Principal component analysis is not the same as clustering. Some of the post seems to make that distinction, while other parts appear to blur it.

If there are clusters, PCA might find them, but PCA might tell you something interesting even if there are no clusters. But if there are clusters, the factors that PCA finds won't be the clusters, but the differences between them. In the simplest case, if there are two clusters, a left-wing party and a right-wing party, PCA will say that there is one interesting factor, the factor that distinguishes the two parties. But PCA will also say that is an interesting factor if there is no cluster, but a clump of people in the middle, thinning towards extremists.

Actually, factor analysis pretty much assumes that there aren't clusters. If factor 1 put you in a cluster, that would tell pretty much all there is to say and would pin down your factor 2, but the idea in factor analysis is that your factor 2 is designed to be as free as possible, despite knowing factor 1.