Inference efficiency is about protecting attention

This article discusses inference efficiency in AI systems, defining it as the degree to which an AI's reasoning is spent on solving the user's problem rather than overcoming tooling. It argues that unnecessary steps, such as verbose CLI output, prose-heavy APIs, or vague error messages, consume reasoning capacity and context window, leading to higher costs and reduced effectiveness. The author emphasizes that attention is the scarce resource, and good tooling design—clear APIs, structured responses, and concise documentation—can preserve it. The article highlights how AI makes the cost of poor developer experience tangible, contrasting with human developers who can skim or ignore distractions. It concludes that improving inference efficiency is crucial for building effective AI agents, as it directly impacts cost, latency, and the quality of problem-solving.