Dylan Patel details the unprecedented scale of AI infrastructure development, where companies like OpenAI, NVIDIA, and Oracle are locked in a high-stakes race for computational dominance. Despite technical advances, growth is constrained by physical limits—electrician shortages, skyrocketing wages, and strained power grids—forcing firms to innovate in power delivery and data center design. The U.S.-China rivalry adds urgency, with Taiwan’s semiconductor leadership posing a critical vulnerability. While algorithmic progress lags behind hardware scaling, value is shifting across the AI stack, disrupting traditional SaaS economics as development costs fall but customer acquisition remains costly. Reinforcement learning and simulated training environments offer paths to better generalization, yet true breakthroughs may require embodiment and real-world interaction. Meanwhile, economic potential looms large: AI could automate trillions in labor, but only if innovation overcomes bottlenecks in talent, capital, and coordination. The buildout is still early, but the stakes—for industry and national power—are already immense.