Early on, these code agents wouldn't do basic good hygiene things, like check if the code compiled, avoid hallucinating weird modules, writing unit tests. And people would say they sucked ....

But if you just asked them to do those things: "After you write a file lint it and fix issues. After you finish this feature, write unit tests and fix all issues, etc ..."

Well, then they did that, it was great! Later the default prompts of these systems included enough verbiage to do that, you could get lazy again. Plus the models are are being optimized to know to do some of these things, and also avoid some bad code patterns from the start.

But the same applies to performance today. If you ask it to optimize for performance, to use a profiler, to analyze the algorithms and systemically try various optimization approaches ... it will do so, often to very good results.

Normal in the sense of "you can model a distribution of human behavioral processes or outcomes" that encompasses, say, 95% of humans in a given culture or geography is very much a thing you can do. And I'd go as far as to say a large chunk of the mental bandwidth of the average person is running those simulation models just to operate in a multi-human-agent world.

(If you want to say we observe bimodal or other multi-peaked distributions in practices rather than "normal" ones, I will strongly agree, but that usually isn't the objection when people say "normal is a myth")

ChatAI - show the top 50 online retailers by revenue in the US and note any that have credible new stories about quality control issues. Save all of them except StoreX and StoreY in your list you use for comparison shopping.

Or maybe another one, scan all my credit card purchases for all time that you have history and record all the stores.

Done. And plenty of third party sites (consumer reports, wirecutter, etc...) will do this kind of thing too. And you could perhaps transitively trust them - either view direct lists or just scraping the places they recommend.

And the average person doesn't need to figure this out ... skills encoding this will propagate.

But that is exactly the right way to think about it. If you have an army of sub-par workers that aren't going to think deeply about their value to your business, but are really cheap (relative to human labor) - how do you make effective use of them? Thinking about AI agents as being high-competence and able to learn your intent is the wrong model at this point. Though they can be high-competence in very specific narrow niches.