Building a Thriving AI Ecosystem with Lisa Su

**[Episode Introduction]**

**Sarah Wang:** Artificial intelligence is just the latest in the wave of technologies to increase the demand for high-performance chips. But it's not the only one. The last decade was filled with the proliferation of cloud, gaming, social networks, IoT devices, and more. Which led to some pretty incredible growth stories. Many are familiar with NVIDIA's, as it skyrocketed to the top market cap stock in the world earlier this year, and as of this recording, sitting at over $3 trillion. But let's not forget other semiconductor companies like TSMC, ASML, or Broadcom, each roughly growing by an order of magnitude over the last decade. But today, you'll get to hear about a 50x growth story over the same time period from Lisa Su, the incredible woman who's been at the helm of AMD throughout that run. For now, I'll pass it over to A16Z Growth general partner, Sarah Wang, to properly introduce this episode. As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com/disclosures.

**[Transition to Sarah Wang Introduction]**

**Sarah Wang:** Hey guys, I'm Sarah Wang, General Partner on the A16Z Growth Team. Welcome back to our AI Revolution series, where we talk to industry leaders about how they're harnessing the power of generative AI and steering their companies through the next platform shift. We have a very special guest this episode, Lisa Su, Chair and CEO of AMD. Lisa is one of the most impressive CEOs in history. Since she took over the helm of AMD, the value of the company has grown over 50 times to a market cap of $250 billion as of the recording of this podcast. Even more importantly, Lisa and AMD are democratizing the benefits of Gen.AI, thanks not only to AMD's top-notch chip design but also through an open ecosystem that allows developers to build AI tools across a huge range of use cases. It's no surprise that Lisa is at the helm of this effort. She's been an innovator in high-performance compute across her entire career, starting from Texas Instruments to IBM and Freescale before joining AMD. This pod is an especially exciting one because Lisa is joined by none other than A16Z operating partner Bob Swan, who is most recently the CEO of Intel, one of AMD's fiercest rivals. It's a huge treat to sit in on Bob and Lisa talking shop and reminiscing about their time together in the industry. In this wide-ranging conversation, Lisa and Bob cover the state-of-the-art in AI compute, the chip supply chain, the role of ecosystem partnerships, and where Lisa thinks AI is evolving from here. Let's get started.

**[Conversation Begins]**

**Bob Swan:** It's great to have you here, and thanks again for spending some time with us.

**Lisa Su:** Thank you so much, Bob. It's great to be here with you. Cheers.

**Bob Swan:** Let's jump in on the Inquisition. All right. So, 12 years at AMD, 10 as the CEO. Tell us a little bit about your career journey and how you got to AMD, if you will.

**Lisa Su:** I grew up as an engineer, engineer at heart, went to school in semiconductor devices and really did the majority of my early career at IBM doing R&D around devices. And then as you think about sort of fun things to do in the world, I was always fascinated with the idea that the work that you do in chips is such that you can influence really the way so many things like technology is so important. And so I just loved being at the forefront of high-performance computing and computing all these years. And that brought me to Freescale Semiconductor for five years where I was CTO for a while and then to AMD 12 years ago. Like I used to say when I would tell people, what do you do? Well, I build semiconductor chips and people are like, well, what's that? Like, why should I care about that? Is that important? And now everybody knows what semiconductors are and why they're so important and why they power everything in our lives. And so that's what's fun about the industry that we're in and the fact that you're able to do things that actually matter in the world.

**Bob Swan:** If you think about when you first started, the role of compute in the grand scheme of things relative to today was relatively narrow. That's exactly right, Bob. I think when you think about even the idea of PCs and people using personal computers and everyone needing a computer and then everyone needing sort of a smartphone and then everyone needing big cloud data centers and now everyone needing AI. I do think it has been an evolution of how semiconductors and sort of the power of chips have really infiltrated every aspect of the business world, our personal lives and for the good, right? We're all much better because we have all this technology. And it's almost like, what would we possibly do if we didn't have all this technology behind our desk, in our hand, in our car, wherever compute is happening, everywhere through all sorts of devices. And then along comes this thing called AI. And it's like compute. It's everywhere. Can you just talk about how you see AI, its relative importance? and then over the longer term horizon, where is this going to take us? And where are you and AMD going to take us?

