Good morning, everybody. Welcome to the final day of the Goldman Sachs Communacopia + Technology Conference. My name's Jim Schneider. I am a Semiconductor Analyst here at Goldman Sachs. It's my pleasure to welcome AMD to the stage today. With us from the company, we have SVP and General Manager of the Compute Enterprise AI, Dan McNamara, and Corporate Vice President of Financial Strategy & Investor Relations, Matt Ramsay. Welcome, guys. Thanks for being here. Thank you, Jim. Thank you. I think the topic almost every session at this conference is AI. Your key enabler of that trend was your infrastructure products. Maybe before we get into those products, how has the AI adoption progressed inside AMD over the past several years from a corporate perspective? What areas have seen the biggest productivity gains, and what lessons from AMD's own AI journey are applicable to enterprise customers today? I can- You want to start? I can jump with that. Look, it's a great question, and I think that when I think about our journey, it's very similar to a number of enterprises. Our team started out, I think it's a multilayer approach to the infrastructure, right? We started out first and foremost with the data layer and optimized that. We talked to a lot of enterprise customers and this is often overlooked, is how you structure your data such that you can actually employ agents effectively. We actually open sourced our solution. It's called OPTIMA. We started there, and then we've been on this journey about four agents driving what I would call automation for efficiency, and that's gone very well. Now, where I would say is we're really in the domain-specific type applications, right? If you think about it, for us, domain-specific is EDA. We're seeing a tremendous amount of upside across coding, debug, and those two key areas along with kernel development and just software development in general. Very strong returns there. Then, of course, across all of the businesses, we're seeing very strong automation and efficiencies across each of the business. I would say that, and it's interesting because we were in New York City last week and I was with our CIO, and we had a roundtable with a number of top enterprise customers in New York City, and he started out and just walked them through the journey. It was a very good conversation about where each one of them are on this journey. I would say that we're advanced in this area. I would say that we took it on very aggressively. We're also looking at how you balance token costs with the value, and we're really doing some advanced things across that too. So overall, very strong adoption. You got to look at us as both a provider and a major adopter of AI. Yeah. Jim, the only thing I would add there is you guys saw us work with a big framework that we put together with Anthropic about obviously them buying up to 2 GW worth of MI450. But there's also a lot of work of not just the OpenAI tools, but the Anthropic Claude tools being adopted across our engineering organizations and unlocking a much faster flywheel of software development, debug, time to production of chip programs, optimizing where our software people are spending their time. We have a huge software organization and trying to figure out what they need to be working on, where can they use tools to accelerate that flywheel versus doing anything manual. My boss, Jean, our CFO, has benchmarked us versus a whole bunch of leading semis and tech companies, and I think we're on the bleeding edge of AI adoption internally. It's come with an increased token cost, but it's come with an even a much greater productivity gain across the organization, and it's allowed us. I think you'll see it allow us to bring hardware and software products to market much more quickly as we go forward. Yeah. Actually, just one last point. I want to just emphasize that, right? So you've got domain specific, and then you've got what I would call general IT automation. One's for efficiency but when you can drive a faster time to market, that's where the real rubber hits the road. That's what we're after. As we go to external enterprises, our goal is to get them time to value very quickly, right? With ROCm and with some of our solutions. So again, we always say we eat our own dog food, right? Everything we build is deployed in our data centers first. It's going very well in terms of driving our time to market with our engineering teams. Yeah. With respect to your customers, from their perspective, how do you think this plays out in terms of model evolution over three to five years in terms of the landscape? Do you think frontier models still going to be leading the charge here? Do we see Small Language Models do a lot more task-specific things, or do you think open-weight, open-source models are going to have a larger role to play? You want to start or I can? Yeah. I think, Jim, the answer is yes. It's not a very helpful answer, but it actually is the answer. Our goal is to make sure that our combination of CPU and GPU roadmaps are very differentiated in terms of driving tokens for dollar outcomes, regardless of whether it's open-weight models, frontier models for our largest customers. I think those obviously the industry is evolving quickly around what are the right use cases. How should we say this? How to apply the right tokens to the right problem relative to the cost of the token versus the return of the token. That's a very large continuum. I think our goal is to make sure that on the GPU side, our hardware and software are driving the right efficiencies, regardless of whether it's open-weight models or our closed-weight models or our Frontier models, and that Dan's business is the right CPU to run agents to drive all of those models, regardless of where they come from. That's our goal. I don't know, Dan, if