The former is currently sitting in my car, and I'll be trying to offload it to someone who actually wants it.
OTOH, I recall Tufte going on and on about cutting the "data-ink ratio" to the point of making graphs that we generally understand at a glance suddenly very unintuitive. I can dig into the book again if necessary, but I recall he essentially argued that box-and-whisker plots became just a few dots. There's meaning conveyed by the boxes and the whiskers, and changing that convention - even if it uses more ink than absolutely necessary - adds significant cognitive load.
That said, intuition is heavily influenced by existing practices and culture. My understanding is that Tufte wrote his first book as a reaction to existing practices and hence the notion was that what was considered intuitive was different than what we both imagine and also suboptimal.
There is a book called Graphis Diagrams released a few years before Tufte's The Visual Display of Quantitative Information that compiled what people previously considered "good" data visualizations pre-Tufte. I'd call Graphis Diagrams more of a collection of art pieces than a collection of good data visualizations, but that was the field before Tufte's work. Some of the visualizations are interesting, but many seem incredibly dated and make heavy use of "chartjunk" (Tufte's term). I'd argue that we wouldn't consider those supposed "good" examples very readable and useful by modern standards, and that just goes to show that intuition can change (for better or for worse).
I do also agree that Tufte tends towards a certain kind of performative minimalism that seems excessive at times to me. Often times the better solution is to present the data in a different way --- transform it using mathematics, use a different visualization, or something else --- rather than just reduce the amount of ink you are using. Box plots and pie charts are just useless to me as media since they simply cannot account for complexity, and no amount of minimalism is going to solve that. The answer is to use another kind of visualization (or perhaps use parallelism/small multiples to build up those kind of simple visualizations into something more meaningful).
I personally find Tufte's book Visual Explanations to be his best book. It focuses much more on how good visualizations can help you explore data more readily (see what otherwise could not be seen), especially in regards to cause and effect. The Visual Display of Quantitative Information is certainly more famous but it focuses on much more low-level implementation details like the "data-ink ratio", etc. and not bigger picture things, in my opinion. I recommend giving Visual Explanations a read if you are interested.