**Lisa Su:** Yeah, I think as you think about all of the various large technology discontinuities that we've seen over the last 30 years, they've all been super important. They start small and they really influence every way we experience technology. I think AI is probably the most important one, I'd like to say, over the last 30 years, 40 years, 50 years, because it's something more than just technology. I mean, it really is. You know, AI becomes the ability for us all to become smarter, more productive, really utilize the incredible data that's out there to help us move forward. So I really see AI, we're just at the very beginning of the AI arc. And it's an opportunity for us to take technology to yet a different level. And for us at AMD, my belief is AI is going to be everywhere in every product that we build. But importantly, it's at the foundation of what enables all of these great applications. And so, yes, we're all building AI compute these days. We're trying to build it as fast as possible so that we can have all of those smart developers really take advantage of the technology. And as you said, it's fascinating because in some ways, AI has been around for such a long period of time. And while new technologies and innovations have a tendency to start slow, this one has moved pretty fast. You're right. It was always around and it was always something that we thought had a lot of potential. But frankly, AI before generative AI was somewhat hard to use. And so it took experts to really unlock the technology. I think the chat GPT moment, as we all remember it, was the moment that AI became easy. We could all talk to our computers and ask it questions. And yes, it is nowhere near as perfect. I mean, we have so much work to do. We still very early. But the fact that we can make technology now so accessible I think is what makes this generative AI arc so interesting. And it's what's accelerated the adoption.

**Bob Swan:** But for AMD, you've always been a high-performance compute company. As you think about the intersection of the company and what it's meant for the industry, and then the overlay of AI, do you see any commonalities with the internet mobility, just the commonalities that positions you so well to capture this opportunity?

**Lisa Su:** Yeah, well, if I just go back a little bit, when I first took over as CEO of AMD, it was like 2014, and it was really a moment where we were like, what should we be when we grow up? And if you remember back in those days, it was like 2014, the whole craze was around mobile phones or tablets. Like everybody was into that kind of thing. And my board even asked me, well, Lisa, AMD can't not be in tablets, right? And I said, well, I'm actually not sure that's our specialty. Our specialty is around high-performance computing. We build big things. As one says, everyone has to know what they're best at, and that's what we're best at. We're best at building large, complex microprocessors or GPUs or with our acquisition of Xilinx, adaptive and embedded computing. And when you look going forward, you see that high-performance computing is really important in the industry in so many places. And it is at the heart of what makes AI possible. Because if you think about what makes AI possible, it's the ability to train these models with hundreds of billions of parameters, trillion parameters, so that they become ultra-smart. And then we can ask it all these questions and it gets most of them right. You need high-performance compute at the heart of that. So it is a great feeling to be. Good place to be, huh?

**Bob Swan:** Yeah, well, it's a great feeling to be in a place where you know that the technology that you're building can really push the envelope on what can be done in the industry. And do you see over time, will it always be the highest performance chip that is going to be the differentiator? Or is it going to evolve for the different workloads and the different multimodalities in the AI world?

**Lisa Su:** Yeah, it's a great point and a great question. I am actually a believer in you need the right compute for each form factor for each application. So right now, there's a lot of energy around building the largest language models and really this large GPUs that are being used for training and inference. But I do see if you look, whether you're at the edge with embedded applications, industrial applications, automotive applications, medical applications, or you're even at the client level in your PC or your phone, you're going to need different types of AI. And so you'll have different engines for that. And that's good. I mean, that's what spurs all of the innovation that's happening around the industry.

**Bob Swan:** The other thing that I found maybe most fascinating about the semiconductor industry is the ecosystem and the importance of the different players. As you think about development of product, how do you deal with both what you need to do, but also the interdependencies with the ecosystem on delivering something requires a bunch of different players? How do you think about that in the context of product development, but to get products to market?

**Lisa Su:** Well, I don't think there's any one company that can do it all. I mean, at the end of the day, we all have specialties and they're things that we're good at. But the opportunity to closely collaborate and partner is so important. We're a big believer in open ecosystems and industry standards and the idea that, hey, I'm building these great processors. They should connect to other people's networking and we should be able to interoperate together. The software ecosystem is super important too. Developers shouldn't have to develop for one company's hardware. Developers should be able to develop what they need and be able to use what's the best hardware underneath. So I think that's part of the evolution of an open ecosystem so that we can get the best innovation out there.

**Bob Swan:** In this constant evolution, and we've seen this debate over time, closed garden, interoperable, what is going to be the predominant winner if there is such a thing in an AI world on open interoperable technologies and interfaces?

