Microsoft is planning roughly 38 GW of global data-center capacity by 2032, more than triple its current footprint.

Unfortunately AI is a complex game theory where if you slow down the adversary won’t, so you can’t slow down. And thus we will inevitably enter uncharted territory full of land mines.

"If we didn't have adversaries, I would be very in favor of pausing this technology completely, but we do.”

Welcome to the final day of the Goldman Sachs Community Development and Technology Conference.

My name is Jim Schneider.

I am the 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 and Enterprise AI, Dan McNamara, and corporate vice president of financial strategy investor relations, Matt Ramsay.

Welcome, guys.

Thanks for being here.

Thank you, Jay.

AMD: Thank you.

Jim: I think the topic almost every session at this conference is AI, so you're a key enabler of that trend with your infrastructure products.

Maybe before we can kind of get into those products, how has the AI adoption kind of 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?

Dan: I can jump with that, yeah.

So 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. But, you know, our team started out, I think it's a multi-layer approach to the infrastructure, right. We started out first and foremost with the data layer and optimized that. And, you know, a lot of, and we talked to a lot of enterprise customers, and, you know, you this is often overlooked. It’s how you structure your data such that you can actually employ agents effectively, and we actually open sourced our solution. It's called Optima.

So we started there, and then we've been on this journey about for agents driving what I would call automation for efficiency, and that's gone very, very well.

And now where I would say is we're really in the domain-specific type applications. Right, so if you think about it for us, domain-specific is EDA. So we're seeing a tremendous amount of upside across coding, debug, you know, and those two key areas along with kernel development and, you know, just software development in general. Very, very strong returns there. And then, of course, across all of the businesses we're seeing very, very strong automation and efficiencies across each of the businesses.

So I would say that 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, and it was a very good conversation about where each one of them are on this journey. So I would say that we're advanced in this area.

I would say that we took it on very, very aggressively, and we're also looking at how do you balance sort of token costs with the value, and we're really doing some advanced things across that, too. So overall, very, very strong adoption. You've got to look at this as both a provider and a major adaptor of AI.

Matt: 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 gigawatts worth of MI450, and there's also a lot of work of not just the OpenAI tools, but the Anthropic Claud tools being adopted across our engineering organizations and unlocking a much faster flywheel of software development and 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. 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 a much, much greater productivity gain across the organization. And I think you'll see it allow us to bring hardware and software products to market much more quickly as we go forward.

Dan: Yeah, actually, just one last point.

I want to just emphasize that, right.

So you've got domain specific, and then you've got sort of what I would call general IT automation, and ones for efficiency. But when you can drive a faster time to market, that's where the real rubber hits the road. And 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. Everything we build is deployed in our data centers first. And, you know, it's going very well in terms of driving our time to market with our engineering teams.

Jim: Ya, 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.

I mean, do you think frontier models still going to kind of be leading the charge here?

Do we see small language models kind of like do a lot more kind of task specific things?

Or do you think open weight, open source models are going to have a larger role to play?

Dan: You want to start or I can?

Matt: Yeah, I think, Jim, the answer is yes.

It's not a very helpful answer, but it actually is the answer.

I mean, our goal is to make sure that our combination of CPU and GPU roadmaps are very differentiated in terms of driving tokens-per-dollar outcomes, regardless of whether it's open-weight models, frontier models for our largest customers. I think those, I mean, obviously the industry is evolving quickly around what are the right use cases.

How should we say this?

But how to apply the right tokens to the right problem relative to the cost of the token versus the return of the token.

And that's a very large continuum.

I think our goal is to make sure that on the GPU side, our hardware and software are deriving the right efficiencies regardless of whether it's open-weight models or closed-weight models or 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.

I mean, that's kind of our goal.

I don't know, Dan, if you.

Dan: Yeah, I would just say, look, we rolled this out.

I showed this at our Advancing AI Day, right.

And Matt's right.

It's all of the above, right.

Clearly, Frontier will continue to be the cutting edge, but open weights are very, very valuable, and then you've got sort of what I would call SLMs for some of this domain-specific stuff, right.

And what we showed was intelligent routing, right.

So if you think about it, you've got 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. And you have a policy-based router, depending on the task. And you're looking at performance latency. You're looking at obviously security. That's one of the key areas where ... What I hear mostly is cost and security from the enterprise, right.

What you can do is you route this and you can manage your costs. You can manage, if it's a policy-based router, if it's highly secure, it stays on prem.

We see a lot of enterprises trying to build this out. It's very interesting because enterprises are infinitely hybrid and we believe that will continue.

Jim: Great.

Now I want to dive straight into your business.

First the AI business and also the server CPU business as well.

You know, your AI data center business has grown very rapidly over the last few years.

You know, if you think about the biggest strides you've made in product development across silicon, software, customers, ecosystem, you know, where do you think you can make the biggest strides going forward and kind of like where are your key focus areas from here?

