Hi, this is Steve Eisman. You know, in the great financial crisis, people sometimes ask me, why did I foresee what was going to happen? And the answer in some ways is that the center of the universe back then was the entire financial sector and that was my area of expertise. So, I knew what was going on. The financial sector has not been the center of the universe for a very, very long time. The center of the universe right now is semiconductor and semiconductor equipment companies because that is where the entire AI infrastructure is being built out. And so today we're going to talk to a recurring guest, Stacy Rasgon of Bernstein, who covers semiconductors and semiconductor equipment companies. And we're going to discuss what's going on, why it's going on, and what could possibly derail it. And afterwards, I'll be back with some closing thoughts.
Hey, this is Steve Eisman and welcome to another episode of The Real Eisman Playbook. And today we're going to discuss a group that I would say is the center of the universe right now, which is semiconductors and semiconductor equipment companies. We're going to talk about it with a recurring guest now, Stacy Rasgon of Bernstein, who we spoke to maybe seven months ago. Stacy, welcome.
So, Stacy, you are the center of the universe. How does that feel?
Like, semis are always interesting. There's always something going on. Um, maybe. So, but you've never been the center of the universe. It is nice to be popular. Like, I'll leave it at that. I always joke, you know, I do well at Bernstein. I'm, you know, but it's not just my sparkling personality, right? I mean, it is a fact that the group is of great interest both to specialists and generalists alike now. And just given the rise of AI, I mean, it's not just semis, right? AI is sort of dragging everything in — I mean, in semis and out of semis along with it right now. Yes. It kind of feels like it's the only thing that's supporting everything else that's going on. Um, so yeah, it's nice to be popular.
I'm glad you're happy to be popular. We'll see how long it lasts. But so let's take a step back. Give me a summary of all the stocks that the group is — a group like what's been since the last time we met. Yeah. When — seven months ago, it's not like things weren't good seven months ago. They were pretty hot then, too. What's happened in the last seven months?
Yeah. You bet. I mean, it's gone into overdrive and what really — what's happened is, like I said, AI has gotten so big, it is now dragging everything along with it. And so, probably seven months ago, we were looking primarily at the compute and accelerator names, the NVIDIA and the Broadcoms of the world. Now, it's everything in semiconductors. Whether it's, you know, it was the accelerators and now it's memory and it's semicap and it's optical and it's power semis and it's CPUs. Now AI is dragging everything. Everything is — you know what it really is is one at a time, all of these different parts of the industry have sort of become the constraint. As AI has gotten bigger and bigger and bigger, different things have now become like the bottleneck. The stocks have actually ripped because investors — as you know, investors love to play bottlenecks. Right. And so one at a time we've seen these different kind of different areas of the broader semiconductor space take off. And frankly you could have owned anything in the space and you would have been just fine. I think semis now year to date — I don't know what the exact numbers now, but at least up 60% year to date, probably more. And the issue — Micron's up well over 100. Oh, and look, you have companies like, say, a SanDisk — I don't cover, my colleague covers — but they just guided to a quarterly EPS that is higher than the stock price was when it went public like 18 months ago. And so this is what I find. Yeah.
SanDisk just guided to an EPS number for next quarter.
One quarter. I can't remember. It was 31 or something like that. 31, 32, whatever the number was, but it is higher than the stock price was when it went public like 18 months or two years ago. That's unbelievable. Unbelievable. And so if you look at the run that the space has had year to date, it's actually all earnings multiples broadly. If you take like the SOX index as a proxy — the SOX index is a broad index of semiconductor companies that people use — the multiples actually come down a little bit. It's actually come down a little bit, right? And so all of the growth we've seen here today has been earnings, right? And so if you are worried about sustainability — and people, I get it. But the thesis you would have to articulate is why are earnings unsustainable? Because it's not like — and the sector is not cheap, but valuations are — it's not egregious at all. Like we haven't gotten anywhere near nuts yet, right? In terms of valuation, not at all. And so —
So give me — what's the PE of Micron right now?