**Lisa Su:** I'm a big believer in open ecosystems. Interoperable is really important. Closed walls usually end up being a problem. If you look at sort of the technology arcs of time and in this world where technology is moving so fast, and whether it's a new model or a new hardware technology or new capability, you want to make sure that it's interoperable. Along the same lines, you and other players in the industry recently announced the new ultra-accelerator link and the Ethernet link standards. Is that an example of how you think about open and how you engage with the ecosystem?

**Lisa Su:** Yeah, I think it's a great example. We're all looking at, when you think about these large AI clusters that you need in the future, networking is such an important piece of it. But you do want this choice as to what hardware are you connecting? What's the processor? What's the networking fabric? What's the overall system architecture? So the Ultra Accelerator Link and Ultra Ethernet Consortium are great examples where competitors and peers can come together and say, you know what, we're going to adopt open standards. And we're each going to innovate on top of that. And those are two great examples. And it includes many companies that do compete, but also can cooperate. And if you think about that, that's exactly what an open ecosystem is supposed to be.

**Bob Swan:** And you talked about competitors, industry players coming together. The demand for compute over the last several years has been maybe unprecedented just in terms of its pace and its distribution. And the ability to ramp up supply to meet these incredible demands when you throw in cycle times to put more capacity in, COVID, supply chain disruptions. Have these disruptions or challenges on the supply side, have they impeded your ability to move faster in some areas? And what have you learned from this that will make the next supply constraint be a bit smoother for the industry?

**Lisa Su:** You know, when you look over the last four or five years, probably the largest disruption to the semiconductor supply chain was really around COVID. It was the moment where everyone needed more semiconductors at the same time, which kind of wasn't expected. Usually what happens in the semiconductor market is you'll have one market up, but one market down. Mobile may be really hot, but infrastructure will be down or vice versa. What we saw in COVID was basically every market at the same time had this concentrated effect. And the semiconductor supply chain is actually really good at meeting demand. Actually, we usually overshoot. As you know, Bob, that happens from time to time. But it takes time, right? It takes 18 months, 24 months to really put that all on board. And so I think the industry as a whole has done a good job at bringing more supply on board. The more recent thing as it relates to AI where it super hard to get GPUs that truly is again nobody forecasted what generative AI would need. And so it has taken some time to really build all this advanced packaging capacity and high bandwidth memory capacity. But again, the semiconductor supply chain is good at that. And we just have to get a little bit better at forecasting what long-term demands are.

**Bob Swan:** I do remember exiting 2019, entering 2020, where that normal cyclical nature of the industry, I think we're all looking at it, expecting 2020 to come down. And then COVID hitting for a short period of time, it looked like things were going further south. And all of a sudden, to your point, everybody needed supply. And in many ways, while there was issues materializing, the way the ecosystem comes together in the semiconductor industry and the sophistication of the supply chain, despite the challenges, is pretty impressive.

**Lisa Su:** That's exactly right. I think we've all gotten smarter and better as a result. I think this idea of, hey, let's try to just eke out every last penny in the supply chain has gone a little bit of ways to let's build resilience into the supply chain. When governments are asking, do you have enough semiconductors? I think that gives us permission to really think more broadly about resiliency.

**Bob Swan:** We'll talk about resiliency and government asking. I think it was roughly two years ago to the day, there was this thing called the CHIPS Act. And for our audience, the government signs into law a bill authorizing $280 billion to help in the design and the manufacturing in the U.S. of semiconductors. And then $53 billion of that was authorized to be spent. I know it's still a work in process, but as you think about the CHIPS Act and some of the challenges from the last couple of years, how do you see that helping resiliency of supply chain going forward?

**Lisa Su:** I have to say I'm a big supporter of the CHIPS Act. I never would have thought that five years ago semiconductors would be high enough priority in the U.S. government's view of what needed clear industrial policy. Some people say, hey, it's not enough or does it make a difference? I think it's made a huge difference. It's made a huge difference because what it's really done is it's put at the top of the priority list, resiliency and semiconductor, both manufacturing as well as research and development in the United States. And of course, there's much, much more work to do. As you said, it's a work in progress, but it's a good thing. It's a good thing for the industry that there's a focus here. I'm actually particularly excited about some of the work that's being done on the R&D side because I think there's a whole opportunity to really train the next generation of leaders who will lead the semiconductor sort of research and development as well as future capabilities. So, yeah, I think it's a great thing. And, yes, it's still early days. And we need to make sure that every dollar is spent, is spent for good reasons, and that we get the return on investment on the other side.

