The demo is always impressive. You describe what you want, the AI writes it, the tests pass, and the code looks reasonable on first read. What’s harder to see - and what’s becoming a serious problem as these tools get more capable - is that the code is often architecturally wrong in ways that won’t surface until three months later when someone needs to change something.
GitHub Copilot, Cursor, and the cluster of similar tools built on models like Claude and GPT-4o have gotten genuinely good at producing syntactically correct, contextually plausible code. That’s the bar they set for themselves, and they’ve cleared it. But syntactically correct and contextually plausible is not the same as appropriate for this codebase, at this stage, with these constraints. The distinction matters more than it used to, because developers are now delegating larger chunks of logic - not just autocompleting a function signature but generating entire modules.
The Fluency Problem
There’s a specific failure mode worth naming: AI coding tools tend to over-engineer. Ask one to write a data processing utility and it will often return something with an abstract base class, a factory method, and three layers of indirection - patterns that make sense in a large system but add pure overhead to a 200-line script. The code isn’t wrong. It’s just solving a different problem than you have.
This happens because these models are trained on vast amounts of open-source code, and open-source code skews toward large, mature projects where abstraction earns its keep. The model has seen more Spring Boot applications than it has seen quick internal tools, so it writes like a Spring Boot application.
Senior developers catch this. Junior developers, who are also the demographic most likely to lean heavily on AI assistance, often don’t - yet. They merge it, it works, and the codebase quietly accumulates weight.
The Confidence Gap
What makes this harder to address is that AI-generated code reads confidently. It’s well-formatted, it’s commented, it follows conventions. The signals developers normally use to identify shaky code - awkward naming, inconsistent style, obvious gaps - aren’t there. You have to actually think about whether the abstraction fits, which is the part that AI assistance was supposed to give you more time for, but often doesn’t, because the output looks done.
The tools will keep improving at the thing they’re measured on: does the code run, do the tests pass, does it match the prompt. Whether it should exist in that form at all is a question nobody’s figured out how to benchmark.