Let's now step back and look at the bigger picture, starting with demand and then supply. On the demand side, AI revenue is growing so fast, but the pace is starting to moderate. The combined AI revenue today is only a fraction of what the industry is spending, so the market is now looking at every revenue number under a microscope. And on the supply side, capacity is sold out for years ahead and pricing remains firm.
From a semiconductor analyst perspective, how should we read these two signals, and who will capture the value along the way? To guide us, we have two leading voices in semiconductors.
Dylan Patel is the founder and CEO of SemiAnalysis, one of the most influential research firms in AI infrastructure.
Stacy Rasgon is managing director and senior analyst at Bernstein, ranked on Wall Street's top semiconductor analyst for more than a decade. Our moderator is Austin Lyons, chief analyst at Chief Strat and former Intel chief engineer.
Please welcome Dylan, Stacy, and Austin. [applause]
All right. Hello, everyone. Y'all ready for this?
All right. This should be good. This should be fun. Okay, so we are going to talk about the industry. We're going to look ahead to 2027, 2028, and hear these two gentlemen's thoughts about where the industry is headed. We'll try to cover it all, from financing to GPU, CPU, down to foundry. So let's get into it. I actually want to start with the finance side. So you know my question for you guys. Lately, a lot of the news has been about the cost of capital. So we know hyperscalers are investing a ton and free cash flow is going toward negative. And now we've got a 10-year Treasury over 5%. Even if you zoom out, like CoreWeave, there's talk about them, you know, bonds at 15% yields. And so my question is, should we be concerned? Is there going to be an impact as companies are investing more and more that it's becoming more and more expensive to finance this? So I'll start with Stacy.
Yeah. I mean, look, there are a lot of constraints in general that are sort of moderating the pace at which this buildout can happen, and these are power and supply and semiconductor tools, and now maybe financing. And if I'm a Wall Street analyst, if you look at the stocks, they're still up a bunch on this, but I mean they're clearly way down from the peaks. And the market is telling you something. The market is telling me that they're worried that we are, you know, that there's a lot of uncertainty in terms of like the pace and sustainability of the buildouts, and maybe the peak is approaching, and clearly financing is one of those concerns. It's probably up near the top. And you mentioned a number of, you know, different reasons like the hyperscalers. These were the most cash-generative businesses in the history of humankind, and like they're burning cash now and probably going to be raising equity. You know, Meta just had a new share class structure that, among other things, will enable them to do that. And we saw this, Google raised, and others. You mentioned CoreWeave's bonds. That's their unsecured debt. It's not like what they're paying right now. That's where they're trading in the market. But that would suggest if they go out to the market today to get new unsecured debt, that's in the ballpark of where they'll be spending. And it's a lot of money. So I think these are all broad concerns right now. You know, though, I think at least for the next year or two, which is as far out, frankly, even than I can look, we're probably okay. But I do think it's pretty clear that we need to have revenues grow to the point where this becomes less of a concern, and maybe that happens. You know, I don't know. SpaceX reported earnings, was it this week, last week? I can't remember.
And I mean, he's running out clearly, he's running out in the spot market, and so things are very, very constrained. The only thing we're hearing is nobody has enough compute. But he's renting capacity out short-term contracts, but you know, $50 billion a gigawatt, something like at those kinds of rates. I don't think it's actually that hard to imagine a scenario where this stuff gets covered and then some. But again, it gets to the question like how sustainable are those, and that has broader questions. It really depends on like what does demand actually look like relative to supply, and how long does a supply-demand mismatch last? And these are questions that actually maybe Dylan would probably have a better idea than I do. But absolutely, it is a concern. It is one of many constraints that are out there that I think are moderating. I'll say one more thing though.
The idea that these constraints are moderating growth, and you know nobody likes that, but at the same time it may be a good thing in some sense too because it stops things from going nuts, right? Right. And you know, we saw this during, you know, even a little before my time, but during the tech bubble, and like there, you know, there's dark fiber in the ground today that's there that was overbuilt, that was never used. And some of these things actually may give us a stronger for longer kind of behavior because it almost forces, you know, that the demand is there and it forces the supply to push out even more and allow things to be better matched rather than, you know, completely going nuts today and then risking having further risk of a crash down the line. So I don't actually think it's necessarily just constraints in general necessarily a bad thing, but I do know how they're perceived, and clearly they are impacting like the trajectory of the stocks like in the near term.
