I mean, yeah, you're not wrong. If it works for you, it works for you. It's problem space dependent, and there's virtually no research that exists suggesting how much, if at all, things will be distorted in the process for given scenarios. For my work, I need to have theoretical bounds on conformal/isometric distortion, the distances involved are infinitely more important than classification accuracy. I work in settings where near perfect classification accuracy is absolutely not expected, so well separation of clusters just will call for question of results.

There have been a number of cases, both theoretical and in practice, where t-SNE and UMAP give results with questionable reliability. I'm sure I could get an example in my space with little effort as well, and I'd rather go through some nonlinear transforms I know well in terms of how they work than spend a bunch of time tuning optimization hyperparameters that could take forever.