A deep dive into the emerging “Silicon Schism”—the split between general-purpose GPUs and specialized AI chips reshaping the global compute landscape. This episode unpacks how NVIDIA’s CUDA-driven dominance is being challenged by hyperscalers building custom silicon (TPUs, Trainium, Maia), and why the future of AI hinges on efficiency, cost per token, and control of infrastructure.

Beyond architecture, it explores the real bottlenecks—lithography, memory, packaging, and critical materials—and the growing divide between training and inference hardware. From nuclear-powered AI campuses to optical computing and AI-designed chips, this is a forward-looking view of the next decade of compute, where geopolitics, supply chains, and economics will define who wins the AI race.