Part 7/16:
In fact, the gap between the increasing demand and the slowing pace of transistor innovation suggests that hardware advances will rely more heavily on architectural innovation and optimization rather than sheer transistor density. Companies will need to adopt more efficient chip designs, like Nvidia's heterogeneous architectures that combine GPUs and CPUs innovatively, and leverage breakthroughs like advanced chip manufacturing (e.g., TSMC's EUV technology).
The Expanding Universe of AI Scaling Laws
AI's growth is now governed by multiple scaling laws:
Training scaling: Larger models trained on more data perform better.
Post-training scaling: Using techniques like reinforcement learning to refine AI outputs.