Why you shouldn't rely on AI alone to fix broken code (and when you still need a developer)

Picture the scenario: it's a Tuesday evening, your online shop or client site has been throwing a PHP error since that afternoon, and you're trying to fix it while keeping on top of support emails. You're not a developer, but you're not helpless either. You've patched things before by following Stack Overflow answers, and when AI tools came along you started using those instead because they actually explain what's going wrong rather than just showing you the fix. You paste in the error message. You get back a clear explanation and a three-line code snippet. You try it, and the error disappears.

That works well enough that you file the approach away for next time.

A few weeks later, a different issue surfaces on a different page, and the same routine gets the same result. A month after that, something trickier shows up: a 500 error that wasn't there yesterday. Another paste, another fix, another apparent success. By this point you've probably saved yourself a few hundred pounds and several rounds of email back-and-forth with a developer, and AI has earned real trust.

Then, two months in, your checkout stops sending order confirmation emails. There's no error on screen, nothing obvious in the logs, just customers contacting support to ask where their confirmation is. When you eventually bring in a developer, they find a codebase that's been quietly accumulating three separate AI-assisted patches, each one locally sensible, but together they've broken a dependency that none of the fixes had any reason to account for.

This is what using AI to fix code without fully understanding it actually looks like in practice: not a dramatic failure on the first attempt, but a gradual erosion of coherence that's hard to notice until it's already expensive. AI is a powerful tool, but like any tool, using it without understanding what it's doing can cause more damage than the original problem.

AI doesn't know when it's wrong

The thing that makes this dynamic so hard to catch is that AI tools don't signal uncertainty the way a human expert would. A developer who's worth hiring will sometimes look at a problem and say they want to check the logs before touching anything, because they've seen enough cases where the obvious fix turned out to be irrelevant once the actual cause was clear. That habit of pausing is a professional instinct, and it's one of the things you're paying for when you hire someone experienced.

An LLM generates the most plausible-sounding response to your prompt, and it delivers that response in the same calm, authoritative tone regardless of whether it's correct, partially correct, or completely fabricated. If you can't independently verify what it's telling you, there's genuinely no way to distinguish between a well-reasoned answer and a confident hallucination, because they look identical from the outside.

A simple example that comes up often: ask an AI tool to fix a deprecated function in a Laravel 8 application, and it will frequently suggest a replacement that exists in Laravel 10. The suggestion looks correct in the abstract, it references real framework methods, and it's presented without any caveat about version compatibility. It just happens to not exist in the version you're running, and you won't find that out until the fix is already in production.

The wider pattern is reflected in how developers themselves have started to feel about these tools. In 2026, only 29% of developers say they trust AI tool output, down from over 70% in 2023, and 45% say debugging AI-generated code takes them longer than writing the equivalent from scratch. These are people who use AI every day and have a precise sense of where it helps and where it creates more work than it saves.

The fix that hides the real problem

When you submit an error to an AI tool, it works with exactly what you've given it: the error message, perhaps the function it came from, and whatever surrounding code you've included. It has no access to your deployment history, your server configuration, your database schema, or the context that explains why the code was structured a particular way in the first place.

This means it pattern-matches against what's visible, treating an error of type X as probably having cause Y and therefore needing fix Z. For isolated, self-contained bugs this produces a reasonable answer often enough to be useful, but for the kind of bugs that actually bring sites down or cause silent failures, the pattern rarely holds.

Consider one of the most common PHP errors: Call to undefined function. An AI tool will typically suggest checking your autoloader, installing the relevant extension, or defining the function yourself, and all of those are plausible responses in the abstract. What a developer does differently is ask why the function was available yesterday and isn't today, because that question leads somewhere more useful: a PHP version change on the server, a Composer update that pulled in a conflicting dependency, a deployment that missed a file. The AI fix resolves the immediate error, but the actual cause remains and will surface again in a slightly different form the next time something disturbs the same environment.

Another scenario that illustrates this well: a site begins throwing 500 errors from a long-running queue worker. The stack trace points to a specific function, AI suggests wrapping it in a try/catch block, and the visible errors stop. What hasn't stopped is the underlying memory leak that was causing the failures, which continues to consume server resources and will eventually produce something worse, just without the helpful error message this time.

The distinction is a simple one even if the implications aren't: AI optimises for making the error go away, while a good developer optimises for understanding why the error appeared.

Three small fixes that quietly break everything

The deeper problem with building a habit of AI-assisted fixes isn't any single mistake, it's the way individually reasonable changes accumulate into something incoherent over time.

A codebase is a network of relationships: routes that call controllers, controllers that query the database, background jobs that trigger emails, hooks and middleware that run on every request. When you change one part of that network without understanding how the pieces connect, you can break something that has no obvious relationship to what you touched, and the failure may not appear immediately.

AI tools work from a static snapshot of whatever you've pasted at the moment you ask. The tool has no knowledge of the fix you applied a fortnight ago, so the suggestion it makes this week may quietly contradict assumptions that fix was relying on, and the one after that may do the same thing again. The pattern that tends to emerge looks something like this: a visible error gets fixed by an AI suggestion, a new error appears somewhere else a few days later, that gets fixed too, the site looks fine for a while, and then something breaks in a way that has no clear connection to anything that was changed.

What makes this particularly difficult to untangle is that by the time the real damage becomes visible, it's often too late to simply undo the changes. The original state is gone, multiple patches have been applied in sequence, and there's no clean record of what each one was assuming about the code around it. Research puts a number on the underlying tendency: AI-generated code produces around 1.7 times as many bugs as human-written code, with the biggest gap in logic and correctness, which are exactly the categories that don't announce themselves as errors straight away but show up quietly in production over weeks.

The costly part isn't the afternoon spent on AI-assisted patches. It's the investigation that follows when the true consequences arrive.

When AI is actually the right tool

None of this is an argument against using AI for code work. It's genuinely useful, and using it well is a skill worth developing.

Where it tends to perform reliably is in contexts where you can verify the output yourself: understanding what an error message means before deciding how to respond to it, generating boilerplate code for a pattern you already understand, making sense of unfamiliar syntax in a framework you're learning, or thinking through the logic of a problem with something that can respond intelligently. In all of these cases you're using AI to move faster through work you could do yourself, which means you're also positioned to catch it when it's wrong.

The failure mode is using it for work where you can't do that: diagnosing a production issue you can't reproduce in a safe environment, touching code that handles billing, authentication, data integrity, or email delivery, working in a codebase that someone else built without a working mental model of how it fits together, or applying fixes to a live site without any way to test them first. In those situations, the confidence AI projects isn't a feature, it's the hazard.

The difference between a tool and a crutch

AI is going to keep improving, and it will keep changing how software is built and maintained in ways that are genuinely significant. That isn't in question, and treating it as the enemy isn't the point of this article.

The value of an experienced developer was never the ability to type code quickly or recall syntax from memory. It was judgment: the instinct to investigate before touching anything, the ability to recognise when an error is a symptom of something else, and an understanding of how a change made today will interact with code that's been running for three years. Those things come from having seen a lot of codebases break in a lot of ways, not from pattern-matching against training data, and they're precisely what AI doesn't have and can't replicate by getting more powerful.

Most business owners and site managers can't supply that judgment independently either, which is entirely reasonable given that their job isn't software development. The question isn't whether to use AI as part of managing a website. It's whether to use it as a substitute for the oversight that comes with actual expertise, and whether the short-term saving is worth the risk of finding out the hard way where the gaps are.