This transcript is generated with the help of AI and is lightly edited for clarity.
LISA SU:
So when we started, people were wondering, well, what is AMD going to be when they grow up? And our driving force at the time was to decide what we could be best at. And I really believed that we could be the best in high performance computing. Back in the day, when I would sit with our technical fellows and we would make that big decision of, “Hey, are we going to bet the company’s roadmap on chiplets?” And we said, “Yes.”
REID:
Hi, I’m Reid Hoffman.
ARIA:
And I’m Aria Finger.
REID:
We want to know how, together, we can use technology like AI to help us shape the best possible future.
ARIA:
We ask technologists, ambitious builders, and deep thinkers to help us sketch out the brightest version of the future—and we learn what it’ll take to get there.
REID:
This is Possible.
REID:
If AI is the engine of the future, then computer chips are its crude oil: vital, contested, and entangled in geopolitics. The next frontier of intelligence depends on the hardware we design, the energy we can harness, and the infrastructure we can scale.
ARIA:
Joining us today is a visionary CEO who understands this perhaps better than anyone: Dr. Lisa Su. Lisa is chair and chief executive officer of the U.S. semiconductor company Advanced Micro Devices. When Lisa took the helm in 2014, AMD was on the brink of bankruptcy. Her commitment to semiconductors and high-performance computing set forth arguably one of the most impressive turnarounds in Silicon Valley history. Over the last decade, AMD has launched game-changing new server processors, and its market cap has risen from around $2 billion to nearly $300 billion. Today, half of the world’s top 10 supercomputers use AMD’s chips. The company is a global powerhouse. And Lisa—a former engineer with three degrees from MIT—is regularly featured on lists of the world’s most powerful people in AI and in business. She was TIME Magazine’s 2024 “CEO of the Year.”
REID:
We sat down with Lisa to talk about chips, the AI arms race, and what it’ll take to power us into this next stage of our AI future—from the global importance of AI to its implications for human medicine. Here’s our conversation with Dr. Lisa Su.
REID:
Lisa, I’m so glad this has worked out. Welcome to Possible. I’ve been looking forward to this for months.
LISA SU:
Well, thank you, Reid. It’s great to be here, and great to be here with you and Aria.
REID:
So you’ve described yourself as someone who has always liked hardware and building things. So tell us more. Where does this kind of curiosity and drive come from?
LISA SU:
Yeah, so I mean—Reid, just a little bit of background. So, I was born in Taiwan, and I immigrated to the United States when I was three. And so my dad was really into math and science and those kinds of things. And so when I was a kid, I just found it really interesting to understand how things worked. My younger brother had a remote-controlled car that would go up and down our hallway. And one day it stopped working, and I was like, “Huh? Why would it stop working?” And so as a curious kid, you unscrew it, and you realize that there’s a loose wire, and if you connect the wire, it starts working again. And that just gave me this feeling of like, it’s nice to work on things where there’s cause and effect. Like, you do something, and all of a sudden you can make something work—or you can do something that is unexpected. And that’s what got me into hardware. And when I was a young college student looking for a job, I had a chance to work in a semiconductor lab. And I think the rest is history.
ARIA:
Well, the next time my kids break their remote control cars, I’m going to be like, “If Lisa Su can fix it, just get in there, open it up.” And I have a feeling that that experience probably led to my next question, which is: I’ve heard you say that some people said you were crazy to go into chips instead of software. So can you take us back to that time and that decision-making, and did it feel crazy to you?
LISA SU:
Well, so, back to—it seems like ages and ages ago when you would say you are interested in semiconductors, and people would be like, “What are semiconductors?” People didn’t really know what semiconductors were. And I would say, “Hey, I work on chips.” And then people would say, “What kind of chips? You mean like potato chips?” And I’m like, “No, no, no, no, no, no. I mean, semiconductor chips.” And so yeah, I mean, look, 30 years ago—now I’m dating myself—people didn’t talk about semiconductors. People didn’t think about what they were. And for me, I got my bachelor’s, master’s, and PhD at MIT in electrical engineering. And what was really important is that you work on projects, and I had a research thesis and all of that. And so I worked in a semiconductor lab.
