So, serious talking. Maybe they're not that serious.
Too many achievements for me to remember everything. Not very easy for me to remember everything.
Now please join me in welcoming the host today, Professor Chen Jang, the president of National Taiwan University, as they take the stage to give us an introduction about our speaker today. Thank you.
Thank you. President Lisa Su of Advanced Micro Devices, distinguished guests, faculty, and students, good morning. It's my great pleasure and honor to welcome Dr. Lisa Su of AMD to give the NTU Li Kuo-ting Lecture today. The Li Kuo-ting Lecture is to invite Nobel laureates, academicians, presidents of prestigious universities, and also industrial leaders. As you may remember, we invited the TSMC founder, Dr. Morris Chang, to give the lectures last December. First, let me introduce Dr. Su's background and remarkable achievements. She received her bachelor's, master's, and PhD from MIT, and then she worked at Texas Instruments, IBM, Freescale Semiconductor, in engineering or management positions. During that period, she developed advanced semiconductor technology such as silicon-on-insulator, which helped make the company a frontrunner in the global scale. Today, she serves as President, CEO, and Chair of AMD, and has been regarded as an industry leader for several years. Her remarkable achievements have received numerous honors, including Time's CEO of the Year and Hundred Most Influential People in AI. Besides that, she received the IEEE Robert N. Noyce Award for business leadership and also served as Chair of the Committee for Economic Development. She is the first female to receive the IEEE Robert N. Noyce Medal and was elected to the National Academy of Engineering. She also served as an advisor on science and technology for the President of the United States. Frankly, we have already had 12 Li Kuo-ting lecturers, and she is also the first female to deliver this lecture. But we have so many 'firsts' and 'most influential' – luckily, NTU has her as the first female Li Kuo-ting lecturer, which is our great honor. As an industrial leader, she has been known as a leader in advanced semiconductor manufacturing technology and developing more efficient chips. Now she is leading AMD in the AI revolution. I think her career illustrates interdisciplinary thinking and innovation. So we have the privilege to hear her speak on AMD's transformation to an AI leader. It's our good fortune to have her here. I believe today's lecture will show us how to fuse electrical engineering, computer science, and AI to make the world better. I believe in the next hour we will enjoy her lecture very much. Finally, I would like to thank AMD staff, especially Brenley, and also Professor Wu for organizing. Please join me in welcoming Dr. Lisa Su to give the speech on the topic 'AI: The AMD Transformation to AI Leaders.' Thank you.
Now we have a quite different speech format. When I checked with the audience—pre-registered audience—there are questions, too many. Actually, when we made an announcement of her visit and speech at NTU, we limited to 500 audience in this lecture hall, and it was fully registered in about one minute. Therefore, too many questions. I cannot ask her to answer all the questions. As you just walked in, you can see there is another room on the third floor where they are using video to listen to what she will talk about today. So we will use a different kind of fireside chat format for her speech. She will accept some questions from the students. After about 10 questions when I chatted with her, she will finally accept some other questions. I think she's very easygoing, so probably after the lecture she can speak with some students and take photos. So that's today's lecture. Thank you.
Wonderful, thank you. How is everyone this morning? So, first of all, President Chen, thank you so much for inviting me. You invited me several times to come to NTU, and I was honored each time, but sometimes when I come to Taiwan it's very busy, especially around Computex timing. So this is actually a great time that I can visit. Thank you for the warm welcome. Professor Wu as well, thank you so much. It's really my pleasure to be here to meet all of you very talented students. You make me feel very old, but hopefully I can share a few stories for your career.
Sure, please. I will ask a few questions first, about 10 questions initially, and then we open to the audience for student questions. The first question: AMD has evolved from a CPU-focused company into a powerful powerhouse across CPUs, GPUs, and adaptive computing. What were the key phases or turning points that drove this transformation?
Maybe let me ask the students: how many students here are in electrical engineering and computer science? Please raise your hands. And how about medical field? Not too many. So I should say my background was in electrical engineering and computer science – that's where I went to school at MIT for bachelor's, master's, and PhD. My specialty was semiconductor devices. For AMD, I've been in the semiconductor industry for over 30 years. The technology moves very fast. I've been with AMD since 2012 and CEO since 2014. The most important thing for any company is to decide what you want to be when you grow up. At AMD, my belief is that our specialty is in high performance computing. It's really at the center of all our lives today. AMD's technology touches billions of people every day, whether it's cloud data centers, the edge of the network, PCs, automotive, or industrial. From a technology company, you must have a long-term strategy and roadmap. Our focus was on three things: first, high performance computing; second, excellent partnerships—TSMC is at the center of our partnership; third, the semiconductor industry is changing. Moore's law is slowing down, and we made a big bet on chiplet technology as a key area going forward. These are some of the important things for AMD. Today, when you look at the top supercomputers, five out of the top ten are based on AMD's technology. I really feel that high performance computing is at the center of what's most important going forward.
Very good. The second question: where do you see the most exciting frontiers in chip design today? Many students view chip design as the most exciting thing.
There's always a movement: sometimes hardware is more exciting, sometimes software. The most exciting part of design is actually holistic design—you have to design hardware, software, and systems together. Especially with AI, you cannot design each piece separately. You have to think about the overall system application, the algorithms and applications, and then how to design your chips.
Okay. Next question: I'll ask a different thing, not from the list. DeepSeek made an announcement in January. It seems to have an impact on the AI industry. What's your comment or opinion about DeepSeek and its impact on AI?