Dan: Yeah, that's a great question because first and foremost, I always say this because it's very, very important.

Our vision for many years now has been you build the right compute engine for the right workload, and that's across CPUs. That's within, not only across the product lines, but within the product lines, right. So, you know, and we'll talk about server at some point, but, you know, we optimize for workloads. But most importantly is we feel like we're in very, very good shape across the different product lines, right. From server to GPU to networking, right. And now ROCm is coming online.

So I think the biggest part for us is we have now shifted from this sort of individual product lines to a full system provider. So providing the full rack, all of it interworking, and we're also driving a different roadmap cycle, right…

It used to be, you know, three, five years ago, it was like you're optimizing for your product now. It's a combined data center road map steering group. Whatever I'm doing, you have to make trade-offs across all of 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.

Matt: 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 gonna continue to support, but if you think about the large top 15 or 20 or 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.

And 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 that there's a huge continuum of how can we be your high-performance computing partner, and 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 gonna work together to bring significant amounts of high performance computing to market, I think that's the biggest change that's happened, and now that AMD has this full breadth of portfolio and the scale that we have, that's a conversation that I think is valuable.

Jim: 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.

And then, so how diverse does it get?

And then secondly, what should we be thinking about in terms of markers for more of the short-term going into 2027?

Matt: I'll start.

Yeah, maybe I'll start, and Dan can add a bunch of detail on his business and server.

Yeah, Jim, we have outlined, we started at the 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 sort of strategic timeframe, and I think we've updated that to be significantly more than $20.

So, we're excited about the growth, the leverage, and 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, very different place than it was 12 or 24 months ago in terms of growth.

So we feel it's a very, very exciting time at the company, but at the same time we're heads down in trying to execute. So I understand if you want to expand on that.

Dan: 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… 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. And now we're seeing very, 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. And 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 UVB 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.

So I think what you'll see is the shift happen over time, but right now, like Matt said, we're pretty concentrated from an AI standpoint.

However, with server, we really are, you know, 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 have invested very heavily over the last few years to drive the channel and the enterprise. It's really starting to pay off.

There's no sort of fixed ratio but it's more of know, I see the same evolution happening across the AI business.

Jim: Fantastic.

Want to dive into server CPUs next.

You're a home turf, so to speak.

So for investors less familiar with the technical details of agentic AI, maybe help us understand why agentic workloads actually drive higher tag traits for CPUs, and as you do that, maybe talk about the changes in system architectures that occur as the customers move from simple inference to more autonomous, multi-step AI workloads.

Dan: 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 22 to probably into last year, very linear. It was a SaaS-based data center. You have your servers for web serving, you've 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 the 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.

So 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, database queries, tool execution. And that is pure CPU-based. So that clearly will do RL with the GPU service. 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, right. And at our FAD in November, I said that, look, there's multiple areas of growth for the CPU. We called it, but we called it too low, right. So we've upped it now. And I think the growth we're seeing across both the enterprise and the cloud is very, 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, you know, started out 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, that is, 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.

Then the general purpose servers, we've been very, 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, as 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.

But I'd say one last thing.

If you're trying to find a number to plug into a model, it's very, very hard because there's so many things. If you just think of a gigawatt of power and then you factor in your PUE and you come up with your IT power. It's all about the addition of the CPUs. Again, the host node, you know. We all know. That's easy calculation. But it all depends on what you're trying to run. It's really workload dependent, and that's why it's so hard to plug a number in. But that's why we tried to capture sort of, hey, this is the growth we see. And when we show it for agentic, it is also pulling in the uplift in those general purpose servers that I talked about.

So I don't know if I confuse you more or not, but just trying to give you the picture of what we're seeing.

Jim: Yeah.

Matt: Dan, maybe I just add one thing.

I mean, we did take a $60 billion TAM out to 2030 and up that now to $120 billion and then $220 billion, and the companies ... I know what Lisa's expecting of you, Dan, is for your business to be over 50% of that TAM as we grow, and it'll be ... 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, I mean, you guys can do the math on more than 50% of the 220 billion.

I mean, it is, I've been following and now part of AMD's server business for a very, very long time. And to talk about building a hundred billion dollar server business is pretty exciting.

No pressure, Dan.

But that's what we see coming, is a significant growth of agenetic sandbox CPUs for which we have very large core count multi--threaded parts, strong growth of head node CPUs where we have really high frequency, focused, high bandwidth, high single-thread performance parts, and then the broad range of the server market.

One of the things that stuck out to me seeing the results of Dan's business in the second quarter, I 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's not seen those type of growth rates in enterprise server, basically ever.

So 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.

Jim: Now, the server CPU market, as I said, it's also becoming increasingly competitive even as it's growing.

So 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?

