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...
I would naively expect something like a 10:1 ratio of skimmers-to-double-readers, though perhaps you have a better UI in mind than I e.g. if you had a cool button on-screen called "Toggle Citations" then reading and toggling it to predict which things were cited could be fun. Of course that 10:1 doesn't include weighting by how much you care about the readers. It's on-the-table that the few people who "get to be surprised" are worth a bunch of people not seeing the second version.
Thinking more, I actually quite like the idea of "Here's the story" followed by "AND NOW FOR THE SAME STORY AGAIN, BUT WITH AN INCREDIBLE NUMBER OF CITATIONS AND ANNOTATIONS". That sounds like it could be fun.