Who Actually Checks the AI-Generated Code in Your Project?

Every developer I know is using AI tools to write code faster now, myself included. That's not the interesting bit. The interesting bit is what happens after the code gets written. Someone has to read it, understand why it works, and decide whether it's actually right for the job. That step is where things quietly fall apart on a lot of projects.

If you're paying someone to build software for your business, whether that's a freelancer, an agency, or an in-house hire, it's worth asking a blunt question: when AI writes a chunk of the code, who checks it, and what does "checking it" actually involve?

Why this is suddenly a real problem

A few years ago, writing code was the slow part. Now it's often the fast part. A developer can prompt their way to a working feature in minutes. The slow part has shifted to reviewing that output properly: understanding the edge cases it missed, spotting the security assumption it got wrong, noticing that it solved a slightly different problem than the one you actually have.

The trouble is, a lot of teams haven't adjusted their process to match. They've sped up the writing without speeding up (or even keeping) the checking. Code gets generated, it runs, it looks plausible, and it ships. Nobody was ever assigned to be sceptical of it.

"It runs" is not the same as "it's right"

AI-generated code is very good at looking finished. It compiles, the happy path works, the demo goes fine. What it's less good at is telling you when it's made a judgement call on your behalf without flagging it. Common ones I see when I'm brought in to fix or extend someone else's codebase:

• Validation that checks the shape of the data but not the business rules behind it (a discount code that can go negative, a date range that can end before it starts).
• Database queries that work fine with ten test records and fall over with ten thousand real ones.
• Error handling that catches the exception and swallows it silently, so the failure just goes quiet instead of getting fixed.
• Security shortcuts, like trusting a value from the browser that should be checked on the server.

None of these show up in a quick demo. They show up three months later, usually at the worst possible time, and usually as a support ticket rather than a code review comment.

What proper review actually looks like

Reviewing AI-generated code isn't about reading every line like a proofreader. It's about asking a small set of specific questions of anything that goes into a live system:

• What was this actually asked to do, and does the code match that, not just something close to it?
• What happens when the input is missing, wrong, or hostile? Not "what should happen", what does the code do right now.
• Does this touch money, personal data, or permissions? If so, has someone who understands the consequences actually looked at it, or did it just get accepted because the tests passed?
• Would I be able to explain this code to a client in plain English if they asked why it works this way?

That last one is the test I actually use. If I can't explain a piece of generated code simply, I don't ship it as it stands. I rewrite it or rework it until I understand it well enough to be accountable for it.

The gap that opens up on bigger teams

On a larger team, code review is meant to be the safety net. In practice, when everyone is under pressure and everyone is using AI to move faster, review can turn into a rubber stamp. A junior developer reviews a senior's AI-assisted pull request and doesn't feel able to push back. A senior reviews a junior's and assumes the AI did the thinking, so they skim it. Nobody is actually testing the reviewer, because everyone assumes someone else already did.

I don't have that problem, but not because I'm special. It's because there's no handoff to hide behind. If I write it, generate it, or accept a suggestion from a tool, it's mine. Nobody else is going to catch my mistake before it reaches you, which means I can't afford to be casual about checking my own work. That's not a claim that one person is safer than a team with a proper process. It's that with one person, there's no ambiguity about whose job the checking was.

What to ask before you sign off on any AI-assisted work

Whether you're commissioning a new feature, reviewing a quote from an agency, or just trying to understand what you're paying for, these are worth asking directly:

• How much of this was AI-generated, and how was it checked before it was shown to me?
• Can you explain, in plain terms, why this particular approach was chosen over the alternatives?
• What happens to this code if the input is wrong, or someone tries to abuse it?
• Who is accountable if something breaks in three months, and will that be the same person who understands why it was built this way?

If the answers are vague, that's useful information. It tells you the checking step got skipped, or got handed to whoever had five minutes free, rather than to someone who actually owns the outcome.

Where this leaves things

AI tools have made writing code faster for everyone, me included, and that's a genuine benefit. But speed at the writing stage only helps if the checking stage keeps up with it. That checking has to be done by someone who understands the business problem, not just the code, and who's still around to be asked about it later.

If you're weighing up a piece of custom software, an integration, or some extra development capacity for an existing project, it's worth asking who that person will be before the work starts, not after something's gone wrong. I'm happy to talk through what that looks like for your project specifically, no obligation, just a direct conversation about how the work would actually get built and checked.