The performance gains from Nvidia's Hopper to Blackwell GPUs come from increased size and power, not efficiency. This signals a potential scaling limit, creating an opportunity for radically new hardware primitives and neural network architectures beyond today's matrix-multiplication-centric models.

Huawei is shifting from shrinking transistors (Moore's Law) to optimizing data flow via advanced chip stacking and interconnects. This "tau scaling law" is an innovative workaround to physical limits, aiming to create competitive AI compute power without access to the most advanced manufacturing processes.

As Moore's Law slows, the path forward isn't just smaller silicon transistors. Tan is investing in new materials like gallium nitride, silicon carbide, glass substrates, and even artificial diamonds to solve bottlenecks in advanced packaging and insulation, fundamentally changing chip architecture.

The next wave of AI silicon may pivot from today's compute-heavy architectures to memory-centric ones optimized for inference. This fundamental shift would allow high-performance chips to be produced on older, more accessible 7-14nm manufacturing nodes, disrupting the current dependency on cutting-edge fabs.

When power (watts) is the primary constraint for data centers, the total cost of compute becomes secondary. The crucial metric is performance-per-watt. This gives a massive pricing advantage to the most efficient chipmakers, as customers will pay anything for hardware that maximizes output from their limited power budget.

Crusoe Cloud's CEO warns of an impending power density crisis. Today's racks are ~130kW, but NVIDIA's future "Vera Rubin Ultra" chips will demand 600kW per rack—the power of a small town. This massive leap will necessitate fundamental changes in cooling and electrical engineering for all AI infrastructure.

While power supply is a current data center bottleneck, a more significant long-term risk is technological disruption. Chip innovations promising 10-1000x more power efficiency could make today's massive, power-centric data center investments obsolete or oversized before they are fully utilized.

Unlike GPUs using slow, dense memory, Cerebras's wafer-sized chip leverages its vast surface area to accommodate faster, less-dense memory. This design sidesteps memory bottlenecks, achieving speeds up to 15 times faster than the fastest GPUs for AI tasks.

Efficiency gains in new chips like NVIDIA's H200 don't lower overall energy use. Instead, developers leverage the added performance to build larger, more complex models. This "ambition creep" negates chip-level savings by increasing training times and data movement, ultimately driving total system power consumption higher.