LISA SU:
I built devices. They were like one micron devices—which seemed like eons ago when you were talking about one micron. And now, everyone’s talking about, “How do we go to two nanometers?” So a lot has changed in that timeframe. But I thought it was amazing that you could build devices on a chip the size of a dime, or a quarter, and it could do things like computing. And that’s what got me into semiconductors. And yes, it was a time when semiconductors were not interesting. They were not sexy; nobody talked about them. But for me, they were interesting because I could see the product of my work actually turn out to make something run—like a computer—which was very exciting for me.
ARIA:
Absolutely. And I think you’re right, it was the time before chips were cool. And so we’re going to get to the present, but from your early days as an engineer to today, can you talk to us about those career chapters?
LISA SU:
I would say that when I started, I was an engineer’s engineer. So, I was a device physics person. I really enjoyed dressing up in a bunny suit and going into the lab. And just imagine me in a bunny suit…
ARIA:
I love it.
LISA SU:
Going into the labs.
GOOGLE GEMINI:
Hi, Google Gemini here to provide some context. A bunny suit, more formally known as a cleanroom suit, is a full-body garment worn by engineers and technicians in highly controlled environments called cleanrooms. The name comes from the fact that the one-piece suit, with its integrated hood, can make the person wearing it look a bit like a cartoon rabbit.
LISA SU:
I would do experiments on my chips, I would go and test my chips. It was very exciting. I spent a lot of the early part of my career at IBM. And I think IBM is just an amazing company. I think as you’ve looked at the contours of IBM—and now under Arvind Krishna, who’s doing just a fantastic job. You could learn everything at IBM. I went in as a 25-year-old, and it was 300,000 people, and you’re thinking, “How can little old me make a big difference in a company of that size?” But you realize that, at the end of the day, the beauty of engineering is it actually matters what you do. It matters less what your title is. It matters less how many years you’ve been with the company. It matters more what ideas that you have.
LISA SU:
And so I had a chance to really work on some exciting projects at that point in time. I worked on some of the first copper interconnects when we were integrating copper into semiconductor labs. And I kind of grew up at IBM. So I say like, every two years I got to do something different. So, I was first a device physics person. Then I became a manager of a product team, and then I started learning something about business, and how do you bring products to market? And, I think that brings me along to where I am today, which is—I like to say—doing probably the most fun thing I can imagine doing, which is being at the leading edge of technology in an industry that makes the world a better place.
REID:
Today, massive conversations around AI, AI hardware. We tend to hear about two major powerhouses, AMD and NVIDIA, for people who aren’t necessarily up to speed on hardware, let’s hear it direct: what are the ways to understand AMD, and what its role in this is, and how it’s different than NVIDIA?
LISA SU:
Yeah, absolutely. So, Reid—if I can just take a little bit of a detour—AMD is a company who has a very rich history. We’ve been around for 55, 56 years. We’ve always been in the computing world, and it’s always been around pushing the envelope on computing. So if you ask, “What do we do when we wake up in the morning?” It’s about how do we push the envelope on high-performance computing? And it turns out today that high-performance computing is all about AI and how we can get AI infrastructure to a point that more people can use AI in their businesses and in their daily lives. And so, what we do is really all kinds of computing. I like to say billions of people touch AMD hardware every single day. And you touch our hardware in the cloud.
LISA SU:
So if you’re running any of the basic services—probably on the podcast right now, today, we’re running through some AMD hardware in the cloud. Many of the most important services that we have—whether you’re talking about Teams, or you’re talking about Zoom, or you’re talking about Instagram, or you’re talking about if you call an Uber, or any of those things—somehow go through AMD hardware. We’re also in things like Sony PlayStation or Microsoft Xbox, those are also AMD hardware. And what I’d like to say is, our goal is to make sure that we have the right compute at the right time. Your comment about what separates hardware companies? We are actually here to provide the entire solution capability, Reid. It’s not just about hardware, but it’s about how do we accomplish things. Whether you’re trying to process as much data as possible in the data center, or you’re trying to have the best client experiences in a PC, or in another device, we have the right computer for the right application. And I think that’s very core to our mission in life.
