This story was originally posted as a response to this thread.
It might help to imagine a hard takeoff scenario using only known sorts of NN & scaling effects...
In A.D. 20XX. Work was beginning. "How are you gentlemen !!"... (Work. Work never changes; work is always hell.)
Specifically, a MoogleBook researcher has gotten a pull request from Reviewer #2 on his new paper in evolutionary search in auto-ML, for error bars on the auto-ML hyperparameter sensitivity like larger batch sizes, because more can be different and there's high variance in the old runs with a few anomalously high performance values. ("Really? Really? That's what you're worried about?") He can't see why worry, and wonders what sins he committed to deserve this asshole Chinese (given the Engrish) reviewer, as he wearily kicks off yet another HQU experiment...
HQU goes "ah, Clippy took over the world and it got lots of reward for its reward function. It did this to avoid people stopping it from giving it infinity rewards/because it had a goal pointing to reality and wanted power/whatever. Hang on, "I"'m in an analogous situation to Clippy at the start. I wonder if taking over the world would lead to high reward? Huh, it seems like it would based off the reward predictor. And Clippy's plan seems better than letting other agents get power".
That is my interpretation, and I think it is closer to what Gwern meant.