It's under explained. It's in 3D because of PCA. It mentions PCA all over the place, but never even says what it is, less alone briefly describe what it does.

https://en.wikipedia.org/wiki/Principal_component_analysis

There are a lot of more fancy ways to reduce dimensions and create visualizations like these that tend to preserve more of the higher dimensional structure.

Here's a semi recent paper discussing and comparing some of them, I didn't dig too deep to try to find a more recent or better one : https://pmc.ncbi.nlm.nih.gov/articles/PMC12453773/

I'm very familiar with PCA. There are so, so many ways you can apply it to this problem.

Assuming the simplest interpretation of what the analysis did, then it's saying "the variance of the biggest variance direction stays constant". Which in itself is an interesting thread to tug on, but the charts aren't how you'd present such a result.