Lisa Su, Chair and CEO, AMD | Behind the Tech with Microsoft's CTO Kevin Scott
LISA SU: No offense, software is very interesting. But at the time, hardware was much more sexy to me and I had the opportunity to see how you could build chips. They weren't the most advanced chips in the world, but to me it was amazing. It was amazing that you could build some transistors on something the size of a coin.
You could look at it in the microscope, you could measure it on a test system. That's how I got into hardware, and that's how I got into semiconductors, actually. KEVIN SCOTT: Hi, everyone. Welcome to Behind the Tech. I'm your host, Kevin Scott, Chief Technology Officer for Microsoft.
In this podcast, we're going to get behind the tech. We'll talk with some of the people who have made our modern tech world possible and understand what motivated them to create what they did. Join me to maybe learn a little bit about the history of computing, and get a few behind the scenes insights into what's happening today. Stick around.
CHRISTINA WARREN: Hello and welcome to Behind the Tech. I'm Co-host, Christina Warren, Senior Developer Advocate at GitHub. KEVIN SCOTT: And I'm Kevin Scott. CHRISTINA WARREN: Today, we're bringing you a conversation with someone who is such an important part of the actual materials that make up our technology. Just an incredibly cool person. KEVIN SCOTT: Yeah. Lisa is
just inspirational. I think in general, given her career arc and the position that she's in now of leadership in the technology industry. But I always like talking to semiconductor people. My bias as a computer scientist is low level system stuff, so I am just a super big fan of Lisa and her company and everything that they're doing. I even have an AMD custom built machine sitting under my desk right now that I've been very proud of owning for the past three years.
CHRISTINA WARREN: Yeah. It's funny, during the pandemic when everyone was building gaming PCs, I was amongst them and I built an AMD system and it was my first AMD system that I'd built in a really long time. But I also helped build a Threadripper system for a friend who claimed that they needed more compute than I think they actually did, but it was really fun for me anyway. I'm right there with you. I don't have the same passion for low level languages and system calls like you do, but I'm a huge fan of Lisa Su and AMD.
I'm really looking forward to this interview. KEVIN SCOTT: Dr. Lisa Su is Chair and Chief Executive Officer at AMD where she's led the company's transformation into a high performance and adaptive computing leader. She's passionate about working closely with partners to deliver the next generation of computing in AI solutions that can solve the world's most important challenges. In 2018, she was elected to the National Academy of Engineering.
In 2021, she was recognized by the IEEE with its highest semiconductor honor, the Robert N. Noyce Medal, and was appointed by President Biden to the President's Council of Advisors on Science and Technology. She also serves on the board of directors for the Semiconductor Industry Association.
Lisa, welcome to Behind the Tech. Thank you so much for joining me today. LISA SU: It's great to be here with you, Kevin. Thanks for having me. KEVIN SCOTT: We always start with folks from the beginning, so I'm just curious how you got interested in the first place in science and technology. Did it start when you were a kid with your parents? How did it begin? LISA SU: Absolutely. Kevin,
I was born in Taiwan. I actually grew up in New York, and my dad was a mathematician. He was actually a statistician, and so we would sit at the dining room table and he would make me do multiplication tables with him. I had to be decent in math.
But look, I've always really enjoyed just seeing how things work and building things. One of my earliest engineering memories was my brother and I were playing with his remote controlled car and it was going down the hallway, and then it just suddenly stopped. I was like, why it did stop? I opened it up and saw there was a loose wire and if I put the wire back in, it started working again.
That was just super interesting, that's how things worked. KEVIN SCOTT: How old were you? LISA SU: I don't know. I might have been like 10 or something like that. My brother was younger. My younger brother. We just got curious about how things work.
KEVIN SCOTT: Yeah. I think it's really interesting, those serendipitous moments when you're young. Where you go from looking at something as this inscrutable, almost magical thing that you don't understand to being able to have some kind of agency over it.
You peer into how the thing works and you understand something. I think they are just super interesting moments. LISA SU: Yes.
Absolutely. Because you're just so proud of thinking, wow, I learned something there and that carries with you. KEVIN SCOTT: Yeah. You were a EE. As you were going through high school, did you know that you wanted to be an electrical engineer before you went to college or did you decide once you got into school? LISA SU: I don't think I could say I knew, Kevin.
Again in high school, I have to admit I was kind of a nerd, so I did things like the math team and projects like that. But I happened to get into MIT and that was my first choice. I went to undergrad at MIT. At MIT, everyone's an engineer, and much of the starting class is either electrical engineering or computer science and so I think that helped me go in that direction. The big decision wasn't, are you going to be an engineer? Was, are you going to be a hardware person or a software person? Or was, are you going to be an electrical engineer or are you going to be a computer scientist? That perhaps was what at least all the kids around me were thinking about, but there was no question I was going to be an engineer, so that was clear.
