I find this line of thinking tedious.

Even if LLM's can't be said to have 'true understanding' (however you're choosing to define it), there is very little to suggest they should be able to ~~understand~~ predict the correct response to a particular context, abstract meaning, and intent with what primitive tools they were built with.

If there's some as-yet uncrossed threshold to a bare-minimum 'understanding', it's because we simply don't have the language to describe what that threshold is or know when it has been crossed. If the assumption is that 'understanding' cannot be a quality granted to a transformer-based model -or even a quality granted to computers generally- then we need some other word to describe what LLM's are doing, because 'predicting the next-best word' is an insufficient description for what would otherwise be a slight-of-hand trick.

There's no doubt that there's a lot of exaggerated hype around these models and LLM companies, but some of these advancements published in 2022 surprised a lot of people in the field, and their significance shouldn't be slept on.

Certainly don't trust the billion-dollar companies hawking their wares, but don't ignore the technology they're building, either.