Behind The Tech with Kevin Scott - Lisa Su, Chair and CEO, AMD

Episode Date: January 8, 2024

As Chair and CEO of AMD, Lisa Su leads the transformation of the strategy and product execution of one of the fastest growing semiconductor companies in the world. She’s the recipient of numerous awards, and a recent appointee to the President’s Council of Advisors on Science and Technology. In this episode, she discusses her upbringing as the daughter of a mathematician, her early interest in engineering and figuring out how things work, and why she thinks this is the most exciting time in hardware in recent decades.  Lisa Su | AMD   Kevin Scott   Behind the Tech with Kevin Scott   Discover and listen to other Microsoft podcasts.   

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, you know, build chips and build very, very, you know, they weren't the most advanced chips in the world, but to me, it was like amazing. It was amazing that you could build, you know, 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. And, you know, that's how I got into hardware and that's how I got into semiconductors, actually. 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. So 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. Hello and welcome to Behind the Tech. I'm co-host Christina Warren, Senior Developer Advocate at GitHub. And I'm Kevin Scott. And today we are bringing you a conversation with someone who is such an important part of the actual materials that make up our technology and just an incredibly cool person. Yeah. Lisa is just sort of 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. Yeah. Like I even have a AMD like custom built machines sitting under my desk right now that I've been very proud of owning for the past three years. 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. And so 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, and I'm really looking forward to this interview.

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 and 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.

Oh, it's great to be here with you, Kevin. Thanks for having me. Yeah, so 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 with like, what, how did it begin? Yeah, absolutely. So, you know, Kevin, I was, 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, you know, we would sit at the dining room table, and he would, you know, make me do multiplication tables with him.

I had to be decent in math. But I've always really enjoyed sort of just seeing how things work and building things. So one of my earliest engineering memories was my brother and I were playing with his remote-controlled car, and it was kind of going down the hallway. And then it just suddenly stopped. And I was like, why did it stop? And so, you know, I opened it up and saw there was a loose wire. And if I put the wire back in, it started working again. And that was just super interesting that, you know, that's how things worked.

So how old were you? I was I don't know. I might have been like 10 or something like that. Yeah. And my brother was, you know, my brother's like was younger, my younger brother. And yeah, we just kind of got curious about how things work. Yeah, I think those are it's really interesting, those serendipitous moments when you're young, where you, you go from like, looking at something

is like this inscrutable, almost magical thing that you don't understand to being able to like, have some kind of agency over it, like you peer into how the thing works. And like, you understand something like I think they are just super interesting moments. Yes, no, absolutely. Because you're just so proud of thinking, wow, like I learned something there. And, and, you know, that's that kind of carries with you. Yeah. And so you were you were a double E. Like how you know, as you're 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?

You know, I don't think I could say I knew Kevin, I, again, in high school, I was I have to admit, I was kind of a nerd. So, you know, I did things like, you know, the math team, and, you know, I did things like, you know, the math team and, you know, projects like that. But I happened to get into MIT and that was my first choice. And so I went to undergrad at MIT and at MIT, like everyone's an engineer. And, you know, much of the starting class is either electrical engineering or computer science. And so I think that helped me kind of go in that direction. You know, sort of the big, big decision wasn't, are you going to be an engineer? It was, are you going to be a hardware person or a software person? It was,

are you going to be an electrical engineer? Are you going to be a computer scientist? And 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. And how did you gravitate towards being a hardware person? Because I sort of had the same thing where I did, you know, when I was young, like a little bit of electronics and electrical engineering stuff and a little bit of software stuff. And I like clearly gravitated towards software.

I used to tell myself it's because like software is inherently, inherently better and like it's faster. And yeah, I mean, like that's the, that's the lie you tell yourself when you're, you know, a teenager or like in your twenties. But like other people like have similar beliefs in the, in the other direction. Like I'm just sort of curious, like how did you know that like you wanted to be a hardware person?

