AI-based programming is becoming an everyday reality for software developers. Today, coders can employ AI-based tools to produce boilerplate, author test code, enhance code, document legacy software, and even suggest design patterns. The potential payoffs are enormous. An hour of work has, in some cases, become just a few minutes of light supervision. The results are significant. Work that initially took one hour has sometimes been reduced to a few minutes.
However, rapid code generation does not necessarily imply good software. One of the biggest downsides of AI-generated code is that it often seems fine before its actual correctness is established. It compiles, is neat, and may even pass some basic tests. But production software is more than just syntax correctness. It is about business logic, integration contracts, security issues, performance limits, and maintainability over the years. These are the types of assumptions that AI can make very convincingly but still be incorrect.