That's been my experience both with Tesla AP/FSD implementation & with LLMs.
Super neat trick the first time you encounter it, feels like alien tech from the future.
Then you find all the holes. Use it for months/years and you notice the holes aren't really closing.. The pace of improvement is middling compared to the gap to it meeting the marketing/rhetoric. Eventually using them feels more like a chore than not using them.
It's possible some of these purely data driven ML approaches don't work for problems you need to be more than 80% correct on.
Trading algos that just need to be right 55% of the time to make money, recommendation engines that present a page of movies/songs for you to scroll, Google search results that come back with a list you can peruse, Spam filters that remove some noise from your inbox.. sure.
But authoritative "this is the right answer" or "drive the car without murdering anyone".. these problems are far harder.
Super neat trick the first time you encounter it, feels like alien tech from the future.
Then you find all the holes. Use it for months/years and you notice the holes aren't really closing.. The pace of improvement is middling compared to the gap to it meeting the marketing/rhetoric. Eventually using them feels more like a chore than not using them.
It's possible some of these purely data driven ML approaches don't work for problems you need to be more than 80% correct on.
Trading algos that just need to be right 55% of the time to make money, recommendation engines that present a page of movies/songs for you to scroll, Google search results that come back with a list you can peruse, Spam filters that remove some noise from your inbox.. sure.
But authoritative "this is the right answer" or "drive the car without murdering anyone".. these problems are far harder.