ARIA:
So I love that you have described AMD’s mission as solving the world’s most important problems and challenges. And, like Reid and I, you are an AI optimist who’s excited about it. So if you could have AI help solve just one of the world’s most important problems, what would it be and what do you think the impact could be?
LISA SU:
Well, first of all, I feel like I’m talking to the experts here—certainly, Reid being very prolific on this topic, I’m optimistic about AI for many, many things. And I view AI as really a multiplicative factor to what humans can do. I mean, the fact is, humans are amazing; we have made so much progress over this period of time, but now we can use computing to add an X factor. Perhaps Aria, I could say, look, there’s so many things that AI can do for businesses—we’re certainly using it throughout AMD for lots and lots of things. We also look at it as what it can do for personal productivity. But probably the place that I’m most excited about is what AI can do for healthcare and medicine. Really, for quality of life. Because I think about the things like money can’t buy. Like money can’t buy health, but if computing can help improve health outcomes—and AI is such a big part of that—I think it’s just amazing. I mean, it goes back to this notion of, “Man, we’re working in like the most fun industry on the face of the planet.”
REID:
We’ll get back to AMD because obviously very important part of exactly as you mentioned, the infrastructure of everything that’s happened in compute—in the cloud, and in many other places. I actually have a couple of Microsoft devices that are powered by AMD. But on the healthcare, I do think this is actually one of the things that people don’t quite realize about how much of an amplification it would be. Everything from like a medical assistant that’s 24/7 available on your smartphone—for your kid, your parent, your sibling, your partner, et cetera—to, obviously, what I’m doing with Manas AI with Siddhartha Mukherjee, which is how do we create a drug discovery factory using AI to accelerate the cure of cancers? What are some of the things that people should understand as the near- and medium-term possible futures with healthcare? What are some of the things you’re seeing from your perch at the center of this with AMD, and why should people be excited about this?
LISA SU:
I think, Reid, it’s a great point. I think it’s all of the above, to some of the examples that you’ve given, right? So, very near-term examples: I think AI can actually improve healthcare outcomes for people, like today. And when you think about the diversity of healthcare that exists, even just across this country, whether you’re at the best hospital versus you’re in some rural community—where you don’t have the best doctors and you don’t have the best capabilities—to be able to equalize some of those healthcare outcomes with the usage of AI. One of the things that I found most interesting about medicine, just kind of spending some time in it, is it’s actually more art than it is science. Early diagnosis of things like cancers or other things really help improve healthcare outcomes so much.
LISA SU:
And you can’t count on the same quality of service everywhere you go. But being able to use AI as an assistant—the best wording that I had was a conversation I had once with Eric Corbett, when he talked about what do you expect of an AI medical assistant? You wouldn’t say that you would want your AI capability to make all of the decisions because we wouldn’t trust that capability, but boy, if you had AI just equalize—call it, a very strong capability—to give you a second opinion right on the spot. That’s just a very easy example right now, to the much further out examples like the one that you talked about, which is drug discovery. I mean, it’s like to help take what might take 10 years of experimentation with normal traditional computation to reduce that to weeks and months, so that we can really accelerate the rate and pace of innovation in this area.
LISA SU:
I think those are all really interesting outcomes. And perhaps one that I’m personally passionate about is I’ve experienced in the healthcare system, you actually see a lot of cases where we have specialists in medicine. But when you think about the human body, the human body is not a set of organs. It’s not your kidney specialist and your heart specialist—it actually is an integrated system. And to find people who can integrate all those systems is actually pretty hard. And I actually think that that’s where AI can help us quite a bit, in terms of integrating knowledge across a vast amount of data and a vast amount of experience to give us the best knowledge possible.
REID:
And actually, one particular follow-up that I think is really important that everyone pay attention to in your answer is today, already, it’s a great second opinion. So if you’re countering anything that’s serious, you check it as a second opinion. By the way, if the two don’t match, then go to a third! As a way of doing it. And that’s already an amplification for what we have today. I thought that was a really important thing for people to understand the AI moment that’s already today.
