Inference Cost

Opening — Why this matters now Enterprise AI is entering its mildly awkward teenage phase: everyone wants intelligence, nobody wants the invoice. For the last two years, much of the AI conversation has revolved around more: more context, more reasoning tokens, more chain-of-thought, more human feedback, more evaluators, more synthetic data, more agents, more dashboards to explain why the agents broke the dashboards. The operating assumption was simple enough: if the model thinks more, explains more, or trains on more feedback, it should perform better. ...

Opening — Why this matters now AI reasoning has entered its awkward managerial phase. For the past two years, the dominant story has been simple enough for a conference keynote: make models reason longer, use reinforcement learning, scale inference-time computation, and let the model “think.” The story is not wrong. It is just incomplete in the same way that saying “hire more analysts” is an incomplete operating model for a research department. More thinking can help. It can also become expensive, slow, noisy, and occasionally theatrical. ...