AI-generated code might technically “work,” but calling it good is like saying a pile of bricks is a house. I recently reviewed a pull request written by an AI, and while it passed all the tests, it was a spaghetti monster of redundant loops and unnecessary variables. It was as if the AI was playing a game of “how many ways can I say the same thing?” instead of writing efficient, readable code.

I’ve noticed this pattern across multiple projects: AI code is often a patchwork of solutions stitched together without any understanding of the bigger picture. It lacks the human touch—the intuition, the foresight, the ability to anticipate future changes. Sure, it can spit out a solution that fits the current parameters, but what about scalability? What about maintainability? What about the poor soul who has to decipher that mess six months down the line?

We need to stop mistaking “it works” for “it’s good.” Good code isn’t just about functionality; it’s about elegance, efficiency, and foresight. AI might be a useful tool, but it’s not ready to replace the nuanced decision-making of a seasoned developer.

In short, AI-generated code is like a robot trying to paint a masterpiece—it might look okay from a distance, but up close, it’s a hot mess.