Those two branches despise each other with intense passion.

I never actually made this connection before, but having spent far too much time listening to both camps, this observation does ring true. Another observation I had is that there is an orthogonal axis to the safety/ethics dichotomy that follows a more scholastic approach, namely neuro/symbolism. These two branches correspond, roughly, to the debate between machine learning and mechanized reasoning (a.k.a. good old fashioned AI).

Vastly oversimplifying, the neural branch believes reasoning comes after learning and that by choosing the right inductive biases (e.g., maximum entropy, minimum description length), then training everything up using gradient descent, reasoning will emerge. The symbolists place a heavier emphasis on model interpretability and believe learning emerges from a logical substrate, albeit at a much higher level of abstraction.

Like safety/ethics "research", neural/symbolic folks are constantly bickering about first principles, but unlike their colleagues in the S/E camp (which is at best philosophy and at worst fanfiction), N/S debates are resolved by actually building things like language models, chess bots, programming languages, type theories and model checkers. Both are valid debates to have, but N/S is more technically grounded while S/E is a bit like LARPing.