you- Yeah, I would just say, look, we rolled this out. I showed this at our Advancing AI Day. Matt's right. It is all of the above. Clearly, Frontier will continue to be the cutting edge, but open weights are very valuable. Then you have what I would call SLMs for some of this domain-specific stuff. What we showed was intelligent routing. If you think about it, you have Frontier. Every enterprise is going to have some distributed model around Frontier, probably GPU as a service in the cloud, most likely an on-prem server that can service and run open weights models. You have a policy-based router depending on the task. You are looking at performance latency, you are looking at, obviously, security. That is one of the key areas where what I hear mostly is cost and security from the enterprise. What you can do is you route this and you can manage your costs. You can manage, if it is a policy-based router, if it is highly secure, it stays on-prem. We see a lot of enterprises trying to build this out. It is very interesting because enterprise are infinitely hybrid, and we believe that will continue. Great. Now, let us dive straight into your business. First, the AI business, and also the server CPU business as well. Your AI data center business has grown very rapidly over the last few years. If you think about the biggest strides you made in product development across silicon, software, customers, ecosystem, where do you think you can make the biggest strides going forward, and where are your key focus areas from here? Yeah. That is a great question because first and foremost, and I always say this because it is very important. Our vision for many years now has been you build the right compute engine for the right workload, and that is across CPUs. That is within not only across the product lines, but within the product lines. We will talk about server at some point, but we optimize for workloads. Most importantly is we feel like we are in very good shape across the different product lines, from server, to GPU, to networking. Now ROCm is coming online. I think the biggest part for us is we have now shifted from this individual product lines to a full system provider, so providing the full rack, all of it interworking. We are also driving a different roadmap cycle. It used to be, three, five years ago, it was like you are optimizing for your product. Now, it's a combined data center roadmap steering group. Whatever I'm doing, you have to make trade-offs across all the products. I think that's the biggest change. What you'll see is getting rack scale solutions at scale is the biggest thing we're focused on right now. Yeah, I think from my perspective, just listening to Dan spoke about it just now, but listening to Lisa and others speak about, we don't necessarily have to force ourselves to be. If you step back and think about the top, there's a long tail of customers that we're going to continue to support. If you think about the large top 15 or 20 consumers of high performance computing cycles in the world, we don't need to necessarily be their CPU partner or GPU partner or FPGA partner or semi-custom partner. We can walk into a room strategically and say, "How can we at scale be your high performance computing partner?" That might look differently at different customers, but it's a very powerful thing to be able to say, "Hey, we just want to be your high performance computing partner, and let's think strategically about what you want to do over the next number of generations and put solutions together that can support that across endpoint, across inference at the edge, across the server on-prem and in the cloud, AI deployments in massive data center scale or in PCs," or there's a huge continuum of how can we be your high performance computing partner. Being able to bring those pieces of IP to the market at significant scale is one of the things that I think is unique about what we bring, is it's not a push approach. It's a how can we be your partner and let's decide how we're going to work together to bring significant amounts of high performance computing to market. I think that's the biggest change that's happened. Now that AMD has the full breadth of portfolio and the scale that we have, that's a conversation that I think is valuable. Great. Now, the company has outlined some pretty healthy revenue growth targets, 60% CAGR over the next several years in data center revenue, 80% CAGR in AI data center revenue. Talk about two elements of that. One is how diverse does this get between the hyperscalers CSPs, Enterprise AI labs over time, even sovereigns. How diverse does it get? And then secondly, what should we be thinking about in terms of markers for more the short term going into 2027? You want to start? Yeah, maybe I'll start and Dan can add a bunch of detail on his business in server. Jim, we have outlined. We started at the Financial Analyst Day back in November, and it's amazing how long ago that seems given how fast this industry's moving now. But we talked about more than 60% growth of the data center franchise, more than 80% of growth of the AI business. And at that time, we thought we were well above where the market was in talking about a $60 billion server TAM, and we've now more than tripled that. So we're, at that point in time, talked about the company growing at more than 35% annually. Lisa and the team have updated the TAM for AMD to be more than around $2 trillion by 2030, and that's a 40% growth rate of the TAM, and we expect to grow faster than that as a company. And we talked about getting to more than $20 in earnings over the strategic timeframe, and I think we've updated that to be significantly more than $20. So we're excited about the growth, the leverage in the model, and we've given a few data points on 2027, much more than doubling the