AMD’s Lisa Su on Experimenting with AI

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As CEO, Lisa Su has transformed AMD into one of the fastest growing semiconductor businesses in the world. She has also seen firsthand the way AI is reshaping companies and entire industries. In this conversation with Adi Ignatius during HBR’s 2024 Leaders Who Make a Difference conference, she explains how leaders can responsibly harness AI to boost their productivity—and stay competitive. Her biggest piece of advice? Experiment aggressively. Su shares what that’s looked like at AMD, and how your company can adopt a similar strategy.

ADI IGNATIUS: So every conversation about AI that I have eventually evolves into something very dark that is ai an existentialist threat in some way to not just our jobs, but to our very existence. I’m assuming you’re a relative techno optimist, but help us out if AI is going to be a force for good, how does that happen? Will it happen? Will technology save us from the downside of technology, or do we, all of us need to be contributing to the discussion now to make sure we don’t get the worst possible outcomes later?

LISA SU: Well, as you said, I’m probably a techno optimist, but I’m actually a very, very pragmatic way of thinking about this is the technology is not perfect, as good as technology is, we’re still in the very early stages of the deployment of ai, and we do know that the ais are not always right. And so part of what we have to do as a set of leaders is figure out how to use the technology for good and also protect the downsides. And look, I think this is a very vibrant conversation. I think all of us are learning in the process. I will say that I’ve personally learned a ton over the last 12 plus months in terms of how to apply AI even within our own company, and also talking to many of my peers how things are going. And I think we’re all recognize that we’re in a learning process, but the key is to be very active in that learning. So my belief, and I know there’s a lot of doomsday theories about how AI is going to take over all of our jobs. I actually am a subscriber to the belief that what we have to do as leaders of companies is to really learn how to harness the power of AI and also bring our employees along with that so that we’re actually making our employees more productive and we’re able to make our companies more productive knowing that there are some areas where we have to be careful with the use of ai.

ADI IGNATIUS: Yep, that’s helpful. There’s also another aspect of this, which is just the balance between speed bringing products out to the market as quickly as possible. Now that there is a market versus caution, and that’s reflected, as you said, you’re learning and there are things we don’t know yet about the technology. How do you think about—from where you are at AMD—how do you think about this balance between speed and caution?

LISA SU: Yeah, I really believe in fast experimentation and implementation. So I don’t believe the answer is let’s slow down. I think what we have to do is experiment. Where we’ve spent time is we actually have a Responsible AI Council. I think all of us as leaders, if you’re leading companies or teams, you have to think about how to utilize the technology responsibly. We think about things about intellectual property, how to protect our intellectual property, as well as protecting our customers and our partners intellectual property. But that being the case, I think the power of AI is finding those use cases that give you very, very significant return on investment. And we’ve seen in some of our workflows, like in some of our design workflows, we’ve seen what used to take weeks and months really come down to days. And when you think about how valuable that is to your enterprise, you have to really push the envelope on using the technology. And there are lots of people who are out there to help in terms of experiences. I know that it’s a very active conversation whenever I’m talking to my peer CEOs these days in terms of what are you learning, where are the use cases that are most beneficial? What are the things to be careful about? So I think this active dialogue is really helpful,

ADI IGNATIUS: And I’d be interested in your advice for people who, well, let’s say when chat GPT came in the market, lots of people experimented with it and played around with it, and now I dunno what wave we’re on, but now it’s like, okay, but how do I actually use it? How do I actually apply it to my company? You mentioned healthcare and people often mention healthcare as a clear use case, but that’s very specialized for the general audience here. What would your advice be? How do people figure out, I guess there are two things, how to protect themselves against being disrupted by AI solutions, but then maybe more pertinently, how do I use AI to improve my business, whether it’s efficiency or something else? What’s your advice for people who are even just trying to think through that problem?

LISA SU: Yeah, I would say again, look across the use cases and the workflows in your business. The places where it’s obvious, very, very near term successes can be in things called copilots or where AI is actually a helper to someone, to your employees. And I think about these types of copilot exercises, whether it’s on the engineering side, we’re using copilots to help us design code and really write code and to help us look at test cases and use cases, improve our quality, those kinds of things. When I look at things that are more business oriented, we’re looking at how we use AI in our marketing and our communications and our content creation. Again, these co-pilots will allow you to, let’s call it, get close to the answer. And then of course the final touches are being done by your expert employees. There are many, many cases like that through every enterprise where you can think about workflows, where you can accelerate your time to get an answer.

The places where of course you have to be a little bit more careful are places that you would rely more on the AI itself to come up with the answer. And there you have to do a lot of testing to make sure that you get the right answers. But again, my advice is lots of pilots, experimentation, and then figuring out where it has the most value. We’ve certainly seen as we’ve deployed AI across our business that there are some places where very high value, very low barrier of entry, and then there are others where frankly the tasks are you have to put a lot more work into making sure that the models and the AI are more adapted to your particular use case. So lots of experimentation and really looking at where you can get the most bang for the buck in the near term.