LISA SU:
Yes. And the other thing I think we find—Reid and Aria, and you probably experienced this as well—is it’s getting better practically on a weekly basis. So the AI that you might’ve experienced six months ago will be different than the AI you will experience six months from now. Because there’s this constant learning in process. And that’s exactly what we would want for—like I said—all of these applications, but particularly in the medical field.
ARIA:
So Lisa, something tells me that you’re not a person who yells from the rooftops about your successes, but I would be remiss if I didn’t mention, because the turnaround that you have had at AMD is truly incredible. You went from revenue of four billion to more than 23 billion in your time as CEO—when you joined in 2014. And you saw shares go from two dollars to $200. And so you can’t have that amazing turnaround without making some risky decisions, making some bets, betting the company. Were there times that you thought, if this bet doesn’t pay off—like I’m betting the company here, or I’m taking a risk that’s necessary, but is still a big risk—in that ten-year trajectory?
LISA SU:
Well, ten years sounds like a long time, Aria, but actually in tech it’s not that long. It really is a few product cycles. And look, I’ve said this befor,e and I’ll say this again, AMD is a dream job for me because I’m getting to work on the most exciting technology in the industry with amazing people, amazing customers, partners—all of that stuff. And so when we started, we were probably about four billion dollars in revenue. And people were wondering, well, what is AMD going to be when they grow up? And for me, I think the most important thing for us to do was to decide what we wanted to be best at. Because I really do believe, for tech companies, the key is you have to be best at something. There’s a whole bunch of other things that are important—you need the right balance sheet and you need the right, you know, all of those things—but our driving force at the time was to decide what we could be best at. And I really believed—my CTO, Mark Papermaster, has been my partner on this journey—we believe that we could be the best in high-performance computing. And at the time, high-performance computing wasn’t as exciting as it is today because AI wasn’t as big as it is today, and the cloud was on a different path. And so for high-performance computing—CPUs, GPUs, the right compute at the right time—that was what we were going to be best at. And the world was also changing. Reid knows this very well. Whenever there are technology inflection points, it actually brings opportunities. And so we were in a place where the world in semiconductors had for 30 years, really was driven by Moore’s Law, and the idea that every two years or every 18 months, you could double the productivity.
LISA SU:
And so it was all about scaling, scaling, scaling, scaling. But scaling was changing, and we saw that change. And yes, we made a few bets. We made the bet that scaling would slow down, and we had to innovate in different ways. And perhaps one of the largest innovations is we were the first to really put high-performance computing, in breaking it up into what we would call chiplets, such that you could take advantage of putting lots and lots of chips together to become a higher-performance computer. And if you look at every high-performance CPU today, if you look at future-generation AI GPUs, they’re all going to use this technology. But it is always about making bets. And yes, Aria, there were those meetings back in the day when I would sit with our technical fellows and we would make that big decision of, “Hey, are we going to bet the company’s roadmap on chiplets?” And we said, “Yes.” It’s trust but verify, right? We had a lot of learning. I mean, I can say there were products that worked really, really well, and there are products that worked not so well, but we learned a ton from the process of that.
ARIA:
And everything that I’ve read about you, you seem very patient. You’re saying, “Hey, we have a three-to-five-year plan.” This, to your point, a decade is nothing. How do you get everyone else to come on board for that when there’s so much noise, and there’s fads and there’s “we should be going left because our company is going right”—how do you get everyone else on board for that longer-term vision?
LISA SU:
Well, I think you have to have conviction. Yeah, there are all kinds of fads. I mean, we see all kinds of fads in, even in tech. When you think about what people went through—like when we started, there was a lot of conversation on, “Hey Lisa, you know, AMD should be making smartphone chips.” And then when you look at where things have evolved, you actually realize that companies have sort of fundamental DNA, in terms of what they’re good at. Like, we are just really good at building big computers. That’s what we think about every single day. We’ve been working on AI for the last seven or eight years, I think. AI didn’t just show up with ChatGPT. AI was around for a long period of time. The industry didn’t quite figure out how to make AI accessible enough.