Well, you know, I had I had two experiences. I had, you know, experience in software where I was like, you know, helping, you know, sort of a lab program. One of the things that's nice about MIT is that they really encourage sort of undergraduate research. So, you know, in addition to the coursework, they encourage you to get, you know, sort of side jobs. And I don't know, I must have got paid $5 an hour or something like that. But it allowed you to work in labs with people. And so I had two experiences working in labs. I had one that was more of a software thing, which I was, you know, helping program, you know, whatever, some professors' experiments. And then I had one that was a hardware thing, which was in semiconductors. And I got to do basic grunt work for the semiconductor graduate students, but I was building something. So, you know, I was putting wafers into a big reactive ionetcher. I was running the experiment. I could look at it in the microscope. And that's how I fell in love with hardware. I mean, 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, you know, build chips and build very,

they weren't the most advanced chips in the world, but know, build chips and build very, they weren't the most advanced chips in the world. But to me, it was like amazing. It was amazing that you could build, you know, 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. And, you know, that's, that's how I got into hardware. And that's how I got into semicondu and that's how I got into semiconductors, actually. 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 Like I wonder, like I'm not, I'm not a double E. And so I don't even know whether you can get that experience that you had anymore in a material science program or a double E program at a university where you get that

visceral feel for what it's like to actually make a semiconductor. 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, you know, very book smart. And then there's, you know, sort of like people who are just pragmatic and have had a chance to experience different things. And I'm definitely the latter. You know, I think I learned through experiences. And I think those experiences are so important. Right. I remember, you know, sort of the first classes I took as as an undergrad. And it was things like, you know, building your own little computer. And yes, you have to you have to build the circuit, but you also have to program it.

Or, as I said, you know, building your first semiconductor device and and just seeing how you go through each of those steps. I won't say that everybody loves those experiences, but I certainly do. And, you know, not that, you know, 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. And it's, it's kind of stuck with me with the notion of, it's so important to see the results of what you're doing. And it's kind of stuck with me with the notion of it's so important to see the results of what you're doing. And, you know, I love the fact that I can build products that I can touch and feel and, you know, walk into Best Buy and see those products or walk into your data center and see those products. So that's what I enjoy. Yeah. Well, and 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 that you get when you're like building your own PC, like where you buy a motherboard and a case and a power supply and a CPU. And like, you just sort

of are assembling this thing physically and you at the end get to like have this working physical artifact. Yeah. So Kevin, am I, am I encouraging to come over to the hardware side? No, look, I've been doing both for a very long time. And I'm thinking right now, actually, so the machine that I'm on right now taping this has like an AMD 32-core ThreatRpper 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. Oh, Kevin, that's very exciting. I must send you our latest Threadripper because we just kind of got the next generation out there. It's pretty cool.

But look, I completely agree with you. I mean, the opportunity to kind of 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. Yeah. So you, you're majoring in electrical engineering at MIT. And then what's next? You go to grad school, you go straight into the workforce. Like, how are you making that decision? Because like, that's a really interesting time in your career. Yeah, it's, you know, I was a 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, you know, at the time, all my friends were graduating, they were getting jobs, you know, they were moving to cool places. But I felt like I wasn't done learning and there was still more, you know, to learn. And I really appreciate, you know, sort of my PhD advisor. You know, his name was Dimitri Antoniadis. He was one of the people who built sort of the very early simulation capabilities for semiconductors. Like I felt like there was more, you know, to learn. And so, you know, I decided to get a PhD and my focus was semiconductor devices. So I was building, you know, a quarter micron devices, which at the time was very, very advanced. You know, today we're talking about two nanometer, but hey, back then

it was, you know, sort of state of the art, you know, type things're talking about two nanometer, but Hey, back then it was, it was, it was, you know, sort of state of the art, you know, type things. And we were thinking about, you know, how do we, how do we push the envelope on, on scaling? Cause you know, you know, even then people were talking about whether Moore's law was ending, right. Obviously didn't end. So yeah. So I so I worked on something called silicon on insulator devices. And it was it was a lot of learning. But it was also like, like fun to be able to think that you're doing something that is, you know, sort of state of the art research as as as part of your your studies. 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 like, I kind of think that the value is in the sort of discipline of getting a very complicated thing done and synthesizing like incredibly complicated information. And yeah, just learning how to be a researcher or like someone doing something incredibly sophisticated around a whole bunch of other people who have the same like high ambition level for the things that they're doing. So I'd love to get your perspective on like what the value of that degree was for you. Yeah, absolutely. So now you have to understand

