All right. Good afternoon, everyone. Hope you are enjoying your time at our Advancing AI 2026 conference. My name's Matt Ramsay. I lead the financial strategy investor relations folks at AMD, and we're delighted that you're here. I'm delighted to be joined on stage by many of the folks that you saw in the keynote earlier. For those on the webcast, it's an audio-only webcast. I'll do a little bit of introduction and then provide some ground rules for the conversation that we're going to have here with the investment community. First of all, joining me on stage here, these probably need no introduction, are our Chair and CEO, Dr. Lisa Su, Vamsi Boppana, who runs our AI business, and Dan McNamara, who runs the server business. Next to me is Forrest Norrod, who leads our overall data center business. We're going to take some Q&A here for the next 45 minutes or so. A couple of ground rules from me. You probably all know that we report our Q2 earnings in about 10 days' time. If you could ask your questions on today's event and the contents of the conference and the keynote and the related press releases, I'd appreciate it. If you ask any questions about our near-term financials, you have wasted your question because I will instruct the folks not to answer it. Secondly, if you could maybe ask one question just so we can get to as many questioners as we can. The third point I'd like to make is I think we're all aware that there's another company in the ecosystem that reports earnings this afternoon, and those numbers may come out during this session. If you ask any questions related to that, I'm going to step in on those as well. Let's just make sure we have a productive session and everybody's on the rails. Liz and Prab from my team are going to be running around to make sure that we get to your questions with microphones. I just want to turn the floor over to Lisa to make a few opening comments. Thank you. Okay, great. Thank you, Matt. Thank you all for being here. I think I've done a lot of talking this morning already. I'm probably not going to have much in terms of opening commentary other than say, it's just incredible how I feel like every time I talk to a group like this, we have so much has happened since just in the last month. We talked a lot today about sort of the large and growing opportunity in AI. We talked about the TAMs for the accelerated business, multi-service compute business. I think we are tremendously excited about the contribution and the launch of Venice and all that entails. Why don't we just jump right into questions? Liz, since you're standing next to Tom. Thank you. Sitting in the front sometimes pays off. Thank you, guys. Appreciate it. It's been a great day. I guess I'll start with the CPU TAM. $220 billion in 2030. You guys have previously talked about 50% of that market. With the bigger numbers, is that still the expectation, and can you update us on what you think the share could be? Yeah, absolutely. We continue to be more and more excited about the CPU market, the agentic AI workloads. The more we talk to customers, the more we understand what's happening. I think we see tremendous growth in CPUs. Yes, we've updated our TAM to over 50% over the next three or four years, reaching over $200 billion. We're still very much focused on getting over 50% of that market. I think we've had really tremendous progress over the last few quarters. With the Venice launch, what we're hearing from customers, what we're seeing in terms of the interest is actually an expanding set of workloads. Venice truly is optimized for AI workloads, head node, agentic AI. We're making tremendous progress in enterprise. All of those things give us confidence that we can continue to grow significantly ahead of the market. All right. As long as you just go with Mark there, since you're right next to Mark. Great. Thanks for the great presentation today. Really appreciate it. I had a two-part on the same topic, if you don't mind, Matt. Part one would be the $280 billion TAM for CPU servers used. How does that split between agent versus standard server versus the head node? The second question is, or the second part of the question, is how do you estimate the TAM here? Because, if I use one agent or if I use 10 agents, or I could use 100 agents, seem like the TAM could grow exponentially. How do you get to a number? Yeah. Well, why don't, Dan, why don't I let you start more so than I can? Yeah. Great question. Look, let's talk about how we did the TAM, right? It's obviously talking to customers, but it's also analyzing our own workloads, right? I talked earlier about some of the work we're doing. We've looked at that very closely. Now, in terms of the breakdown, in the outer years, we believe that the agentic part of it, which is more sandbox type application, will be probably like 50% of it. Then, kind of do it linearly. You could argue, because there's a bit of an argument in terms of general purpose, because general purpose gets floated up too, due to some of the calls and things like that. That's probably the number