End-to-end ML can be much better than hybrid (or fully rules-based) systems. But there’s no guarantee and you have to actually measure the difference to be sure.

For safety-critical systems, I would also not want to commit fully to an e2e system because the worse explainability means it’s much harder to be confident that there is no strange failure mode that you haven’t spotted but may be, or may become, unacceptable common. In that case, you would want to be able to revert to a rules-based fallaback that may once have looked worse-performing but which has turned out to be better. That means that you can’t just delete and stop maintaining that rules-based code if you have any type of long-term thinking. Hmm.