KEVIN SCOTT: How did you gravitate towards being a hardware person? Because I had the same thing where I did, when I was young, a little bit of electronics and electrical engineering stuff and a little bit of software stuff. I clearly gravitated towards software. I used to tell myself it's because software is inherently better and it's faster. Yeah. I mean, that's the lie you tell yourself when you're a teenager like in your '20s. But like other people, they have similar beliefs in the other direction.
I'm just curious, how did you know that you wanted to be a hardware person? LISA SU: Well, I had two experiences. I had experience in software where I was helping a lab program. One of the things that's nice about MIT is that they really encourage undergraduate research.
In addition to the course work, they encourage you to get side jobs, I don't know. I must have got paid five dollars an hour or something like that, but it allowed you to work in labs with people. I had two experiences working in labs. I had one that was more of a software thing, which I was helping program whatever some professor's experiments. Then I had one that was a hardware thing which was in semiconductors.
I got to do basic grunt work for the semiconductor graduate students, but I was building something. I was putting wafers into a big reactive ion etcher. I was running the experiment, I could look at it in the microscope and that's how I fell in love with hardware. No offense, software is very interesting but at the time, hardware was much more sexy to me.
I had the opportunity to see how you could build chips and they weren't the most advanced chips in the world, but to me it was amazing. It was amazing that you could build some transistors on something the size of a coin. You could look at it in the microscope, you could see, you could measure it on a test system. That's how I got into hardware and that's how I got into semiconductors, actually.
KEVIN SCOTT: Yeah. Look, I think the thing that all of us realize at some point is if you are in computing, it's actually both pieces that are very important and we'll get back to that in a minute when we start talking about the work that we've been doing together, like Microsoft and AMD for so many years. But I want to first poke on this practical experience that you have. Because one of the themes that we have talking to computer scientists and engineers, is this notion that the abstraction layers that we have built up over the decades sometimes obscure some of the low level things, I wonder, I'm not a EE. I don't even know whether you can get that experience that you had anymore in a material science program or a EE program at a university where you get that visceral feel for what it's like to actually make a semiconductor.
LISA SU: Yeah. I think it's so important, Kevin. I mean, I'm a big believer. I mean, there are people who are, let's call it, very book smart and then there's people who are just pragmatic and have had a chance to experience different things, and I'm definitely the latter.
I think I learned through experiences and I think those experiences were so important. I remember, the first classes I took as an undergrad and it was things like building your own little computer. Yes, you have to build the circuit, but you also have to program it. Or as I said, building your first semiconductor device and just seeing how you go through each of those steps. I won't say that everybody loves those experiences, but I certainly do. I won't say that school is supposed to be job training.
It's not supposed to be job training, but it's supposed to help us think through what do we like to do in life. It's stuck with me with the notion of it's so important to see the results of what you're doing. I love the fact that I can build products that I can touch and feel and walk into Best Buy and see those products or walk into your data center and see those products. That's what I enjoy. KEVIN SCOTT: Well, there is just this fun thing I think about hardware in general because even when you're writing a program, you don't get the same sense as you're building it. Even if you're assembling it piece by piece, you don't get the same sense that you get when you're building your own PC.
Like where you buy a motherboard and a case and a power supply and a CPU, and you just are assembling this thing physically and you at the end get to have this working physical artifact. LISA SU: Kevin, am I encouraging you to come over to the hardware side or? KEVIN SCOTT: Look, I've been doing both for a very long time and I'm thinking right now actually. The machine that I'm on right now, taping this has like a AMD, like 32 core Threadripper CPU, which I very custom configured and I'm getting ready to do another build and I'm probably going to hand assemble this next one because I haven't built my own PC in a long time. LISA SU: Kevin, that's very exciting. I must send you our latest Threadripper because we just got the next generation out there. It's pretty cool.
But look, I completely agree with you. The opportunity to build tech and touch tech I think is super cool and it's great for getting students into STEM, that I know is something that's very important to you and to me as well. KEVIN SCOTT: You're majoring in electrical engineering at MIT. Then what's next? You go to grad school, you go straight into the workforce? How are you making that decision? Because that's a really interesting time in your career.
LISA SU: I was what they call a lifer at MIT, so I did my undergraduate Master's and PhD at MIT. It wasn't a super easy decision. I think you're right, Kevin, because at the time all my friends were graduating, they were getting jobs, they were moving to cool places.