Oh. Well, so the memory names are probably trading at single-digit PE. Micron's — and that's because, you know, look, memory is known to be a very cyclical industry and typically in cyclical industries when earnings —
Wait, what's the difference between a memory chip and a CPU chip?
Different types of processing, different types of applications, different economics in the industries. CPUs are — they're a subset of a broader space which is known as logic. They tend to use very advanced manufacturing technologies and they're used for computation, right? Memory chips — I mean, they're for memory. They're used to store.
So Micron's a memory chip company.
So if Micron's a memory chip company, which of your companies are CPU companies?
Sure. I cover — well, a lot of them. I've got some CPUs and some that want to be CPU companies. So the traditional CPU companies I have are Intel and AMD. Intel and AMD. AMD. NVIDIA, who makes accelerators and GPUs, they actually make a lot of CPUs as well. And they are articulating a CPU story that could be even bigger than the other two right now. I've got other names like Qualcomm, for example, which is known to be — they make chips for mobile phones mostly. They are now trying to get into the data center space and they have CPUs as well. Okay. So yeah.
So let's start with the granddaddy of them all, NVIDIA.
Sure. So, you know, for my weekly wrap, I went over the numbers with like a actually I actually went over numbers really carefully and I I would say if I mean you'll give me more details, but I would say if you could boil down the whole story to like a sentence, it would be the revenue growth was 85% and two quarters ago was 65%. It's actually accelerating and so it's accelerating and the gross margin a year ago was 60% and it's now 75.
Be careful. The gross margin a year ago had an impairment in it. So it was too low. It was 75ish or 70 I can't remember 72 or 73 without the impairment.
Okay. So my first question is Nvidia gave some different disclosure this time. What was it and what does it reveal? And let's start with that question.
Okay. You bet. So they did two things. They took their data center segment which they used to split up into compute and networking. Now they're splitting it up into what could you could take as hyperscale versus sort of non-hyperscale. So hyperscale would be the large the Googles and the Metas of the world the ones that are building the massive data centers and then non-hyperscale would be everything else. So the enterprise customers and the neoclouds and the software.
Would that be Anthropic there or would they be in hyperscalers?
Well, it depends on I think where Anthropic is getting the compute from, right? Because they're Anthropic may be building some and they may be also going to the neoclouds, but anyways, but yeah, so that was one thing they did and they kind of because there's this big concern with Nvidia about customer concentration. So what they actually said and they showed it is is the hyperscale and non-hyperscale are about equal sized in terms of revenue for data center revenue, they're about 50/50.
Okay. And data center is what percentage of total revenue? Uh oh it's 90% right? Used to be used to be gaming.
Oh yeah yeah yeah 10 years ago was almost get to my second change in the disclosure but but 10 years ago was gaming and there was crypto and all that nonsense. Right. Yeah but they're giving us some visibility and saying you they still have concentrated customers but they also do have they're not quite as concentrated as maybe you might think. They do have a long tail of other customers and those are also growing very rapidly.
Just to wrap, almost just to wrap, the data center business is basically divided equally between hyperscalers and everybody else.
Last quarter it was equal. They bounce around a little bit quarter to quarter but it's pretty big.
Second is they took all of their other segments which is gaming, professional visualization like workstation stuff, automotive and this other bucket and they lumped those all into a single segment which they're now calling I think it was edge computing or edge AI.
And that's a small percentage of the total revenue.
Pretty small, yeah. So they're telling you two things. One, well the biggest thing is they're telling you none of that stuff really matters anymore. Right. But the other I think it gets to the longer term. They've talked about longer-term drivers, physical AI and robotics and even the autonomous driving. And so I think they're expecting hopefully if we're looking out, you know, 5 years, 10 years, maybe that other bucket will get bigger as some of that other stuff takes off. But I don't think it'll be gaming. It'll be things like robotics and automotive hopefully. But they're taking all the other segments and lumping them together. They did not move stuff in between the segments or anything like that. Sometimes when companies resegment, they play games and they move stuff from one segment to the other and it's hard to they didn't do any of that. They just took the big one and split it up and they took the other smaller ones and shoved them together.
I see. Before we move on, just define for us the difference between GPU and CPU.