**Bob Swan:** Yeah. I couldn't agree more. The dynamics of, we've talked about the ecosystem working together and the importance of the ecosystem to throw the government in there. Obviously, it creates challenges, but industry and government working together to solve really big problems, I think, is a real necessity in some areas. And this is the one where I'm thrilled about the CHIPS Act itself, but the deployment and the proof points, I think, are still in front of us. So it'll be an exciting time. And I hope that the challenges around resiliency will be remembered or rearview mirror.

**Lisa Su:** Yes. Yeah, exactly. Remembered is the best way to frame it. At a time when innovation is happening all the time, you still are a relatively long cycle development time frame. Yeah, very long cycle, really. How do you guys deal with long cycle development with short cycle innovation and what inherent challenges or opportunities that creates for you in the industry?

**Bob Swan:** Yeah, I think the most important thing in our world, especially in hardware, is one needs to try to have a crystal ball. You're never going to predict the future entirely, but you do need to be able to say, hey, these are the disruptions that are coming up. These are the things that we need to pay attention to. Probably the best example that I can think of, and this is one where we had a lot of debate internally, is what's the future of Moore's Law? That's been debated just a little bit. I remember that. I remember those debates. And by the way, I'm a believer in Moore's law has been extended so many times because people are super smart and able to come up with different ways of extending the same principle of more transistors, more capability every couple of years. But for example, bets like advanced packaging. And when do you go to 2.5D and 3D packaging? And for us, we use this technology called chiplets. We didn't know. Like we didn't know at the time when we were making that decision, was it going to be the right bet? But we knew that we had to make that bet. And you really don't figure that out until three to five years out. So your question about how do you know, you don't know, but you try to make sure that you're betting in the right direction. And then you have to be agile enough to adjust accordingly. And that's what this whole world of sort of high performance computing is about.

**Lisa Su:** You talked about the right bets. And you guys have had incredible success on making the right bets. What is the balance between how you learn from your customers about the right bets to make, but also how you lead your customers given the development cycles? How do you strike that balance at AMD?

**Bob Swan:** Yeah, our top two priorities that I tell the company all the time. The first one is you're a tech company. Our job is every day to wake up and build great products. But we do that through having very deep customer relationships because I really do believe that they go hand in hand. Our customers are some of the largest, whether it's cloud manufacturers or OEMs or enterprises in the world, that they see the problems that they're trying to solve. And that's where it's most beneficial is talking to our customers about, hey, what problems are you having? What are you trying to solve two, three, four years out? And then our technologists can really come up with ideas for how to solve those problems. So it's not like it's one for one where we listen to everything people say, but we do listen a lot because that tells us that we're working on the right things. Because whatever we do, you want to ensure that the technology you're building is something that will solve somebody's problem.

**Lisa Su:** Hyperscalar market. Tremendous progress over the last several years. Congrats. Correlation learnings from winning in hyperscalar with this rapid growth from AI. Is there learnings that you've been able to extract from what it takes to win into one? And then how do you translate to winning in AI?

**Bob Swan:** So when we started in the hyperscaler market with our first generation products, our Zen product portfolio, I think we were about maybe 1% share of the server market. And actually, the whole idea of having deep partnerships with customers is really, we needed to be able to say that, hey, it's all about roadmap, right? Yes, it's great the product you have today but it all about can you keep a sustained level of constant innovation many generations out. And I think we have made a lot of progress in the hyperscalers. I love the relationships that we have across the top brands whether it Microsoft or Amazon or Google or Oracle or Meta. It's always about how do we innovate together. I think the AI arc is very similar in the sense that these are big bets that the hyperscalers are making on who their technology partners are going to be. And we want to help them accomplish that. So it is about putting out great technology, but also being very consistent in execution and offering a long-term roadmap.

**Lisa Su:** The progress that you've made on that less than 1% market share pre-Zen is unbelievable. I remember those less than 1% days, not fondly either, just so you know. It's a tough market. It's a tough market as we know, but we must earn it every day. So I'm very cognizant of that. Well, that's what keeps you ahead of the game and progressing forward.

**Bob Swan:** So many years ago, before your arrival, you were not a fabulous company, but post a spin out of what's now Global Foundries, you are dependent on the ecosystem, the manufacturing ecosystem. Can you talk a little bit about the challenge of not only integrating tightly with your customers and the hyperscalers, but also the need to integrate tightly with the fab players as well.

