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...
How is this plausible? Per Wikipedia:
As an attempt to model Gwern's likely motivations, this seems terrible. You really think there's no reason to include lots of details in scenario-building or fiction-writing outside of wanting to deceive debate opponents??
You really think the primary motivation of Gwern Gwern.net Branwen for finding the fine details of ML scaling laws interesting (or for wanting to cite sources) is 'I really want to deceive people into thinking AI is scary'?
Have you met Gwern??