Because the models do what you ask them. "Claude, do this thing, be thorough, no mistakes" is not a good prompt.

Maybe for some value of "what you ask them."

I have a data pipeline with 6 steps, A -> B -> C -> D -> E -> F. I asked Codex to make some specific optimizations to step B and benchmark them. It did what I asked. Then it decided to also benchmark the entire pipeline, and after noticing that step E was slow it decided to make some optimizations that I had not asked for on step E. It was at this point that I wondered why it was taking so long, saw what it was doing, and stopped it.

This is GPT-6.1 Sol High.