AI Hardware Bottlenecks: Can Scaling Continue?

The rapid expansion of artificial intelligence is colliding with the physical limits of hardware, signaling an end to the era of "infinite" compute. While industry focus often remains on data or capital, the primary bottlenecks are now thermal density and energy consumption. The breakdown of Dennard Scaling has led to "dark silicon," where chips contain more transistors than can be safely powered without melting, exemplified by the cooling failures in NVIDIA’s Blackwell servers. Simultaneously, Jevons Paradox ensures that efficiency gains only drive higher total power demand, forcing tech giants like Microsoft and Amazon to bypass the public grid for nuclear energy. This shift necessitates a transition in software development from prioritizing delivery speed to optimizing execution. Future industry demand will pivot toward systems architecture, latency masking, and high-performance languages like Rust as developers must learn to "cheat" the speed of light and manage the physical constraints of the Von Neumann bottleneck.