On Memory. I too agree that this is a cyclical phenomenon albeit super-cyclical. The current demand for HBM has no prior analog. It's as if a multi-trillion market was created out of nowhere because the hypercalers have converted their savings into CapEx. In and of itself this action should give us all some pause. Given the long lead times on building new capacity, the multiyear agreements signed by Micron that lock in pricing and estimates that there will be no relief till at least 2028. do you sense that the typical 4-year cycle is shorter than we should expect and this sell-off is a bit premature?
I think that’s right. The traditional four-year memory cycle may be a poor framework this time because HBM demand is structurally different and new supply takes years to come online. Micron now expects tightness beyond 2027 and has locked significant volume into multi-year take-or-pay agreements, so I think the market may be pricing the downturn prematurely—even if cyclicality ultimately returns.
Good analysis and worth a read and re-read. The HBM cycle I am questioning a bit given the reported HBM is sold out already for 2027 and that the industry is moving towards long term agreements. The cyclical dynamics in memory may be less pronounced going forward if LTA are the future of the industry which Micron has also announced in their earnings call. Disclosure I am long SK Hynix and NVIDIA.
I’ve been thinking about the operations layer and where value accrues. In construction, if productivity is increased by leveraging AI who wins? My gut feel is because of competition in the contractors, designers, and supply chain the value will end up with the clients and developers.
This isn’t my wheelhouse, interested in other views.
NVIDIA's moat is beyond CUDA these days? They are delivering the highest value and capturing maximum margin. It would be interesting to see how that moat appears now and the competitive threats surrounding it.
The important distinction is between capex intensity and value capture.
The companies spending the most on AI infrastructure are not necessarily the ones earning the highest returns from it. Profit accrues where substitution is hardest: advanced chip design, leading-edge foundry capacity, HBM, lithography and high-speed networking.
That means the AI stack should be analyzed less like a software market and more like an industrial bottleneck map.
The strategic question is not who spends the most.
It is who controls the layer everyone else cannot bypass.
Harder they rise harder they fall. Durable layer is cyclical… clouds will continue to grow as demand and AI usage grows… a token costs the same to generate per watt - no matter what it’s sold for.
Useful framing, thank you. Curious as to why AMD wasn’t included? Also, you call out the critical if often overlooked role of optics, but don’t include Corning or the like. Best
Great points. AMD definitely deserves a spot as do optics players. I have covered some of the optics players in other posts on networking and should do more!
And where is “open source” stands in the stack? The efficiency and depreciation of the massive silicon capex dictates if the value capture is durable enough
The distinction between structural scarcity and cyclical scarcity is the key here. HBM can generate extraordinary margins today while still deserving a different terminal multiple from CUDA, EUV, or leading-edge foundry capacity.
1846 is your chokepoint map in another century: Britain approved thousands of miles of railway, and the margin stuck to locomotive and rail makers, not the debt-loaded operators.
Really enjoyed this piece, particularly the way you worked layer by layer through the AI stack asking where value is actually captured rather than simply following revenue growth.
One layer left me wanting more: optics. You make a strong case that optics is a genuine physical bottleneck, perhaps one of the least appreciated chokepoints in the stack, but unlike Nvidia, TSMC, ASML, Arista, Vertiv, etc., you don’t really identify who you think has the durable moat and captures the economics there.
You might enjoy the work of Ren @ SemiAnalysis. He’s approached the AI infrastructure problem in a remarkably similar way, working backward from physical bottlenecks rather than headline beneficiaries, and has done some particularly interesting work around photonics, InP lasers and the optical supply chain.
You two seem to be attacking the same problem from different directions: where does scarcity actually live, and who gets paid for owning it?
I’d be very interested in your take on his work, and especially whether it changes your view of who the real winner is in the optical layer.
Thanks — really appreciate this. I agree optics deserves a deeper treatment. It’s arguably one of the most interesting parts of the stack because the bottlenecks are real, but the value capture is less obvious and may shift as architectures evolve.
I’ve read some of Ren’s work and think it’s excellent. I’ll dig further into his pieces on photonics, InP lasers, and the optical supply chain. I’m actually working on more around optics, so this is very timely. The question of where the truly durable scarcity sits — components, lasers, packaging, or the systems layer — is exactly what I’m trying to understand.
The key question is not just who captures the most revenue, but where pricing power sits across the AI infrastructure stack. The margin versus growth split makes that especially clear.
On Memory. I too agree that this is a cyclical phenomenon albeit super-cyclical. The current demand for HBM has no prior analog. It's as if a multi-trillion market was created out of nowhere because the hypercalers have converted their savings into CapEx. In and of itself this action should give us all some pause. Given the long lead times on building new capacity, the multiyear agreements signed by Micron that lock in pricing and estimates that there will be no relief till at least 2028. do you sense that the typical 4-year cycle is shorter than we should expect and this sell-off is a bit premature?
