It's been this way for a long time, basically since deep learning became the "default" for ML. I remember back in 2018 taking a "Deep Learning" course and one of the most emphasized aspects of the approach is how much of a "black box" it is and how difficult (basically impossible at any non-trivial scale) it is to "understand" the outputs of a deep neural network compared to more classical methods like decisions trees or basic regression. This has only gotten more extreme as things have gotten more complex, abstract, and large.