But I felt like I wasn't done learning and there was still more to learn and I really appreciate my PhD advisor, his name was Dimitri Antoniadis, he was one of the people who built the very early simulation capabilities for semiconductors. I felt like there was more to learn and so I decided to get a PhD and my focus was semiconductor devices. I was building a quarter micron devices, which at the time was very advanced.
Today we're talking about two nanometer, but hey, back then it was state of the art type things and we were thinking about how do we push the envelope on scaling. Because even then people were talking about whether Moore's Law was ending. It obviously didn't end. I worked on something called silicon-on-insulator devices and it was a lot of learning, but it was also fun to be able to think that you're doing something that is state of the art research as part of your studies. KEVIN SCOTT: I'd love to get your perspective on what you think the value of your PhD was. Because I think a lot of people think that most of the value is that contribution that they're making to the state of the art.
But I think that the value is in the discipline of getting a very complicated thing done and synthesizing incredibly complicated information and just learning how to be a researcher or someone doing something incredibly sophisticated around a whole bunch of other people who have the same high ambition level for the things that they're doing. I'd love to get your perspective on what the value of that degree was for you. LISA SU: Absolutely. Now you have to understand my perspective. I was so impatient, Kevin, as a student.
As soon as I started the PhD program, I was like, I need to finish as soon as possible. I was a little bit impatient. But I think the value of a PhD for me, and as I advise other people is, it is not job training. Whatever project you do, some are great projects, some are more esoteric projects.
But it's an opportunity really. For me, it was how to learn to think about solving very difficult problems. If you think about, it's a problem that nobody else has solved out there, so you can't go to a book and say, hey, this is how to solve it.
You actually have to think through, how do I solve this? How do I contribute something to the industry or to academia where the answer isn't clear? The process of doing that, for me, it was four years, 3, 4, 5 years, really gives you the confidence that you can contribute at the highest level in a certain field. That's what I take away from it. I just think it teaches you how to think. What we do, what you do, what I do all day long is, how do we solve very interesting tough problems? That time gave me the confidence that you can do that. Even at the time, it was also very much about a team. I loved working with the other grad students and we worked together on how to solve some of these problems.
KEVIN SCOTT: I think the thing that you said about they're very tough problems and they're also ones that no one else has solved before. You can't go consult someone to say, hey, how did you do exactly this or skip ahead to the answer. There's no skipping ahead, you just got to sort it out. After MIT, what was your first job? LISA SU: My first job actually was at Texas Instruments in Dallas, and I wasn't there super long. I was there about a little less than a year.
I was kind of homesick. I live in Texas now, but at the time it was so far from home for me. I ended up spending the majority of the early part of my career at IBM in New York. I joined what was TJ Watson Research Center, and then the IBM Microelectronics team.
KEVIN SCOTT: Were you working on the RISC processors that they were building there? LISA SU: I was. I spent my time in the process technology area as we were continuing to look at the next generation technologies. But yeah, the first processor, I've been working on processors like forever. It's been, I don't know, now 30 years. The first processor that I worked on at IBM was actually a PowerPC processor that actually went into PCs as well as into some of the larger server systems at IBM.
KEVIN SCOTT: I remember I went to a science and technology Governor's School which had us do a bunch of internships. I interned at a place that had a PowerPC, one of the very first one that I just remembered what an amazing thing that was. LISA SU: It was super fun. Again, the idea of RISC processing and for us it was, how do we get the performance and power and all that in the right place? KEVIN SCOTT: It's an interesting thing.
I don't know whether you want to talk about this or not, but we had this big revolution, I'm guessing early in both of our careers where there was all of this innovation on instruction set architectures and there was PowerPC and PA-RISC and MIPS. The deck part like 21 164, 264 series. LISA SU: The Alpha stuff also. KEVIN SCOTT: Alpha. What ended up happening just in terms of where the bulk of the compute in the world went was to processors running this x86 instruction set, which you build them, Intel builds them. Now we've got this interesting world where you've got ARM in a bunch of places that are risk construction set machines.
Then you've got these GPUs which are an increasingly large volume of compute. It's interesting a lot of the things happening now feel to me like the things that were happening 30 years ago, 20 years ago. I don't know if you reflected any on that. LISA SU: It's interesting, I hadn't actually bridged the parallel between the two.
