The podcast explores the practical realities behind training large AI models like Claude. While scaling laws suggest predictable gains from more compute, data, and parameters, real-world bottlenecks—such as faulty GPUs, network latency, and power limits—often dictate progress. Anthropic’s early focus on custom infrastructure highlights how hardware awareness is critical. Teams must balance specialization with broad expertise, and debugging spans from code to silicon. As pre-training gives way to reinforcement learning, concerns grow over data quality and synthetic content polluting future training sets. Evaluations must be fast and meaningful, and alignment is increasingly guided by constitutional principles. Rapid iteration remains key, requiring full-stack engineers who can navigate both ML frameworks and low-level systems. The future may favor architectural efficiency over raw scale, especially for startups aiming to innovate within constrained resources.