Maybe more all that is essential for a molecular biologist isn't necessary for a general practitioner? It's just... those conference calls where you're explaining that because the classifier is working really well now doesn't mean that we can use it in production, those calls can get difficult and annoying, and sometimes the "other side" wins - with predictable results.
You bring up a very important point and a difficult one which is, if the decision making is in the hands of someone who does not understand the nuances too well nor has the time or inclination, what do you do ?
If your salary is going to depend on how many models you pushed out and not how well they continued to perform, many will optimize over the number of models pushed out.
A major source of problem (and sometimes a gift) is that you cannot prove a empirical statistical claim true or false in finite time. There is always this non-zero probability that the weirdest thing would happen. It could be just sheer bad luck that the model did so poorly in this cycle.
ha ha ha!