that I would give you in terms of the agentic in particular. Like Lisa said, is we do believe we're extremely well-positioned for that with Venice and as she showed today with Florence. You guys may add too. Maybe the only thing I would add to that, Mark, is we've talked about CPU to GPU ratios and how do we think about those CPU to GPU ratios. If you think about the various categories, if today, in the head node type configuration, the CPU to GPU ratio may be 4: 1. That is four GPUs to one CPU. We certainly expect that to tighten as we go into future generations. When we add agents, certainly for the new TAM, we're expecting that the CPU ratio will actually be greater than one. Maybe we get to the point where it's two CPUs for one GPU. It's hard to call exactly, but we're certainly seeing from a workloads standpoint, the migration to needing a lot more orchestration around the whole end-to-end workload. I think we can go to Stacy over here on the side, probably. Thanks. Thank you, guys. I appreciate it. I had a question on the Helios ramp. Lisa, just from your comments, I just wanted to clarify. It really sounded like it was getting going in Q4 rather than Q3, and it doesn't fundamentally matter to me which side of the line it lands on, I just want to make sure that I understand that properly. I just wanted to ask about Anthropic. You talked about 2 GW. The start of the first gigawatt, I guess, ramping in the first half. Do you guys expect, of 2027, do you expect to get that first full gigawatt in 2017 or does it stretch out farther? That $15 billion-$20 billion kind of content bill that you've talked about in the past, still kind of the right number for that? Yeah. Stacy, you have successfully asked three or four questions. It's all around the same question. Let me make sure I get through each of them. Starting with where the Helios ramp is. Actually, we will start first shipment here in the third quarter. You should expect first shipment of Helios to start in September. It will ramp into the fourth quarter, and it will continue to ramp into the first half of next year. We've actually built the ramp this way because it is a complex system. We want to make sure that we're letting our ODMs really get a chance to really get the manufacturing process fully tuned out. It also corresponds very well to the data center build-up for our largest customers, so we know which data centers these Helios systems are going into. That's one. On Anthropic, we're very excited about Anthropic, and I think having really Anthropic, OpenAI, Meta, all moving into Helios is a big deal for AMD. In terms of the Anthropic timeline, as we said, we will start the first gigawatts of shipments in the first half of 2027. I don't know if I will say exactly all in 2027, but would expect to be fairly aggressive on the ramp of the first gigawatt. Our plans are to get as much of that into 2027 as possible, and it's more just aligning with the data centers and when they are ready for production. Was there another question in there? Content. Again, not talking about any specific customer, but in the same zip code. Liz, maybe Chris Caso is there next to you. Thank you. Chris Caso from Wolfe. One of the things that came up during the presentation was comparison between Venice and the Arm ecosystem. Kind of attempted, it seemed like you put to rest some of the performance and power characteristics there. Could you speak to that a little bit more and maybe give some indication of where you think your market share may be relative to some of the Arm solutions in the market? Sure. Forrest, why don't you take that, I can add. Sure. Yeah. First off, we're very pleased with what the team has done on Venice. The whole family of parts, we think, is exceptional. We've really tuned Venice, as you heard, for a number of different workloads, a number of different deployment scenarios. In that, we think we have achieved the highest performance any way you want to measure it. Highest performance per core, highest performance per socket, highest overall throughput performance for just about any workload. We also are demonstrating, we believe, outstanding power performance efficiency at each one of those operating points. From our perspective, what we're trying to do is provide the best CPU for any workload, regardless of architecture. I think that the teams have absolutely achieved that. For us, it's less about the ISA, it's less about an x86 versus Arm discussions. It's about how do you produce the best CPU for any given workload to deliver the best TCO, we're having that conversation. Can I jump in? The only thing I would add there, completely correct. If you think about the Arm solutions out there, they're very uniquely optimized for one point. That covers even some of the cloud Arm solutions, right? They're very optimized. Like Forrest just said, look, we're optimizing, we're building a complete portfolio to solve multiple problems and hitting those different optimization points also. I think that's the thing that never comes out in this conversation, and that's