I think what has been the case, and I'm actually curious your thoughts on this, is the instruction set certainly back in those days when there were so many different ones and the consolidation, I don't think it's been so much about the instruction set. People ask me all the time about ARM versus x86 and I'm like, look, it's not about ARM versus x86, they're both great instruction sets. It's really about the applications and the ecosystem and what you're trying to run on top of it. There's a lot of reasons that you want to be at scale. If I think about what happened 20 years ago, it's just that you had too many instruction sets and many of them were not at scale, and so it didn't scale.
Now you look today and what we're doing going forward, the workloads are changing. That's what's made the GPU so important. Now as we think through AI, as the workloads change, you need somewhat different compute. That's where the choices get made.
KEVIN SCOTT: Look, I think I'm one of the weird people who cares about instruction sets because I started my career writing a lot of assembly language code. In grad school I was a compiler and computer architecture person, having written a software decoder for x86. I think you care if you're implementing something at the lowest level of the stack. But that's such a tiny little minority of the development activity and like everybody else, what you want is low power, high performance and cheap.
It's those three things. To your point, I think, scale drives that. LISA SU: Would you agree, Kevin, that as you think about where things are right now, the relative benefit of programming at the lowest lowest level is perhaps less just because there's so much computing power and that's why people are moving up the stack for speed and agility and all of that stuff? What do you think about the relative tradeoffs at the lowest level programming? KEVIN SCOTT: I think it's going to be the same thing. I think there's going to be this very small minority of folks and I think it's going to get to be a smaller and smaller minority over time who need to deal with the lowest level details of the compute stack because they're trying to do something that's right on the edge of possible or they have to wring every last bit of performance out of the lowest level things. I think eventually most of that gets abstracted away for most developers. That's what you want. It's always happened.
LISA SU: I completely agree. That's certainly everything that we see. Technology is changing so fast. The basic foundation of the computer is getting so fast that you can make up for whatever, let's call it loss of abstraction pretty easily.
KEVIN SCOTT: I do have this concern though, I mentioned this a few minutes ago. Even though that's true, you still need this population of engineers who are excited about those low level details. You need to hire them, we need to hire them for folks building low level system software.
I do worry when I look at what some kids are learning in computer science curricula now. Your abstraction level that you're operating at is so high, whether or not you really deeply understand the full stack that you're operating on top of and then have the opportunity to even get interested in being one of those systems people who's poking around at the low levels because that doesn't go away at all. LISA SU: Actually, Kevin, I think you're totally right. As we think about how do you have the best engineers or the brightest people, you want them to have that breadth of experience. It's really important. You can still specialize,
but the breadth of knowing really know how computers work and what do you need to do to enable that, I think, is super important. One of the things that happens for a lot of the folks that I talk to is how do we get enough people interested in hardware, because software is a sexy place. I try to say, look, everyone's different in their interests, but there is so much that can still be done around the optimization of hardware and driving that. KEVIN SCOTT: More now than in the past 20 years, I think this is the most exciting time in hardware that we've seen in a few decades. LISA SU: I completely agree. KEVIN SCOTT: I go into work every day and I'm just completely surprised at the things that I'm working on.
When I was in college and grad school, I did internship at the National Center for Supercomputing Applications and wrote a whole bunch of stuff for the Thinking Machines, CM-5 supercomputer. I had an internship at Silicon Graphics right after they bought Cray Research and was working on the Origin 2000, this big cash coherent pneumo-machine that they built that was super innovative. Then I left grad school and precisely none of that mattered for 20 years. Now, all of it, it matters again. You really do have to think about some of these old high performance computing principles to write some of the software that we're building today. Computer architecture matters again. It's awesome.
LISA SU: It is. Certainly for some of the stuff that you're doing and Microsoft's doing, you are absolutely pushing the envelope on everything that we think about as it relates to hardware and systems. KEVIN SCOTT: Let's go back to your career progression. You were at IBM and then at some point, was the next step, joining AMD? You've been there for a while now.
LISA SU: Yeah. I was at IBM for 12, 13 years. Did a lot of stuff around our semiconductor R&D and the next generation processor technologies. I'm a semiconductor person.
That's who I am through and through, and from an opportunity standpoint, getting to be able to influence at a larger scale. I actually went to Freescale Semiconductor first. KEVIN SCOTT: Interesting. LISA SU: I was at Freescale for five years. I had your title. I was CTO at Freescale as the company was thinking how to reshape their portfolio and then I ran their networking and multimedia business for a couple of years.
By that time I'd already moved to Austin. Now I'm officially a Texas person. Then I had the opportunity to come join AMD. I joined AMD about 12 years ago, something like that.
It's been a great ride. We've gone through a set of things as we reshaped the company. But like I said, I haven't been far from processors anywhere near my career.
