Why Is AI Code Suddenly “Hard to Debug”?
Debugging is easy when someone carries the mental model. With AI, nobody does.
Lately I keep seeing the same refrain: “AI-generated code is hard to debug.” And almost every time, it’s followed by a pitch, usually a framework, a product wrapper, or a “new process” that promises to make AI-written code safe to use.
But think about it. When a human team ships code, we don’t stop and ask: “Will we be able to debug this?” Debugging is assumed. The same people who built the feature are also able to repair it.
So why does that logic collapse when the author is AI?
The Two Camps of Solutions
Watching the AI code-gen space, I notice two camps forming.
1. The Wrappers
These teams build shiny products around AI. Guardrails, retry loops, clever UIs. They don’t actually make the AI better at debugging — they just make failure look smoother, until a human quietly steps in.
2. The Engineers
These folks take a more honest tack. They double down on engineering discipline: structured prompts (requirements), better documentation (context), and test-driven development (safety nets). They don’t pretend AI can debug itself. They admit debugging only works when there’s a shared model of how the system is supposed to behave. They work to make that model explicit so both humans and AI can use it.
What “Hard to Debug” Really Means
The phrase “AI code is hard to debug” isn’t really about debugging. It’s about the absence of a mental model.
Humans can debug their own code because they remember why it was written that way. The design tradeoffs, the shortcuts, the assumptions. All of that context lives in their heads. When something breaks, they can repair it.
AI doesn’t work that way. It generates, then forgets. When a bug shows up later, the original “author” is gone. No context, no memory, no model. That’s why debugging AI code feels uniquely brutal: there’s no one carrying the story of how the system is supposed to work.
I wrote earlier that the real bottleneck isn’t code — it’s shared understanding. The same truth shows up here. Debugging is hard not because the code is “AI-generated,” but because nobody, not even the AI, holds the mental model anymore.
Where This Leaves Us
So when someone says “AI code is hard to debug, but, our product/process makes it easier,” what they’re really admitting is that AI doesn’t carry a mental model. And without one, every bug-fix is a shot in the dark.
The wrappers try to gloss over that weakness. The engineers try to solve it with practices that externalise understanding.
Debugging has always been about more than fixing mistakes. It is about knowing how the system is supposed to behave. With AI in the loop, that knowledge has to live outside anyone’s head. It belongs in tests, in structure, and in documentation. Otherwise, it does not exist at all.
Maybe the hardest part isn’t the code at all. It’s the trust gap. We debug human code assuming purpose. We debug AI code assuming chaos. That alone changes how much patience we bring to the task.
And for those who say they can’t debug AI-written code, I have to ask: why? Have you not ever stepped into code written by someone else? Have you not fixed bugs in parts of the system you didn’t author? AI is just another “someone else.” The only difference is whether the team has made the system’s intended behaviour explicit or left it implicit.