And you're missing the point of the field if you're making the trivial observation that working out an explanation decreases the usefulness.

That is the point. We want to decrease it's usefulness and increase its accuracy in fields where the accuracy is paramount. This is akin to the relationship of physics and math. In physics, we routinely make unjustified steps to make our models work. Then in math, we try to find a reasonable framework in which the unjustified steps are justified. Saying "math reduces the usefulness by requiring an explanation for seemingly okay steps" is to miss the point of what mathematics is trying to do.

This misses the entire point of what explainable AI is. Asking humans to explain their intuition as a precondition for their intuition to be applicably valid is definitely limiting for humans. However, explainable AI isn't that we ask AI to explain itself. It's rather being able to exactly or with high probability pinpoint the exact dataset on which AI is basing it's prediction. This is definitely useless, and so limiting, when it comes to machine learning applications to, say, predicting what food you might like the best. It's however immensely important in areas like medical imaging, because we want to ensure that the input, on which AI is basing its decision, isn't some human-errored spot on the x-ray.

As such, it is for these fields that explainable AI is studied, where limitations of AI are far less significant than us being sure that AI isn't making a mistake. As such suggesting explainable AI is a dead-end is inaccurate, if not a mischaracterisation.