What baffles me is that everyone talks about software quality but very little organization actually measure their software quality. Not just some made up metrics but exactly how well the software meets its requirements.

And not that we don't know how. For any ML model, we have a validation data set and it is imperative that we measure how well a model performs on this data set and not the training data set. We know that without a validation, a machine will overfit its model into the data it has. Programmers... do the same thing. We're very good at passing tests and, unless we have a separate independent validation and verification process, we convince ourselves that green tests = quality. So our tests are always green but our backlogs are always red. And nobody seems to notice the contradiction.