Writing
Enterprise AI in 2026 is not a contest between frontier models and local ones, or between NVIDIA and commodity hardware. It is a placement problem: five workloads — pretraining, fine-tuning, inference, R&D and agentic systems — spread across a stack that now runs continuously from a 230 kW rack to a desktop, under three constraints that do not negotiate: silicon supply, electrical power, and jurisdiction.