
Nvidia’s ability to ship GPUs depends on three critical bottlenecks, none of which it controls: HBM memory (sold out through 2026), advanced packaging (overflowing orders), and logic fabrication (competing with Apple and Qualcomm for capacity).
Bottleneck 1: HBM Memory
The HBM market is a triopoly: SK Hynix (~50% share), Samsung (~40%), and Micron (~10%). All three have their 2025-2026 HBM production sold out.
OpenAI’s Stargate project alone may require 900,000 DRAM wafers per month by 2029 – roughly 40% of current global DRAM output. Google, Amazon, Microsoft, and Meta have placed “open-ended orders” with Micron: take everything available, regardless of price.
The shortage may persist 3-4 years.
Bottleneck 2: Advanced Packaging (CoWoS)
TSMC’s CoWoS advanced packaging capacity is the binding constraint on AI chip production. Nvidia has secured over 70% of TSMC’s CoWoS-L capacity for 2025.
Orders from Nvidia, AMD, Google, Amazon, and custom ASIC developers are “overflowing.” TSMC is preparing to outsource parts of its packaging workflow beginning in 2026 – an unprecedented step.
Bottleneck 3: Logic Fabrication
TSMC’s 4NP process node is less constrained than packaging, but it’s still limited. Apple, AMD, Qualcomm, and other major customers compete for capacity. Nvidia’s datacenter revenue now rivals gaming, but TSMC must balance allocations.
The Physics Problem
Money cannot immediately solve supply constraints. Converting capacity requires different equipment, different processes, and different factories. The gap between demand and supply keeps widening.
Key Takeaway
As AI data center analysis shows, access to hardware will determine competitive outcomes. The companies that secured supply early will lead. Others will wait.
Source: The Economics of the GPU on The Business Engineer









