This is nicely put. It reminds me a bit of David Perkins's [0] "whole ballgame" approach (i.e. the best way to learn how to play baseball is to play a few games or approximations thereof, e.g. tee-ball; you'll struggle mightily at first, but you'll ultimately benefit by having a sense of how the game as a whole works, and it provides valuable context and motivation for subsequently working on specific skills like throwing the ball, swinging the bat, etc. And the ultimate result is greater mastery than if you had started from the skills and worked your way up to playing the game piecewise.) Fast.ai's pedagogy is based in part on this principle, and (as someone inclined to go down rabbit holes) it's been a huge help to me as I go through the course and book. [1]
I'd add too, recently released personal knowledge base apps (Obsidian, Roam, et al) are very well suited to fractal learning. I partition my notes into content (summaries) and concepts (with some Zettelkasten-style ephemeral notes mixed in). When I come across new concepts, I create a stub; then I fill it in when necessary with the necessary degree of detail, adding new details, revising, and forcing myself to make my account of it coherent as I encounter different facets of the concept in different books, articles, etc.
[0]: http://www.pz.harvard.edu/resources/making-learning-whole-ho...