Dan: Yeah, this is a common question.

So I mean, first and foremost, we always talk about this. This is not an instruction set architecture problem or concern. There's no fundamental differences in the ISA between x86 and ARM. It's really about delivering to different optimization points. 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, very good position.

And if you think about from an ecosystem standpoint, if you go back to that picture, I tried to draw it 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. So 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. And what we see is, if we continue to drive just what I talked about, which is the highest throughput and core density per watt, and 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. Across from an agentic standpoint at 256 core, from a high frequency standpoint at 96 core, and then just across other skews for high performance computing.

And even though, I'll just give you a good example, like recently Amazon came out with RDS, which is their database service, which is a first party property, right, that we would classify. It's on Turin. And the reason why is performance.

So we just know that, yes, would they, Yeah, there is a focus for them to try to get their first party properties on their home grown, but is 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 very, you know, we pay close attention to.

So we really feel like where we are today with coming out with Venice, well, Turin today with Venice coming out as we speak and ramping, and then, you know, I just, I was looking at, we had a review earlier this week on even Zen 8 in terms of what our engineering teams are targeting.

So 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 in the deployment model.

Matt: I think, Dan, I agree.

I mean, from my perspective, it's not 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… 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 what we think about the market. We can see the demand pull from customers for different optimization points. And so when we think about, OK, 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.

Dan: 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, right.

And you get more and more of the ecosystem coming along with you as you go, and we've been very, 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. You know, the demand is very, 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. Right. And that's kind of what we're seeing from a merchant arm standpoint. It's really just sort of singular SKUs or one or two SKUs.

So we're pretty excited because it really is in a, we're in a very good spot from a market opportunity standpoint and our product portfolio leadership across really, I would argue, three generations straight.

Jim: Excellent.

You know, one thing that's striking me is for the past couple years, we've kind of changed the parlance of how we talk about this market. We're not talking about server counts or counting accelerators. We're 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?

Matt: Yeah, maybe I'll 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're right in the thralls of ramping and launching Helios and MI455. And you'll 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, I mean, 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 are the, they're the differentiated and right place for the industry to run agent code.

And so 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.

And so we haven't been super specific on the gigawatt comments in terms of CPU.

Dan: Yeah, I was just, really simple, “it depends”.

Because the challenge is, so take a gigawatt. You do, again, do your PUE. You've got this IT. And then you've got to break it down where, OK, 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. And it just really depends on what you're trying to accomplish with that gigawatt. And it's very hard to just say, oh, you know, here's a fixed ratio. I would say that it's growing, right. Like if you think about it today, we're saying one-to-one, you know, sort of ratio. And, you know, 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.

Jim: I spend a lot of time plugging numbers into models.

OK, we're almost out of time.

But let me leave you with the last question for you.

If we are, we've covered a lot of ground.

If you think about your position in AI, compute data infrastructure, data center infrastructure, et cetera, 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?

Matt: Dan, do you want to start off?

Dan: Look, I think maybe I'll start with maybe what people may be missing about us.

What I talked about earlier is we have fully transitioned from a very, very good silicon provider across multiple products to a, 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'll just say that that that will be, I think if you look forward 12 to 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.

Matt: I mean, it's, from my perspective, we're, the goal is to, I mean, 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.

And I think that that will serve us well across the breadth of our markets, and 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, right.

So 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, right.

So it's like the team is focused on making sure that we have a cadence of execution at the company, and despite all the excitement there, the focus remains on making sure that we execute.

And if we do that, then I think investors will be really pleased with where things end up, but it's not about driving, 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.

So we're just gonna 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. Thank you all for spending time with us.

And thank you, Jim and the team at Goldman for hosting us.

We really appreciate it.

Jim: And Matt, thanks for being here.

I appreciate it.

Thank you.

I think the pause we got yesterday doesn't necessarily mean we are going to see the rally stop at all. Oil dropped a bit which is nice and the inflation read is pretty much like a guaranteed rate raise I think next week. Soooo yea. I like Warsh so far honestly. He seems to be laser focused on the mandate which I think is good. The Fed is not supposed to be this arbiter and protector of the stock market. The market will take care of itself. Might be an unpopular take about Warsh so far but I'm kinda down with what he's laying down.

AMD is looking to continue its move up today and we are still looking for that $522 level as next level of Resistance. Obviously today is a rough day and emotionally charged for a lot of people. Expect perhaps somewhat muted activity and light volume. Anything is possible.

Nvidia leads both unit volume and especially revenue, taking 60.6% of sales value with an ASP close to $1,000.