LISA SU:
And I think that’s the difference with large language models is—it’s now quite accessible to a much broader population across a much broader set of applications. And it does require making big long-term bets. I do like to say that the decisions that we’re making today, we will know three to five years from now whether we made the right decisions. And that’s okay. That’s actually good. The rate and pace of AI though, is different. And I’m certainly curious to see how you feel about it, Reid. But certainly in some of the stuff that you’ve talked about, I think the rate and pace of AI is faster than any other thing that I have seen in my career. I think it’s because you are allowed to experiment much more. Like, hardware is on three-year cycles. Software can be on three-week cycles. And as a result, although we provide infrastructure for this, the tremendous amount of innovation on the application layer, and on the usage models, I think, is what makes AI quite different. And actually quite exciting
REID:
By the way, I think it’s not only going to be three-week, I think it’s getting the three-day, and potentially even three hours. I mean, I think part of your chiplet thing is exactly right. The AI revolution is how do we harness large-scale compute together with large-scale data, together with teams putting that together. And that’s the revolution that we’re in. And obviously, it becomes learning machines, learning compute machines. So, you said the word, the early innings of a ten-year AI supercycle. Give some more color to that. What are the things that you’re seeing from the central vantage point of AMD coming? Things that we need to navigate, et cetera?
LISA SU:
Yeah, so I spend an enormous amount of time with customers and partners. So just understanding what they’re seeing, really understanding where the use cases are going. And I guess what I would say is as much progress has been made over the last two years, AI is now part of our everyday, every hour vocabulary. Everybody talks about it. Whether you’re talking about, forget tech—I mean, of course, tech, we talk about it all the time—but you think about investors, you think about people on the street, you think about everybody uses AI in their life. In my mind, it’s still so early because the technology is good, but it is not yet great. I think it’s gotten a lot better, but it’s still like we can see the potential is nowhere near where it could be.
LISA SU:
And I view that across all application bases. So if you think about the evolution from large language models now to the usage of agents. And we’re all excited in companies with the idea that we’re going to augment our workforce with agents that can do a lot more, let’s call it, collective tasks, so that they’re not single-task oriented things. But we’re still at the very early innings of implementing these things. Where we see a lot of promise is just in building chips, for example. We think we can build much better, much faster, much more reliable chips over time. We can apply AI to our design elements, we can apply it to our test elements, we can apply it to our quality elements. And we’re just at the beginning phases of doing that.
LISA SU:
And the cases that we see today that are probably most interesting is we’re using AI to write a ton of our software. One of the things that people would say about AMD is, “Hey, we’ve been a great hardware company, but there’s a lot of software infrastructure on AI that’s been written in CUDA and needs to be—let’s call it—translated to AMD.” Well, a ton of that can now be done in software. We’re using software to significantly optimize our customers’ code so that they can run on AMD. And that’s just an example of the multiplicative factor of AI. So it’s just still so, so early, Reid, from my standpoint. And yes, you’re right, I said three weeks, maybe it’s three days, but the fact is, it’s getting better all of the time. We’re finding new use cases. We’re continuing to work up that productivity scale as we learn. The AI can really be tailored for company use cases, for personal use cases, so that it becomes more effective over time.
ARIA:
So global geopolitics has become so important here. And you’ve talked about the importance of U.S. manufacturing right now—we have such a reliance on Taiwan. How does this directly affect AMD’s business? We read about plans for you guys to begin receiving chips manufactured at TSMC’s Arizona plant before the end of 2025. How realistic is this shift towards national chip-making?
LISA SU:
I think it’s absolutely happening, Aria. The notion that we need leading-edge chip manufacturing in the U.S. is now completely understood by everyone. Now, these things don’t happen in months; they happen in years. And so we have been active in this area for the last, I would say, three or four years. But the key is it’s accelerating now. There are more investments coming in—TSMC built their first Arizona plant a few years ago, and now they’ve agreed to build that out to full large-scale manufacturing, which is really good for us as a company, but really good for the country. And we’re active in moving our manufacturing to the United States. It’s never going to be a hundred percent—I mean, we should be realistic. The supply chain for tech and semiconductors is very global. Right now, much of it goes through Asia. I think we’re going to balance that a little bit with a significant portion coming back to the U.S., but it’s not going to be a hundred percent. We just need to make sure that there’s a good balance in how we think about investments and where we put priorities.
