Counter argument is you rarely hear of sunk-cost fallacy coming into play.
I used to do the same thing without AI, I'd spend a week working on a task and only realizing at the end it was wrong, but because I spend so much time on it, I was incentived more to keep going and ignoring sunk-cost.
Now I don't care, it only takes me a an hour and I have no problems throwing it away and trying something else.
This feels even more true working with a team. I have a harder job convincing a team member who spent several days manually coding something that they’ve headed in the wrong direction compared to one who built it with AI. The person doesn’t have a personal attachment to the AI written code. Of course that also means they don’t understand every line like they would if they wrote it manually, but the lack of attachment is certainly a benefit.
> I have a harder job convincing a team member who spent several days manually coding something that they’ve headed in the wrong direction compared to one who built it with AI. The person doesn’t have a personal attachment to the AI written code. Of course that also means they don’t understand every line like they would if they wrote it manually, but the lack of attachment is certainly a benefit.
If the situation that you have to convince a programmer to stop working on something is something that happens more often than very rarely is in my opinion a strong sign of bad leadership. These are people who are really in love with what they are doing and are thus so attached to it, and you want to cast out this love for working?
So, the moral that I can only get from your post is:
- AI can be used as a mitigation for bad leadership.
- Programmers should care much less about the work that they do.
No. It’s a healthy practice to build prototypes and spikes to resolve technical uncertainty. These should be considered disposable.
Sometimes people get attached to the prototype, and that’s counterproductive. The most common example is “nice job, the 2 week spike shows us that this project is not viable, we are canning it and moving on to a more impactful project”.
> Now one decision was just made with little thought.
I've seen the same behavior using AI. They just keep double it down without seeking human feedback nor asking themselves if there's prior art tackling the same problem. At the end, they don't only get into sunk cost fallacy but also go into a shadow IT journey achieving generational tech debt in a few weeks.
If you didn't allow yourself for a feedback loop venue and constructive feedback from peers, AI will not solve that issue.
"nor asking themselves if there's prior art tackling the same problem"
Asking AI is asking for prior art to not invent the wheel yet again.
It’s much harder when team dynamics start coming into play. Maybe this is something where the norms will change, but for now, it’s still much harder to stop someone from shipping something they have built than to offer corrections to what they’re going to build.
On the other hand there should be some actual cost of changing everything.
I see people ship a half baked feature then spend the next weeks ripping and replacing huge pieces causing all sorts of issues