data center business and those are things that we feel really good about. And now it's just putting our heads down and making sure that we scale the AI business in terms of building racks. And Dan's business is in a very different place than it was 12 or 24 months ago in terms of growth. We feel it's a very exciting time for the company. At the same time, we're heads down and trying to execute. Dan, if you want to expand on that. Yeah. I would just say, look, the way I look at our AI business is very similar to the way I looked at the server business five years ago, right? It's a very deliberate approach. You get in, and if you look at what we did in server, it was strong in cloud and national labs, and then we evolved into the enterprise, right? Now we're seeing very strong growth in the enterprise. I think you'll see the same thing happen. Like Matt said, we're very focused on delivering to our top customers right now with Helios, but the spread will happen, just like I just talked about. The enterprises are really thinking through what their overall infrastructure needs to look like. It will include cloud, but if you think about AI, it's the exact opposite of what happened in general purpose compute. General purpose compute started on-prem and went to the cloud. It's the exact opposite. We are seeing many of the mainstream enterprises look at building sub-rack scale, whether it's PCI card type deployments or eight way server, UBB-based deployments to do exactly what I just talked about in terms of what is the right balance and what's the distributed architecture that you need for the long term. I think what you'll see is the shift happen over time, but right now, like Matt said, we're concentrated from an AI standpoint. However, with server, we really are. Lisa and Jean talked about the results we're seeing across the enterprise as well as cloud, and it's growing quite dramatically right now in terms of share gains across all of the mainstream enterprise and the channel. We've invested very heavily over the last few years to go drive the channel and the enterprise, and it's really starting to pay off. There's no sort of fixed ratio, but it's more of a I see the same evolution happening across the AI business. Fantastic. Want to dive into server CPUs next. Your home turf, so to speak. For investors less familiar with the technical details of agentic AI, maybe help us understand why agentic workloads actually drive higher tag rates for CPUs and as you do that, maybe talk about the changes in the system architectures that occur as the customers move from simple inference to more autonomous multi-step AI workflows. Yeah. Look, this is a hot topic, and I think I would start with saying that this is more of a distributed systems architecture problem as opposed to a simple linear problem. If you think about the world of ChatGPT from November 2022 to probably into last year. Very linear, right? It was a SaaS-based data center. You have your servers for web serving, you got your application servers, you've got your database storage, you've got caching, and then you've got sort of this GPU server, right? Which everyone understands a GPU server, right? You know the ratios. Everyone can calculate that very easily. And that was very linear. Prompt response, right? That's what it was built for. Well, with agentic, as you all know, it's an entirely continuous flow. It's a completely different compute paradigm. It's 24/7 churning, within a sandbox, spawning numbers of different agents. If you just think of the picture I tried to just draw for you, if you think of your traditional servers here and your big GPU servers here, you kind of open it up and you pull in a whole new class of compute, which is for agentic. Control plane, API calls, database queries, tool execution, and that is pure CPU-based. So that clearly will do RL with the GPU servers. So the GPU servers grow also. But if you think about those general purpose servers, those get uplifted too, because more and more calls to those. So you're seeing an uplift in a whole new class plus the traditional general purpose. And we're just seeing that dramatically grow. At our [Advancing AI Day] in November, I said that, "Look, there's multiple areas of growth for the CPU." We called it, but we called it too low. So we've upped it now, and I think the growth we're seeing across both the enterprise and the cloud is very exciting. And then lastly, what I would say is, with Venice, we are hitting on the three main focus areas for CPU, right? You've got your GPU server that everyone knows and loves in terms of, started out on one to four, a CPU to GPU. Then you've got this agentic sandbox CPU, where with Venice, with our high core count 256 core device. If you think about agentic, it is really threads per watt with the right level of per core performance. If you think about the head node, it's really about IPC and high frequency driving and keeping the GPUs busy. And then the general purpose servers, we've been very strong there for many years, and we're going to continue. When you think about it, we feel like not only with Turin today leadership, Venice, we launched it already, and as it comes online here through the back half of this year, we are extremely well positioned to capture this growth. I would say one last thing. If you are trying to find a number to plug into a model, it is very hard because there are so many things. If you just think of a gigawatt of power, right? Then you factor in your PUE, and you come up with your IT power. It is all about the addition of the CPUs. Again, the host node, we all know that is easy calculation, but it all depends on what you are trying to run. It is really workload dependent, and that is why it is so hard to plug a number in. That is why we tried to capture sort of, hey, this is the growth