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.

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.

> 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.

In my experience I have seen far more division of labor than you describe. Real quants don’t do work like making sure your data is right or even much of implementation; they delegate that to software engineers. But a cheap quant shop might be too cheap to hire SWEs so quants end up doing this work instead. The real quant work is just hunting through poorly understood markets.

The days of doing some calculus, having a moment of brilliant insight, and writing down a pricing formula then getting paid millions probably only ever existed in people's imagination.

Fancy math definitely is part of derivatives pricing. However financial world has become too complicated for simple models. Adding things like the risk of counterparty default to your pricing equation quickly leads you into the world of equations without closed form solutions. The common approach these days is some kind of huge multi factor Monte Carlo model. These are still solving pricing equations but the challenge is more about numerical methods than brilliant algebraic gymnastics.