Separating training and inference is the most expensive waste in the RL era

When finance people read tech news, our reflex is to look for cost items. Let me start with an analogy: A company has two budgets, one for R&D and one for marketing. Policy dictates they cannot be cross-used, yet by year-end, both departments are scrambling to spend everything, because if you don't use it up, your budget gets cut next year. Today, most AI companies deploying training and inference compute operate on this exact siloed accounting logic. Training clusters are separate from inference clusters—they buy separately, schedule production separately, and calculate utilization rates separately.