The Underlying Logic of Bottleneck Propagation in the AI Computing Power Industry Chain
The article analyzes the evolving bottleneck progression within the AI compute supply chain. Initially constrained by GPU chip and advanced packaging capacity (2022-2024), the primary bottleneck shifted to HBM memory (2024-2025) due to massive model parameter growth. As cluster scale expands, physical limits of copper interconnects are making optical interconnect technologies the next critical phase (2025-2026). The ultimate, emerging constraint is power delivery and advanced liquid cooling (from 2026 onward), driven by skyrocketing rack power densities exceeding traditional infrastructure limits. The core thesis is that AI compute demand follows a "Leontief" production function where solving one bottleneck immediately exposes the next in the sequence: Compute (GPU) → Memory (HBM) → Interconnect (Optics) → Power & Cooling. Each shift reallocates value and investment across the semiconductor and infrastructure landscape.
marsbit05/22 12:25