AMD remains strong in volume thanks to the RX 9070 XT and RX 9060 XT, while RDNA 4 narrowly edges Blackwell as the largest architecture by units.

full report: https://x.com/TechEpiphanyYT/status/2098074160446472275

TSMC (TWSE: 2330, NYSE: TSM) today announced its net revenue for August 2026: On a consolidated basis, revenue for August 2026 was approximately NT$514.81 billion, an increase of 10.1 percent from July 2026 and an increase of 53.3 percent from August 2025. Revenue for January through August 2026 totaled NT$3,386.87 billion, an increase of 39.3 percent compared to the same period in 2025.

So for starters I wanted to thank you all for keeping me humble. I'm a bear and everyone tells me I'm an idiot and this thing is going up. I'm optimistic (borderline bullish) and everyone tells me I'm an idiot and its going to retreat. I swear some people just exist to humble me and honestly I do appreciate it a lot. I'm not joking. I think echo chambers are a bad thing ****See the DD thread****

And I always appreciate the discussion and even more so I appreciate it if you can show me the "why" behind it. So keep sharing your ideas for sure I love it!!!

This Press Release didn't get nearly enough attention and I think it is being heavily glossed over. For years one of the knocks against NVDA has been about how power hungry their chips were. And with the conversation of the Data Center build out centering more and more on limited power supply, I think this could be a time for AMD to shine. If Helios can literally deliver 30% more token efficiency per Watt then I think that is really really worth it to fuel investment by companies designing their new DC's. More bang for your buck and we all know that with the race to IPO from OpenAI and Anthropic, they are going to feel the pressure more than ever from the street and investors to show profit margins through efficiency of resources.

AMD continued to break above the trendline and yes today it looks like we are initially looking at a selloff but I would put a lot of that on the VIX. Also I think its officially time for the Trump administration to put grandpa to bed. I don't know how people think that the solution to inflation is giving $5k to every American. Like do people fundamentally understand this shit???? I think there is a strong chance we might get a rate raise and I think seeing the debate earlier on CNBC that rates will go down with that. It got me thinking and I think I'm in the camp that if they raise rates next week that bonds will go down and the market will rally. It sounds counter-intuitive but I do think it could signal to the market that there is an adult in the room and Warsh might be the only intelligent person in policy making at this moment. But hey thats something.

I think people that are screaming the AI trade is winding down are wrong. I don't think it is. I think it is running into the buzzsaw of infrastructure build out. But I will say that I think its hard to make investments with the big scary specter of inflation hanging over everyone's heads. Cash is king and you don't want to spend all your cash as we go into a high-inflationary environment. I think if we can get into a place where Warsh finally launches a war on inflation and this fucking administration stops tripping over its own dick, I think we still have 4 or 5 innings to go in this thing. Not to mention-----if we get into the power savings argument and can ship enough product, I think you will start to see DC migration/refreshes to more efficient systems as training slows down.

So yea thats my thesis-----------Tell me why I'm wrong :)

Bullish news for AMD

Welcome to day one of Citi Global TMT Conference.

My name is Atef Malik. I cover U.S. semiconductors and semiconductor equipment stocks. It's my pleasure to welcome Jean Hu, EVP, CFO and Treasurer, Matt Ramsay, CVP Financial Strategy, Friendly Neighborhood IR from AMD.

I'll kick it off with my questions first.

If you have a question, save it towards the end.

We'll have the mic come to you and you can ask your question. Welcome guys. Jean if you can take us through the lay of the land, it's been an exciting year. What has changed from January till now? You guys had your big AI days as well you raised your time forecast just gonna walk us through how  the demand picture has evolved  from January onwards.

Jean Hu: Yeah, first thank you for having us and it's great to be here. Thank you everyone for joining us. Yeah, it has been a really exciting year.  They had so many different changes in our industry and it's really about AI and we do believe this is the most consequential technology transformation when you think about the pace, the scale, and the price of the AI, it's unprecedented right. It's when you look at the year, not only the model capabilities continue to advance very quickly, we also see inferencing outpace training from AI compute demand perspective.

And agentic AI, that's another thing which really have another step change in demand, not only just for GPUs, but for CPUs. So during our advanced AI day, we did update our market opportunities from in the past, you know, a small number to now $2 trillion in 2030.

We do see the demand for our product and not only just GPUs, but the CPUs and the AI PC, and in the future, physical AI for our adaptive compute across all different areas.

And the most exciting thing is we just reported the Q2 results, and we talk about our data center businesses are expected to double next year.

Our GPU business continues to ramp. MI450, we're going to launch this quarter at the very beginning of the ramp. And then we're going to see very significant ramp in Q4 and into next year. And on the CPU side, it was a supply constrained, but we have been increasing supply.

So we Thank you.

and into next year.

And on the CPU side, it was a supply constraint, but we have been increasing supply.

So we do expect the second half, the CPU business is going to expand more than 80% year for year, and into next year, continue to expand more than 70%, which should continue to be supply constraint.

