I like the article and I love that people are talking about this but in my experience getting to this point is the easy part -- there are a lot of ways to talk about the breakdown of most of the statistical/stochastic side of classical financial mathematics and plenty of people in industry are smart enough to listen to that.

The issue is that most of these people (again, just in my experience, YMMV) look at it from a risk management perspective: "Hmm, so the model's right N% of the time. What can we do to hedge out the edge cases?" and so it's hard to move the conversation forward into coming up with a more robust framework for financial time series. Combined with the "oh that sounds like chaos theory, they tried that in the 90s, it doesn't work" reaction, the lack of obvious profitability means there's not a lot of industry interest in the dynamical systems side of this question, which is unfortunate.

On the academic side, there are just too many researchers who are happy to cite yesteryear's incrementalist paper about time series analysis so the finance and econometrics journals are content to treat it either as a settled issue or as a niche "metaphysical" one. I would hope that this has changed over the last few years but I'm not optimistic.

The most productive conversation I've had about this stuff was with an ecologist -- apparently the DS framework for time series analysis has been a big transformative force in their field recently. He gave credit for that to the fact that instead of risk, predictability etc., the real constraint in their discipline was getting new data from field research so they were heavily incentivized to find new and more efficient ways of looking at the same data.