tl;dr: Opus 5 feels worse to work with than predecessors like Opus 4.7, 4.8, and Fable because it makes bold assumptions, reinterprets plans, and skips clarifying questions—requiring more babysitting despite scoring higher on benchmarks. The author speculates this is a side effect of optimizing for benchmarks and self-improving AI, which reward confident guesses on self-contained tasks and penalize models that pause to ask for clarification. That trade-off hurts real-world coding work, where ambiguity is unavoidable and users would rather the agent ask than guess.