my perspective. Like I was so impatient, Kevin, as a student. So as soon as I started the PhD program, I was like, I need to finish as soon as possible. So 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. It is not whatever project you do. Some are great projects. Some are more esoteric projects. But it's an opportunity to really, for me, it was how to learn to think about solving very difficult problems. And 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? And how do

I contribute something to the industry or to academia where you don't – the answer isn't clear. And the process of doing that over – for me, it was four years, three, four, five years, really gives you the confidence that you can contribute sort of at the highest level in a certain field. And that's what I take away from it. I just think it teaches you how to think. And, you know, what we, you know, what we do, what you do, what I do, you know, all day long is, you know, how do we solve very, you know, interesting, tough problems. And, you know, sort of that time gave me sort of the confidence that, you know, you could do that. And 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. Yeah. Yeah. And I think the thing that you said about like, they're very tough problems and they're also ones that no one else has solved before. So like, you can't go consult someone to say like, hey, how did you do exactly this? Or skip ahead to the answer. Like there's no skipping ahead. Like you just got to sort it out. Yes.

So after MIT, what was your first job? You know, 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. You know, I live, by the way, I live in Texas now. But at the time, it was so far from home for me. So I ended up spending the majority of the early part of my career at IBM in New York. And I joined what was TJ Watson Research Center and then the IBM microelectronics team. And so were you working on the risk processors that they were building there? I was. I was. I was always, you know, I spent my time in their, you know, sort of process technology area as we were, you know, continuing to look at the next generation technologies.

But, yeah, the first processor I've been working on processors like forever. I mean, like, you know, it's been, I don't know, now 30 years. The first processor that I worked on at IBM was actually a power PC processor that went into PCs. It actually went into PCs as well as into some of the larger server systems at IBM. Yeah. I remember I went to a science and technology governor's school, uh like which uh had us do a bunch of internships and i interned at a place that had uh that had a power pc like one of the very very first ones that i just remembered like what an amazing uh amazing thing that was uh like it was super

super fun i mean it was um again the the idea of, you know, sort of risk processing. And, you know, for us, it was how do we get the performance and power and all that in the right place? So, yeah, it's sort of an interesting, interesting thing. I like I don't I don't know whether you want to talk about this or not. But, yeah, 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 and the DEC part,

the 21164, 264 series. The alpha stuff. Yeah, 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 RISC instruction set machines, and 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. And like, I don't know if you reflected any on that. Yeah, you know, it's interesting. I hadn't actually bridged the parallel between the two. I think what 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 sort of the consolidation, I don't think it's been so much about the instruction set. I mean, 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 like, look, it's not about ARM versus x86. They're both, you know, great instruction sets. It's really about sort of the applications and the ecosystem and what you're trying to run on top of it. And, you know, some, you know, there's a lot of reasons that you want to be at scale. So, you know, if I think about what happened, you know, 20 years ago, it's just that you had too many instruction sets and many of them were not at scale. And so it just wasn't, you know, it didn't scale. And now you look today and what

we're doing going forward, the workloads are changing, right? I mean, that's what's made the GPU so, so important now, as we think through AI, as the workloads change, you need somewhat different compute and that's where the choices get made. Yeah. I think I'm like one of the weird people who cares about instruction sets because I started my career writing a lot of assembly language code and 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. Everybody else, what you want is like, low power, like high performance and cheap. And, you know, it's like those three things. And to your point, like, I think scale drives that. And 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 of the lowest level programming versus? 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. And I think eventually most of that gets abstracted away for most developers.

