Earlier quoted context omitted.
Setting aside the silliness of that definition of magic, there's a huge leap between "I can't explain it" and "It can't be explained". There are plenty of explanations of how LLMs work, by their creators, incidentally.
Yet there are emergent behaviours from these LLMs that are both surprising and not immediately understood. [1][2][3] Everyone has theories, of course, but still pretty "magic" considering these behaviours weren't theorised in papers prior to observation. 1 - https://www.jasonwei.net/blog/emergence 2 - https://arxiv.org/pdf/2206.07682.pdf 3 - https://www.quantamagazine.org/the-unpredictable-abilities-e...
[1] Is a summary of [2], by one of its authors, not a separate source.
[2] Defines "emergent behaviors" in a way that you're clearly misunderstanding (because "emergent behaviors" is an extraordinarily poor way of communicating this--it's partly the fault of the researchers who chose this ambiguous language). All it's saying is that bigger models can do things that smaller models can't, which should be surprising to no one. It's NOT saying that the capabilities are anything more than the sum of the input data.
[3] Is written by a journalist, not an AI researcher, and so it's limited by the things the journalist is excited about. The journalist, for example, downplays sections like, "The other, less sensational possibility, she said, is that what appears to be emergent may instead be the culmination of an internal, statistics-driven process that works through chain-of-thought-type reasoning. Large LLMs may simply be learning heuristics that are out of reach for those with fewer parameters or lower-quality data." If you're going to try to gather things from journalists rather than subject matter experts, you need to understand how journalists work, and how subject matter experts work, and look for paragraphs like that to understand what's actually happening.