I have some serious reservation about the grokking papers and there's the added complication that test performance is not a great proxy for generalization performance. It is naive to assume the former begets the latter because there are many underlying assumptions there that I think many would not assume are true once you work them out. (Not to mention the common usage of t-SNE style analysis... but that's a whole other discussion)

It is important to remember that there are plenty of alternative explanations to why the "sudden increase" in performance happens. I believe if people had a deeper understanding of how metrics work that the phenomena would become less surprising and make one less convinced that scale (of data and/or model) will be insufficient to create general intelligence. But this does take quite a bit of advanced education (that is atypical from a ML PhD) and you're going to struggle to obtain it "in a few weekends".