Best Place To BuildProf. Kamakoti, Director, IIT Madras |"No substitute for hard work to become a great engineer"|Ep.23
At a glance
WHAT IT’S REALLY ABOUT
IIT Madras Director on engineering rigor, Shakti chips, startups
- Prof. Kamakoti frames great engineering as the outcome of rigorous conceptual thinking and sustained hard work, not merely exam ranks or early pay packages.
- He explains the RISE Lab’s long-term systems vision and how the Shakti indigenous RISC-V microprocessor program catalyzed a full-stack semiconductor startup ecosystem spanning core design, SoCs, verification, physical design, and hardware security.
- He outlines IIT Madras’ CS curriculum as three pillars—Theory, Systems, and Applications—emphasizing “NAND-to-Tetris”-style stack-building that makes students understand computing end-to-end.
- He argues JEE Advanced is difficult because it tests higher-order concept application across topics within the +2 syllabus, while cautioning that rank is not destiny and there is “no bad course” when aligned with student interest.
- As director, he credits IIT Madras’ sustained performance to cohesion, alignment with national priorities, execution against a faculty-owned strategic plan, and targeted improvements for global rankings (especially sustainability, research networks, and citations).
IDEAS WORTH REMEMBERING
5 ideasHard work and conceptual rigor are the real differentiators of great engineers.
Kamakoti emphasizes that IIT-level outcomes come from training students to apply concepts (often across multiple topics) and endure demanding practice over four years; there is “no substitute” for that effort.
Building an indigenous chip is not a single-project achievement—it requires an ecosystem.
Shakti’s impact is presented as full-stack: core IP, SoC integration, verification, physical design/PCB, and security, with different startups specializing in each layer so real products can be built end-to-end in-country.
A strong CS program should teach the entire computing stack, not just ‘coding’.
The curriculum described pushes students from logic gates to architecture, compilers, OS, and applications, so graduates understand how software maps to hardware and systems behavior rather than only writing high-level code.
Specialization works best after foundations are common and deep.
All students take core systems and theory courses early; only after 5th–6th semester do they choose deeper tracks (theory/systems/apps), which reduces premature narrowing and improves long-term fit.
AI education must be cross-disciplinary and infrastructure-backed to be credible.
He argues AI now belongs to every domain (bio, finance, management, humanities), so the AI/Data Analytics program is designed to bridge disciplines—while requiring significant investment in compute, storage, and accelerators.
WORDS WORTH SAVING
5 quotesThere’s no substitute for the hard work that you need to put… to become a great engineer.
— Prof. V. Kamakoti
If you come with an idea, we will give you the way to make it a unicorn—provided you work hard.
— Prof. V. Kamakoti
We have this entire innovation and entrepreneurship stack.
— Prof. V. Kamakoti
There is no bad course.
— Prof. V. Kamakoti
That forty-five marks of QS is out of syllabus for me.
— Prof. V. Kamakoti
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