Let me say the obvious thing first so there's no confusion: I use AI to write code every day, and I'm not giving it up. This isn't a cautionary tale about staying away. It's about a risk that's easy to miss precisely because the tools work so well.
The risk is this. Across a lot of companies that adopted AI coding quickly, tools are getting built that genuinely help — and that nobody on the team can actually read. Someone non-technical described what they wanted, the AI produced something that worked, and it shipped. The fact that it works is the trap.
The seductive promise
The pitch is intoxicating, and it's not entirely wrong: anyone can build software now. Describe the thing, get the thing. We've seen people with no engineering background stand up tools that solve real problems. One person we came across was on the 24th version of a tool they'd built entirely on their own machine — no version control, no backup, no second pair of eyes.
Twenty-four iterations of useful work, and one laptop failure from gone. That's not a story about a bad employee. It's a story about a capable person who was never given the surrounding structure that makes software safe to depend on.
The hidden cost
AI will finish a task by any means necessary. That's a feature when you can review the result and a liability when you can't. To get to "it works," a model will sometimes invent an approach that holds together today and quietly can't be maintained tomorrow. It doesn't flag that. It just hands you working code and moves on.
62%
of AI-generated code solutions contain a design flaw or known vulnerability, even with current models
Cloud Security Alliance, 2025
24.2%
of AI-introduced issues still survive in the codebase months later, across 304,000+ commits
Large-scale study, 2026
"AI code is technical debt by default."
That phrase is harsh, and it's directionally right. The debt accumulates in an afternoon and gets paid back over months — usually by whoever inherits the tool after the person who built it has moved on.
What a reviewer catches
An experienced engineer reviewing AI output catches the things the tool won't tell you about: the shortcut that won't survive the next change, the security hole, the assumption that only holds while the data stays small. They also do something subtler — they keep the work in a place others can find it, with version history and a path to maintain it.
The most immediate risk usually isn't the code quality. It's that the work lives on one person's machine with no version control. Fix that first, even before you fix the review process.
The takeaway for leaders
Here's the part to hold onto. The tool got cheaper. The judgment didn't. AI dramatically lowered the cost of producing software, but it didn't lower the cost of producing software you can trust, maintain, and hand off. That still takes someone who can read what the machine wrote.
So the practical rule is simple: don't hand AI to non-technical staff and walk away. Let them build — it's a real unlock — but pair every AI builder with someone who can review the output and put it somewhere safe. You keep the speed, and you don't wake up to debt you can't see.
Key Takeaways
- AI coding is a real unlock — this is about how to use it safely, not whether to.
- Studies consistently find a large share of AI-generated code ships with a flaw or vulnerability.
- The most immediate risk is often process, not code: work trapped on one machine with no version control.
- Experienced reviewers catch the shortcuts, security holes, and assumptions the tool won't surface.
- The tool got cheaper; the judgment didn't. Pair every AI builder with a reviewer.