The problem here is we're going to let the machines dictate the ontology, and we will lose expressiveness by reverting to the mean. All in order to communicate more cheaply with the machines - but on their terms.

Forgive the anthropomorphizing, it was necessary - "terms" has double meaning there, and each of us can understand that without an ontology - because we can access nuance. I can read schema, structure, and shape, and understand; but only one of those triggered some readers.

Anyway, the machines will choose terms which are most well-represented in the training data. This will normalize communication by chopping off the top-end where nuance lies. This is token-efficient.

Ontologies, in their implementation in the Linked Data ecosystem, do offer the ability to relate one understanding-system to another, but this is not token-efficient. I would love to see each domain, each business even, define their view of the world in the most expressive and culture matching owl representation, but I don't see it happening. "Shape" it is.