Dylan Patel argues that AI inference will become a market larger than oil, with costs dropping 60x annually per unit of quality, as tracked by his InferenceX benchmark. He emphasizes that hardware-software co-design, optimizing models, kernels, and silicon together, yields 100x improvements, with memory bandwidth as a key bottleneck. Patel explains how model architecture choices make Google's TPUs and Nvidia's GPUs better suited for different AI companies, and that the CUDA moat is evolving as model companies prioritize custom kernels for any chip. He notes that AI model improvements expand economic value faster than compute grows, creating a pseudo-recursive self-improvement loop. Patel also discusses why neoclouds like CoreWeave outperform hyperscalers due to CPU-optimized features hurting GPU performance, and how Jensen Huang's strategy of funding neoclouds creates a multipolar world to prevent hyperscaler dominance.