From in-depth experience I completely concur with your bullet points, they are all valid (spot on really), and in some cases huge blockers for larger scale adoption. I've had plenty of existential angst regarding the amount of time I've invested in the ontology world. Navigating, inferring, understanding, all the things necessary to make the use of ontologies, need
a lot of work, particularly new interfaces (but- hmm, sounds like Science). IMO, however, these issues don't invalidate the underlying effort or goals. Biology is vast, and difficult. In my view these ontologies are focal points that force biologists to think about what they are doing, this, by itself, is enough, to me, everything else is bonus.
I've seen communities of biologists come to a new awareness as to how bad their existing scientific-terminology is when they go through ontology-building exercises. Scientists often use terms they think they know the meaning of because their academic ancestors all used those terms. Simply having scientists work through these issues is of value (again, Science == Slow).
Good luck with using AI to understand human labels, you're going to need more structure (formalized scientific consensus). Ontologies are one way to contribute to this structure/consensus. Of course they, like every other knowledge-base, are not a stand-alone answer.
This leaves me with: "I am saying that as someone that uses GO a lot."- but why!? Since you're following this "dead-end" I suspect you're not a scientist, but rather someone selling something, and as such you have no problems using a tool to make a $, even knowing it's pointless in the long run?