A nitpicky comment and two questions:

1. The name of the factor analysis technique is principal component analysis (PCA). The author uses a common misspelling instead.
2. Related to the same graph, can someone explain to me how to interpret the two factors? Does factor 1 describe the similarity between the two distances, while factor 2 their dissimilarities? The power of a PCA analysis is to reduce dimensionality of a set of data. Here, we are looking at a 2D graph, so extracting 2 factors really doesn’t help that much, as it is just a transformation of displacement and gas milage into F1 and F2.
3. I am still struggling with the author’s idea of “squishification” and its meaning in a PCA. Does that mean that using Mahalanobis distance makes a PCA perform worse (i.e., getting the two components closer to a circle means that the two factors are worse)? I am also confused because it looks like you want to run the PCA on the raw data and not on the data transformed using the distance.