The point I'm making isn't (in life or in this blog post) that LLMs do a better job than humans. My point is that LLMs can get pretty close with the right context, and in some circumstances they can build context better than the typical human translator would.

Most CAT tools do vet for ML/LLM translations these days, so "cheap" human translation is mostly ML-assisted and human-edited, but my entire point is that they're not to blame, the majority of translation fails happens because of the lack of context, not because the human didn't try. It's about setting the site and l10n infrastructure out in a way that gives them the freedom to translate in the way that makes most sense for their language without being forced into "English in a costume".

It is unfortunately a fact that one can be both things: a startup and a language learning app. But my entire point wasn't about LLMs vs. humans, it was about laying out the architecture in a way that enables great l10n, whether we do that with LLMs today or humans tomorrow once we can afford it.