Subvolts take IBM exec Amith Singhee details India's AI race, focusing on infrastructure, talent, and global model improvements. Apr 24, 2026 - Developers
- Learn about IBM's research in foundation models, agentic systems, and continual learning, and how diverse development environments foster better AI.
- Business
- Understand how India's growing AI capabilities and IBM's hybrid cloud AI deployment strategies can impact enterprise solutions globally.
This interview provides an insider's perspective on a major country's AI development and its impact on global research.
#338 Amith Singhee: Can India Catch Up in AI? IBM's Amith Singhee on What It Will Take
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Craig Smith interviews Amith Singhee of IBM Research India on India's AI progress. Singhee discusses why India has lagged in AI development, citing a lack of investment and infrastructure. He explains how this is changing and details IBM's research in foundation models, hybrid cloud AI, agentic systems, and quantum computing. Singhee also highlights how developing AI with fewer resources and more linguistic diversity can improve global models.
India has smart people but lacked AI tools. Now, IBM's Amith Singhee explains how India is building better AI by working with less data and more languages, which helps make AI smarter for everyone. This approach forces engineers to create more adaptable and capable AI systems.
This episode offers a ground-level view of India's AI potential and challenges from an IBM executive. It's for anyone interested in global AI development, particularly those tracking how emerging markets are contributing to the field. The discussion on foundation models, agentic systems, and continual learning connects to ongoing work at major AI labs like Google Gemini, OpenAI, and Anthropic, showing how diverse development environments can lead to more robust AI.
- HostCraig S. Smith
- GuestAmith SingheeDirector of IBM Research India and CTO of IBM India and South Asia, IBM
- Guest: Amith Singhee, Director of IBM Research India and CTO of IBM India and South Asia
- Show: Eye on AI
- Episode Title: #338 Amith Singhee: Can India Catch Up in AI? IBM's Amith Singhee on What It Will Take
- Length: 47 minutes
- IBM Research India Presence: 27 years
- Research Areas: Foundation models, hybrid cloud AI deployment, agentic systems, quantum computing
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
- foundation models
- Amith Singhee
- Large AI models trained on vast amounts of data that can be adapted for many tasks.
- agentic systems
- Amith Singhee
- AI that can act independently to achieve goals, often involving planning and decision making.
- catastrophic forgetting
- Amith Singhee
- When an AI model loses previously learned information after learning new information.
- continual learning
- Amith Singhee
- AI that can learn new information without forgetting what it already knows.
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WithAndrii Hrytseniuk
Questions people ask
- Why has India been slow to compete in AI?
- India has historically lacked the necessary investment, compute infrastructure, and coordinated institutional intent to match its engineering talent in AI development.
- How can developing AI in India benefit global AI?
- Working with less data, less compute, and greater linguistic diversity forces better engineering, making IBM's models more generalizable and robust for worldwide use.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Craig S. Smith.