I think that’s right. The traditional four-year memory cycle may be a poor framework this time because HBM demand is structurally different and new supply takes years to come online. Micron now expects tightness beyond 2027 and has locked significant volume into multi-year take-or-pay agreements, so I think the market may be pricing the downturn prematurely—even if cyclicality ultimately returns.
SRAMs are eclipsed by strategy and pricing points of use of HBM in HPC …high yield stacks and MR MUF packaging already covers 60:/: market share.
Good analysis and worth a read and re-read. The HBM cycle I am questioning a bit given the reported HBM is sold out already for 2027 and that the industry is moving towards long term agreements. The cyclical dynamics in memory may be less pronounced going forward if LTA are the future of the industry which Micron has also announced in their earnings call. Disclosure I am long SK Hynix and NVIDIA.
I tend to agree! I am bullish as well. The pricing power currently is extreme. Let's see how long it lasts!
Really interesting.
I’ve been thinking about the operations layer and where value accrues. In construction, if productivity is increased by leveraging AI who wins? My gut feel is because of competition in the contractors, designers, and supply chain the value will end up with the clients and developers.
This isn’t my wheelhouse, interested in other views.
NVIDIA's moat is beyond CUDA these days? They are delivering the highest value and capturing maximum margin. It would be interesting to see how that moat appears now and the competitive threats surrounding it.
This was great
Brilliant. Real analysis . Thoughtful . Totally agree with your thesis .
Thank you!
The important distinction is between capex intensity and value capture.
The companies spending the most on AI infrastructure are not necessarily the ones earning the highest returns from it. Profit accrues where substitution is hardest: advanced chip design, leading-edge foundry capacity, HBM, lithography and high-speed networking.
That means the AI stack should be analyzed less like a software market and more like an industrial bottleneck map.
The strategic question is not who spends the most.
It is who controls the layer everyone else cannot bypass.
Harder they rise harder they fall. Durable layer is cyclical… clouds will continue to grow as demand and AI usage grows… a token costs the same to generate per watt - no matter what it’s sold for.
Useful framing, thank you. Curious as to why AMD wasn’t included? Also, you call out the critical if often overlooked role of optics, but don’t include Corning or the like. Best
Great points. AMD definitely deserves a spot as do optics players. I have covered some of the optics players in other posts on networking and should do more!
And where is “open source” stands in the stack? The efficiency and depreciation of the massive silicon capex dictates if the value capture is durable enough
The distinction between structural scarcity and cyclical scarcity is the key here. HBM can generate extraordinary margins today while still deserving a different terminal multiple from CUDA, EUV, or leading-edge foundry capacity.
1846 is your chokepoint map in another century: Britain approved thousands of miles of railway, and the margin stuck to locomotive and rail makers, not the debt-loaded operators.
Really enjoyed this piece, particularly the way you worked layer by layer through the AI stack asking where value is actually captured rather than simply following revenue growth.
One layer left me wanting more: optics. You make a strong case that optics is a genuine physical bottleneck, perhaps one of the least appreciated chokepoints in the stack, but unlike Nvidia, TSMC, ASML, Arista, Vertiv, etc., you don’t really identify who you think has the durable moat and captures the economics there.
You might enjoy the work of Ren @ SemiAnalysis. He’s approached the AI infrastructure problem in a remarkably similar way, working backward from physical bottlenecks rather than headline beneficiaries, and has done some particularly interesting work around photonics, InP lasers and the optical supply chain.
You two seem to be attacking the same problem from different directions: where does scarcity actually live, and who gets paid for owning it?
I’d be very interested in your take on his work, and especially whether it changes your view of who the real winner is in the optical layer.
https://renstocks.substack.com/p/you-want-the-light-here-are-three?r=63eqd&utm_medium=ios
https://renstocks.substack.com/p/lumentum-let-there-be-lite?
Thanks — really appreciate this. I agree optics deserves a deeper treatment. It’s arguably one of the most interesting parts of the stack because the bottlenecks are real, but the value capture is less obvious and may shift as architectures evolve.
I’ve read some of Ren’s work and think it’s excellent. I’ll dig further into his pieces on photonics, InP lasers, and the optical supply chain. I’m actually working on more around optics, so this is very timely. The question of where the truly durable scarcity sits — components, lasers, packaging, or the systems layer — is exactly what I’m trying to understand.
The key question is not just who captures the most revenue, but where pricing power sits across the AI infrastructure stack. The margin versus growth split makes that especially clear.
Tremendous effort.
Who’s buying the end product that enables a return on this capital investment?
The labs are the biggest buyers but also AI apps, enterprises, etc.
Understood. But currently the labs and so on are spending using capital raises , not from recurrent cash from sales of and end product to customers…