andrew feldman

Individual data center campuses now consume more power than midsize cities, with buildouts across US, Canada, Nordics, Europe, Middle East, and Central Asia; total projected power demand surpasses the previous 50 years of global electricity growth, creating massive demand for power infrastructure, cooling, and compute density.

Unlike fiber/rail bubbles where supply led demand, AI data center construction cannot keep up with current demand. Cerebras, Nvidia, AMD all have backlogs. $25B backlog at Cerebras alone. Multi-gigawatt facilities now normal; 500GW discussed. This is demand-pull, not supply-push.

HBM is the #2 bottleneck after TSMC wafers. Only Samsung, Micron, Hynix produce it. Capacity additions are step-functions: $40B fabs taking 5 years. Micron earning 80-85% gross margins. If AI demand stays high, shortages persist for several years. Cerebras avoids this via on-chip SRAM.

Cerebras' novel architecture avoids the diminishing returns of 20-year-old GPU designs by optimizing for inference workloads directly, enabling 15x faster token generation and a performance trajectory that doubles every ~9 months vs traditional 18-month Moore's law, critical for reasoning models that require massive inference-time compute.

Cerebras' 58x larger chip eliminates inter-chip communication bottlenecks, delivering 20x faster inference; simultaneous hardware-software co-design with frontier model builders (OpenAI, Google TPU/DeepMind) creates compounding advantages that traditional GPU architectures cannot match.

Inference speed has no upper bound for hard problems. 6.7x speed advantage means solving 6.7x more problems per unit time. History of semiconductors is massive reduction in cost per unit compute. Software engineer token spend alone could reach $5T (47M engineers × $100k/year). Slow inference is like dial-up — zero market.

AI workloads shifted compute demand from training to real-time inference where latency is existential; memory-bandwidth bottleneck solved by wafer-scale integration placing SRAM adjacent to compute cores. Cerebras demonstrates 15-18x speedup vs H100 for inference, making GPU-like architectures structurally disadvantaged for new entrants.

Cerebras' wafer-scale engine solves the memory bandwidth bottleneck by using massive on-chip SRAM, delivering 15-20x inference speedup with minimal multi-chip penalty — a fundamental architecture shift away from GPU/HBM paradigm.