Somehow processors find me or I find them. KEVIN SCOTT: When in your career did you decide that you wanted to lead teams of people? That leadership was a thing that you either enjoyed or believed was necessary to get done, what needed to be done? LISA SU: It was probably in my early years at IBM. One of the things that I had to decide is, someone asked me, I think my manager asked me, hey, do you want to be an IBM fellow or do you want to be an IBM vice president? At the time, I was like, that's an interesting question. I actually thought what was most interesting to me. Yes, I liked working on my own research and I had some good ideas, but what was much more fun was seeing teams get together and do things that frankly we didn't think were possible.
The early memories of, hey, you're on a project, you know you have to ship something to a customer at some given time and nothing works. That's what I enjoyed the most. I enjoyed thinking about, how do I pull this together? How do I pull teams together? The answer to that question was, I don't think I'm smart enough to be an IBM fellow, so I guess I'm going to try to be an IBM vice president. I had the opportunity to lead small teams that became medium teams, that became larger teams. But that's actually really what I enjoy the most, Kevin.
The technology is super fun, but it is even more rewarding to see teams come together and do something that you know, truly is groundbreaking. That's what I've always enjoyed in my career. KEVIN SCOTT: I want to dig into what the career progression was or has been at AMD. But I think the whole arc of your career is extraordinary. LISA SU: Well, thank you. KEVIN SCOTT: You're the daughter of immigrants to the United States and you go from first generation American to Chair and CEO of one of the most important semiconductor companies in the world.
Your path through that was just being technically excellent, like getting a STEM degree and leveraging technical excellence to get into this fantastic leadership position. That's an inspiration for a lot of people and probably particularly for both immigrants and young women who are thinking about their path. You're probably going to blush me asking.
LISA SU: You're making me feel uncomfortable, Kevin. KEVIN SCOTT: But look, whether you like it or not, you are a role model. So I wonder, how do you think about that job? So in addition to being CEO, like you are a role model for folks who aspire to be like you. LISA SU: Look, thank you for that, Kevin. I think a few things. First of all, as good as one is, one also needs to be sort of in the right place at the right time.
I think I've been somewhat fortunate in the sense that I've found the right place at the right time. I mean, AMD, when I joined, many people asked me, why would you join AMD at that point in time? Actually I never thought about why wouldn't I join AMD. Look, in the United States, how many companies are building high performance processors? There are just not that many that are doing that. I thought this was a place that I could help.
I was passionate about what we were doing. For me, I never said I have to be a CEO. That wasn't my thing. What I said in my mind is it was really important for me to work on something that I thought was important. I love semiconductors. I wanted to be in an industry and in a place where I can make an impact on the industry and AMD has been a great platform for that because I do think that high performance computing and this technology is so foundational for what we have to do.
But to your point about being a role model or helping, a lot of people help me get to where I am. I think the mentors that helped me the most were the ones that told me when I screwed up, frankly. Because everyone can tell you how great you are, but it's much better when someone tells you when you've made a mistake. I've appreciated that.
I think my job or I hope what I can do is, is also help others feel like, hey, it's absolutely possible for you to follow your aspirations and dreams. You'll make some mistakes along the way, but that's okay. I get an opportunity to meet a lot of women who are earlier in their career and much of what I encourage them to do is actually be ambitious and feel like you can actually tell somebody what you want to do. Because there are plenty of people who want to help, but sometimes people feel shy or like, oh, I can't say that. I'm like, yes you can. Yes, you can.
You can absolutely do incredible things and people will be glad to help you along the way. Of course, you have to be good. Of course, you have to work hard.
All those things are true. But I think it's just good to encourage people that, yeah, you can do some amazing things. KEVIN SCOTT: Yeah. I could not
agree more with that advice. Telling people that you have permission to be ambitious and you should advocate for your ambitions is so important. It's crazy to me how many people don't do that or don't have a clear sense in their head of what their ambition actually is. You have just in your time at AMD seen a pretty incredible set of developments in the semiconductor industry. We've continued to make just amazing improvements in process technology.
I know since I've been a professional computer scientist, which is a very long time now. People have been talking about the end of Moore's Law and yet we continue to. I mean like Dennard Scaling is done right, but we have been able to figure out how to economically get more and more and more compute over time. We have figured out how to put that compute to use for crazy things like the whole mobile ecosystem, like what you can do today with PCs, like what you can do with high performance computing for scientific workloads and like now all of this AI stuff. I wonder, over your 12 years what you think are the most interesting trends.
