I’ve been reading a lot about AI-assisted development lately, and one thing keeps bothering me: everyone talks about speed, but almost nobody talks about what happens after. In my last project we used AI to scaffold most of the backend services, and at first everything looked amazing because we were shipping features really fast. But after a month, the codebase started feeling “heavy” — duplicated logic, inconsistent patterns, and small shortcuts that didn’t seem important at first but started piling up. I started wondering if we’re actually reducing work or just pushing complexity into the future. This article this read more got me thinking that maybe the real skill is not generating code anymore, but controlling what AI produces before it becomes a mess.
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I’m not a developer, but I sit in enough product and delivery meetings to notice a pattern here. Whenever AI gets introduced, the conversation always starts with speed and output, but after a while the discussion shifts toward coordination and maintenance. It kind of feels like AI doesn’t remove work, it redistributes it into different phases of the lifecycle. Instead of writing code, teams spend more time validating, aligning, and cleaning up. From the outside, it looks like the real challenge isn’t technical anymore, but organizational — making sure everyone treats generated output as something temporary, not something final.