One thing I can say about the AI industry is that every week there is a new innovation coming out. DeepSeek was one of those areas. When it came out in January, there was some surprise. What they've done is clever technology—gathering innovations using mixture-of-experts models. The main thing is that when you make AI more accessible and cheaper, you get more usage and application. So DeepSeek has really accelerated the application of AI. That's a technology statement, not a market statement.
After this DeepSeek, the United States still wants to invest about $500 billion in AI infrastructure. Will they continue such investment after DeepSeek?
The key point is that there is no one-size-fits-all for the computing ecosystem. You have very large frontier models where the few who can invest hundreds of billions will go as fast as possible to build the best model. But that's just one application of AI. You'll also see many medium-sized and smaller models, and different algorithms. My expectation is that there will continue to be very significant investment, including very large investments, as well as more affordable ones.
Very good. The next question is what differentiates AMD's AI strategy from others, and what role do you see for the open ecosystem and partnerships in accelerating AI adoption? Actually, I'd like to ask how you will compete with Nvidia or others. How are you going to beat Nvidia?
The most important thing is that the AI market is huge—expected to be $500 billion over the next few years. In such a market, you need all different kinds of computing. No one company has all solutions. You need CPUs, GPUs, ASICs, FPGAs. Our AI strategy is number one: have a complete solution including hardware, software, and systems. Second: foster an open ecosystem and collaboration. DeepSeek was an open model, and many people innovated on top of it. The market is huge, and AI is throughout every aspect of our business, from largest data centers to the PC level. So there's a lot of market for all of us.
You already answered about the core principles guiding the AI transformation. So I'd like to ask another question: what is your suggestion for Taiwan to compete in future AI technology? We are very good at semiconductor manufacturing and AI servers, but how can we maintain our advantage?
The Taiwan ecosystem is very special. Starting from TSMC's ecosystem, which has built incredible manufacturing capability, and including all the ODM/OEM manufacturing and design ecosystems. I think more investment in software and systems will come. At AMD, we are very proud of our team in Taiwan—it's a very important part of our R&D. But also, ecosystems today are very interconnected. The US relies a lot on the Taiwan manufacturing ecosystem, and building resilience into that ecosystem is important.
I think family and education are very important to your early life and your career. Could you talk about that? And how it connected to the difficult period when you turned AMD around?
Do you think President Chen should go from being a university president to a talk show host? I hope so—I could make more money. I was born in Tainan. My parents immigrated to the US, which was a big move for them. The key thing they instilled in me was that education is so important. When I was at MIT, after my bachelor's I wanted to finish and go to work. My parents strongly suggested I get a PhD. At the time I was impatient, but they were right. University and graduate school give you the opportunity to learn many things and build confidence to solve future problems. I wasn't happy with my parents then, but they were absolutely right. Becoming CEO of AMD was my dream job—from semiconductor engineer to business person to leader. It wasn't easy, especially in the early years. My family has always been very supportive.
I'm curious: industrial leaders like Bill Gates, Zuckerberg, Steve Jobs did not finish university, but newer leaders like Jensen Huang and you finished your degrees. What's the difference?
There are many different kinds of leaders, and we respect all of them. But I truly believe university education is so helpful. These years are the best time to learn and decide what you enjoy, so you can decide your next career step.
I see. In Taiwan, you have to finish your education. In the US, maybe not. Here, the education system and job system require a degree, and Chinese parents ask their kids to finish. Another personal question: I know you and Jensen are relatives. Do you exchange ideas on running an AI company?
First of all, I have tremendous respect for Jensen. He built an amazing company as founder of Nvidia. But we run independent companies. In the semiconductor industry association in the US, all semiconductor companies exchange some ideas, but we are separate.
Next question: for students who want to make an impact in technology, what advice would you give about taking risks, choosing mentors, and building resilience?
The best advice I received from a mentor was: whatever you do, pick the hardest problems. Run towards problems. I think that's the best advice. Choosing a project, problem, or company where you can make a big impact—you will learn so much and contribute. I learned the most from my biggest mistakes. Mistakes are not a bad thing; they are a good thing because you learn and become better.
That's good advice. When I submitted my first paper to NTU, it was rejected. I looked at the comments and found I hadn't examined the experimental data carefully. I revised and submitted to a better journal and it was accepted. We learn from mistakes. Now we open the floor to student questions. Only students, please raise your hand.
Good morning Dr. Su and President Chen. My name is Jan Ling from the Department of Political Science. In September 2023, a book from Columbia Law School described three models of the internet world: rights-based from the EU, market-based in the US, and state-based from China. Throughout 2023-2025 we see AI developments. My first question: regarding the EU's rights-based model with many regulations, do you think current regulation will hinder innovation in Europe? Second: what is your evaluation of the US political environment, especially AI policy between the Biden and Trump eras?
Political science is not the easiest question—technology is easier. I'll talk about my thinking on AI. It's such a new technology; we're all learning together. Initially, AI could be dangerous without good guardrails. But you also don't want to stop innovation. I think we have to innovate as fast as possible while having regulation—but not too much to slow progress. That's my philosophy. Countries recognize it's a dynamic time. Generally, countries that embrace technology will benefit, and many are building AI policies.
Any more questions? Yes, please. We need a female to ask.
Hello, good morning Dr. Su and President Chen. Thank you for the marvelous speech. One of my relatives says Taiwanese people are most likely to achieve in semiconductors and AI. My two questions: first, how can students not in engineering play a role? I'm a freshman in languages and literature. Second, Taiwanese Americans are among the most highly educated ethnic groups with the highest income in the US. How do you think that will affect the status of Taiwan in the future?
