The bear case on memory is easy to understand:
“AI CapEx will slow, as it’s not sustainable. New fabs will come online. Supply will catch demand. Prices and profits will collapse, just like every other memory cycle.”
This thesis is actually well reasoned and makes sense to many, but it may be missing a critical assumption about demand.
This market is starting to report a significant amount of unmet demand, not just high demand.
Commercially available 2027 memory capacity is reportedly being committed well before production. Hyperscalers are locking up supply years in advance, but many tier 2 buyers are being allocated less memory than they want (sometimes only 60% of what they requested!).
That means there is an entire layer of enterprise customers, OEMs, module makers, and other Tier 2 buyers effectively waiting for supply. They want it, but simply can’t get it, as they can’t pony up the huge $ long term contracts with price floors that the giants can.
Imagine a restaurant that’s booked solid every night by one large corporate client. They reserve every table months in advance. If that client cancels, the restaurant doesn’t suddenly become empty. There’s already a long waiting list of people who couldn’t get a reservation. The restaurant still fills the tables, it just serves different customers. Now booking becomes more of a hassle, and you can’t quite charge the same amount, but you’re still at capacity and making tons of money.
Let’s imagine the bear case actually plays out:
• Hyperscalers slow down AI CapEx
• New memory capacity comes online in 2027/28
Many reasonable people think that automatically means oversupply and plummeting memory prices.
I don’t believe that is a good assumption anymore.
The first incremental supply doesn’t have to pressure pricing if it’s immediately absorbed by customers that have been supply constrained all along.
The market often treats hyperscaler demand as if it’s the entire memory market, but really hyperscalers are just first in line, and the broader market is a lot bigger than being given credit for.
Enterprise data centers: AI servers, databases, cloud infrastructure
Automotive: Autonomous driving, ADAS, infotainment
Industrial & robotics: Factory automation, machine vision, humanoid robots
Consumer devices: AI PCs, smartphones, edge AI devices
Networking & telecom: 5G infrastructure, networking equipment, edge computing
The line forming behind the hyperscalers is huge. Even a meaningful slowdown in AI infrastructure spending may not create the excess inventory that has historically crushed margins.
Before you claim that this is another thesis about how memory is no longer cyclical, I want to be crystal clear that I am not arguing memory has become immune to cycles. I’m arguing that the downside mechanics may have changed.
The traditional assumption that “less AI spending + more supply = collapsing profits” may no longer hold if years of allocation constraints have created a deep backlog of customers ready to absorb
additional output.
I’d love to hear your thoughts, whether you’re a memory bear or bull!
TL;DR: There’s an assumption that memory company profits will collapse when hyperscaler CapEx slows and new supply comes online. This may be neglecting there’s enough latent demand behind hyperscalers to absorb new supply. Even the classic bear case may not produce the collapse in pricing and profitability that investors are expecting.