LISA SU: You're absolutely right. We've been talking about Moore's Law either slowing down or ending for the longest time. It really hasn't. It has slowed down, but it has also allowed us to think differently about how we put chips together.
By the way, Bob Dennard was like one of my heroes as a young engineer. I often say to my teams and to others that it's so important to make the right bets on technology because it does take so long for it to really play out. The fact that Moore's law has slowed down has meant that there are different ways to put together chips.
Probably the most important decisions that we've made at AMD, and this was back in, I would say 2014, 2015 timeframe. It is we decided that if Moore's law is slowing down, then the better way to put together chips is to really break them up in these things called chiplets. That was like a really important decision. I remember when we made that decision and I was like, it was almost a bet the company decision, frankly, because we were trying to get a very competitive road map out there. The thought process is, this is the future. This is the future of, how do you put chips together? We have to figure out how to make them smaller because they yield better, they're much more cost efficient, but the interconnect between them is so important.
So how do you make that happen? How do you ensure that from a programming standpoint it doesn't affect software too much? We've seen that idea now on steroids. We just launched our newest AI chip. By the way, Kevin, thank you so much for being with us at that. It's like chiplets on steroids.
It's like 12 chips stacked sideways, up and down and all that stuff. If you had asked me 20 years ago as a semiconductor student or as a semiconductor engineer, I'm like, this stuff is never going to work. It's never going to work. It's too complicated. It requires too much precision for it to work at a level of like 150 billion transistors. But that's the beauty of our industry. It's like, you know what, we found a way to make it work.
I think as we look forward, that's what we have to be looking for is that there are inflection points and technology which enable you to take the next big step. Making those decisions at the right time are the things that I think about because you do have these fundamental limitations that are there in terms of physics. But boy, we have just incredibly smart people. You give them a problem and they figure out there's a way to get around that problem. It's just you have to invest in it. KEVIN SCOTT: Yeah. So for folks
who aren't chip people like let me see if I can try to explain what you just described and you can correct me where I get it wrong. When you open up a PC and you see the thing that is the CPU, that is not the chip. That's the package that for the longest kind of time a single monolithic die set in.
You would print bunch of semiconductors onto a single piece of silicon, you would put it inside of that package, and you would very carefully connect the perimeter of that piece of silicon out to the pins or the balls or whatever the connectors were on that package. What you are describing now is a bunch of the innovation is in the packaging technology and these components. So chiplets are little pieces of semiconductors that you place inside of that big package. They're very complicated ways to interconnect them and then a separate set of ways to connect all of that out to the outside of the big package so that they can sit on a motherboard and do its job. How close did I get? LISA SU: That was perfect, Kevin.
Perfect. To the casual observer, I think they don't have to care about any of those things. All they have to care about is you're going to get more performance at a better cost point.
We talk all the time about in computing, we want to ensure that Moore's law that you would really double the performance every couple of years. Just going the normal way, you can't do that. But using all of these techniques and technologies, you really can extend that performance curve.
Which just gives people like you, Kevin, much more compute for all the great things that you're doing and building. So that's our goal in life, is to make sure that the compute does scale, that you do get more, and that enables applications to do lots and lots more. KEVIN SCOTT: God knows we need it. I was a little bit depressed for a while with computing because it seemed like we had lost our imagination for what you could do with dramatically more compute.
We had gone inward and you were like, okay, how can I power optimize compute, get a certain set of capabilities available on a small battery, which is very important. But the thing that's always really excited me it is like, what can you do if you'd just cost efficiently had so much more compute? I think this current generation of generative AI is one of the answers to that question. It's just remarkable. I don't think we're anywhere near the end of the scaling laws for things. It feels exciting to me more in the way that the Internet did than mobile, just because we are so quickly deploying so much compute and have so many people with these very inventive ideas about what they can go do with this vastly expanded compute landscape that's available. LISA SU: I completely agree.
For the last 10 years, and we've had a lot of computing evolution, it's been more about form factor frankly. We've done lots of great things, but I think going forward, this is to me the most exciting time in my career. Absolutely, hands down. I would've said a very similar thing, I think AI is the most important technology of the last whatever, 40 or 50 years. You can see there's so much untapped potential. As much progress as we've made in what computing can be used for, it's still hard to use.
It's still not quite as accessible as it should be, and generative AI just brings a whole new dimension for how we can use computing. I completely agree with you. It's just amazing what's in front of us right now.
KEVIN SCOTT: The thing that always excites me is when you let people have less constrained imagination. When I was a little kid and I was reading science fiction books and watching the first Star Trek series and all of these optimistic science fiction movies and books that were around then, the computers were incredibly powerful. This is before personal computers even existed, these folks were imagining what a computer could be. Maybe some of the reality of what the computing revolution actually became, in a way constrained people's imagination. I think what's happened in the past, handful of years has unconstrained people's imagination.
