A country can help design the world’s chips and still have little control over the products it builds. That distinction sits at the heart of this AI Vey conversation: what would it take for India’s semiconductor talent to turn into companies, products and decisions of its own?
Our guest, Binoy Oommen, works in electronic design automation at Siemens EDA. He began his semiconductor career at Mentor Graphics and has spent much of it in functional verification: checking whether a chip design behaves as intended.
We begin with cars, transistors and the extraordinary compounding behind modern computers. Then we ask why NVIDIA’s advantage extends beyond its chips, where India fits into the semiconductor industry, and whether AI could help Indian engineers build verification products of their own. By the end, we’re talking about a crowded engineering lab, the cost of discouraging children from asking why, and a choice between money for education and ten times as much for industrial policy.
Highlights
A chip, explained through a car: Binoy separates design from manufacturing before taking us into the much smaller world of transistors.
What decades of doubling actually mean: Moore’s law, compounding, and the gap between knowing the formula and grasping its consequences.
Why faster computers can still feel slow: Navin explains how software absorbs the hardware improvements we have come to expect.
The limits to shrinking: Heat, quantum tunnelling and the rising cost of fabrication complicate the next generation of chips.
NVIDIA’s software advantage: Binoy argues that CUDA and the ecosystem around it help explain why a competing chip alone is not enough.
Why AI needs so much hardware: The connection between parallel computation, GPUs and the workload behind AI systems.
Talent is only part of the story: Binoy distinguishes having engineers in India from owning a product and making its strategic decisions.
An opportunity in verification: His proposal to build on India’s existing expertise through AI-assisted test generation, bug localisation and coverage optimisation.
The problems between specialities: Why Binoy sees opportunities where chip design meets manufacturing, and hardware meets software.
The habit of asking why: All three speakers connect classroom habits with the questions people do—or do not—ask at work.
AI in an engineering syllabus: Binoy asks what an AI course should help an electronics student actually do, and argues for closer work between industry and universities.
Choices, Choices: Education or industrial policy; teachers or bureaucrats; tea or beer; and a career answer that only an old friend predicts.
Links & resources
TSMC’s 28nm technology, for context on the mature nodes discussed in the conversation
Connect with Binoy
Binoy welcomes conversations with students interested in semiconductors and is happy to help with mentoring.
