So, how is this different from years ago when there were software libraries but in class we still rolled our own implementation to understand the algo. For example in machine learning, yes you could just use a Matlab toolbox or libsvm or scikit-learn. But then you wouldn't learn the SVM training algorithm, so we had to implement it ourselves. We knew that we wouldn't roll our own SVM at a job, in production. The goal of the exercise was to learn the algorithm and how it actually works.

Today we have AI, which is basically like having infinite libraries available that do what you ask for. But you will not learn if you just take that code, similarly to how you don't learn if you just call scikit-learn to train your SVM.

And of course students grumbled back then also and said why do we need to do this when all those libraries exist?

Learning often requires not taking the most efficient path for every project.

Precisely my point. But unfortunately companies are rushing to get the features out. No cares if you’re learning and augmenting your own understanding or not.