Again, sometimes in weird ways but I think in mostly incredibly optimistic ways. That's exciting because, I don't know about you, I'm of the firm belief that my imagination is not enough, we need everybody imagining. LISA SU: I completely agree. KEVIN SCOTT: Let's talk a little bit about where you think AI compute is going.
You just announced this really, what I would call a breakthrough in AMD's AI compute roadmap with the MI300 which is a very powerful GPU tuned for AI workloads. We've done a lot of work together to try to figure out how to get the most powerful AI workloads working on this system. But that's just one point in time.
Obviously y'all see the same future trajectory that we see and you must be thinking all awesome things about what that future looks like for the semiconductor world. LISA SU: Yes, absolutely. First of all, Kevin, again thank you for your partnership, the tremendous partnership between Microsoft and AMD. From my perspective, AI is such an empowering technology and it's empowering in many dimensions. We talk a lot about the data center from the standpoint of these large language models, the great work that Microsoft and OpenAI and others are doing in just training the largest models in the world. You need lots of compute for that and that's where we come in.
It's actually quite a continuum as to where AI is going to impact our lives. We look at it as, in the data center, you need, it's called the big iron, so that you can train and infer the most complex models. You have the opportunities at the edge when you think about all the data that's at the edge. Then you have the opportunities at the client and reshaping what PCs do, what mobile phones do, and all of that requires AI capability. Although it may not necessarily be exactly the same technology, I think they all want to interoperate together. It has been a super busy several years from the standpoint of really extending our roadmap from, let's call it more general purpose processing to more of the AI capability.
As we go forward I think we're going to see AI in all of our computer products. Whether data center, edge, or client. It's a fun place to be. We're super happy with the work that we're doing, certainly in Azure, but also on the PC and Windows side. We're very excited about what's going on with the Windows evolution and the Copilot capabilities there as well.
KEVIN SCOTT: I think one of the interesting things, and again it just gets abstracted away from folks because you don't need to think about all of this to make an API call to the Azure OpenAI API service. Y'all have understood this for quite a while because you have some of the most powerful supercomputers on the top 500 list for scientific workloads. When you're building these big systems, you have to think about everything, how you get power into the data center, like how you cool things, how you design racks, how you build your networks.
I think that's the other really exciting piece here. It's not just about the chips, but it's about everything that you have to put around the chips. You really have to have partnerships like the one that we have to think about the full system design, because otherwise you have one highly performant thing and if everything around it is not equally high performing, then it just doesn't work.
LISA SU: I think that's what we all view as the opportunity. The opportunity is through deep partnerships like what we have to really move computing to the next level of, yes, chips, systems and then also just what you're doing in terms of the model development itself. Bring those things synergistically together, we can build better overall systems going forward. KEVIN SCOTT: Cool, well, we are just about out of time here, and so the last thing that I ask everyone on the podcast, even though your job is one of the most demanding in the world right now, and it's gotten nothing but more demanding over the past year for sure.
But I do want to ask you what you do outside of work for fun? LISA SU: Well, I have to say you're right. I think work is fun. I think you think work is fun too. KEVIN SCOTT: It's balance.
LISA SU: Work is tremendously fun, but outside of work, I like to play golf. I have to say my golf handicap though, has definitely gone up in the last few years. I haven't played enough, and I'm a foodie Kevin. We love to have great food and a little bit of Bordeaux wine once in a while. And it's just an opportunity to relax and enjoy all the wonderful things in life.
KEVIN SCOTT: Awesome. Well, thank you so much for taking time out of your very busy schedule to do this with us today. It's been great to hear more about your story. Again, I'm so grateful, not just for the partnership, but for everything that you've done in your career and the inspiration you are for hopefully generations of young engineers. We need more Lisa Sus in the world for sure.
LISA SU: Well, thank you so much Kevin. We need many more Kevin Scotts in the world too. It's really an honor and pleasure to be here with you today and look forward to all that we'll do together. KEVIN SCOTT: Awesome. Thank you. CHRISTINA WARREN: What a fantastic conversation with Lisa Su.
There were so many things that stuck out to me. Her background, it's impressive just to see her career arc and all the different places that she's been. I loved listening to the two of you reminisce about how the hardware industry, silicon industry has evolved over the last 30 years or so. But one thing that really, I think was great for me as a woman, and I think also just for people in general to hear, was her comment about how important it is for people to ask for what they want their careers to be and to be open with their ambitions. What a remarkable and simple comment, but also something that I think you made this comment a lot of people don't do, and I was just really struck by that. KEVIN SCOTT: Yeah, it is so important.
