#338 Amith Singhee: Can India Catch Up in AI? IBM's Amith Singhee on What It Will Take
Episode
46 min
Read time
2 min
Topics
Career Growth, Productivity, Investing
AI-Generated Summary
Key Takeaways
- ✓India's AI readiness formula: Three elements must converge simultaneously — sustained investment, deep tech talent, and consolidated GPU infrastructure — before India can compete in AI development. As of 2024, the India AI Mission has committed funding, but data center capacity remains in ramp-up, with consolidated clusters of 4,000-plus GPUs still being assembled and activated.
- ✓Enterprise AI deployment gap: Deploying frontier AI models inside regulated enterprises requires far more than model capability. Security, identity authorization, auditability, tool description quality, and on-premises portability all need engineering solutions. IBM's hybrid cloud architecture prioritizes model portability across any cloud or on-premises environment, giving regulated clients like banks control over where AI workloads run.
- ✓COBOL modernization as continual learning testbed: IBM's Watson Code Assistant for COBOL releases updated model versions every four to six weeks, making it a live production environment for continual learning research. The model explains legacy code in plain language, writes new COBOL, and translates COBOL to Java — addressing a critical risk as engineers fluent in legacy languages retire.
- ✓Low-data model customization techniques: When enterprise clients have limited proprietary data for fine-tuning, IBM researchers apply data mixing strategies, curriculum training sequences, and synthetic data generation to prevent catastrophic forgetting while embedding domain-specific knowledge. The goal is maintaining general model capability — keeping benchmark scores above 90 — while adding business-specific skills without full retraining from scratch.
- ✓Career strategy for the AI era: Engineers should prioritize domain fundamentals alongside AI fluency, since hiring managers consistently choose candidates who understand underlying principles and use AI over those who only use AI tools. Beyond skills, the ability to continuously learn and rapidly apply new knowledge is now the core career asset — a shift academic institutions need to structurally support.
What It Covers
IBM Research India Director Amith Singhee examines why India has lagged in AI development despite abundant engineering talent, what conditions must converge for India to compete globally, and how IBM's enterprise-focused AI research — spanning hybrid cloud deployment, Granite LLMs, COBOL modernization, and agentic systems — addresses real-world business constraints.
Key Questions Answered
- •India's AI readiness formula: Three elements must converge simultaneously — sustained investment, deep tech talent, and consolidated GPU infrastructure — before India can compete in AI development. As of 2024, the India AI Mission has committed funding, but data center capacity remains in ramp-up, with consolidated clusters of 4,000-plus GPUs still being assembled and activated.
- •Enterprise AI deployment gap: Deploying frontier AI models inside regulated enterprises requires far more than model capability. Security, identity authorization, auditability, tool description quality, and on-premises portability all need engineering solutions. IBM's hybrid cloud architecture prioritizes model portability across any cloud or on-premises environment, giving regulated clients like banks control over where AI workloads run.
- •COBOL modernization as continual learning testbed: IBM's Watson Code Assistant for COBOL releases updated model versions every four to six weeks, making it a live production environment for continual learning research. The model explains legacy code in plain language, writes new COBOL, and translates COBOL to Java — addressing a critical risk as engineers fluent in legacy languages retire.
- •Low-data model customization techniques: When enterprise clients have limited proprietary data for fine-tuning, IBM researchers apply data mixing strategies, curriculum training sequences, and synthetic data generation to prevent catastrophic forgetting while embedding domain-specific knowledge. The goal is maintaining general model capability — keeping benchmark scores above 90 — while adding business-specific skills without full retraining from scratch.
- •Career strategy for the AI era: Engineers should prioritize domain fundamentals alongside AI fluency, since hiring managers consistently choose candidates who understand underlying principles and use AI over those who only use AI tools. Beyond skills, the ability to continuously learn and rapidly apply new knowledge is now the core career asset — a shift academic institutions need to structurally support.
Notable Moment
Singhee reframes India's AI ambition away from competing for global AI dominance and toward a more achievable near-term goal: becoming fully capable of applying state-of-the-art AI independently for India's own benefit — treating those as two entirely separate questions requiring different timelines and metrics.
Episode Transcript
What's your view of why India has been slow? And then do you think it's possible for India to catch up? What I have seen is India's taken more time to bring in the investment and the intent at the level needed that some of the other countries have done. It is perfect storm of meaningful investment coming together with the talent and with infrastructure in a single entity. So once all these three things come together, then it's gonna move really fast. I don't know if we can become number one or number two in the next two years. But if we look at it from the lens of, are we maximally capable of applying the latest AI tech for the benefit of India on our own, I think we can get there. There. Whether we can make the next $3,000,000,000,000 startups, that's a whole other question. I'm Amit Singh. I'm, director of IBM Research in India, and I also play a CTO role for the business here, in terms of, sharing our technology vision with the ecosystem and, engaging on that front. I've been in IBM almost eighteen years now. And, my background is in engineering. I studied electrical engineering at, the Indian Institute of Technology in Kharagpur, IIT Kharagpur. After which, I went to The US, did my masters at Carnegie Mellon. This was in, in a field called electronic design automation. So I switched a little bit into kind of the, semiconductor and microelectronics area. I worked for a couple of years and went back to my PhD back at CMU, and then, joined IBM Research in York Town Heights. Oh, really? Yes. I spent, almost eight years there. Beautiful building. Where did you live? I used to live in Yonkers. Oh, yeah. Because my wife used to work in the city, so it was convenient. Yeah. And I started off working in semiconductors from the, design automation lens, which is basically creating software tools for chip designers. Right. So it was at the intersection of computer science, electrical engineering, math. And, and then it's been eighteen years in IBM. The last ten have been in India. I've moved around in terms of, teams and areas over the years. I've been to more, more AI and applications and smarter energy. This was in, twenty ten to 2015 time range. That was the period of big data and analytics. Yep. And lots of utility companies were looking to use that because they're data rich. And we did some great projects at that time internally and also with, utilities like, DTE Energy in Michigan and PG and E in California in trying to simulate the weather and its impact on the grid. Once I moved back to India, I was more focused on things where we could leverage collaborations in the market here. So we looked more at IoT and retail and fashion and how deep learning can make a difference there. And then in around 2019, 2020, …
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Books, tools, and gear mentioned in this episode
SignalCast may earn commission on purchases via these links.
Tools
- Granite LLMsBy guest
by IBM
“IBM's enterprise-focused AI research — spanning hybrid cloud deployment, Granite LLMs, COBOL modernization, and agentic systems — addresses real-world business constraints.”
- Watson Code Assistant for COBOLBy guest
by IBM
“IBM's Watson Code Assistant for COBOL releases updated model versions every four to six weeks, making it a live production environment for continual learning research. The model explains legacy code in plain language, writes new COBOL, and translates COBOL to Java.”
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