But the specificity required for a machine to deliver an apt and snark-free answer is -- somehow -- even more outlandish?
I'm not sure that I see it quite that way.
Like... In most accounting things, once end-dated and confirmed, a record should cascade that end-date to children and should not be able to repeat the process... Unless you have some data-cleaning validation bypass. Then you can repeat the process as much as you like. And maybe not cascade to children.
There are more exceptions, than there are rules, the moment you get any international pipeline involved.
It's not underspecified. More... Overspecified. Because it needs to be. But AI will assume that "impossible" things never happen, and choose a happy path guaranteed to result in failure.
You have to build for bad data. Comes with any business of age. Comes with international transactions. Comes with human mistakes that just build up over the decades.
The apparent current state of a thing, is not representative of its history, and what it may or may not contain. And so you have nonsensical rules, that are aimed at catching the bad data, so you have a chance to transform it into good data when it gets used, without needing to mine the entire petabytes of historical data you have sitting around in advance.
LLMs AFAIK cannot do this for novel areas of interest. (ie if it's some domain where there's a ton of "10 things people usually miss about X" blog posts they'll be able to regurgitate that info, but are not likely to synthesize novel areas of ambiguity).
If we used MacOS throughout the org, and we asked a SW dev team to build inventory tracking software without specifying the OS, I'd squarely put the blame on SW team for building it for Linux or Windows.
(Yes, it should be a blameless culture, but if an obvious assumption like this is broken, someone is intentionally messing with you most likely)
There exists an expected level of context knowledge that is frequently underspecified.
Now, humans, other than not even thinking (which is really similar to how basic LLMs work), can easily fall victim to context too: if your boss, who never pranks you like this, asked you to take his car to a car wash, and asked if you'll walk or drive but to consider the environmental impact, you might get stumped and respond wrong too.
(and if it's flat or downhill, you might even push the car for 50m ;))
There is an endless variety of quizes just like that humans ask other humans for fun, there is a whole lot of "trick questions" humans ask other humans to trip them up, and there are all kinds of seemingly normal questions with dumb assumptions quite close to that humans exchange.
I don't know if it's a lack of intellect or the post-training crippling it with its helpful persona. I suspect a bit of both.