Sure. So, they're both logic. They both use advanced transistors. Um they and they both do processing, but they process differently. Um I'm going to grossly simplify, but CPUs,
You can think about CPUs as doing computations sort of serially like one after the other, right? Um you can think about a GPU as doing computations in parallel. So for example, a lot of these compute chips have a certain number of compute cores on them like they've got different pockets of transistors on the chip that can handle logical operations. And a CPU might have anywhere from, you know, a few cores to a few hundred cores on it, depending on what that thing is being used for, a laptop chip or a server chip or whatever. GPUs would typically have thousands of cores on them. Um smaller each core would be less performant than a CPU core, but there's a lot more of them. And the GPUs tend to do certain types of math exceedingly well, but not as useful for like the general purpose math that a CPU would do. Um GPUs tend to be used this is why they were used in gaming and other things. They tend to be used for um it's called matrix manipulation, matrix multiply and addition and I don't want to go into what those are but it's the type of compute operations that were very useful for graphics. And as it turns out are actually exceedingly useful for artificial intelligence and machine learning. It's the same kind of math. That's why GPUs which were developed for graphics turned out to be very useful for artificial intelligence applications.
Okay. So quick question on Nvidia's stock since the entire story basically I mean if you look at, you know, CPUs, memory chips, semicap equipment, the entire business basically hinges on Nvidia. In other words, if Nvidia is growing revenue 85%, everybody else is going to do great. And then we could have a discussion about who's doing better here or there, you know, what's pricing. You know, you get into the weeds. If tomorrow Nvidia announces that revenue growth goes from 85% to 120%, everything goes up. If it announces that revenue growth is going from 85% to 40%, everything's going down. So my question is, why is Nvidia only up 14% this year and sells on a multiple that's much lower than a lot of these other companies whose entire businesses hinge on them? And I'm very upset about it because I own Nvidia and I don't get it.
It's a great question. It gets to what I said earlier about yeah. I mean, try not to be upset, but you're right. It's lagged. Um now, to be fair, it's up I don't know what it is. It's up a crazy percentage. Whatever. You know, that's talking about this year. So, there's a few reasons. So, that is one. It had a big run already. Fine. Um secondly though, and it gets back to what I said earlier where I said AI was sort of dragging all of these other segments along. Um and investors have been rather than playing like the GPU or the compute names, they've been playing the constraints, right? And again, remember what I said for just to pick on memory for example, some of the earnings revisions we've seen in some of the memory names, um where you've gone up an order of magnitude or even more in terms of the earnings power in the stocks, you're just not because pricing has gone crazy. Exactly. You're not seeing that kind of a thing from Nvidia. And so the investors who love to play constraints have been playing the constraint names and they've been going from one to the other like I said memory to semicap to optical to power right to CPUs. So that is part and it's
Interesting because it brings up this very interesting divergence between the two, because one of them has to be wrong, to your point. The other stuff cannot work if...
Doesn't work. Impossible.
So I think that there is an opportunity because I do think that that has to normalize one way or the other. Either the constraints are going to go down, or Nvidia I think has to come up. The valuations I think have to normalize.
But there's no constraint on GPUs right now.
Well, there is and there isn't. So there's leading-edge logic and what's called CoWoS, which is the packaging technology, and we can talk about that if you want, to put the chips together. That's always tight. And memory is tight. But Nvidia has been very good at securing supply across the value chain. They saw this coming, right? So if there's anybody out there that has enough...
Jensen is an excellent CEO.
Oh, yeah. Oh, yes. Oh, yes, he is. So they've been very good at securing the supply that they need to meet the growth, so they can accelerate.
Mean supply from like Taiwan Semiconductor?
Well, and also the memory guys, and also the packaging, and now we know he's getting into the optical and silicon lasers and all kinds of other stuff, right? But he's been very good at doing that. So they have supply. But this divergence has been very interesting. That's another reason it hasn't worked as well. And I think the third is just, I mean, look, I can't remember what the market cap is. Is it 5 trillion, 6 trillion now?