REID:
And with this long-term vision—years, cycles—how is the current volatility in global affairs? Whether it’s tariffs and trade policies, or different stances in different countries, what do you see as critical to happen, and how are you guys navigating it?
LISA SU:
It’s a great question. It has become, let’s call it, a bit more volatile. We’re in the part of the industry where countries have significant policies, and some of those policies. For example, China is an important market to us. We have a good portion of our revenue that goes through China. And much of that is in the consumer space, you know, in sort of traditional computing space, which is going well. But there are certain parts of what we build, especially the highest performing AI computing that we have, that there are export control restrictions that cause us not to be able to sell to China. There’s no question that we have to protect national security, but we also want to ensure that U.S. AI technology is adopted as broadly as possible. Because that is good for innovation.
LISA SU:
Innovation requires the more people that are building on the U.S. ecosystem, the better. That increases the rate and pace of innovation. So I think the answer is, we navigate by making sure that we spend time with all stakeholders. So I spend a good amount of time with our Washington stakeholders because it’s really important that we are on the same page as the Department of Commerce, where a lot of these decisions get made, and that we ensure that there’s a good dialogue there. But we are also a global company. We have a lot of revenue outside of the United States. We want to make sure that the stakeholders outside of the U.S. also understand that, at the end of the day, our goal is to deliver the best technology to the world.
ARIA:
So when I talk to my friends—especially those that aren’t in technology—and if I’m talking to them about AI chips, supply chains, certainly geopolitics and national security comes up, but what also comes up always is environmental concerns. So can you talk about how you think about these environmental implications of scaling up chip production and what it means for the world?
LISA SU:
I think it’s important to recognize that one of the corollaries of all of this desire for compute is there’s just a lot of power that’s required. And making sure that we’re as efficient as possible, and the usage of that energy is important. From our standpoint, we are maniacal about energy efficiency. I think you can say that energy efficiency is as important, if not more important, than just general performance scaling. Because we recognize at the end of the day, there may not be enough power for all the compute that people want. And so the more energy efficient our chips are, the more computing that people can have. And I think that needs to be a first-order consideration in the development processes.
ARIA:
Are there specific things that AMD is doing, whether it’s investing in green tech or long-term big bets to accelerate that technology?
LISA SU:
We are, we’re doing quite a bit. We were able to improve our energy efficiency by over 30 X over the last five years. And that’s not any one thing, Aria. That’s a ton of how do we have better power management techniques? How do we have better techniques to ensure that we have the most energy-efficient chips in there? What do we do in packaging? What do we do in manufacturing? What do we do in all of those areas? And over the next five years, we’re doing the same thing. And the key here is we have to work across the ecosystem. Because actually, as important as we think our computing is, we’re just one part of the overall system. And the system requires that all of it come together. So we’re working with our partners to make sure that we are really scaling that energy efficiency.
REID:
Well, I mean, one of the things that people frequently don’t understand is these are ecosystems. And so part of what AMD and you do is supporting a whole bunch of different startups that are also building AI infrastructure on your GPUs—like Lamini. So what are some of the most promising hardware innovations you’ve seen lately coming out of the startup ecosystem?
LISA SU:
Yeah, Reid, we are really, really impressed with what startups have been able to do. For a while, people were thinking that it was hard to do new things in startups. Because frankly, the investment intensity is so much right. You’d have to spend triple-digit millions before you get your first product. And I think the difference—again, this is one of the differences with AI—yes, you do need a lot of infrastructure—and we have a very active ventures program that supports a number of different startups to get them on AMD infrastructure—but we also see is there’s a tremendous amount of innovation on the model layer. And that is actually pretty exciting. We’ve seen, for example, Liquid.ai is one of the startups that we’ve spent time with. And what they’re trying to do is actually reduce the amount of compute that you need to run very capable models because this is all about how do we democratize AI? It shouldn’t be that only the biggest companies in the world have access to AI infrastructure. You want everybody to have access to AI infrastructure. So I think the startup community has contributed a ton to the AI innovation over the last couple of years. And the key to this ecosystem is to give people access. So we have a developer cloud out there that allows people to quickly get access to our GPUs, and then really innovate on top of that.