I would say, you know, echoing Stacy's point that in any buildout, everyone's going to want to build, build, and then it's sort of you get closer. There we go. So it's kind of like crack. Do you guys have a spare microphone for him?
We'll just swap off in a second. Thank you, Stacy. I'll never give it back.
But anyways, I think when we look at any buildout over the last few decades, we've always had the industry double order, triple order, and too much capacity get built out, and supply chains are really bad at forecasting demand. This is, you know, as Stacy mentioned, there is a bit of self-moderation, especially on the finance side. If we go back a year or two ago, any cloud that wanted capital could, they would pay a pretty high interest rate, but they could get it. Now, if you don't have really high-grade offtake, you're not able to build it if you're some random cloud. And so ultimately, the hyperscalers, sure, they can take their free cash flow down to zero, they can raise some debt, and their earnings are obviously growing, so their capacity that they can build out grows, but they can't build, you know, they can't do north of two trillion capex next year. They can't do 2.5 trillion capex next year. So that sort of moderates how much they can spend. And then when you look at the Neocloud industry, which accounts for, you know, non-hyperscaler demand, accounts for nearly half of the stuff that's being built out, if they don't have offtake from a hyperscaler, how are they going to go get it financed? And then, you know, the market's already saying, hey, if you've got offtake from Anthropic or OpenAI, we're going to price it at, you know, 13, 14, 15% debt. And so we saw that recently with Volta. They're making a cluster for Anthropic, and their debt is basically a 12% coupon, but they sold the bonds for 95 cents. So it ends up being like 15% interest rate. And if we look out at everyone who's not getting offtake from these companies, hyperscalers, the major AI labs, how do you build that capacity? How do you build capacity to service, you know, smaller enterprises? How do you build capacity to service, you know, new startups? Well, you just literally cannot unless it's coming from a hyperscaler. And so that's Elon's whole gambit is he's making all this capacity that he can sell at very high pricing on short-term rental contracts to startups, to the labs. But ultimately, it is quite a dampener. And so we've seen, you know, Nvidia recognized this as a very big problem for them because, you know, hey, they have five customers who can buy as much as they want or as much as they have capital for, but what about the other part of the market? And how do they enable that? And so they've gone and done really creative structures where with Sharon AI and Fermi and a few others, they've taken revenue shares on the cluster in order to guarantee that it's financed, right? So they'll say, 'Hey, we'll rent the cluster from you at a guaranteed price of $3.50, which is the cloud making zero money, and then if you rent it to anyone else above that price, we get a revenue share.' And we're starting to see more and more of these structures. In fact, even to build a cluster for Anthropic, Lambda had to do a revenue share with Nvidia. In this case, it wasn't a 50% like Sharon and Fermi had, but it was still a 10% revenue share. And so, you know, how these clouds can get the capital to over, you know, sort of build and get to that point where actually they build everything they want to build, and get to the oversupply scenario is really tough just because of the financing of this all. And so we're seeing more and more turn from, you know, these long-term loans and long-term debt to something that's more shorter term. So Broadcom's done the same thing with Anthropic where they're basically leasing them the chips in a sense, right? It's a rent-to-own. They're stretching out the payments over a few years. And the whole name of the game here is if anyone wants to get capacity, if you're not the big companies, you've got to come, you know, with a deal, a structure that has, you know, Nvidia or Broadcom or AMD backstopping these clusters. And so ultimately, the financing of the space is a big dampener. You know, the hyperscalers are also, you know, very, very large off-balance sheet and on-balance sheet commitments. Like Meta has $1.4 trillion of commitments for compute and data centers and so on and so forth. And that's then able to get financed by the whole market where they go loan, buy a data center, rent a data center from someone, or rent GPUs, and then those guys can go turn around and get capital for it. But ultimately, there's a big struggle for anyone to do more than that. And also, a lot of people have worried about even Meta's financial structure. I mean, Meta is completely fine, but, you know, a lot, even the capital markets are getting spooked by that. So this is, you know, financing is part of the holy sort of trinity. You know, it's land, power, shell, it's chips, and it's financing.
