Good analysis and great of them to share their thinking. Does feel like this could have been a tweet that said the necessary condition for successful ML solution is applying it to a problem that has asymmetric upside.
Great for telling people they should get tested for diseases, terrible for diagnosis. In the alerting first case, consequences of being wrong are no better than base rate as they wouldn't have been tested otherwise, and the upside saves a life. In the latter diagnosis case, the consequences of being wrong are catastrophic, and it is substituting for the best available judgment. Similarly, it's great for fraud detection, terrible for making credit decisions, because the false negative rate is essentially externalized. It's good for finding opportunities, bad for providing services. So funnels and conversion pipelines it's great for.
So perhaps there's an ironic Turing test for ML solutions that is related to the relationship between the size of a group of people and the effect of mean reversion of their collective intelligence on their behaviour makes them indifferent to the perceived intelligence of the model, whereas a given individual will find the results of the model unsatisfying. From an indifference perspective, AI can fool some of the people all the time, and all the people some of the time, but no confusion matrix satisfies all the people all the time. Economically, ML will be useful for creating simple and cheap services that people who can't afford better will use, and substitute up from them when they can afford better, known as "inferior goods." There may be a hard limit on ML providing "normal goods," to individuals at scale for this reason. Lots of money to be made, but lots to be wasted tweaking your ROC curve to in the hope of creating a normal good.
I yell from the rooftops every chance I get that "the confusion matrix is the product." That is, your FP/FN/TP/TN rate is your product, and you are optimizing your system for the weights your customer assigns to those variables.
There is another ML/DL use case I'm hacking on that is about enabling privacy, but even this reduces to the asymmetry of the upside/downside of the confusion matrix. Obviously the article is more nuanced than this, but I think this heuristic is a key tool for reading articles like it.