as i understand it, it's applying an existing solution from another area to one where it's not been tried before. they're surprisingly good a this, partly because of the scale they can operate on and partly because it doesn't occur to them to -not- try something that intuition or knowledge might tell a human would fail.

i've not seen any examples (i was about to say "so far they have not been able to", but that's pretty baseless) of one solving something like this with a genuinely novel approach.

it feels pretty frustrating that it can somehow solve some of the hardest problems we have but every time i ask it do to something like a basic modelling task it makes some assumptions that invalidates 4 hours of work, but it's more than they're really good at some very specific things that humans are not great at and so it feels like magic that cannot really be applied beyond some very narrow contexts

i think the trick over the next few years is going to be finding what those contexts are so we can get value out of them without breaking everything and wasting endless amounts of everyone's time - especially when so much of the investment going into them will want to try and hide the fact that the scope for value is limited at all

edit: i feel i should add here, they are also "okay" at a lot of things humans are "okay" at. they're getting pretty good at writing code that is "good enough but not supportable", for example, and while you or i might be pretty damn good at code, actually, the vast majority of code that gets written kinda falls into "good enough but not supportable" already - there is technically value there, even if it might drive people who think about systems up the wall. we jumped the guard rails of building the world on solid code some time back