Editing Openai/6960ed20-8dfc-8005-a089-1bb9023ccffa (section)

=== Assistant: Short answer: no, it probably won’t “eat through” global RAM demand nonstop until 2027 — but HBM AI-grade memory will s… ===
Short answer: no, it probably won’t “eat through” global RAM demand nonstop until 2027 — but HBM (AI-grade memory) will stay extremely tight until ~2026–2027, while regular DDR RAM pressure should ease earlier.

Let’s break this down cleanly.

==== : ====

This is the key correction:
* OpenAI was founded in 2015
* ChatGPT launched in Nov 2022
* The infrastructure shock started in 2023, not OpenAI itself

So the “5-year compressed buildout” people talk about is roughly:

2023 → 2027/28, not 2022 → 2027

You’re about ~2.5 years in, not 3+.

==== When headlines say “RAM shortage”, they usually mix two very different things: ====

===== - Used in PCs, laptops, servers =====
* Supply is already improving
* Prices are cyclical
* AI is not the dominant consumer

👉 This demand should normalize by 2025–2026

===== - Sits on AI GPUs (H100, H200, Blackwell) =====
* Extremely hard to manufacture
* Few suppliers (SK Hynix, Samsung, Micron)
* This is the real choke point

👉 This stays constrained until ~2026–2027

So no, OpenAI isn’t starving all RAM — it’s pressuring HBM specifically.

==== AI infrastructure growth is S-curve, not linear. ====

===== - Everyone races to build training clusters =====
* Over-ordering happens
* Massive upfront memory demand ✅ We are here

===== - Core capacity mostly in place =====
* New orders = incremental, not exponential
* Optimization starts ⚠️ Coming soon

===== - Same hardware reused more efficiently =====
* Better models, better memory usage
* Inference > training 📉 Demand growth slows

So demand peaks before infrastructure “finishes”.

==== More precise answer: ====
* 2024–2025: yes, very aggressive consumption
* 2026: still heavy, but stabilizing
* 2027: no longer dominant growth driver

Why?
* Training runs don’t scale forever
* Inference scales differently (often less HBM per user)
* Model efficiency improves faster than people expect

Also: Google, Meta, Amazon, Microsoft catch up, so OpenAI’s relative share drops.

==== A lot of these wafer / HBM deals are: ====
* reservations
* hedges
* “just in case” capacity locks

Historically, this leads to:
* temporary shortages
* followed by sudden gluts

Semiconductor cycles always overshoot.

==== - HBM tight: until ~2026–27 ====
* Regular RAM tight: mostly eases by ~2025
* AI memory panic: peaks in 2025
* Post-2027: demand still grows, but sanely

So no — OpenAI won’t be “eating the world’s RAM” nonstop till 2027.
It’s more like 2023–2025 is the chaos window.

===== : =====

If you want, next we can talk about what would actually cause demand to crash early (there are a few real risks).