Lisa T. Su

Advanced Micro Devices CEO

AMD chair and CEO Lisa Su, an MIT alumna, reflects on her undergraduate and graduate years at MIT, including her UROP research experiences and the development of what she calls an 'engineer's instinct' to break down hard problems. She discusses her career path through IBM and AMD, the promise of AI to accelerate discovery in fields like medicine, and argues that people, not technology, determine the future. She urges graduates to choose ambitious problems, run toward the hardest ones, and make their own luck.

“Technology itself does not decide what the future looks like. People do.”

On technology and purpose Official transcript ->

Key moments

01 Recounting her arrival at MIT and early UROP research in semiconductor clean rooms
02 The mentor advice to 'run toward the hardest problems'
03 Becoming CEO of AMD and betting on high-performance computing
04 Framing AI as a tool that makes people more capable, especially in medicine
05 Closing advice to be ambitious, take risks, and make your own luck

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Lisa T. Su at MIT, 2026

A commencement address about engineering instinct, hard problems, AI as a human multiplier, and making your own luck.

Speech arc

01MIT roots
Opening move
02UROP labs
Speech beat
03Engineer instinct
Speech beat
04IBM years
Speech beat
05AMD bet
Speech beat
06AI for discovery
Speech beat
07Hard problems
Speech beat
08Make luck
Final charge

Origin: MIT trains an engineer's instinct

Su begins with the habits shaped by MIT: breaking down difficult systems, staying close to the work, and learning in research settings where theory meets silicon.

UROP

Clean-room research turns coursework into craft and gives technical judgment a physical setting.

Method

The useful habit is decomposition: make a hard problem legible enough to attack.

Confidence

Repeated contact with demanding work becomes the instinct to keep going.

Su begins with the habits shaped by MIT: breaking down difficult systems, staying close to the work, and learning in research settings where theory meets silicon.

Career: The path compounds through hard choices

From MIT to IBM to AMD, the career story is built around choosing larger problems before the outcome is obvious.

Advice

Mentors push her to run toward the hardest problems instead of optimizing for comfort.

Leadership

The CEO role joins technical judgment, long time horizons, and appetite for risk.

Turnaround

A company-scale comeback is made from many disciplined engineering bets.

From MIT to IBM to AMD, the career story is built around choosing larger problems before the outcome is obvious.

Technology: AI expands what people can solve

Su frames artificial intelligence as a tool for human capacity, especially in medicine and discovery, rather than a substitute for human responsibility.

Multiplier

High-performance computing and AI increase what researchers and builders can attempt.

Agency

People, not technology alone, decide how powerful tools shape the future.

Standard

Progress needs ambition paired with accountability.

Su frames artificial intelligence as a tool for human capacity, especially in medicine and discovery, rather than a substitute for human responsibility.

• Key takeaways •