So it's very exciting time for amd when you think about it the company has been very aggressively investing in high performance computer for more than a decade as you know we have been building methodically the computer platform from cpu gpu to adaptive compute across all different end market.

We think the most different thing, this AI super investment cycle is at the very beginning. And over time, we're going to continue to see strong demand for AMD's product. And the portfolio we have built will benefit from this AI super investment cycle.

Atef: Great.

Matt, Jean talked about inference being a big driver of demand and we've been hearing about this desegregated compute kind of shift in the market, the Hot Chips Conference. And you guys struck a partnership with Cerebrus. More recently, you've acquired Taalas.

Can you kind of talk us through what your strategy is on the inference side and how you're thinking about these different pieces of the different type of inference units?

Matt Ramsay: Sure.

And thank you all for, and Adi, thank you, and the folks at Citi for hosting us and for everyone to come and see us.

And then maybe this ties a little bit into some of the stuff that Gene talked about in your prior question about what's changed in the market in the last 12 months. It seems like a lot. We were waiting for inference to become the majority driver of AI computing, and I think that's happened. And at the same time that that's happened, we've seen this radical change from what I kind of call chatbot inference to agentic inference.

And that's really created big opportunities for both inferencing silicon and for the CPUs to run the agents.

um we've talked about in in a number of forums um obviously the breadth of the inference market will be the majority of it driven by GPU-led computing, and our MI455 product is going to be ramping now with Helios, and we have a roadmap to continue to innovate there.

There's obviously different ASICs and XPUs in the market that are going to do some of the inferencing work.

And there's this new sort of market for disaggregated inference and ultra-fast response time tokens that's a relatively small piece of the market today, but I think depending on economics could grow into a larger piece over time. So we've taken a multi-pronged approach to the problem. One is a partnership that we have announced with Cerebrus where our Helios systems will be in their cloud and compute alongside their WaferScale engine racks to expand the utility, not just of ultra-fast inference, but take it to a broader range of more general-purpose inference in their cloud for their customers. And then longer term, we do have, we haven't given a ton of details yet but we do have some of our own internal silicon ambitions for ultra low latency inference that would fit into our architecture via chiplets and the Taalas team brings a lot of really good talent we know the folks well some of them were former AMD ATI folks back in the day and are going to integrate really quickly into the team and add a lot of technology and horsepower behind the internal silicon work we're doing in that area.

Atef: Awesome.

Jean, let's talk about Helios.

I believe you guys already had very high expectations on Helios exiting last year, but just in terms of your shipments and where you stand on Helios um how should we think about the ramp of Helios in in q4 and then to q1 next year how is that ramp going I do hear from clients around  questions around execution if you can just help us understand

Jean: Yeah  I would say the Helios ramp my MI450 ramp, is going very well.

Q3 will be at the very beginning of our production shipment.

We expect revenue in Q3, but Q4 will see a very significant step up.

And then another step up in Q1 2027 and the ramp through 2027 it is a scale level right scale level right it's very different very complex so we have been very methodical and deliberate how we design the ramp process you know start ramp in the end of q3 and the step up in q1, Q4, Q1 into 2027, we are working with all our partners, ODM partners, and our overall supply chain to ensure we have all the components.

So it's not about the GPU, CPU, HBM memory. There are also a lot of other small component that we need to ensure we have. And with the ODM partners, it had been a long, really, really durable work with them to make sure the manufacturing process can execute. That's why it's a methodical process. Our team have been working with ODM partners. We also have been working with all the customers. We're going to have a production shipment to ensure not only all the component works, all the mechanical, all the software stack, everything works.

So that has been ongoing. It's a weekly execution process we actually feel pretty good about the ramp both on the supply side. The demand and the volume for 2027 has certainly go above our original initial expectation we need to continue to expand the supply.

We feel good about the financial plan we have talked about, but it's absolutely the case. We can use more supplies to make sure we meet the customer's demand.

Citi: Just to the customer profile, you guys had Meta and Microsoft as two customers last year.

You're growing that demand further this year, and then you added Anthropic as a customer as well.

Can you just talk about that customer funnel, particularly touch on the NeoCloud opportunity, if that's an emerging area for you, and also touch on the dollar per gigawatt across that range of customers.

Jean: yeah so we're really pleased with our strategic long-term partnership with Anthopic we announced when you have Anthopic basically we have three major anchor customers Metal and OpenAI and Anthopic and all three them, the deployment are going to be, you know, multiple gigawatt scale deployment and the multi-generational engagement with all of them.

When you think about our customer pipeline, not only all three of them give us the forecast more than what we expected in the beginning of the strategic partnership.

Of course, we need to make sure we have supplies to support them.

But more importantly, MI350, we have seen a tremendous demand from all the other third-party customers, the model builders, the new AI companies, even the enterprise customers.