It's big, right? And it's, you know, it's 8% of the S&P or something. And so for some of the large, like especially the large longies, it's hard for them to own more of it because they're already there. It's even hard to be at market weight because it's so big. And so that's, you know, it's like, okay, it's 5 trillion, is it going to go to, you know...
Posing, just so our viewers understand: if Nvidia is at 8% of the S&P, there are plenty of institutions out there who have rules that say you can't do anything more than X%. So they can't — they have to — so by their own rules, they have to underweight Nvidia.
Yeah. Okay. So there's some technical reasons, I think, as well.
At the same time, look, I mean, the thesis on it has been pretty simple, like number go up, right? [laughter] It hasn't — you haven't needed any more than that. And I think it started to work a little bit better the last few weeks. Part of the reason there is, you know, people get very excited about the CPU names and people realize, oh, wait a minute, they sell CPUs too. And then on the last earnings call they were trying to articulate — they could have been a little clearer about — I think we'll hear more at GTC. They were articulating a CPU thesis and I...
Oh, so he was articulating a CPU opportunity. They're going to do, they think, $20 billion worth of CPU revenues this year. So that's basically all in the second half. $20 billion is about as big as Intel and AMD's CPU businesses.
And why are they going to be selling so many CPUs?
So two reasons, and this is one reason. Now, why are they going to sell them, not somebody else?
Well, others will as well. So there's two things that they're selling. One is in the large GPU racks that they sell. So their current mainstream product is something called a Grace Blackwell — well, it's like a GB300 NVL72. They have all these, but it's a big rack and it's got 72 GPUs and...
GPUs and it has [clears throat] 36 CPUs. Now, these are Nvidia's own CPU design. It's called a Grace CPU. It's based on the ARM architecture. We could talk about that if you want, but every GPU rack they sell has these CPUs. Okay, so that's one thing. However, there is a much bigger demand for CPUs now just in general. And the reason is the rise of something called agentic AI. So this is over the last couple of years, the big push in AI has been what's known as generative AI. So...
LLMs and, you know, AI slop, like I ping the thing and it makes a photo or a video or like what, it's generating content, which is interesting and nice, but ultimately it's not...
Agentic AI would be: I want to go do something. I want to — I want to go — I want to plan a trip to Paris.
Go book it, give me the whole itinerary.
Yeah. Or, you know, what we use it for now is coding, like I want to write an app to go do something like that. And [clears throat] the thing is, like when I'm generating AI, that's running on the GPU, right? When I'm doing something agentic, I've got an actual agent — the model is actually creating an agent to go out and do a task, and most likely that task is a real-world task and it's running on a CPU. And so just to give you — to use your...
Why would agentic AI run more on a CPU?
Right, my travel to Paris.
Yeah. So I ask my model, 'I want you to plan me a trip to Paris. I want to go on these dates. I want you to look for pricing of tickets between this range, and I want to stay in four-star hotels in these cities.' So what is that? And it's going to have an agent that's going to orchestrate this. So that agent is going to go spin up a bunch of sub-agents, and each of those sub-agents, they're going to go ping, you know, American Airlines and Delta and United and whatever, and all the different hotels.
There's a lot of computation going on.
A lot of it. All of those things are going to be running on CPUs, right?
Well, there's a GPU that's running all this as well, right? But American Airlines' server is not running on a GPU. Like when it's actually going to do the physical task to make the travel happen, that's running on CPU. Another example: I'm using Claude to make an app and I'm coding something and I tell it I want to do this. So that agent is going to go spin up 20 sub-agents. The agents can spin up agents. Each of those sub-agents is going to open up a virtual machine, like basically a representative computing environment that's running on a CPU, or more likely on a compute core on a CPU someplace. And each of those sub-agents is going to be writing a block of code, and there'll be other sub-agents going to put all the stuff together and orchestrate it and review it and everything. And so that one GPU with that one task can be spinning up a ton of CPU compute while it's running.
The way people tend to think about this, they tend to think about it in terms of attach rate. So, for example, in the old style, if you go back a couple of years, the CPU attach was like 8:1. I had like, you know, I had like eight GPUs and one CPU in this box, in a server. And then it went to 4:1. And then if you look in these big GPU racks that Nvidia sells, it's 2:1. A lot of people talk about, oh, it's going to — with agents, it's going to go to 1:1 or even...