ADI IGNATIUS: Yeah, thank you for that. So here’s an audience question. This is Melissa Quillan, not sure where Melissa is, but question is, when it comes to ai, how are you anticipating returning real-time data mining so that you can pivot your business almost immediately to current trends or to resolve issues that pop

LISA SU: Up? Yeah, absolutely. We have done, there is quite a bit of work and we’ve also done work ourselves on looking at things like being more predictive in sales cycles and looking at some of the data that comes into those trends. I would say that requires a bit of training on your business because not every business is different and there does need to be a bit of training on your specific data, but I do think that you can get some very nice patterns and trends that come give you insights of where to dive to the next level of detail going forward.

ADI IGNATIUS: Yep. So I want to ask you about your run at a MD. You’ve been CEO now for about 10 years. You said earlier on that one of your goals was to bring focus to the company. How do you determine which business to prioritize and how do you get think about focus in that role?

LISA SU: Yeah, so I’ve been at a MD about 12 years, CEO for almost 10 years. And one of the things that is true in every business around the world is that you have more opportunities than you have people or resources or leadership bandwidth. And so for us at a MD, it was deciding really what are we going to be best at? And our heritage has been one of high performance computing and really building at the bleeding edge of technology. And that was really our focus item. So there were things that we had to choose not to do. For example, mobile phones are very interesting part of semiconductors. There are lots of great companies in that area that wasn’t the perfect area for a MD, and we just had to really choose the things that we were best at. So our focus was a high performance computing before high performance computing was sexy. And now we can say between high performance computing and ai, we are in perhaps one of the most exciting areas, if not the most exciting area in semiconductors. And it has a lot to do with our heritage and focus.

ADI IGNATIUS: So I know your goal is to stay at the cutting edge of technology, the next innovation. This is a competitive industry. And when you’re up against big players like Nvidia, how do you do that?

LISA SU: Well, the beauty of technology, and I like to say this very much, it is about building great products. And to really do that, we actually have to see the future. We need to decide, hey, where’s the industry going over the next three to five years? And we need to place big bets on technology. And I think from that standpoint, it is one of those areas that is very rewarding if you make the right big bets. And we’ve made some very good bets. I think as we look at technology going forward, I’m super excited about what we’re doing in ai. It is sort of a confluence of events. I mean, generative AI has come into fruition and the fact is everybody needs AI compute technology, and we’re one of the very few companies in the world that can do that. And we’ve been really investing in this space for the last 10 years. So it is one of those places where you have to kind of see across the horizon. And with that, we invest very heavily in r and d and the key technologies to enable the next generation of products.

ADI IGNATIUS: I love your observation. Who knew this industry would be sexy, but you’re having your moment, so that’s great. So here’s another audience question. This is Gaja and Yoga Suran, who’s asking how expensive versus accessible will AI technology be in the medium term? And the point is, given the large cost of materials required for building semiconductors for employee headcount at the big producers like a MD, do you see the cost of accessing this technology will limit the ability of certain people, certain companies to take advantage of what it could offer?

LISA SU: Yeah, the great thing about technology, especially when you think about usage curves is we’re very cognizant of the fact that for technology to be most broadly adopted, you do actually need to get sort of the cost to a very, very reasonable point. So one of the things that we’re working on today are things like if you think about there are all kinds of large language models that are used in ai. There’s some who are the most advanced, the largest, which require tens of millions, hundreds of millions of dollars, maybe even billions to train. But frankly, there are ways to really access more fine tuned models that don’t require that kind of investment. Or if you think about how much it costs to ask a question to chat GPT or one of your copilots these days, we call that an inference opportunity. We’re absolutely looking at reducing the cost of that by factors over the next couple of years. So I don’t believe that this is going to be an overall issue where the cost is prohibitive. I think it is an issue of you have to decide where your return on investment is and where are you going to see the largest productivity enhancements. And that is very much what we’re driving as we look at advancing the technology going forward.

ADI IGNATIUS: So your industry seems very complex and the supply chain seems very complex. On top of that, you have the uncertainty of political and trade issues. As I said, it’s a sensitive industry. China recently said at least it was prohibiting A MD and Intel chips from government computers. How do you respond to that? Can you do anything to try to move the needle on policy issues like this?

LISA SU: Well, I would start with the notion that, look, every country has to do what they believe is in the best interests of their national interests. That being said, the particular question that you have about China’s policies around government procured processors, that actually wasn’t new news. That was telegraphed actually late last year. And so it is something that, again, we look at the breadth of the market that we have. We are a global company. We work in all markets. China is a large market for us. And so within that, as long as we can plan across the different markets, I don’t see it as a significant factor in the business. I think the more important conversation is we’re very much about driving deep partnerships across the globe, and that’s with both large companies as well as small companies, startups and companies are very regionally focused, and we’ll continue to drive deep partnerships across the world.

ADI IGNATIUS: As I mentioned at the start, you may well be the most prominent woman in the technology industry. How do you think the industry is doing now in terms of gender equity?