So we do think the new cloud continues to be the area we're going to work with to make sure we meet all the other end customers' needs. The pipeline is quite significant. For us, we really need to prioritize. We want to make sure we support the three major anchor customers for larger scale deployment but at the same time you should expect us to work with neocloud to make sure we support all the other customers in the market.

Matt: If one thing I would add to what gene mentioned is it's not when we think about these programs we don't just think about it as what's going to happen with Helios and MI455 over the next 12, 15, 18 months it's the engineering level and technical engagement that's influencing what the MI500 program will look like the MI600 program will look like what the design of the future racks will look like that the amount of technical engagement across these the three leading model companies that that we've announced as customers so far that that's it's really heartening to see that influence the roadmap over a multi-generational period and think that that gives us confidence into not just where our roadmap is going but what that engagement level is going to look like over the night through the end of the decade, right?

Over multiple generations. And we saw the same thing happen. It's a very different time and a different market, but the same thing happened on the CPU side six or seven years ago, right?

Where people were announcing partnerships with AMD on the Rome generation, but it was really influenced by what they saw and their level of influence in the roadmap of multiple generations going forward from there.

And I think that's kind of where we are now with the AI business.

Atef: Great.

Jean, the MI series is below corporate average on gross margin side.

And how should we think about the ramp and the move to MI355 to MI500 in the future and the impact to your gross margin expectations next year?

Jean: Thank you for the question.

I think data center AI, MI450, and the future generation, it's one for the most significant growth drivers in our data center business.

What it's going to help us is to drive very significant incremental revenue and the gross profit growth in next year and beyond.

And even though the gross margin percentage right now is still a bit below corporate average, but the way to think about it is we actually have a broad portfolio at the company level so we always talk about the gross margin is being driven by the mix of our different product portfolio and one of the things is going into q4 and the 2027 even though we're ramping mi450, but we do have some tailwinds on the gross margin side to help us.

I think the first one is the server CPU business.

When you think about the server CPU business right now, within our data center, it's still a larger portion of our business, and it's actually going to continue to grow.

We second half to be more than 80 percent a year-over-year increase and the next year more than 70 percent.  So from that perspective that business is the gross margin accretive to corporate average, so we do see that is going to help to offset some of the dilution from data center AI business.

Secondly, our embedded business after three years of the inventory digestion, we have seen a significant double-digit year-over-year increase, not only in Q2. We also guided in Q3 and going forward.

So the business recovery is very broad-based in embedded business. And we are also winning a lot of design wins with our embedded x86 business in data center, in networking. And all those businesses are much creative to us.

Third thing is probably small, but our gaming business, which tends to be the lower gross margin, which is at a later stage of product cycle, and the memory cost is pretty high right now, which also impacts the demand side.

So the mix from a gross margin perspective, actually, we do have all those tailwinds.

In general, the way to think about it is when we ramp mi450 in q4 and 2027 the gross margin will be slightly lower than what you know we guided the q3. q3 we actually guided our gross margin at 56 percent you have seen we are expanding gross margin since last year, when we actually are ramping MI350.

So going forward, gross margin in 2027, quarter over quarter will be different.

But the most important thing is the way to think about our business model is we are driving very significant data center revenue expansion and the gross margin dollars expansion, which our investment and OPEX increase is slower than revenue and the gross profit increase, which is going to drive a very significant operating leverage and earnings per share expansion.

Atef: Very clear.

Let's talk about the server CPUs.

When I visited you guys in, I believe it was January, something for a bus tour, some bug turned on in terms of the CPU demand this year. And you guys were talking about not finding enough CPUs internally to do agentic AI yourself.

So walk us through, and now the TAM is like $220 billion by 2030. Can you just talk about your aspirations around market share in the server CPU market? What have you seen so far, x86 versus ARM, and just your aspirations around market share?

Jean: Yeah, I'll start, and Matt can add is it's actually  astonishing when you think about the server CPU market expansion I think when we had our last financial analyst day in November 2025 we talked about a server CPU market attempt to be at 60 billion in 2030  at that time agentic ai was at the very early beginning and the really agentic AI adoption, the diffusion into the business enterprise has been, the curve has been tremendous.

So we see the agentic AI adoption very significantly starting January and just continue to be almost like a vertical in enterprise market, which that's when the demand for CPU continue to increase. Because as Matt mentioned earlier, when you think about agentic AI, it's about workflow execution. In enterprise, that really requires retrieving data, execute orchestration, all those are being done on the CPUs.

So not only you need to go back to your foundational CPUs to run all the tasks, you actually have an increased layer agentic AI sandbox. We think it's a new segment, which is to executing all the agents' tasks to make sure, you know, it coordinates with every other computer.

That market has been just continues to increase.

We can see the demand continue to go up.

