I pointed this out to my buddy who's a psychology doctoral student, his reply is below:

I don't know enough about g to say whether the people talking about it are falling prey to the general correlation between tests, but this phenomenon is pretty well-known to social science researchers.

I do know enough about CFA and EFA to tell you that this guy has an unreasonable boner for CFA. CFA doesn't test against truth, it tests against other models. Which means it only tells you whether the model you're looking at fits better than a comparator model. If that's a null model, that's not a particularly great line of analysis.

He pretty blatantly misrepresents this. And his criticisms of things like Big Five are pretty wild. Big Five, by its very nature, fits the correlations extremely well. The largest criticism of Big Five is that it's not theory-driven, but data-driven!

But my biggest beef has got to be him arguing that EFA is not a technique for determining causality. No shit. That is the very nature of EFA -- it's a technique for loading factors (which have no inherent "truth" to them by loading alone, and are highly subject to reification) in order to maximize variance explained. He doesn't need to argue this point for a million words. It's definitional.

So regardless of whether g exists or not, which I'm not really qualified to speak on, this guy is kind of a hugely misleading writer. MINUS FIVE SCIENCE POINTS TO HIM.