why we believe we're extremely well-positioned to continue to gain share. Yeah. Maybe just to finish off on the market share point. Look, we're very proud of the progress that we've made. Certainly across all of the largest clouds are deploying Turin, and that has gone really well. The important point on Venice is we think our share grows. That's not just because the market is larger. Of course, the market is larger. We think our share grows because we're seeing the breadth of workloads that people are wanting to put AMD on. I think that says a lot about the portfolio. We're excited about the market in really being the CPU partner across a broad set of workloads. Share with x86 overall market. It's shared within x86, and it's shared within the overall market. All right, Prab. I'm going to go to Josh next, and Liz, I'll do Joe next. Just to give you a time to walk around with the mic. Thanks, guys. Congratulations on the informative day. Me following up on Stacy's question, the language in the release for the Anthropic deal was very specific. I think the 2 GW for MI450. Could you speak to, one, how the deal, I guess, came together from a background perspective, also, should we assume that it's multigenerational? It was different language than your OpenAI and Meta deals. Thank you. Yeah. Well, I think, Josh, what you should expect is that every customer is a little bit different, every deal is a little bit different. Every one of these things which we're doing, these large strategic engagements, is different. With Anthropic, in particular, what we announced was the MI450 engagement, that was a choice. I think up to 2 GW, very large scale, really ensuring that the first gigawatt gets delivered as soon as possible. To Stacy's question, I think the vast majority of that will be in 2027, if not all of it. To the framing of where we go from here, I think what you should expect is nobody wants to choose an accelerator for a single generation. It's just too much work. No matter how good Claude or Codex is, it's a lot of work to get the teams fully integrated. We are actively talking with every one of our largest customers, including Anthropic, about what's beyond MI450. A lot of excitement about MI500. We're getting more and more positive feedback about how that design point is put together, then a lot of discussion about where workloads are going in the future and starting with MI600. You should assume that we view, just like we did with EPYC, it's very, very similar, where you start with a deep relationship, you expand into more and more workloads over time. You heard that from Santosh and Meta. That was exactly what we've done is start with an initial installment and move forward. I think the difference today with the foundational model companies is there are no small deployments. There are no pilot deployments. These are at scale, large deployments for the sake of ensuring that you're amortizing all of the engineering work appropriately. Josh, before maybe moving to the next question with Joe. Vamsi, since Josh brought up the agreement and some of the things there with Anthropic, maybe you could spend a little bit of time talking about the Claude collaboration between the two companies, because I think that's quite important, and it would be good for you to expand on that. Yeah, I think there's two aspects that I would bring up. Part of it you saw in my keynote. We made some choices in terms of the strategy for how we make it easier to access our platforms, relying on open source and abstractions. What has really helped is because AI now has surface area across all the things that we put out in the open, unlike some of our competition, whether it's instruction sets or compilers or tool chains. They actually learn all that pretty readily right off the bat, and they're productive even now. What makes it even more uniquely special is we've been doing work with them to further tune and extend Claude's capabilities to be able to target high-performance optimization. Start with what's out there, which is already pretty good because of our strategy, and then further optimize it. Thank you, Vamsi. Joe, go ahead. Thanks. Yeah. Joe Moore, Morgan Stanley. Wonder if you could talk about the Cerebras partnership and how tight you see that integration going. You talked about disaggregation. How closely do you need to work together to be able to handle those sort of disaggregated workflows? Yeah. Yeah, I can take that. It's gone really well to date. What I can say is that with our 350s, we won't be here if we didn't do work already on 350s. We have things up and running in our lab infrastructure between 350s and their Wafer-Scale Engines, and the disaggregation software stack is all serving well. We see excellent performance, which gave us confidence to jump forward to what we would do with Helios. Early work on Helios in terms of analysis and simulation is also progressing well. We expect these deployments for their initial version, which is basically token service under Cerebras Cloud, to happen by the end of this year. Prab, maybe