78% of developers claim AI makes them more productive. 14% say it's a 10x improvement. So where's the flood of new software? Turns out those productivity claims are bullshit.
AI coding assistants have made my life a lot easier. I’ve created multiple personal projects in a day that would’ve taken me multiple days of figuring out frontend stuff.
It’s also helped me in my work, especially in refactoring. I don’t know how y’all are using them, but I get a lot of efficient use out of them.
I’ve had “success” with using them for small one-off projects where I don’t care too much about correctness, efficiency, or maintainability. I’ve tried using various AI tools (Copilot, Cursor agents, etc) for more serious projects where I do care about those things, and it was counter-productive (as studies have shown).
Hmm, I was curious if ChatGPT still gives inefficient code when asking it to write quicksort in Python, and it still does:
defquicksort(arr):
iflen(arr) <= 1: # Base casereturn arr
pivot = arr[len(arr) // 2] # Choose middle element as pivot
left = [x for x in arr if x < pivot] # Elements less than pivot
middle = [x for x in arr if x == pivot] # Elements equal to pivot
right = [x for x in arr if x > pivot] # Elements greater than pivotreturn quicksort(left) + middle + quicksort(right)
That’s not really quicksort. I believe that has a memory complexity of O(n log n) on the average case, and O(n^2) for the worst case. If AI does stuff like this on basic, well-known algorithms, it’s likely going to do inefficient or wrong stuff in other places. If it’s writing something someone is not familiar with, they may not catch the problems/errors. If it’s writing something someone is familiar with, it’s likely faster for them to write it themselves rather than carefully review the code it generates.
AI coding assistants have made my life a lot easier. I’ve created multiple personal projects in a day that would’ve taken me multiple days of figuring out frontend stuff.
It’s also helped me in my work, especially in refactoring. I don’t know how y’all are using them, but I get a lot of efficient use out of them.
How dare you! Can’t you see there’s a circle jerk in progress?
I’ve had “success” with using them for small one-off projects where I don’t care too much about correctness, efficiency, or maintainability. I’ve tried using various AI tools (Copilot, Cursor agents, etc) for more serious projects where I do care about those things, and it was counter-productive (as studies have shown).
Hmm, I was curious if ChatGPT still gives inefficient code when asking it to write quicksort in Python, and it still does:
def quicksort(arr): if len(arr) <= 1: # Base case return arr pivot = arr[len(arr) // 2] # Choose middle element as pivot left = [x for x in arr if x < pivot] # Elements less than pivot middle = [x for x in arr if x == pivot] # Elements equal to pivot right = [x for x in arr if x > pivot] # Elements greater than pivot return quicksort(left) + middle + quicksort(right)That’s not really quicksort. I believe that has a memory complexity of O(n log n) on the average case, and O(n^2) for the worst case. If AI does stuff like this on basic, well-known algorithms, it’s likely going to do inefficient or wrong stuff in other places. If it’s writing something someone is not familiar with, they may not catch the problems/errors. If it’s writing something someone is familiar with, it’s likely faster for them to write it themselves rather than carefully review the code it generates.