we see. When we show it for agentic, it is also pulling in the uplift in those general purpose servers that I talked about. I do not know if I confused you more or not, but just trying to give you the picture of what we are seeing. Yeah. Dan, maybe I would just add one thing. We did take a $60 billion TAM out to 2030 and up that now to $120 billion and then $220 billion. The companies that I know what Lisa is expecting of you, Dan, is for your business to be over 50% of that TAM as we grow. We can do a relatively small number of chiplets and put them together in configurations that can be a significant number of SKUs and a full coverage of the platform. So, you guys can do the math on more than 50% of the $220 billion. I have been following and now part of AMD's server business for a very long time, and to talk about building a $100 billion server business is pretty exciting. No pressure, Dan. That is what we see coming, is a significant growth of agentic sandbox CPUs for which we have very large core count multi-threaded parts, a strong growth of head node CPUs where we have really high frequency focused, high bandwidth, high single thread performance parts. Then the broad range of the server market. One of the things that stuck out to me seeing the results of Dan's businesses in the second quarter, it seems like forever ago we talked about the second quarter, but even the enterprise part of the server business grew more than 70%. The industry has not seen those type of growth rates in enterprise server basically ever. We're very excited about all parts of the server business and the breadth of SKUs and the breadth of platforms as we roll out Venice and then move into the Florence generation is something that we're really excited about. Yeah. Now, the server CPU market, as I said, you talked about it's also becoming increasingly competitive even as it's growing. What advantages do you think the x86 ecosystem continues to provide for the enterprise specifically, and how do you think about the durability of x86 in the hyperscaler environments, especially for some of these internal workloads where customers are developing their own silicon? Yeah. This is a common question, right? First and foremost, we always talk about this, right? This is not an instruction set architecture problem or concern, right? There's no fundamental differences in the ISA between x86 and ARM. It's really about delivering to different optimization points, right? It's perf per watt per dollar ultimately, and we know that if we continue to drive along the three swim lanes that we just talked about and optimize for that performance per watt, we're in very good position. If you think about from an ecosystem standpoint, if you go back to that picture I tried to draw with my hands, all those general purpose servers that I talked about, x86 based today. Lots of software built for x86. So the ecosystem is built around x86. All that growth comes on x86. Now, if you look at sort of the hyperscalers, each one of them are doing some form of their own. What we see is if we continue to drive just what I talked about, which is the highest throughput and core density per watt. Then we hit these other points. We feel extremely good about the design-in that we have right now across all of the major cloud vendors in the world. From an agentic standpoint at 256 core, from a high frequency standpoint at 96 core, and then just across other SKUs for high-performance computing. I'll just give you a good example. Recently, Amazon came out with RDS, which is their database service, which is a first-party property that we would classify. It's on Turin, and the reason why is performance. We just know that, yes, there is a focus for them to try and get their first-party properties on their homegrown, but it doesn't fit for everything. And again, even when you go high density, it's that perf per core sweet spot and that optimization point on the VF curve that we pay close attention to. We really feel like where we are today with coming out with Venice, Turin today, with Venice coming out as we speak and ramping. Then, I was looking at, we had a review earlier this week on even Zen 8 in terms of what our engineering teams are targeting. I feel very, very good about where we are, in terms of delivering the optimization points. That's the key, right? It's really optimizing for the different workload and the deployment model. No, I think, Dan, I agree. From my perspective, watching the teams internally, the investor focus tends to be much more around instruction set, and it is important for the enterprise pieces of the server market, whether that's on-prem deployment or in-cloud deployment. But the economics of rolling out the server market to unprecedented scale that we talked about with the TAM, it's about building the best server parts, period, never mind the instruction sets. And I think that's what we, from a scale and supply chain point of view, from a, like you said, optimization points and the number of SKUs that we can roll out, the number of platforms that we can roll out, the significant amount of optimization you can do for different places in the roadmap. I feel really good about where we are. And it's not just of what we think about the market. We can see the demand pull from customers for different optimization points. When we think about, okay, this is where the demand pull is, and these are conversations that are multi-generation in nature, I think we feel really good about where the server business is. I would just final point on that is for Venice, and I'm pretty sure Lisa talked about this at our last earnings, but with each generation we built builds on the next. And you get more and more of the ecosystem coming along with you as you go, and we've been very focused on that. But with Venice, it's the