LISA SU: Well, that’s very kind of you to say that, ADI, I appreciate that. Look, I consider myself extremely lucky to be where I am. This is kind of my dream job to be a part of an industry that is so important and essential to the world and be leading a company like a MD in tech. Look, there are not enough women. I mean, I think we can say that it’s one of those areas where we’re consistently trying to drive more sort of more gender diversity as well as just overall diversity of thought. And the reason for that is, frankly, is we want to build the best business and we want to build the best products. And to do that, you do need diversity of experiences and thoughts. I’m a big believer in the best thing that we can do is give people opportunities. I was very lucky in my career and I got a chance to really experience many things early on in my career, which helped give me some great experiences. And so that’s very much what I’m focused on doing is giving women sort of more exposure to the industry overall and then opportunities to shine and sort of demonstrate their capabilities going forward.

ADI IGNATIUS: So there’s also the question of I guess, age diversity. David Dawson, a viewer asked, do you see any clear opportunities or gaps where new perspectives? And I think by that he means new graduates, young workers will be beneficial, and let’s say particularly in ai.

LISA SU: Yeah, look, we are always looking for new talent. I mean, we’ve significantly grown as a company. When I first started as CEO, we were about 8,000 people. We’re now about north of 25,000. So lots of growth over the last 10 years. And I think the key for that is a diversity of perspective is super important. And what I like to say, especially when we’re looking for new graduates, we don’t view hiring somebody at a school as job training. We’re not looking for that exact software skill to plug into a software team. What we’re looking for is people who are great thinkers, who are great problem solvers, who are here to build a career and here to learn a lot of different things. And along the way, we’re going to need your hardware skills and your software skills and your problem solving skills. And so yes, I think diversity of thought is really important. We love new graduates out of school and we hire across the world that new hires every year, and we’ll continue to diversify our talent base going forward.

ADI IGNATIUS: So you can’t do an HBR interview without getting at least one classic HBR question. So here’s my classic HBR question. In your decade as CEO, what’s the most important lesson that you’ve learned in these 10 years?

LISA SU: Yeah, so I think the most important lesson that I’ve learned is to really be very ambitious in the long-term goals that you set for a company. I mean, if you think about where we were, we were a 4 billion company in 2015, and we’re now north of 22, 20 3 billion last year. I think setting very ambitious goals for the team while having very clear milestones for how we show progress along the way. Certainly in our business it’s about long-term thinking and charting a strategy for that, but everyone needs some near term milestones as well.

ADI IGNATIUS: So there’s an audience question that has gotten lots of up votes you could do with it whatever you want. This is from Bahar Dunno where Bahar is from. But Bihar’s question is, what are you reading now?

LISA SU: Oh, wow. That’s a great question. I read a lot of things online actually. And believe it or not, I’m a pretty avid user of both Reddit and X because they actually helped me get very good real-time information of what’s going on in the world.

ADI IGNATIUS: Okay. And last question. So a couple of people have asked, are you using on the topic of sustainability, is AI helping AMD achieve its goals for sustainability? And then more broadly, do you see AI playing a role in influencing sustainability or CSR efforts for you or for others?

LISA SU: Yeah, let me turn it around the other way. I mean, our technology is actually very focused on sustainability. So the idea of where technology is going, think about it as not just about high performance, but it’s about what performance can you get at a certain PowerPoint. So we’re all about, when you think about today’s limitations, frankly power will be a limitation as you go forward. And so we’re constantly looking at how can we be more efficient with our products, which help the overall sustainability conversation. Now as it relates to ai, I do absolutely believe that AI will help us in sustainability from the standpoint of it will get us to answers more efficiently. And with that you need less power for that, that being the case. There’s also the reverse trend, which is we are using a lot more computing to help us modernize our businesses. So a lot of focus on sustainability. What I would definitely say to this audience is that the newer the technology, frankly, the more sustainable it is because you do have all of the benefits of newer technologies being much, much more power efficient. So you need much less power to get the job done.

ADI IGNATIUS: So Lisa, I want to thank you for being at this event. I’ve long admired you and long admired A MD, so it’s really nice to have this conversation. So thank you for being here.

LISA SU: Thank you so much for having me this morning.

HANNAH BATES: That was AMD CEO Lisa Su in conversation with HBR Editor at Large Adi Ignatius.

We’ll be back next Wednesday with another hand-picked conversation about business strategy from the Harvard Business Review. If you found this episode helpful, share it with your friends and colleagues, and follow our show on Apple Podcasts, Spotify, or wherever you get your podcasts. While you’re there, be sure to leave us a review.

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This episode was produced by Dave DiIulio, Elie Honein, Terry Cole, Julia Butler, and me—Hannah Bates. Curt Nickisch is our editor. Special thanks to Ian Fox, Maureen Hoch, Erica Truxler, Ramsey Khabbaz, Nicole Smith, Anne Bartholomew, and you – our listener. See you next week.