to make your life easier, you can go with Ben, then we can move to Simon and Aaron since they're all sitting next to each other. Hey, thanks a lot. Ben Reitzes with Melius. It's great to be here. This is probably for you, Vamsi. ROCm.ai, the software, how are you looking at that in terms of disrupting or impacting the ability to run apps versus CUDA? You think it's revolutionary? How should we think about? Is that a game changer? Is it evolutionary? Does that help level the playing field, do you think, with developers? It's a great question. Obviously, I have enormous passion for this. We truly believe it's the biggest leap that we have made, maybe since the early days where we laid out our strategy. We've made excellent progress every year. If you have to point to, is there a moment in time where we say, "Okay, this is actually the biggest leap and spring forward," I would point to this time. I don't mean that by August 14th everything is different. The inflection that's happening now, what is likely to happen that we build over the next many months, t ogether with the biggest labs, our collaborations with OpenAI, where Codex gets better, our collaboration with Anthropic, where Claude gets better. It's going to be a significant differentiation in terms of how easily accessible the platform would be relative to any time in the past. Over the coming months, you can expect the productivity of people to access platforms to be quite different. Thank you. Thanks. Simon Leopold with Raymond James. When we think about the new TAM outlook, I wanted to see how you're thinking about the biggest risk to that, in particular, the ability of your customers to get power to their data centers or your ability to get manufacturing capacity, wafers, et cetera. How has that factored into your view on that, and what's your consideration? Thanks. Sure. When we think about TAM, especially with the accelerator TAM being as large as it is, I think we look at all of those components. Not just raw demand, but we also look at what is the rate and pace that power is coming up, as well as what is the rate and pace that our suppliers are adding capacity. From that standpoint, I think, in the near term, I think we have very much planned capacity for significant growth in 2027 as well as 2028. Over the longer term, as you're thinking about 2029 and 2030, I think it's a rate and pace of growth that would require the entire ecosystem to be building at the same pace and have the same vision. Probably the largest change that we've seen is everyone has been thinking about the accelerator TAM growing very fast, that has sort of been in the numbers. The fact that the CPU TAM has accelerated as much as it has required some adjustments to overall capacity. We're very happy with the supply chain relationships that we have, we do see significantly more capacity coming online to satisfy those larger TAMs. Aaron, go ahead. Perfect. Aaron Rakers at Wells Fargo. Thanks for doing this and congrats on all the announcements today. I guess I want to build on that question. Maybe it's not the supply chain, it's your ability to actually stand up these massive rack configurations. Lisa or Forrest or anybody, if you were to conceptualize like you've got 6 GW here signed up for Meta, OpenAI, 2 GW of Anthropic, how do we think about the pace of your ability to ramp from a gigawatt per quarter basis? How quickly can you stand up that much capacity? Any kind of color would be helpful. It's a great question. If we start answering it from the rack level on up, because I think Lisa's already addressed it from the rest of the supply chain. First off, we're working very closely with our key OEM and ODM partners. Sanmina, Wiwynn, et cetera, as well as the OEMs, to ensure that we've got the manufacturing capacity in place to build the racks. To build, integrate, test, and validate the racks at the right pace. That is an important part of it, because the better you can get at that, the easier it is to actually support the deployment in the data centers. Shipping a very high-quality rack is an important part of making sure that you can turn them on very quickly in the data center. Beyond that, we see the next choke point is in actually deploying both logical as well as physical as well as logical deployment of the racks. That's something that we're working, again, very closely with our manufacturing and OEM partners. One of the things that we acquired as part of the ZT acquisition was we acquired a large services arm, which we have retained as part of AMD, and we are actually using that team right now, not just to support some legacy customers, but also do all of our internal deployments within AMD, and then to help our customers deploy very rapidly, both MI350s as well as MI455 systems in their data center. With that set of capabilities and training our partners, we're pretty confident that we'll be able to stand up to build at the pace required and then to stand up and provision and get turned on the systems in the customer's data centers. Maybe the only thing I would add