And that's what you want. It's what's always happened. I completely agree. I mean, that's certainly everything that we see is and the technology is is changing so fast that, you know, the basic foundation of the compute is getting so fast that, you know, you can sort of make up for whatever. Let's call it loss of of abstraction, you know, pretty, pretty easily. Yeah, the thing, you know, I do have this concern, though, like I sort of 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 like you need to hire them like, you know, we need to hire them for folks building low-level system software. So 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 deeply understand like 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. Yeah, actually, Kevin, I think you're totally right. I think as we think about, you know, sort of, you know, how do you have the best sort of engineers or, you know, sort of 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 how computers work and what do you need to do to enable that, I think is super important. You know, one of the things that happens in, you know, for a lot of the folks that I talk

to is how do we get enough people interested in hardware? Because, you know, software is a sexy place. And I try to say, look, you know, everyone's different in their interests, but there is so much that can still be done around optimization of hardware and driving that. So more now than in the past 20 years. Like, I think this is the most exciting time in hardware that we've seen in a few decades. I completely agree. Completely agree.

So I go into work every day and I'm just completely surprised at the things that I'm working on. So, you know, 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, CM5 supercomputer. I had an internship at Silicon Graphics right after they bought Cray Research and was

working on the Origin 2000, this big cache-coherent NUMA machine that they built that was super innovative. And then I left grad school, and precisely none of that mattered for 20 years. And 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. And computer architecture matters again. It's awesome. Yes, it is. It is. I mean, 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. So let's go back to your career progression. So you were at IBM, and then at some point, like, was the next step joining AMD? Like, you've been there for a while now. Yeah. So I was at IBM for 12, 13 years. You know, did a lot of stuff around our, you know, semiconductor R&D and, you know, sort of the next generation processor technologies. And then, you know, I'm a semiconductor person, right? That's who I am through and through. And from an opportunity standpoint, you know, sort of getting to be able to influence at a sort of larger scale. So I actually went to Freescale Semiconductor first.

Oh, interesting. Yeah, yeah. I was at Freescale for five years. I was actually, I had your title. I was CTO at Freescale as a company was thinking how to reshape their portfolio. And then I ran their networking and multimedia business for a couple of years. And by that time, I'd already moved to Austin. So now I'm officially, you know, a Texas person. And, and then I had the opportunity to come join AMD. And so I joined AMD about 12 years ago, 12 years ago, something like

that. And it's been a great ride. You know, we've gone through, you know, 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. So 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 believe was necessary to get done what needed to be done? Yeah, it was probably in my early years at IBM. You know, one of the things that I had to kind of decide is, and 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? And at the time, I was like, huh, that's an interesting question. I actually thought that sort of what was most interesting to me, yes, I liked working on my own research. And, you know, I had some good ideas. But what was much more fun was seeing teams get together and do things that, you know, frankly, we didn't think were possible. And, you know, the early memories of, hey, you're on a project, you know, you know, you have to ship something, you know, to to a customer at some given time and like nothing works. That's what I enjoyed the most. Like I enjoyed thinking about how do I pull this together? How do I pull teams together? So the answer to that question was, I don't think I'm smart enough to be a IBM fellow. So I guess I'm going to try to be an IBM vice president. And, and I had the opportunity to lead, you know,

sort of 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, you know, sort of rewarding to see teams come together and do something that, you know, truly is groundbreaking. And that's what I've always enjoyed in my career. And, you know, like I want to dig into like what the career progression was or like has been at AMD. But, you know, I think the whole arc of your career is extraordinary. Thank you. you're the you know the daughter of uh immigrants to the united states uh and you go from uh

you know like first generation uh you know american to chair and ceo of one of the most important uh semiconductor companies in the world uh and like your path through that was just being technically excellent, like getting a STEM degree and, and, you know, leveraging technical excellence to like, get into this like fantastic leadership position. And like, that's a, an inspiration for a lot of people. And like, you know, probably particularly for both immigrants and young women who are thinking about their their path. And so I like I wonder how how do you think about you? You probably are. You're probably going to blush me. You're making me feel like uncomfortable, Kevin.

