To The Moon

​A lot of people panicked recently when tech giants like $Meta Platforms, Inc.(META)$  started optimizing their data centers, thinking the AI chip boom was over and memory stocks would crash.

The initial panic that wiped out billions in global semiconductor and South Korean memory market caps following reports of Meta’s intent to commercialize its "excess capacity" represents a fundamental misreading of structural industry mechanics.

As tracked in Wells Fargo Securities data. Meta is simultaneously acting as a capacity vendor and the absolute largest customer for high-end specialized infrastructure. Meta’s massive $35.2 billion in back-to-back commitments with CoreWeave, including the massive $21 billion Nvidia Vera Rubin contract signed on March 31, 2026, ensures that aggregate demand for physical silicon platforms is structurally insulated through December 2032

Demand of HBM

​But here’s the real ground truth after running the numbers: The global shortage of high-speed AI memory (HBM) isn't going away anytime soon. Here is why:

​🧠 1. Software can't save us fast enough

Bears argue that new software updates make AI models way more efficient, so they won't need as much hardware. But guess what? That’s already factored into the math! AI data traffic is exploding by 4x to 7x a year, while memory demand is only growing around 40%. That massive gap is already assuming the software is running at peak efficiency. To actually cause an oversupply, software would need to get another 25% faster every single year—and we are already hitting the hard mathematical limits of physics.

​💾 2. You can't replace premium memory

Some think tech companies can just use cheaper flash storage or standard server RAM to save money. Nice try, but the "hot" data that an AI chip needs to look at for every single word it generates is physically trapped on the ultra-fast HBM chip by latency. Moving that process to slower memory completely breaks the system. The boundary is drawn in silicon.

​📈 3. Jevons Paradox in full effect

In technology, whenever you make something more efficient, people don't use less of it—they find bigger, crazier ways to use more. Every time software engineers save a byte of memory, developers immediately spend it on longer context windows, bigger data batches, and complex AI agents that burn millions of tokens per task. No major GPU generation has ever shipped with less memory than the one before it. The numbers only go up:

👉 80GB ➔ 141GB ➔ 180GB ➔ 288GB+

​The bottom line: The core engine driving global AI tech isn't slowing down. The structural, high-margin demand for next-gen memory remains deeply locked in.

​Keep your eyes on the long game! 🚀

$Micron Technology(MU)$  

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$CSOP SK Hynix Daily (2x) Leveraged Product(07709)$  

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