What if the country that trains the world's engineers finally built the infrastructure to match its talent?

In this episode of Eye on AI, Craig Smith sits down with Amith Singhee, Director of IBM Research India and CTO of IBM India and South Asia, to explore where India actually stands in the global AI race and what it will take to close the gap.

Amith gives an honest, ground-level assessment of why India has been slow to compete. The talent has always been there. But until recently, the investment, the compute infrastructure, and the institutional intent hadn't come together in a sustained, coordinated way. That's changing, and Amith explains exactly what's different now.

He walks through IBM Research India's 27-year presence in the country, the research it's doing on foundation models, hybrid cloud AI deployment, agentic systems, and quantum computing. He also explains why building AI from India doesn't just help India. Working with less data, less compute, and more linguistic diversity forces better engineering and makes IBM's models more generalizable for the entire world.

We also get deep into the technical frontier. Why catastrophic forgetting is one of the key unsolved problems standing between current AI and anything more capable. How IBM is already shipping continual learning in practice through its COBOL modernization tools, helping enterprises decode decades of legacy code before the engineers who wrote it are gone. And why agentic AI, for all the hype, still has a mountain of unglamorous enterprise engineering left to climb before it becomes truly reliable.

Plus, what Amith would tell an 18-year-old engineer in India today about what skills will actually matter in an AI-driven world.

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(00:00) Introduction and Amith Singhee's Background

(06:26) Why IBM Set Up Research in India

(11:45) Can India Compete in AI

(15:18) How IBM Collaborates With Indian Universities

(19:25) Why India Has Been Slow in AI

(24:50) IBM's Hybrid Cloud AI Research Focus

(27:34) How Data Scarcity in India Makes Better AI

(31:18) Fine-Tuning Models Without Losing General Knowledge

(35:03) Continual Learning and Catastrophic Forgetting

(38:25) COBOL and Legacy Code Modernization

(42:11) Agentic AI Hype vs Enterprise Reality

(48:09) What Young Engineers Should Study Today