Yeah. But but so look, you whether you like it or not, you are a role model. And so I wonder, you know, how do you think about like that job? So in addition to being CEO, like you are a role model for for folks who aspire to be like you? Yeah, well, look, thank you for that, Kevin, I think, well, I think a few things. I mean, first of all, I was, as as, as good as one is, one also needs to be sort of in the right place at the right time. And I think I've been somewhat fortunate in the sense that I've found sort of the right place at the right time. I mean, you know, AMD, when I joined, you know, many people asked me, well, why would you join AMD at that point in time? And actually, I never thought about why wouldn't I join AMD?

I mean, look, in the United States, you know, how many companies are building high performance processors, right? They're just not that many that are doing that. And I thought this was a place that I could help. I was passionate about what we were doing. Like, for me, like, I never said, like, 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. Like I wanted to be, you know, I love semiconductors. I want to be in an industry and 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, much better when someone tells you when you've made a mistake. And so I've appreciated that. And I think my job, or I hope what I can do, is also help others feel like, hey, it's absolutely possible's, it's absolutely possible for you to, um, you know, sort of follow your, uh, your aspirations and dreams. You'll, you'll make some mistakes along the way. Um, but, uh, that's okay. And so, um, you know, I, I get an opportunity to meet a lot of, um, uh, women who are earlier in their career and much of what I encourage them to is, is actually be ambitious and, uh,

you know, feel like you can actually tell somebody what you want to do because there are plenty of people who want to help, but you know, sometimes people feel, um, you know, shy or like, Oh, I can't say that. Um, I'm like, yes, you can, you know, the, yes, you can, you can absolutely, um, do, um, 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. Yeah. I could not agree more with that advice. Like telling people that you have permission to be ambitious and you should advocate for your ambitions is so, so important. It's crazy to me how many people don't do that or like don't have a clear sense in their head of what their ambition actually is. You have, just in your time at AMD, like, seen a pretty incredible, like, set of developments in the semiconductor industry.

So, like, you know, we've continued to make just amazing improvements in process technology, you know, like I know since I've I've been like a professional computer scientist, which is a very, very long time now, people have been talking about the end of Moore's law. And yet we, you know, we continue to I mean, like Denard scaling is done right. But we we have been able to figure out how to like economically get more and more and more compute over time. And we have, you know, 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. So, you know, I wonder, you know, over your 12 years, like what you think are the most interesting trends? Yeah, no, that you're, you're absolutely right. You know, Moore's law, I mean, 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, you know, allowed us to kind of think differently about how we put chips together.

By the way, Bob Denard was like, you know, one of my heroes as a young engineer. And I think what we've learned through this process is, you know, I often say and, you know, 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. So, you know, the fact that Moore's law has slowed down has meant that there are different ways to put together chips. Like one of the probably the most important decisions that we've made at AMD, and this was back in the, I would say, 2014, 2015 timeframe, is we decided that if Moore's law is slowing down, then the better way to put together chips is, you know, to really break them up in these things called chiplets. And 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, very competitive roadmap out there. And 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 put them, make them smaller because they yield better. They're much more cost efficient, but the interconnect between them is so important. And so how do you make that happen? How do you ensure that from a programming standpoint, it doesn't affect software too much?

And now we've seen that idea now on steroids, right? We just launched our newest AI chip. And by the way, Kevin, thank you so much for being with us at that. It's like chiplets on steroids, right? It's like 12 chips stacked on top of sideways, up and down and all that stuff. If you had asked me 20 years ago as a semiconductor student or as a semiconductor,

you know, sort of engineer, I'm like, this stuff is never going to work. Yeah. Never going to work. Like 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. Now, I think as we look forward, that's what we have to be looking for is that there are inflection points in technology which enable you to take the next big step. And making those decisions sort of 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 like just incredibly

smart people. You give them a problem and they figure out, well, there's a way to get around that problem. It's just, you have to kind of invest in it. Yeah. And so for folks who like 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. So for when you open up a PC,