So you'll need more CPUs per GPU.
By the way, I think that the attach rate model is wrong, but it's useful, right? It's not — I don't think that these CPUs are necessarily directly attached to the GPUs, but the GPU, when it's orchestrating these kinds of agentic tasks, it's going to require the usage of a lot of CPUs. So the attach rate model, like all models, are wrong, some are useful. I think that's a wrong model, but it's maybe a useful model. We need more CPU content as you do real-world tasks with these models. So that's one reason why the demand for CPUs is going up.
And Nvidia is actually — not only are they selling CPUs in their racks, they're actually going to sell standalone CPU racks now.
Okay, that's interesting. Okay, let's switch gears just for a sec. Hi, Steve.
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So, Michael Burry, many, many months ago, put out a thesis about — not per se NVIDIA, but about all the hyperscalers — arguing that all the hyperscalers had lengthened their depreciation from like three or four years to five to six years and mathematically speaking that increases obviously earnings. You can't argue with that, that's a fact. And he thought that that was illegitimate because — because the speed — because you keep hearing from Jensen, I got a new GPU every six months, I got a new GPU. So what does that — how — what could that possibly say about the old GPUs? They're obsolete. That was the argument. What do you make of that?
That was the argument. I do not think it's correct. Although I understand the logic and I'm not going to knock on Burry, you know, fine, but I don't think it's accurate in the current environment. In fact, you know, you can look at the — this is data that exists — like you can look at the rental prices of GPUs by generation, okay? And take the older generations, the Hoppers, which — Hopper came out in 2022. You can even look at Ampere, which is the generation — the NVIDIA generation before Hopper. And by the way, just to level set you, the current generation — the current generation that's about to come out this year is called Rubin. The current one that's shipping is called Blackwell, right? The one that came before that was Hopper, 2022. The one that came before that was Ampere, 2020. Okay. Okay. And we can look at the rental prices because these GPUs are out there, right? They're available if you want to rent them. Rental prices — even for the older stuff — are going up, not down, right? And even for the stuff that's fully depreciated, they're very profitable to use. So these GPUs do not disintegrate after three years, right? They are still usable. So the question is, is it economically viable to continue using them when you have better GPUs that are out there? And at least at the moment it is absolutely economically viable to use and you can see it in the data. Like, I think there's no question. The rental prices are going up, not down. Why is that? Because demand is off the charts and demand is so strong we need every bit of compute that we can possibly get right now and — even old compute — even whatever is there. And that's been the interesting thing is more and more compute — with every generation you get more compute out of it, right? So you've had exponentially more compute coming online and yet the demand has been accelerating even more than that. And so we need every bit of compute that we can have and the stuff is getting monetized, no problem. If we run into a scenario where demand falls off and I don't need that old compute, then I worry about lifetime and depreciation, but we're screwed anyway, so like who cares, right? I do not think that that is a — if we want to articulate viable bear cases on AI, we can. I do not think that the depreciation point is a viable bear case.
Okay, we're going to come to the potential bear cases later. Let's keep going down some of the companies. Let's talk about AMD.