That's good. That's interesting. So okay, it's getting more difficult to finance. Although the hyperscalers are fine, maybe a little bit harder for neoclouds. We're seeing these new creative structures come around to help that. If we set finance aside and go back to the traditional maybe bottlenecks that are limiting ultimately the pace of the growth of the industry, talk to me about where you see that 2027, 2028. You know, there's obviously ASML says they're sold out in 2028. TSMC always has more demand than they have supply, even though they're doubling capacity for two nanometers. And then, you know, people talk about power as a bottleneck. Dylan, I know that your team recently, this week maybe, put out a post about up to 50 gigawatts coming online next year. So maybe I'll start with you. How about you stack rank some of these different parts of the semiconductor supply chain, and where should we be looking at the constraints?
Yeah. So I think on the semiconductor side, the biggest constraint, of course, besides memory, is substrates. Substrates, PCB materials, substrate materials, all of these components were severely underinvested in. Much of the supply chains that create the materials in Japan, those firms are sort of a lot more conservative and unwilling to invest heavily in capacity. And so we've seen, you know, tremendous bottlenecks there in terms of what can be made over the next two years, you know, both for accelerators as well as CPUs and every other component in the supply chain. So substrates is, I think, the biggest bottleneck of all the bottlenecks, including, you know, land, power, shell, semiconductor tools. I'd say the next bottleneck would be, of course, memory. And then following that, there's a lot of bottlenecks around the desire to ramp optics to a really, really large degree, right, with co-packaged optics or near-package optics. There's humongous constraints in ramping that. You know, we just simply cannot ramp, you know, even if everyone wanted to do CPO in 2028, or even one major company wanted to do CPO in 2028, they could not satisfy their volumes in terms of lasers, in terms of fiber attach, in terms of all these different components that go into it. And then, you know, as far as land, power, shell, power is definitely a big constraint. But one thing that our research sort of shows that I think is counterintuitive to much of the market is that next year there is, you know, obviously there are data center constraints, but it's a lot less than people realize because there are nearly 50 gigawatts of data centers being built globally, you know, seven or eight gigawatts in China, and then most of that, you know, about 35 in the US, and then a bunch of rest of everywhere else, you know, Australia, India, Malaysia being some of the big places, but really all over the world, data centers are being built at a tremendous pace. And while power is a huge bottleneck, people can find creative ways around that, whether it be, you know, turbines on site or reciprocating engines on site or fuel cells on site. All of these things are helping alleviate the power bottleneck. So I think actually power, you know, as far as what most people think, oh, data center and power are the biggest constraint, I think they're constraints, but I think they're actually a lot less than substrates, than memory, than lasers, and other things in the optics supply chain, because there are things that people can do to ramp power capacity on a shorter time span, right? They are just significantly less complex than the semiconductor supply chain.
Yeah, you bet. I think it's really interesting on the power side. You know, like so Jensen and Hock, like Nvidia, Broadcom, have given some pretty sizable guidances, and Jensen said he'll grow his revenues to at least 70%, Hock said 100, and those are actually constrained numbers, and they're looking primarily, I think, at land, power, and shell as their constraints. Maybe that's because they're semiconductor companies. They know what they can make, I guess, in terms of the semis. But on the land, power, shell side, I think I agree there are ways to get around this. Although even for some of the things that are going on site, I mean, I don't know what the lead time is on a turbine these days. Musk says he's going to go start building his own turbine blades to get around that. I think on the memory side is really interesting. You know, the memory stocks are all trading at two, three, four times price-to-forward earnings, and pricing has gone through the roof. I mean, I don't cover the memory stocks, but it's a colleague of mine, but you take a look at like a SanDisk that, you know, they guided to a quarterly EPS that's higher than the stock price was, you know, 24 months ago. It's like really amazing because pricing has gone through the roof, and they're all trading at, you know, single-digit PEs because that's how you trade things at the end of a, at the peak of a cycle, and everybody's worried that especially as you get into '28, supply does start to come online, and they're worried that the pricing collapses. And I sit here and I always wonder like what happens if it doesn't. You can look inside these racks.
