When I was a young nerd, I was a rabid fan of logic, objectivity, science. Anything which smacked of spirituality (or, worse, religion) was immediately dismisse

When I was a young nerd, I was a rabid fan of logic, objectivity, science. Anything which smacked of spirituality (or, worse, religion) was immediately dismissed as useless sentimental nonsense. I recognized that some people might _enjoy_ thinking about these fripperies, but I was convinced that any phenomenon which claimed an effect but did not explain the science-based interactions that mediated it (think astrology or religion) was hogwash. One day I was deathly bored on a school trip, and the only entertainment available was a book on Feng Shui. During the introduction, it advised the practice of imagining a dragon entering a living space through a door, coiling their way through and around the space, and then exiting. The following sentence stated that this was often a good heuristic for helping the human mind judge a space for sight lines, common travel paths, and avoiding clutter. My mind was blown. The dragon wasn't the point - the dragon was a way to hack the human brain (itself a messy, complex, poorly-understood mechanism) to a better outcome than it would get by a straightforward series of explicable evaluations. If the process demonstrably leads to better outcomes - if it is "useful" - then it can still provide value even if you can't explain _why_ it works[0] All of which is a roundabout way of getting to my point, which is: no, I don't get nervous about architectural patterns which _appear_ to only exist to make unit testing easier, because overwhelmingly I have found that "is this architecture easy to test?" is a good heuristic for "does this architecture have well-divided areas and layers of responsibility?". No matter your opinion on the value of tests _themselves_ (and there are very cogent arguments that an over-reliance on unit testing alone can lead to brittleness and false confidence), ceteris paribus making your architecture "more testable" will usually be a good thing in itself, even before any tests are written. [0] this attitude needs some nuance, of course, when it is actually important to explain how and why the process reaches conclusions. See: ML/LLMs/AGI/AI/whatever-the-current-initialism-is, and recourse in the face of disputed judgements or unforeseen effects.