The “fancy math” associated with quant usually refers to pricing derivatives like options.

The thing is that there isn’t really strong institutional demand for exotic derivatives, people are happy using existing methods and just applying those to current markets.

The other type of fancy math has to do with deriving alpha, which is also not that complex, from a statistics perspective you’re mostly using linear regression or other basic forms of regression.

The hard part of quant is implementation, making sure your data is right, hunting through poorly understood markets, and managing risks carefully and understanding them.

There’s also ML but that’s equally complex in quant as it is anywhere else.

In finance, if you have a better understanding of how to price a certain asset then you can make better trades than your competitors and thus make money.

Some assets are very easy to price, like bonds. Others are esoteric, like Exotics which are options or other securities with uncommon pricing structures. This is where the "fancy math" kicks in. But as time goes on, more and more firms figure out how to better price all the various assets that they trade, which erodes the competitive edge between market participants.

The assertion is that today, most firms have most things mostly figured out as far as how to value them. There's little to no competitive advantage to be mined from esoteric stochastic calculus. In contrast, it's rumoured that one of the most successful firms of all time, Renaissance, owes a large part of their success to their absolutely pristine data which comes from a massive data ingestion and cleaning pipeline, allowing them to get a clearer statistical picture of what the current market forces at play are, and how they're going to manifest.