India’s Leapfrog Moment in AI: Why Building Data and Infrastructure Matters More Than Chasing ChatGPT

India’s Path to a Leapfrog Moment in AI Research: Insights from B Ravindran

Professor B Ravindran, head of the Wadhwani School of Data Science and AI at IIT Madras, offers a clear perspective on India's progress and future in artificial intelligence. He emphasizes that India must prioritize AI penetration, data availability, and infrastructure development instead of merely chasing high-profile models like ChatGPT.

Ravindran highlights that large language models (LLMs) like China’s DeepSeek or OpenAI’s ChatGPT are the results of strategic, long-term investments by governments and institutions. For instance, China’s pursuit of DeepSeek began over a decade ago with deliberate funding and policy support. India’s efforts, in contrast, need a similarly sustained commitment and a focus on building foundational elements.

Focus Areas for India’s AI Advancement

• AI Penetration: Expanding AI usage across sectors and democratizing access to AI tools and knowledge.
• Data Ecosystem: Creating and curating vast, high-quality datasets that reflect the diversity of India’s population and industries.
• Infrastructure: Investing in computational infrastructure and cloud capabilities that support large-scale AI research and deployment.

Rather than chasing after the latest global AI trends or replicating existing models, India can carve a unique path by focusing on practical AI applications that serve its population and industries effectively. Initiatives like BharatGPT, Sarvam, and the India AI Mission are steps in this direction, aiming to build AI solutions tailored for India’s specific needs.

The Importance of Strategic Patience

Ravindran points out that breakthroughs in AI do not happen overnight. Long-term strategic investment and policy frameworks are essential. Countries like China have demonstrated that a decade-long vision can position a nation at the forefront of AI innovation.

India’s AI community, including researchers and policymakers, must align efforts to establish a sustainable ecosystem for AI research and commercialization. This means prioritizing ethical AI practices and ensuring technology benefits are inclusive.

Conclusion

India’s AI future hinges on building strong foundations rather than chasing headline-making AI models. By focusing on data, infrastructure, and broad AI adoption, the country can create its own leapfrog moment in AI research.

For professionals interested in enhancing their AI skills relevant to India’s AI landscape, exploring curated AI courses can provide practical support. Resources like Complete AI Training’s latest AI courses offer focused learning paths that align with emerging AI needs.