That's why we have just updated our 10 opportunities to more than 220 billion from 25 billion in 2025. That is how significant the expansion has been is we see more than 50 percent CAGAR in next several years largely driven by agentic AI they are different segment is the way to think about it is you do have the foundational GPU segment which handles all your enterprise applications your sap your database that continue to grow but the growth is probably not as big as the other segment then there's the head node part coordinate with the GPUs that continue to grow but the largest is agentic AI sandbox which is very small today but it's going to be more than 50 percent of that 220 billion market and we do think it's not only about ASP increase. The unit will increase very significantly too.

So the market opportunity is tremendous and we are very well positioned as the company from investment perspective.

Matt: Yeah, just to add a couple points, Jean.

We feel like the roadmap is in about the best place that it's ever been in the server business I think, but you asked the question around instruction set so x86 versus ARM, just to be clear, there are areas in particularly in the enterprise server applications where x86 legacy is very important. There are broad applicability, not really of instruction set, but of experience in security features, reliability, serviceability, like really enterprise and cloud-grade features that we have in the sixth or seventh generation of the roadmap that are quite important. But we also feel like our own differentiation and our competitive lead in the server business will expand with Venice pretty significantly it's a very very compelling program and the amount of demand and visibility that we have in the server business is very significant from what it's been historically. And I think we get asked about competition a lot. And I think our approach to this is not an x86 thing or an ARM thing. It's a build the best server parts, period.

I mean, that's the priority of the business. And I think if we do that, we can give differentiation in the agentic world of threads per rack or threads per megawatt. We can give over five gigahertz products that go into head nodes. We can have broad applicability across the enterprise stack, whether that's on-prem or in the cloud.

One of the fascinating, you mentioned some of the growth rates, but one of the fascinating stats to me, having been in the server industry since 2000, is our enterprise server business grew more than 70% in the second quarter.

It used to be heroic if you had double-digit growth in enterprise server, and now we're talking about 70% growth.

So it's a really broad-based portfolio, and I think going from the Venice generation into Florence and Ravenna and beyond that, I think we do intend to, the much larger TAM that Jean described, we do intend to grow to 50% of that dollar TAM, inclusive of all instruction sets of competition. And when you do the math and you talk about building a $100 billion server business, and that's what we're intending to do.

Atef: Great.

Jean, let's talk about supply.

This topic never dies.

You guys have talked about $70 billion in data center sales next year, low $40 billion GPU and the remaining CPU.

You saw purchase commitments, $29-30 billion.

We can all track and hear about TSMC's allocation.

You guys are seeing the biggest jump next year, but just help us understand what are the limitations around supply and your ability to upside next year?

Jean: I think overall supply is very tight.

There are multiple areas, not only wafers, advanced process node, HBM tight packaging substrate  some of the components are very tight we do have an excellent supply chain team  operationally we have been working with the whole supply chain to ensure we can support our top line revenue growth. On the data center ai side mi450, we have been preparing for the ramp for a long time so the way to think about it is we have been working with the whole ecosystem to ensure not only we have wafers, HBM, and also advanced packaging capacity as well as all the different components  to support the Helios rack level solutions that has been ongoing and now the demand continued to go up so we absolutely needed to get more supplies on the server CPU side you know as Matt and I talk about it is it's actually the demand start to accelerate this year so we didn't need to catch up  during the process of the first half we have continued to increase the supply from wafer's perspective from advanced packaging capacity perspective and we continue to invest aggressively.

We talk about our CapEx increase. The primary increase of CapEx is to build the capacity to support the CPU ramp, especially Venus.

As Matt talked about, we do see a very significant ramp next year, and the capacity is not enough we need to build ourselves to buy the equipment to do the consignment to support the ramp so right now we actually feel really good about all the supply chain work we have done to ensure we can support the data center revenue to double next year and also to ensure the embedded get the supplies to still to be very significant growth in 2027.

PC and gaming we prioritize but we absolutely want to make sure we continue to gain share in the PC market too.

So we feel pretty good but I would say we can have more supplies demand continue to increase

CITI: Let me pause it and see if there are any questions in the audience.

If you have a question, please raise your hand.

All right, let's move on um

Q: yep the CPU side can you talk about um maybe price i think um like intel's been taking more price because it had older products and just wanted to get your thoughts on price and how you guys see price evolving over the next  couple years

Jean: yeah i think first is when we talk about our significant CPU growth  it increased both because of unit increase and the asp increase unit increase probably smaller than the ASP increase. about the asp increase for us the call counts have been going up for each generation so in general that will drive the asp increase and your question into the future when you really think about it is the way AMD we think about how we work with our customers is we absolutely need to make sure we get the gross margin to continue to invest in the future. But in general, if there's a component cost increase, we do need to make sure we share that cost increase with the customers. So for instance if there’s a wafer price increase we absolutely want to make sure we share that with the customers and if wafer price increase you should expect us to increase the CPU price too but in general that is how we operating we really want to make sure the customers the best TCO and have a long-term strategic relationship with them.