**Lisa Su:** Yeah, absolutely. So it was the right answer at the time for AMD. It was before my time, but to separate the manufacturing operations from the design operation, we just didn't have the volume, the capex, the business model to make that work. Now, what it is today is we get to focus on what we're good at, which is design. And that is what we are focused on. However, we do have to be very tightly partnered with our manufacturing partners. TSMC is our main manufacturing partner for advanced node technologies. We're plotting out far beyond the next few years. We're really looking into the five plus year timeframe of what we need to do. And it is something that you learn. You learn how to partner well, and you learn how to really get advice on these other areas, like where's technology going and how do we optimize our designs. But yes, I think that's part of the ecosystem now. And it's even more complicated because it's not just about silicon. It's about packaging and really how do we put these chips together for very complex, multi-node, multi-chip type things.

**Bob Swan:** And recently, I mean, you talk about the integration and how it's not just about chips anymore, but M&A has been a really important part of your strategic agenda in many ways. And you've done some incredible acquisitions at incredible times. ZT Systems, maybe just talk a little bit about how important M&A has been for you and then illuminate a little bit how you see the role ZT Systems will play in the evolution of solving customers' problems.

**Lisa Su:** Yeah, absolutely. We've used M&A to really round out our portfolio. So if you look at over the last five or six years, we've probably acquired about six companies or so, So some small, some larger, you know, Xilinx was the largest semiconductor acquisition. I think it's still the largest semiconductor acquisition. And that was bringing in, you know, the FPGA and adaptive computing portfolio into AMD, which really brought in our portfolio. We announced the acquisition of ZT Systems. And we're talking a little bit about AI and how fast AI is moving. What we've seen certainly going forward is it's not just about the silicon. The silicon is important, and we're pushing every ounce of getting more computing technology on the silicon in the package. The software is incredibly important, so being able to get just enough AI software people so that we can help our customers and partners utilize our technology. But we're also finding that the integration of hardware, software, and then really systems is critical because now you're building these very large clusters of high performance computing, CPUs and GPUs, and everything from how do you connect them from a networking standpoint, a thermal standpoint, just a reliability standpoint is so important to actually make it productive. That's what ZT Systems is. So it's the third leg of our stool, if you think hardware, software, now solutions. So yeah, I'm very excited about it. It's really an expansion of the problem that we've been solving around how do we enable our customers with the best high-performance compute, and that now extends into the system.

**Bob Swan:** As a student of what's going on in the industry, and you guys in particular, whether it's organic development or M&A or partnerships, each step you make always seems to be skating to where the puck is going as opposed to necessarily where it is. So a lot of the audience is in early stage startup land. Can you talk a little bit about how you see the role of startups in semiconductor broadly, but more AI specifically? Is the CEO of a large company how you see the role of startups in the industry?

**Lisa Su:** There's so many good ideas out there. And the beauty of a startup is you can get a good idea and you get some backing from great venture capitalists like yourself. And you can really innovate and experiment and learn on those ideas so fast. And that's really, really valuable. I'm really enjoying the work that we're doing with startups. We've decided to become much more active in how we're working on this. One is we want to help many of these companies. So by the way, if anybody needs GPUs, we'd love to work with you. Did everybody catch that? Did anybody need GPUs? Small advertisement. Yeah, I got it. It's okay. But I think the role of startups, especially right now, has never been stronger. Cutting edge innovation, experimentation, really what I've seen, and maybe you've seen it as well, Bob, is I think even large enterprises who typically used to be, let's call it much more conservative in working with startups are also becoming much more open. Because, again, this is back to the disruption I talked about. Nobody wants to be behind in AI. And so they want and need people with good ideas to help them implement in this complex world. And if it's a startup, that's great. And we've learned a ton from startups, actually. And the rate and pace and speed at which people are moving is fantastic. I mean, in some ways, given the evolution of the ecosystem, the barriers to enter SEMI over time have been relatively large because you have to find who's going to make my product for me. And the capital you raise, if it has to go to build your own server farm or your own fab, the lack of innovation takes place in the startup ecosystem. But with the hyperscalers and the role they play to make getting started much simpler with the world-class foundry capabilities that exist, and we love interacting with you and being a part of that.

**Bob Swan:** I can't thank you enough for doing this. It's been such a treat to chat with you, and congratulations on what you guys are doing at AMD. I admire your leadership and the role you've played in the industry. Thanks so much for spending time with us.

**Lisa Su:** Thank you so much, Bob. It's a real pleasure and really appreciate all the collaboration. Cheers.