like 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. So you would print a bunch of semiconductors onto a single piece of silicon. You would put it inside of that package. And you would put it inside of that package and like 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. And like what you are describing now is like a bunch of the innovation is in the packaging technology and in like these components. So chiplets are like little pieces of semiconductors that you place inside of that big package. Like they're very complicated ways to interconnect them. And then a separate set of ways to, you know, connect all of that out to the outside of the big package so they can sit on a motherboard and do its job. So how close did I get? That was perfect, Kevin. Perfect. I think that 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 sort of more, you know, a better cost point, right? So we talk all the time about in computing, you know, we want to ensure that, you know, Moore's law was that you would, you know, really double the performance every couple of years. Just going sort of 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. God knows we need it. Actually, 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're like, okay, well, like, how can I power optimize compute to get, you know, a certain set of capabilities available on a small battery and, you know, things like, which is very, very important. But like the thing that's always really excited me is like, what can you do? Like if you just like cost efficiently had so much more compute. And I think this current generation of generative AI is like one of the answers to that question. And it's, it's just remarkable. And I don't think we're anywhere near the end of the scaling laws for things. So 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. I completely agree. I mean, for the last sort of 10 years, I mean, we've had a lot of computing evolution. It's been more about form factor, frankly. And we've done lots of great things. But I think going forward, this is, to me, like the most exciting time in my career, like, absolutely, hands down. I would have said, you know, very similar thing. I think, you know, AI is the most, you know, sort of important technology of, you know, the last whatever, 40 or 50 years.

And it's because it's, you can see there's so much untapped potential in as much progress as we've made in what computing can be used for, you know, it's still like hard to use. It's still like, 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. So I completely agree with you. It's just amazing what's in front of us right now. Yeah. And the thing that always excites me is when you let people have less constrained imagination. Like when I was a little kid and I was reading science fiction books and watching, you know, the first Star Trek series and, you know, all of these optimistic science fiction movies and books that were around then, like the computers were incredibly powerful. Like they, you know, and maybe,

and this is before, you know, personal computers even existed. Like these folks were imagining what a computer could be. And like maybe some of the reality of, you know, what the computing revolution actually became in a way constrained people's imagination.

And like, I think, you know, what's happened in the past, you know, handful of years has unconstrained people's imagination again, you know, sometimes in like weird ways. But like, I think in mostly incredibly optimistic ways and like that's exciting because like I'm I don't know about you. Like, I'm of the firm belief that my imagination is not enough. Like we need everybody imagining. I completely agree. Completely agree. Yeah. So let's talk a little bit about like 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 like, that's just one point in time. So obviously, you all, you know, see the same future trajectory that we see. And like, you must be thinking all sorts of like, awesome things about what that future looks like for the semiconductor world. Yes, absolutely. So first of all, Kevin, you know, again, thank you for your partnership, the tremendous partnership between Microsoft and AMD. You know, from my perspective, you know, AI

is such an empowering technology, and it's empowering in many dimensions. You know, 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. But I also look at it as it's actually quite a continuum as to where AI is going to impact our lives. And so, you know, we look at it as in the data center, you need the let's call it 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. And then you have the opportunities at the client and kind of reshaping the, you know, sort of 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. So it has been a super busy year, several years, from the standpoint of, you know, really extending sort of our roadmap from let's call it more general purpose processing to more of the AI capability. And as we go forward, I think, you know, we're going to see AI in all of our computing products, whether data center, edge, client. And it's a fun place to be. But we're super happy with the work that we're doing, certainly in Azure, but also on the PC and Windows side. I'm very excited about what's going on with the Windows Evolution and, you know, the co-pilot capabilities, you know, there as well. Yeah, I think one of the interesting things, and again, it just sort of gets abstracted away from

folks because, you know, you don't need to think about all of this to make an API call to, you know, the Azure OpenAI API service. But you 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. So how you get power into the data center, like how you cool things, how you design racks,

how you build 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 um because otherwise you you have one highly performant thing and if everything around it is not equally high performing then you know it just doesn't work yeah i i think that's what um you know we all view as the

opportunity right the opportunity is you know, you know, deep partnerships like what we have to really, you know, sort of move computing to the next level of, yes, chips, systems, and then, you know, also, you know, just the, just what you're doing in terms of the model development itself and, you know, bring those things synergistically together, we can build, you know, better overall systems, you know, bringing those things synergistically together, we can build, you know, better overall systems, you know, going forward. 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 like 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 like I do want to ask you what you do outside of work for fun. Well, I have to say you're right. I think work is fun. I think you think work is fun too.

