Writing Code Is Easy. Reading It Isn't.
Drawing on years of contract work, the author argues that the real cost of software lives in reading, not writing: every codebase demands a mental model, built by tracing function definitions, return types, database paths, caches, error handling, and call sites. Understanding one getUserPreferences-like function often means opening five other files. Because LLMs can now emit code faster than anyone can consume it, they enlarge the reading burden; the lawyer who filed fictitious ChatGPT-sourced cases failed not at generating but at verifying through reading. The practical conclusion is to point AI at comprehension—explaining existing code, uncovering side effects, compressing context—rather than at producing bigger diffs. Team throughput should be measured by how quickly members construct accurate mental models, not by lines generated. A grounded opinion piece for engineers who want AI assistance without mistaking output volume for velocity.