data security and privacy by keeping AI models trained on locally sourced data within national boundaries.

• Affordability and Accessibility: AI solutions tailored to Indian businesses and startups can be more cost-effective and widely available.

India’s Growing AI Ecosystem

India is already making strides in AI research and development. Initiatives like Bhashini (an AI-driven language translation project) and efforts by organizations such as IITs, NITI Aayog, and the Ministry of Electronics and IT (MeitY) are helping build an ecosystem for indigenous AI models. Additionally, startups and companies are exploring ways to train AI on vernacular languages, local business data, and diverse consumer behaviors.

Challenges and the Road Ahead

While India has the talent and resources to build its own AI models, challenges such as computing infrastructure, access to high-quality datasets, and funding for AI research need to be addressed. Sood called for greater collaboration between government, academia, and private industry to accelerate AI development in India.

The government is expected to roll out policies and funding schemes to support AI-driven innovation, ensuring that India does not rely solely on imported technology but becomes a global AI leader.

Conclusion

Ajay Sood’s call for foundational AI models tailored to India’s demographics underscores the importance of AI independence for national growth. With the right investments, policies, and collaboration, India has the potential to develop AI solutions that empower industries, enhance governance, and improve lives while positioning itself as a key player in the global AI race.