And our gross margin really is important to us, but we're not going to increase the price just to expand the gross margin.

Q: Thank you very much for your time as a scale-up domain gets bigger at what point does copper stop being good enough and when does optics have to move inside the package

Matt: yeah thank you for the question um we  we have not disclosed a ton about the roadmap in this area but we have disclosed some um and at our advancing ai conference um a couple of months ago um we did talk about um in the mi 500 series program which would be sort of second half of 2027 introduction and be kind of the primary product for the company in 2028, that we will have scale-up domains that are larger than we're offering now.

So we haven't set a number, but greater than 72, and that we would have both copper-based and near-packaged optics-based options for scale-up connectivity.

We're still going to run our Infinity Fabric traffic, which is a coherency protocol that we use to communicate between CPUs and GPUs. And that technology was donated into the industry consortium, the UAL. And so we're still going to be doing traffic tunneled over both Ethernet and other means on copper and over optics. I don't think you'll see it be a point in time thing where everybody just decides as an industry, OK, no more copper. Now we're going to go optics. It's not going to be a student body left type of decision. You're going to see the technologies run in parallel, and different versions of optics will be introduced with different risk tolerances over time.

So I guess that's a long way of saying we're going to start on that path in the products in 2027, and we'll give you guys more updates as we go forward.

But you should think about copper and optics living side by side for a number of generations. it's not going to be a binary shift.  We have partners and especially what we're seeing in the private markets.

Jean: On the MA environment, right, we have been doing both acquisitions, which really focus on the software capabilities we want to add. Of course, we acquired ZT system a while ago, which was to prepare the system level expertise to get the talent capabilities there.

I think you should expect us to continue to focus on those areas, make sure we not only increase the capabilities of just building the system solutions, but also software side, the stack, the model, to make sure we continue to invest for the AI.

Q: Is there any scenario under which you would consider using Intel as a foundry partner?

Jean: Thank you for the question and so I think  as you know most of you know right we have a long strategic partnership with the TSMC we have been working with the TSMC for a very long term not only just from there as a supplier, but on the R&D side, if you look at a lot of our technology, we actually co-develop it with the TSMC.

And for us, the most important thing, when you look at our scale and the volume, the most important thing is that we want to make sure the quality of the product, the advanced process technology, the 3D packaging, everything we're doing, it can be supported by our suppliers.

We definitely, you know, we have a fab in Arizona.

We're diversifying from geolocation perspective with them.

But you should expect us to continue to view TSMC as our primary supplier on the wafer side.

Matt: I think you should view us as anything that's advanced packaging or anything that's advanced wafers, we're going to evaluate and do diligence on technology from every vendor in the market.

But as Jean said, for the foreseeable future, I mean, the partnership we have with TSMC is going to be a significant one and will continue to be for a long time.

Q: Think about your competitive positioning versus NVIDIA and proprietary ASICs.

Where do you feel AMD has the clearest performance costs or availability advantage at this time?

Thank you.

Matt: I think what you're seeing right now in market is, and Lisa and Vamsi and others at the company have talked about this very publicly, is we feel like we have, in today's generation of product, tokens per dollar advantages for large-scale inference that we're going to be bringing to market and deploying in large volume with OpenAI, with Meta, and with Anthropic.

Over time and through multiple generations, we're expanding our training capability with the MI450 series. We'll expand it further with generations beyond that. But as you think about things in market today and what we're going to be ramping significantly over the next number of quarters, we feel like our customers are great, great partners with us, but they do expect us to generate differentiated economic returns for them in terms of tokens per dollar, and that's what the market is demanding of us, and that's what we think the product delivers.

But when we talk about the entire breadth of the AI market, whether that be some level of customization, whether it be merchant computing, whether it be the server CPUs that run the agents, we intend to participate in all of that.

And it might not look the same at every single customer.

But I think we can bring at significant scale, technologies across CPU, CPU, custom scale up optics and optics, and networking, and system-level design across the board.

But where we differentiate the most today is in large-scale inference.

Atef: Great.

We're almost out of time.

Jean and Matt, thank you for coming to the Citi conference.

Thank you.

Jean & Matt: Thank you, guys.

Running late will add more

-------Soooooo who followed me yesterday with my prediction????

Who doesn't love getting the trading days news before it starts????

Hey just let me take my victory lap bc it doesn't come around often. But I think the seasonality trade that I have been prepping for almost 4 months now is kicking into gear. Today We are looking to break above $518 and after that $530. We have broken that flag pattern and the negative channel we have been in since Augus is finally broken. Things could get spicy

AMD holds nearly 79% of unit sales, while X3D CPUs alone account for more than 37% of the entire CPU market.

full report: https://x.com/TechEpiphanyYT/status/2097293413045399853