Index of everything on this site: https://ai.hiina.space/index.md
The four questions turn out to be one question asked at four scales: what happens to the party that stops doing the work once a stronger helper takes it over. For model weights, the evidence says the answer depends on the training recipe. Narrow SFT on tool traces produces habit capture (the model keeps the tool-shaped loop and invents the tool's output), while RL and mixed-data recipes mostly preserve unaided skill. For people, deskilling is real in automation-heavy and "practice with AI" settings, but several famous results do not replicate. For the dark room question, theory alone cannot say whether a relieved agent idles or wanders; the deciding variable is whether it has uncertainty left that its own actions can reduce. For supervisor agents, neighboring evidence predicts deference, duplication, polling and model-specific drift, and production systems have so far avoided the question by blocking. For hierarchies, LLM worker and manager skill already come apart in at least one published table, which is the precondition for a Peter effect, but noisy evals will produce a Lazear-style regression that looks the same unless an experiment separates them.
All five candidate experiments are cheap by research standards. The tool-off audit (X1) gives a real number on a confirmed gap in about a day of inference, and the idle supervisor tick loop (X4) answers the dark room question per model while also producing a practical oversight number: how often a supervisor rubber-stamps a planted error as a function of relative strength. For anyone supervising a fleet of coding agents, that last number is the one most directly worth knowing.
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