Image Generated With GPT Image 2.0 Compute Scaling Alone Is No Longer Enough For decades, semiconductor progress was driven primarily by transistor scaling. Smaller transistors enabled higher compute density, faster performance, and lower power consumption, allowing continuous system level improvements across multiple generations of computing infrastructure. Today, that scaling model is changing. Modern Artificial Intelligence (AI) systems are increasingly constrained not only by compute capability, but by how efficiently data moves between compute engines, memory, accelerators, and distributed infrastructure. Training and inference workloads require enormous bandwidth across highly parallel architectures, making communication efficiency critical to sustaining performance. This challenge is becoming more visible as accelerator performance scales faster than memory bandwidth and interconnect capability. Modern Graphics Processing Units (GPUs) and AI accelerators can process massive workloads internally, but maintaining