It's two parts I think you have to be clear with yourself, what it is you want and why you want it, and then you need to tell people and act on it. I think this is consistent, good advice that a lot of people give, like Warren Buffett's advice is the best investment you can make is in yourself. CHRISTINA WARREN: Yeah. KEVIN SCOTT: It always amazes me how self-conscious people feel sometimes about expressing their ambition and asking for help and opportunity. I think they would be really surprised at how open people would be to like getting on the journey with them. CHRISTINA WARREN: No, I mean, I totally agree and I think it's a few things.
I think one, it's that there is this idea in society that maybe you're not supposed to do that, that you're supposed to be more humble, be less outward with your ambitions. I know that's true, especially for women, which is one of the reasons why I really appreciated what Lisa said, and I appreciate especially having people like her as examples to the rest of us. But I think the other thing, to your point, about asking for help, I think a lot of us are just afraid to admit we might need help. Even though when someone asks us for help, I think very few of us ever would look at that as anything but wanting to offer that if we can and certainly not looking upon that request with judgment. KEVIN SCOTT: If you're making a judgment at all, the judgment is thank God this person is asking for help. Because we're aligned in wanting to get something ambitious done and if they need help, please ask for the help as soon as you need it.
CHRISTINA WARREN: No, no, you're exactly right. I think even though these things make sense and we hear very successful people say these things, I think that it can be hard for us to sometimes take that action on. But again, I think it's so great that we have people like Lisa who are examples of this. Her career is so interesting and what's been happening in silicon and computing GPUs over the last 5, 6, 7, 8 years has been just remarkable. AMD has been leading the way with that.
For you as a person who is both into hardware and software, was there a moment when you started to notice, we're now back to where things are interesting again? KEVIN SCOTT: Yeah, I really do think it's been the past two or three years. Look, I don't mean to throw any shade whatsoever on the mobile revolution, because I was one of the leaders in one of the companies that helped with the mobile revolution. I was very excited about it. But there is a thing that we are returning to now that I haven't seen since the beginning of the Internet, where you just have an abundance of compute all up, and lots of interesting ideas with this new AI platform about what to go do with it.
It feels really liberating in a sense, like just the things I can imagine doing with it and then just seeing everybody around me having these great ideas and then having a platform that they can go use to try these ideas out, it feels very Internetish to me. CHRISTINA WARREN: Yeah, I think I agree with you. At first, I was a little bit hesitant because I think I have a stronger affinity towards what the mobile revolution meant. But if I'm being honest, a lot of the ideas of the mobile revolution we didn't have the compute to really take advantage of what people wanted to do. I think maybe that's the difference whereas when the Internet became a thing, we were at the point where hardware technology and the software were aligned and it felt limitless.
With mobile, it took a few years for mobile chipsets to become powerful enough, or wireless Internet to become powerful enough. But now to your point, I do feel like maybe we are at that correct alignment where we have these massive amounts of compute, thanks to companies like AMD, that people can access from clouds or they can build their own Threadrippers if they want to be like you. But they have access to the compute and so finally the two goals are aligned. KEVIN SCOTT: Yeah, and it just it's exciting to see entrepreneurs be entrepreneurs like it really, really is. I can't even imagine what I would be doing if I were you know like a 20-something-year-old young engineer right now. It's exciting for me like as an older engineer later in my career.
But I would just be so happy right now and so energized to have all of this power to do crazy things that were hard to imagine just a year ago. CHRISTINA WARREN: Yeah. KEVIN SCOTT: Imagining everything that is still ahead of us. It's so much fun right now. CHRISTINA WARREN: I completely agree, it's so much fun.
I'm with you like if I were in my early 20s I think I'd probably be at an AI start-up because there's so much exciting things happening. As you said, it does just feel like one of those moments where the possibilities are limitless and a lot of that is because of the hardware. Hardware is sexy again and that hadn't been the case for a long time. KEVIN SCOTT: Absolutely, true. Yeah, and like that to my compiler nerd, computer architecture nerd beginnings makes me very happy.
CHRISTINA WARREN: Definitely. That's all the time we've got for today. A big thanks to Lisa Su for joining us. If you have anything that you would like to share with us, please e-mail us any time at firstname.lastname@example.org and you can follow
Behind the Tech on your favorite podcast platform or you can check out our full video episodes on YouTube. Thanks for listening. KEVIN SCOTT: See you next time.