HBM demand isn’t rising like a normal semiconductor category; it’s compounding through a self-reinforcing flywheel. Every layer of the AI stack — from model developers to hyperscalers to enterprise adopters — funnels into the same bottleneck: memory bandwidth. As argued in The AI Memory Chokepoint (https://businessengineer.ai/p/the-ai-memory-chokepoint), compute is no longer the limiting factor. Memory is.
This shift turns HBM from a component into a gravitational center. Once you understand the flywheel, the industry’s behavior makes sense: NVIDIA can’t get enough HBM, hyperscalers are rushing to secure their own supply, and governments are pouring capital into memory capacity instead of GPUs. When memory is the constraint, demand becomes structurally infinite.
The HBM market is expanding at a pace that no other semiconductor segment matches:
2024 Market: $16.2B
2030 Projection: $79.6B
CAGR: 58%
Price Premium: HBM is 10× more expensive than DDR
GPU BOM Share: 50–60% of total GPU cost is now memory
Demand > Supply through 2026+
You cannot explain these numbers without understanding the bottleneck: compute has scaled 10,000× in two decades, while memory bandwidth only improved 10×. That 1,000× divergence, mapped in The AI Memory Chokepoint piece, is what forces every serious AI player to chase HBM with urgency.
2. The Flywheel — How HBM Demand Reinforces Itself
HBM demand is not linear. It accelerates through a cycle that repeats faster as models grow:
More GPUs → Bigger Models
As data centers deploy more H100s, B200s, TPUs, and MI300s, the feasible size of frontier models increases. Bigger models demand exponentially more memory bandwidth per token.
Bigger Models → More Users
Higher modelquality → more adoption → more usage → more inference traffic.
More Users → More Revenue
The economic engine of AI — from ChatGPT Plus to enterprise copilots — funnels revenue straight into GPU and accelerator capex.
More AI success → more GPUs → more HBM → more AI success. HBM demand compounds faster than GPU shipments.
This is why every major GPU roadmap — NVIDIA Blackwell, AMD MI350 series, Google TPU v6, AWS Trn3 — consumes significantly more HBM than its predecessor.
3. The Big Buyers — Who Actually Drives HBM Demand?
Only a handful of companies determine the trajectory of global HBM consumption. Each has its own logic for aggressively acquiring supply, but all participate in the same bottleneck dynamic.
NVIDIA — The Dominant Consumer (~50–60% of all HBM)
Across all of them, the common denominator is the same: models get bigger, throughput gets higher, and memory becomes the bottleneck.
4. The Structural Insight — Why Demand Outruns Supply
“Demand is effectively infinite — supply is the bottleneck.”
The real constraint isn’t GPU demand. It isn’t model size. It’s manufacturing physics.
HBM production is limited by:
a 3-company oligopoly (SK Hynix, Samsung, Micron)
advanced packaging capacity at TSMC
yield limits on 3D stacking
TSV count limits
2–3 year fab lead times
geographic concentration (88% in South Korea)
Even if the world doubles HBM demand, supply can only grow slowly. Even if models pause in size, inference traffic still explodes. Even if compute plateaus, memory bandwidth is still lagging.
This is why in The AI Memory Chokepoint (https://businessengineer.ai/p/the-ai-memory-chokepoint), the hourglass structure matters: every model, every accelerator, every application eventually squeezes through the memory layer — and that layer is defined by HBM availability.
The Bottom Line
The HBM demand flywheel isn’t an economic cycle you can slow down. It’s a physical consequence of how transformers work. Every token touches every other token, weights must be read on every pass, and GPUs sit idle waiting for memory bandwidth.
As long as AI scales, HBM scales faster. As long as AI adoption grows, HBM becomes more constrained. As long as compute outpaces memory, HBM is the kingmaker.
And until the bottleneck shifts again — which isn’t happening soon — HBM remains the most strategically valuable commodity in AI.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
