Comment on I Went All-In on AI. The MIT Study Is Right.
dejected_warp_core@lemmy.world 5 days ago
To quote your quote:
I got the product launched. It worked. I was proud of what I’d created. Then came the moment that validated every concern in that MIT study: I needed to make a small change and realized I wasn’t confident I could do it. My own product, built under my direction, and I’d lost confidence in my ability to modify it.
I think the author just independently rediscovered “middle management”. Indeed, when you delegate the gruntwork under your responsibility, those same people are who you go to when addressing bugs and new requirements. It’s not on you to effect repairs: it’s on your team. I am Jack’s complete lack of surprise. The idea that relying on AI to do nuanced work like this and arrive at the exact correct answer to the problem, is naive at best. I’d be sweating too.
fuck_u_spez_in_particular@lemmy.world 4 days ago
The problem though (with AI compared to humans): The human team learns, i.e. at some point they probably know what the mistake was and avoids doing it again. AI instead of humans: well maybe the next or different model will fix it maybe…
And what is very clear to me after trying to use these models, the larger the code-base the worse the AI gets, to the point of not helping at all or even being destructive. Apart from dissecting small isolatable pieces of independent code (i.e. keep the context small for the AI).
Humans likely get slower with a larger code-base, but they (usually) don’t arrive at a point where they can’t progress any further.
MangoCats@feddit.it 4 days ago
Notable exceptions like: peimpact.com/the-denver-international-airport-aut…