Look inside an AI rack. 70%, 80% or so of the silicon area inside that rack is memory. It's mostly DRAM. You know, there are other issues when you're making high-bandwidth memory, the memory that goes into the AI chips. There's something called a trade ratio, but it takes three or four times as many wafers to make a gigabyte of high-bandwidth memory as it does to make a gigabyte of standard DDR5 DRAM that would go into a PC or go into a smartphone. So you're sucking up wafers from that. I mean, we need a ton of memory. And this feeds to one of the other bottlenecks, which is semicap, the actual tools to build this stuff. And, you know, we had a company report this week. They actually gave a preliminary outlook for WFE, for wafer fabrication equipment, for next year, and he said more than 200 billion. I think a lot of investor view was it was going to be a lot higher than that even, which I found really surprising because I just don't think it's possible. We don't have the fabs. Like, there's your other constraint is we don't have, and it takes, there's no clean rooms, like they're all full. They've actually been squeezing as much as they can out of the existing footprints. That's why the WFE estimates beginning of this year were, you know, 120 maybe. We'll probably end the year 155. That's all finding other better ways to use the footprints that's there, but now they have to build, and it takes years to put the stuff into the ground. The clean rooms do start to come online a little in '27, more into '28 and '29, and at that point, I guess we'll know if there's a supply-demand mismatch or not. But if the demand profiles continue at the rate that I think people seem to think they are, memory may still continue to be a constraint.
You talked about substrates and PCBs and other things. Again, to go back to the tool vendors, they're all complaining about short-term supply constraints that are actually impacting their own ability to upside even in the near term. And it's exactly that kind of, it's PCBs and it's actually semiconductor chips and it's even other components, you know, valves and mass flow controls, everything, but it's simple stuff that's actually affecting other parts of the space as well. We saw a little bit of this sort of thing during COVID. I do think that at least the semicap players and most of the folks in the supply chain did learn some lessons from COVID, and that I do think they're managing that sort of thing better now than they used to. But at the same time, we've got all kinds of constraints that are in the spaces that need to get fixed before the other constraints can actually get fixed. So you have these sort of layers, I guess, to things. I actually don't really see, I think things are going to be tight for a while, frankly. Like, I think this year is fine. I think next year is going to be the same thing. 2028 will probably still be tight. 2029, I don't know. I mean, that turns into a demand question. But I suspect we're going to be tight for a while.
Okay. So if we're tight for the next couple of years, and we talked about prices just skyrocketing, you know, memory industry's making margins in the 80%...
Yeah, yeah, 88, 90, right? So do you just see, you know, all those players, everyone's constrained up and down the whole stack, do you see every industry in there just continue to kind of charge for their value?
Well, you know, it's funny. So this is an industry where pricing coming down was just sort of taken as a given, right? That was just, and in fact the semiconductor industry as a whole historically was a massive value destroyer. It never covered its cost of capital. Like all of the value flowed to the consumer. I did a piece, I used to work at McKinsey many, many years ago, and I did a piece of work there. It was called value creation in the semiconductor industry. And, you know, we defined value very classically. It was economic profit, ROIC minus WACC times your average invested capital. We aggregated it up. It's like 150 different companies over like 12 different subsectors over like a full cycle. I think the initial work was like '96 to '04. I think over that period collectively the industry destroyed $5 billion in aggregate economic profit. And if you took Intel out of the mix, it was 50 billion destroyed, which was a big number back then, right? So the industry didn't actually really start covering its cost of capital, meaning that all of the economic profit you created before that was flowing directly to the consumer. It was a wonderful time to be a customer for semiconductors. But post-'08 there was a lot of rationalization and consolidation, and at least we started to kind of come in and meet the cost of capital. And then COVID happened, and what the industry discovered in COVID was that they have pricing power, which I think was something that they did not feel in their bones before that. And, you know, we had input costs going up and everything, and I cover a bunch of companies, and I didn't have a single one experience margin compression because of input costs going up. They all raised their prices to offset it, and they raised their prices at their margin structure. So like if their cost went up by 100 basis points, because, you know, they raised it by 160 if they had a 60% gross margin, and they had no problem passing it along. And right now we're seeing, and I've said this, inflation saved the industry during COVID. If you look at industry like unit shipments, like in 2023 or 2024, I mean they were flat to 2019, and ASPs blended for the industry were 50% higher, 50 even if you take out memory, right? And we're seeing that on steroids right now. So clearly memory pricing has gone through the roof. The industry, by the way, we're going to do a trillion and a half dollars this year, maybe more. We're going to be up probably 100%. 70 or 80% of the growth we're seeing this year is memory ASPs. A big chunk of the rest of it is also like non-memory ASPs. And we got, I don't know, units are up 10% or something like that. Whatever it is, but the industry is now flexing that pricing power a little more. And by and large, I think customers are accepting it. They don't really have any choice, right? So, yeah, it may stay tight. I mean, so if it stays tight, we will probably continue to see this, I think.
