AI-generated code promises to revolutionize development: write a prompt, get working code, ship faster. The reality looks different. AI produces code that compiles, runs, and does something. Whether it does what you actually need is another question entirely.

The training data problem

AI learns from the code available on the internet. That includes elegant solutions from experienced developers, but it also includes countless examples of questionable code written by people still learning, deprecated libraries no one should use anymore, and security vulnerabilities that were fixed years ago. AI doesn’t distinguish between brilliant and terrible. It learns patterns from everything it sees and generates new code based on those patterns.

The result is what developers call “AI slop.” Code that might work. Code that probably doesn’t work the way you want it to. Code that introduces subtle bugs you won’t notice until production. AI can produce a function that calculates loan interest, but it might use a formula from a Stack Overflow answer that was marked incorrect in the comments below.

When good enough isn’t good enough

A development team recently used AI to generate a data validation routine for customer records. The generated code looked clean, handled the basic cases, and passed their initial tests. They deployed it. Three weeks later, they discovered it was silently corrupting records that contained certain special characters within address fields. AI had learned a pattern from examples that never anticipated international addresses with non-ASCII characters.

Finding that bug required stepping through the actual execution with a debugger, watching how the code processed different inputs, and understanding where the logic went wrong. The debugger revealed the problem in minutes. Without it, they would still be adding print statements and guessing.

Debugging skills matter more, not less

The executives who think AI eliminates the need for developer expertise have it backwards. AI makes debugging skills more critical because you’re not just debugging your own logic anymore. You’re debugging someone else’s pattern recognition system that learned from millions of examples of varying quality.

Using AI effectively for coding means understanding what the generated code actually does. You need to read it critically, test it thoroughly, and know how to trace through its execution when something goes wrong. A developer who can’t debug AI-generated code is just copying and pasting solutions they don’t understand, hoping for the best.

AI as a tool, not a magic wand

The most productive developers treat AI as a drafting tool. It can generate boilerplate content, suggest approaches, and speed up routine tasks. But those developers verify everything. They step through the generated code, assisted by a strong debugger. They understand the logic before integrating it into their systems. They catch the subtle bugs before deployment.

AI changes development workflows, but it doesn’t change the fundamental need to understand what your code does and why. Hands-down, the debugger remains your most important tool for obtaining that understanding, perhaps now more than ever.