broadest true launch that we've had in terms of OEMs, ODMs, cloud vendors, the ISVs on day zero support. The demand is very strong. Due to the three swim lanes that I talked about, I think our customers in the ecosystem are seeing that one SKU doesn't solve every problem. And that's what we're seeing from a merchant ARM standpoint. It's really just singular SKUs or one or two SKUs. We are pretty excited because we are in a very good spot from a market opportunity standpoint and our product portfolio leadership across, really, I would argue three generations straight. Excellent. One thing that is striking me is over the past couple of years, we have changed the parlance of how we talk about this market. We are not talking about server counts or counting accelerators. We are talking about counting gigawatts of capacity, and every single presentation at this conference has done that. So maybe as you think about these multi-gigawatt AI deployments, how should investors be thinking about CPU content per gigawatt? Do you want to start? Yeah. Maybe I will start. We think the focus that we have at AMD broadly in our data center business is to make sure that we provide very compelling tokens per dollar and TCO for our GPU business, and we are right in the thralls of ramping and launching Helios and MI455. And you will see us be a very large partner to some of the leading model companies in the world to run their inference workloads. Separately, Jim, regardless of whether the inference runs on our GPUs or NVIDIA GPUs or TPUs or whatever XPU. Dan can expand on this, but I think what we're focused on in the server business is to make sure that AMD's Venice portfolio and going forward, they're the differentiated and right place for the industry to run agent code. We haven't been super specific about what that ratio is in terms of gigawatts of deployment, because it does look different depending on what customer it is. But we want to grow a very large AI business, and I think Dan's business is positioned to be a significant majority of the industry running agents to power agentic AI. We haven't been super specific on the gigawatt comments in terms of CPU. Yeah. I was just really simple. It depends. Because the challenge is take a gigawatt, again, do your PUE, you've got this IT, and then you've got to break it down where, okay, I've got clusters of GPUs over here training, I've got clusters here doing inference, then I've got to support it with a general purpose, and then the agents. It just really depends on what you're trying to accomplish with that gigawatt. It's very hard to just say, "Oh, here's a fixed ratio." I would say that it's growing. If you think about it today, we're saying 1:1 sort of ratio, and it's going to continue to grow. But it's just very hard to pinpoint, plug this into a model and you'll get what you're looking for. It's very highly dependent on what the end customer is trying to accomplish. I spend a lot of time plugging numbers into models, so- Yeah. Okay. We're almost out of time, but let me leave you with the last question for you. We've covered a lot of ground. If you think about your position in AI, compute, data infrastructure, data center infrastructure, et c, if we're up in here on stage again in five years and we look back, what do you think the one thing or two things that investors are going to be most surprised about in terms of the performance of the company? Dan, do you want to start off? Look, maybe I'll start with maybe what people may be missing about us. It's what I talked about earlier is we have fully transitioned from a very good silicon provider across multiple products, and we are transitioning now to full rack scale, and our software has come. Even over the last six months, the gains we've seen. We're becoming more of a software company and a systems company today than we were even six months ago. I would just say that that will be, I think if you look forward 12- 24 months, I think it'll become very clear how we have made that transition quickly, and we've driven a software stack that is truly focused on time to value for our customers. I think that's where I'd leave it in terms of what you'll see over the next few years. From my perspective, the goal is to. Dan started the conversation this way, Jim, where we want to provide the industry that consumes high performance computing with the right type of computing for the right type of workload. I think that that will serve us well across the breadth of our markets. We're right now at one of the more exciting times that the industry's seen and more exciting times for the company. We've talked about much more than doubling our data center business next year, and driving gross margin dollars very significantly faster than expenses. We're at that inflection point, and I think it's important for the investor community to understand that Lisa and the whole team. It's funny, we were doing a meeting in a room at the conference just an hour before we came on stage here, and Dan was on an execution meeting with Lisa and the team. The team is focused on making sure that we have a cadence of execution at the company, and despite all the excitement there, that the focus remains on making sure that we execute. If we do that, then I think investors will be really pleased with where things end up. For us, it's about driving outcomes for customers, and then that'll translate into outcomes for the investment community, not the other way around. We're just going to put our heads down and execute because it's a super exciting time. But as we wrap up here, the little blinking light is on. But thank you all for spending time with us, and thank you, Jim, and the team at Goldman Sachs for hosting us. We really appreciate it. Yeah, Matt, thanks for being here. All right. Appreciate it. Thank you. Thank you.