to that is now when you talk about overall data center power, we're also very active in that process with our customers so that as they're planning power, we're planning the GPUs and the Helios systems that go along with that. It's much more involved than it was in the past, where somebody just places an order. I think there is easily 12 to 18 months of visibility. Maybe we'll go to Srini here. If I've missed people out there, the quality of these spotlights is spectacular, I'm not doing it intentionally. They are kind of on the bright side. Yes. Thank you. Srini from RBC. Lisa, I have a question on your roadmap that you talked about, in particular the scale of networking. I think you mentioned optical and copper with MI500. I'm just curious if you think the market and the ecosystem is ready for optical or will be ready for optical next year. If so, do you have all the pieces of the puzzle to be able to support that? Also, as part of that, I saw ESUN highlighted a bit more than you at UALink. I just want to hear your thoughts on which, I guess, scale-up you will be supporting going forward. Thank you. Yeah. Do you want to start that? First off, I'll take both of those pieces in order. We do see the MI500 generation as the one where we begin transitioning from a purely electrical interconnect for scale-up networking to start to see optical play a role as well. It's going to be a transition. We don't view this as a light switch. We don't view this as, hey, we're going to hit a generation, whatever, MI500, MI600, and everything is going to flip to optical. Instead, we see MI500 starting the transition. We're working very closely with a number of partners across the ecosystem as well as we've been investing in optics for quite some time. We're highly confident of our ability to begin that transition, that the terminus of that, generations out in the future is, co-packaged optics on all the major components and optical really being the backbone of many connections within the rack as well as between. That's going to take a little bit of time to get there. We think, again, doing it in this phased approach allows us, our customers, and our suppliers, our partners in the supply chain, all to gain experience and to make sure that we're moving at the appropriate pace and not taking any operational disruptions. On the scale-up protocol, look, on MI450, we support UALink transported over Ethernet. ESUN is a set of extensions to Ethernet, which is helpful with that, and it actually is going to continue to evolve. We do expect to see that protocol brought forward and, again, UALink over Ethernet brought forward and being available in MI500 as well. That's not the only thing we're doing. We'll unpack more around scale up as we get closer to the MI500 timeframe. Yeah. Atif, maybe you want. Lisa, did you want to expand on that at all? No. I kind of jumped the gun there. Atif, go ahead. Yeah. Atif Malik, Citigroup. I have a question on the MI500 ramp as well. HBM content is a very important part of your performance and token economics, a couple of your peers have cut their content for HBM memory in the future because of the availability of the memory. My question is if your thinking has changed or evolved, maybe in the last six months or so, on how you're thinking about the content increase for MI500. We obviously study the workload characteristics and how capacity impacts. First order. We separate our bandwidth and capacity. Bandwidth has a tendency to lift more boats in terms of more workloads directly getting impacted. That's one order of consideration, then you look at capacity after that. One advantage is because of the way we have our chiplet architecture, it actually gives us more flexibility and options in terms of how we are able to optimize capacity while preserving top-order bandwidth constraints. That's the uniqueness of our architecture. We've leveraged that with our existing products, and we do expect to leverage that with future products as well. We're not sharing the exact configurations of what 500 would have or the roadmap, but that's one unique piece that actually we believe will play to our advantage. Maybe if I just add to that. I think the way to think about it is we absolutely think our chiplet architecture gives us the ability to be very flexible in terms of memory bandwidth, as Vamsi mentioned, but memory capacity is useful. I mean, our customers have told us. The fact that we have more memory on MI450 is one of the reasons that we're getting better inferencing performance. I think the key is, as we're going forward, and all of our customers are doing this, I mean, this is an ecosystem discussion, that we need to make sure that the memory that is there is really being used because it is such a larger piece of the TCO. We are doing some memory optimization along the way, and that's true on both sort of the CPU systems as well as the integrated Helios type systems. memory is definitely super important. We will just make sure that every amount of memory that we're using is valued by the customer