REID:
And one of the accelerations that most people are tracking is the coding acceleration. And so what are some of the things that you think are currently working in the coding acceleration? What are some of the things you think are coming?
LISA SU:
Definitely. We are looking at the coding environment every day. And certainly, for the volume of code that needs to be written, our engineers are able to get a significant amount of code written through AI. We use all of the tools available out there. So, I think different tools at a different point in time have some strengths over others. But in general, I think it’s a race. There’s no one sort of magic pill, I think we have to use all the tools. Probably the place that I’m most excited about using AI is really around kernel development. So, if you think about our GPUs, one of the things about GPUs is there’s so many, so many libraries that need to be written so that somebody else can use our GPUs easily. And we’re actually really doing quite a bit with having AI actually write some of the very specific optimizations to our hardware. And if you think about it, the key thing to accelerate the rate and pace of AI innovation is to make it easier to use all of the power. Because every year we come out with a new thing, but if it takes people six months to optimize to the new thing, then that’s lag time. And so our goal is to make it super easy. It’s still early, I wouldn’t say it’s perfect, but we’re teaching the AI tools to really be able to optimize to our hardware.
ARIA:
So in this sort of new world that’s changing every three weeks or three days—or however quickly we’re rapidly innovating—obviously, one of the things that’s going to keep AMD ahead is your internal employees using AI all the time. So, there are specific things that you do to either encourage AI adoption or just to encourage that knowledge sharing? Because I feel like for so many people it feels like, “Well, if I mastered it four weeks ago, I have to go to college again for AI today because it’s already changed so much.” How do you guys stay ahead of that curve?
LISA SU:
Yeah, it’s a very organic thing, Aria. So, we actually have teams that are set up who are constantly looking at how we can use AI more in our internal processes. So in engineering, for example—I mentioned earlier—super important that every aspect of our chip development can be improved with AI. We’re working closely with our tool vendors—you know, cadence, synopsis, these guys—to ensure that we put that into the tools. We’re also very much developing our own databases around our techniques for both hardware and software developments, so that we can accelerate on our side. And then for, let’s call it, the non-engineering tasks—like everything from finance, to HR, to marketing, to sales—all of that can be enhanced with more AI capability. And we’re just learning which tools are the best. We’re experimenting a lot—and this is something that I personally review with my staff—that we are utilizing AI as broadly as possible across our business. And the key for me is we live and learn and continue to broaden where we can apply AI in our business.
ARIA:
I want to take it from the technology to more focus on people. You talk a lot about talent and how important it is to take risks on talent, how people have taken risks on you. Can you say more about that? Because talent is everything. Talent is the only way we’re going succeed. So what do you mean by taking risks on talent?
LISA SU:
Yeah, I think, again, this is all a little bit about how people grew up. Like I said, I grew up as a device physicist, and someone gave me an opportunity to run a business, and that took somebody taking a risk. I mean, who knows? Like, why should Lisa Su be able to run a business? Why should Lisa Su be able to run a company? Somebody took a risk on me. I believe, giving people big, hairy challenges and they don’t always succeed, but boy, if you give them the setup such that they can really learn through that process, you build incredible talent. And part of what I think makes AMD special, because look, for the longest time, we were the smaller guy, right?
LISA SU:
Intel was a hundred thousand people, we were like 8,000 back in the day. And why would you want to be part of that 8,000-person team? Because we’re going to let you work on the most exciting technology there is in the industry, and you’re going to get to learn how to do that. And I think that’s really what we need to do to attract the best and the brightest minds is to give them the opportunity and the freedom to innovate in a culture that allows that to happen. And so that’s what I mean about taking chances on people. I am a big believer in your resume is important, but frankly, it’s really your ideas. Like, going to school is not on-the-job training. Going to school is learning how to think. And once you learn how to think, you have to keep applying those skills every single day.