Yeah, I would say that, you know, the semiconductor industry is very interesting in that some people flex their pricing power in the good times and they suffer really bad in the bad times, but then others they just sort of eat margin...
The entire way through. So, if you look at TSMC over the last decade, they've risen from 45% gross margin to now 60. And it's been a very steady climb up. Yes, they've taken more when times were good, but they haven't given back much when times were bad. You know, and you look at memory, right? They've gone from basically negative margins just a few years ago to now, you know,
People forget we're coming off the worst memory cycle since the tech bubble, like just like 24 months ago, right? Negative gross margins.
And so like when you look across the supply chain, some of these folks are taking tons of pricing because it is a structure of also like what is the market like, right? And so TSMC, where they're a near monopoly and they're a very long-term partner, has not really jacked up pricing even though they could. Whereas memory companies, you know, if Apple's going to screw them in the downturn, they're going to screw Apple over in the upside of the cycle, which is pretty clearly what's happening. So when you look at this sort of market, right, to build a gigawatt, you know, in 2024, it was roughly $40 billion, right, across chips, land, power, shell, everything. Now it's north of 60. So the price to build a gigawatt of compute has skyrocketed. Now, of course, not many people were building a gigawatt, or really no one was building a gigawatt in 2024, and a lot of people were building gigawatts in 2026 in
Getting more out of the gigawatt now, too, by the way.
Yeah. And then that gigawatt's profitability is huge, right? And so I think everyone is recognizing profitability extends. You know, a lot of the mechanics I mentioned earlier, which is you can't build a cluster as a cloud unless you have offtake from a hyperscaler, which means you've locked in pricing ahead of time. That is sort of being exploited by Elon in the sense that, okay, well, if chips and land, power, shell are a constraint, if I just build these and then I don't sign a contract until it's actually up, I can instead of making 15, you know, billion dollars per gigawatt, can make more. And then the AI labs who are making, you know, Anthropic in their recent sort of disclosures is making on the order of 70 to 80 billion a gigawatt off of their inference capacity. You know, they're kind of forced to eat it, right? You know, yes, their margins are, you know, 75, 80% when they're spending 15, you know, billion dollars a gigawatt for compute, but if they're spending 50, their margins are much smaller. And so it's, you know, not clear who's going to eat all the cost. You know, Nvidia's had to keep their margin roughly the same, slightly decreased, but they've had to eat the memory pricing increase and other parts of the supply chain like substrates and optics and all these other things. And so the prices that they charge their customers have soared despite the fact that their margins are flat. And so this is sort of we're seeing really interesting, you know, who gets to keep their margin, who gets to expand it, and who's going to eat margin. And I think it's a very fun time to be on the pricing side because you're real time discovering, and yes, you can sign a deal at many different prices. But the best sales guys are going to get really insane pricing and hopefully they're rewarded for it.
Hilarious. So, okay, Stacy, I kind of like what you said about COVID. It was almost like a dress rehearsal for AI for the industry to start to realize like, hey, we can charge for our value here. And Dylan, you know, you make an interesting point about like a system builder like Nvidia having to decide of all the components as their prices are going up, do we pass it on or do we keep our margins the same? Now, I'm feeling a little tension of, okay, there's still going to be buildout all the way through. You know, we are not sure what's going to happen in 2029, but clearly, supply is limited and demand is high for the next couple years. And yet, we started by saying that it's getting harder and harder to finance. And so I'm wondering if there becomes a point where those people at the top of the stack, the Metas and the model lab companies, if they're ever going to push back and say this is just getting too expensive, or beggars can't be choosers.
Remember, you have to look at more than like the margins and the cost. You also have to look at what they're getting out of it. And so I mean I'll take Nvidia as an example. You know, again, their pricing is going with every generation. So you but the what you're getting out of it is going up more than the price, which I think is important. So you went from like an Ampere to Hopper.
