Text is an LLMs input and output, but, under the hood, the transformer network is capable of far more than mere re-assembly and remix of text. Transformers can approximate turing completeness as their size scales, and they can encode entire algorithms in their weights. Therefore, I'd argue they can do far more than reassemble and remix. These aren't just Markov models anymore.

(I'd also argue that "understanding" and "functional brain" are unfalsifiable comparisons. What exactly distinguishes a functional brain from a turing machine? Chess once required a functional brain to play, but has now been surpassed by computation. Saying "jobs that require a human brain" is tautological without any further distinction).

Of course, LLMs are definitely missing plenty of brain skills like working in continuous time, with persistent state, with agency, in physical space, etc. But to say that an LLM "never will" is either semantic, (you might call it something other than an LLM when next generation capabilities are integrated), tautological (once it can do a human job, it's no longer a job that requires a human), or anthropocentric hubris.

That said, who knows what the time scale looks like for realizing such improvements – (decades, centuries, millennia).