appropriately. Blayne, you want to go ahead? I think Liz is on your other side there. Thanks. Blayne Curtis at Jefferies. I just want to expand on Ben's question on ROCm.ai. Just kind of curious where you are on this AI journey, internal use of AI. I know Jensen drew out half a person's salary, which is a big number. Just kind of curious, are you tracking that? If you could talk about where you are in terms of day zero support and automating that with AI, then where else are you using AI? Yeah. I'll comment specifically on ROCm.ai, then maybe there's also a broader sort of corporate usage comment in here. As far as ROCm.ai goes, there's actually both internal acceleration of existing features and capabilities, externally, what we can put in the hands of developers that come with the platform. What I mean by that is, imagine you have a profiler or a debugger feature that needs to be built. Our engineers in the past used to say, "Okay, this is going to be a team of 20 people, six months." Now, that's actually dramatically cut down because those profilers, debugger features can get out much faster because of the ability of AI. That all comes part of the ROCm accelerated release. The piece where it actually helps significantly from an external perspective is the platform now becomes native in terms of AI agents being able to access it, and that is what we are going to start shipping starting August, both from just general out-of-the-box usability, but performance optimization and running these models through it becomes much easier. Almost everybody on the ROCm team, they are all AI native, more or less because of the group they are in, are pretty much using AI assist to be able to accelerate their plans. To just give you a sense for how fast or how extensive that is going within AMD. Maybe to the broader point, we are seeing AI usage ramp up across AMD extremely quickly. I would say every single month, we are seeing token usage, the amount of It is not just the number of tokens, but it is the quality of what we are able to get from AI. Dan mentioned what we are doing in terms of serving different models across our AI stack. I would say it is a very much a part of our development process across hardware and software, and I see it continuing to ramp and these deep relationships with Anthropic and OpenAI, as well as a number of the other model companies are helping accelerate that rate and pace. I think there is a question over here, Prab, to your left. Hi, this is Bhavtosh, CLSA. Lisa, you started your presentation with this 35 quadrillion tokens number, which is already all over the media because it is a shockingly high number for today's environment. My question is, how is AMD projecting demand beyond your conversations with your partners in the ecosystem? Do you have a fundamental way of thinking about where token use will go, given current cost of compute? A lot of investors worry about the cyclicality of this industry, and that is where this question is coming from. Thank you. I think the way we project demand is really quite holistically. We start with customers. We look at workloads. We look at adoption rates. We certainly look at the free cash flow of our customers to make sure there's the capital behind it. When you put that all together, every projection that we've put out seemed like it was really high, and then the market has actually gone faster. We continue to see just very significant demand across virtually every part of the portfolio, and I think that gives us a lot of near-term confidence in these higher market projections. That being said, we have to see how things develop over time, so I wouldn't say that our crystal ball is perfect, but I can say that it's self-consistent. It's self-consistent that assumes all of the aspects of is power available, is supply available, is capital available, and is productivity going to be able to close that loop when we're looking at TAMs. All right, folks. I think I'm going to try my best here to wrap this session up and keep my executives here on time because they have a lot of other commitments. I thank you very much for coming out. Lisa, and the whole team, it was a great day and a great conference. I think it's really, really exciting to be in a place where there's so much diverse demand for high-performance computing across what's now, what, a $2 trillion TAM. Lisa, if you have any closing remarks, I think we'll close the session if you do. I'll just say thank you for spending the time with us. It's been a really exciting day. It's a culmination of a lot of work from across the company. What I would like to say is we really think about AI as a complete compute picture. We talk a lot about CPU TAMs, GPU TAMs, Helios systems, all of that. We really think about AI as every aspect of compute. This is a place where we can be quite differentiated in the end-to-end story. Hopefully, you heard a little bit of the comments from Jeremy at AT&T, the work that we're doing with Cisco, the work that we're doing in physical AI. This is like we're on this five-year super cycle of just.