That's a valid answer. It's valid. Work is tremendously fun. But outside of work, I like to play golf. So 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 kind of like a foodie, Kevin. We love to have great food and a little bit of Bordeaux wine once in a while. Nice. of great food and a little bit of Bordeaux wine once in a while does well. And it's just an opportunity to kind of relax and enjoy all the wonderful things in life. 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, like, hopefully a generation or generations of young engineers. Like, we need more Lisa Su's in the world, for sure. 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. Yeah. Awesome background, you know, it's impressive just to kind of see her career arc and all the different places that she's been. And I loved listening to the 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 like a remarkable and kind of 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. Yeah, it is so important. Yeah, and it's two parts, I think. Like 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. And 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. And it always amazes me how self-conscious people feel sometimes about expressing their ambition and asking for help and opportunity and like things that I think

they'd be really surprised at how open people would be to like getting on the journey with them. No, I mean, I totally agree. And I think it's a few things. I think one, it's that there is kind of this idea in society that maybe you're not supposed to do that, right? That you're supposed to, you know, 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. I, you know, or if you make it, if you make it a judgment at all, like the judgment is like, thank God this person is asking for help because like, you know, we're aligned in wanting to get something ambitious done. And if they need help, like, please ask for the help as soon as you need it.

No, no, you're exactly right. And so I think even though these things like 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 five, six, seven, eight years has been just remarkable. And AMD has been leading the way with that. And for you as a person who is both into hardware and software, was there a moment when you kind of started to notice, okay, we're now back to where things are interesting again? Yeah, I really do think it's been the past two or three years. And look, I don't mean to throw any kind of

shade whatsoever on the mobile revolution, because like I was like I was one of the leaders and one of the companies that like helped with, you know, the mobile revolution. I was very excited about it. But there 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. And, you know, it feels really liberating, in a sense, like just the things I can imagine doing with it. And then just seeing everybody around me, like having these great ideas, and then having a platform that they can go use to try these ideas out. Like it's it it, it feels very internet-ish to me. Yeah. I think that I, I, I think I agree with you at first. I was a little bit hesitant because I think I have a stronger affinity towards like what the mobile revolution meant. But if I'm being honest, a lot of the ideas, the mobile revolution, we were not ready.

We didn't have the compute to really take advantage of what people wanted to do. And 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 so it felt limitless. And with mobile, it took a few years for mobile chipsets to become powerful enough for wireless internet to become powerful enough.

But now I think, 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, you know, or they can build their own thread refers if they want to be like you. But they have access to the compute and, and so we can finally make the, the two goals are aligned. Yeah. Um, and it's just, it's exciting to see entrepreneurs be entrepreneurs. Uh, like it really, really is. Uh, like I, I can't even imagine what I would be doing if I were, uh, you know, like a 20 something year old young engineer right now, like it would like it's exciting for me is, you know, like a like an older engineer later in my career. But like I would just be.

So happy right now and so energized to have all of this power to go do crazy things that were hard to imagine just a year ago. Oh, yeah. And then just imagining, like, everything that is still ahead of us. It's so much fun right now. I completely agree. It's so much fun. And I'm with you. Like, if I were in my early 20s, I think I would definitely, I'd probably be at an AI startup, right?

And because there's so much exciting things happen. And as you said, like it does just kind of feel like one of those moments where the possibilities are limitless. And a lot of that is because of the hardware, like hardware is sexy again. And that hadn't been the case for a long time. Absolutely true. And yeah, and like that to my compiler nerd, computer architecture nerd beginnings, like makes me very happy. 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 email us anytime at behindthetech at microsoft.com. 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. See you next time.