Yeah, look, that stock has done great. So I can't argue with it, but AMD is a smaller company than NVIDIA. Almost every company is small. Almost every company is small, but quite a bit smaller. I can't remember what AMD's revenue growth was in the first quarter. Oh, it was pretty strong. How strong? I can't say. I don't think it was — I'm not sure. I'm not sure because they've got other businesses. So just articulate the AMD — you're recommending it. We just upgraded it. I saw you. To be fair, I will say that my track record with AMD bull cases is horrific. So we — however — we all have our crosses. We do. I've been much more wrong by not recommending it. I mean, clearly, like, we've been — we upgraded on earnings. And I'm actually kind of kicking myself because when we had previewed the quarter a week and a half before they reported, I came this close to upgrading it into the print and I chickened out. I lost my nerve. So like, no guts, no glory. Okay, I'll tell you the reasons we were looking to upgrade and then the reasons why we did, though. Okay. Into the print. Again, people were getting very excited about this agentic CPU story and they have very good CPUs. And not only is their data center — their CPU business growing a lot, they actually have really good products. They're taking a ton of share from Intel. And just to give you an example for how strong CPU demand is right now: Intel had about 200 basis points of margin upside last quarter because they were selling previously written-off garbage that was like lying around. By their own admission, their server products are not competitive. Customers right now do not care. The stuff was written off. It was lying around. They just — they could sell customers like, we'll take it. They'd already written it off to zero. So they sold it at 0% cost basis. AMD actually has good products that customers want. They've been taking — they took out oodles of share. AMD — 10 years ago, AMD's market share in x86 servers was 0.1%. Say that again. AMD's market share in x86 server chips 10 years ago, in 2014, 2015, 0.1%. So 99.9% market share — 99% — this on a revenue basis. So basically 100% versus zero. Zero. Today AMD's revenue share in x86 servers is like — I can't remember — low to mid-40s. And Intel — it's the opposite, the 60s. But AMD's taking share, of course, they actually have products that customers want to buy. That's — and then the other thing that we were looking at is, you know, they are selling — they also have GPUs and they've signed a couple of big deals, OpenAI and Meta. And this buy side, I think, was there, but the sell side, for whatever reason, had not put Meta in the numbers yet. So I started to look into '27 and for the first time in a while my numbers were quite a bit above. We didn't upgrade. We didn't pull the trigger on it at that point because the street was already modeling the CPUs up 50% this year and I figured, well, people must know Meta is coming. It looks like now the server CPUs for them are not going to be up 50% this year. They'll be up 70%, like maybe more. Okay. We'll see. And how are they doing in the GPU part of the business? They're doing okay. A couple of things. So — because that was the story. It still is part of the story, but you have to remember — so the GPU thesis on AMD goes something like they're going to go from being a marginal player to a slightly less marginal player. [laughter] They go from 4% market share to 10 or 11 in a market that's growing to a trillion dollars plus. Right. Now, to be fair, you know, to the —
Bears on AMD to sign these big deals, and they signed two of them right now: OpenAI and Meta, multi-gigawatt, multi-billion-dollar deals. They basically had to give away chunks of the company to get there. They gave warrants.
Each one was about 10%. If they're all fully exercised, there are purchase commitments required for the warrants. So there's also stock price thresholds. I will be honest, I would rather see them sign big deals without having to sign the warrants, but I understand the warrants. The reason they did it is, I mean, like they had to be on the rocket ship, so they bought their ticket. It was an admission on their part that the products as they stand today are not good enough yet to get the kind of share that they need. But they have to build scale rapidly. It's not just enough to have the parts. You have to have the developers and the ecosystems and everything behind you. And it's a chicken-and-the-egg, right? Developers are not going to develop for your product if you don't have anything in the marketplace. So they're doing everything they can to get the products into the marketplace to build that ecosystem. And I don't like it, but I understand it. And frankly, if the stock goes to $600, which was the high end of the stock price threshold to exercise the warrants, I figured investors are not going to care anyways. And the stock price looks like it's maybe going there. So they reported earnings. Everything looked good. Our server estimates, which I thought were high, looks like they needed to go higher. And I started running my numbers and it was like, well, now I'm not... the street had been at like 11 bucks for next year. I had been at 13 and change. Now I'm at like 14 and change. And if Lisa, the CEO, if Lisa Su is correct, and the server CPU thing does not level off, if it keeps going, because she also doubled her estimate for where she thought the CPUs could go.
She thought by 2030... they had analyst day a couple of months ago. They said, 'We think in 2030 it'll be $60 billion TAM.' Now she thinks 120. If that's true and things don't moderate, they continue to grow, all of a sudden I'm looking at 2028 and I'm pretty close to 20 bucks in earnings, which was their 2030 target. And at 20 bucks in earnings in '28, you can start to underwrite quite a bit of upside. So I admitted it. It's late. Like, I get it, but better late than never. And so we pulled the trigger on it after earnings and upgraded it.
Seven months ago, Intel was here, and now it's up here.