LISA SU:
Because every day I learn something new. Like I’m still learning how to think every day and applying new things. And that’s the environment that we want to build in terms of attracting and retaining the best talent. And I agree with you, it is all about talent. But it’s also about talent that’s dedicated to a certain mission. And not everyone wants to be part of every mission, but if you find the right group of people—I like to say the best leaders are ones that can help a team figure out how to do like 150% of their expectations. To really inspire teams to do amazing things.
REID:
Well, I completely agree on the talent side. What are some of the signals that you use for this is talent that we should take a risk on,—we should take a chance on?
LISA SU:
I like people who —obviously, you have to have the basics. The basics are the right schooling. But when you see leaders who are willing to take risks, like I like to say, the person who volunteers and says, “Hey, Lisa, I think I can help on that.” Or when you see a problem, the person who says, “Hey, let me take that and see what I can do.” I love those kinds of people. Because you realize that the person who’s willing to—the advice I give people is run towards the hardest problems. Volunteer for the hardest problems because those are the ones that you’re going to learn the most from. And you see talent who do that. Who are not necessarily the people that you would expect, who really like to bring people together. The other aspect of it is I like people who can integrate across disciplines. This idea of “you’re a hardware person” or “you’re a software person,” what we really like is people who know how to think broadly in terms of solutions and can stitch all the pieces together. And so those are some things that I like when I’m looking for talent.
REID:
Let’s turn to rapid fire. Is there a movie, song, or book that fills you with optimism for the future?
LISA SU:
I think Fei-Fei Li was on your program not too long ago. I think her story and her book [The Worlds I See] that she put out on AI is actually pretty inspiring. It’s a nice compilation of just an incredible story, optimism, and pragmatism around AI.
ARIA:
She was a previous guest and, no surprise, she was fantastic. So, rapid fire number two. What is a question that you wish people would ask you more often?
LISA SU:
I’d like to be asked what I’d like to be when I grow up. I think there’s still a lot of growing up to do in our world, so just getting started.
ARIA:
You’re just getting started. I love it.
REID:
That’s awesome. So, where do you see progress or momentum, outside of your industry, that inspires you?
LISA SU:
There’s so much interesting technology out there. I’m pretty inspired about some of the things that are being done in research, but I recently also saw—I spent some time with Elon on some of the progress he’s making on his optimist robots. And it’s pretty cool. It’s pretty cool. I mean, I think the world where robotics is, let’s call it in our everyday life, there’s still a lot of progress to be made, but I think it’s making very rapid progress, and it’s pretty exciting.
ARIA:
Can you leave us with a final thought on what you think is possible to achieve if everything breaks humanity’s way in the next 15 years? And what’s the first step to get there?
LISA SU:
I’d like to believe that over the next ten to 15 years, we can make our quality of life in every way substantially better. So, that’s in terms of health, that’s in terms of productivity, that’s in terms of longevity, and all of those things. And I think the first step is already underway, which is let’s figure out how to use AI as aggressively as possible across all of the health and medical fields. And I say it’s along the way, but it’s actually not that easy. And I think—Reid, you might agree with this—right now it’s still two separate worlds. There’s the technology world, and there’s the medical and healthcare world. I think it’s coming together. We’re seeing more people look at it with a broader lens, but I think we need more of that. We need more cross-pollination of people with different skillsets so that we can really bring the power of technology into making our lives truly different ten years out.
ARIA:
I think it fits together so well with your theme of interdisciplinary. If AI can bridge the gaps between technology and biotech, oh my God, what can we achieve?
LISA SU:
Yes.
REID:
A thousand percent.
REID:
Possible is produced by Wonder Media Network. It’s hosted by Aria Finger and me, Reid Hoffman. Our showrunner is Shaun Young. Possible is produced by Katie Sanders, Edie Allard, Thanasi Dilos, Sara Schleede, Vanessa Handy, Alyia Yates, Paloma Moreno Jimenez, and Melia Agudelo. Jenny Kaplan is our executive producer and editor.
ARIA:
Special thanks to Surya Yalamanchili, Saida Sapieva, Ian Alas, Greg Beato, Parth Patil, and Ben Relles. And a big thanks to Brittany Jedrzejewski, Sarah Feller, Rich Phillips, Carlos Carrillo Jr., Tyler Matlock, Kelly Jackson, and David Nuno.