A product leader sits in a sprint review on a Thursday afternoon. The demo is clean, the feature works, the team looks happy, and the AI-assisted code that built it shipped in half the usual time. She nods along, and a quieter question sits unanswered in the back of her mind: what is actually in that code, and would I even know how to ask?
If that question feels familiar, you are not behind. You are one of many product and engineering leaders who now approve, fund, and depend on code they will never personally read. That used to be fine; the senior engineer who wrote the code carried the judgment in their head, and you trusted the person.
AI changed the arrangement. The code still ships, but the judgment that used to come bundled with it does not always ride along.
The comfortable lie about AI-generated code
The comfortable lie is that AI-generated code is fine as long as the tests pass and the demo works. It sounds reasonable. It is also the exact assumption that gets teams in trouble.
In October 2025, the security firm Ox Security analyzed more than 300 code repositories and found that AI-generated code is not more buggy per line than human code. The problem is not quality per line. The problem is that AI reproduces the same structural shortcuts over and over, and it removes the natural slowdowns (code review, debugging, the careful second pass) that used to catch them before they reached customers.
Source: Ox Security, Army of Juniors report, October 2025: ox.security/resources/army-of-juniors-report-resource
Their name for the pattern is blunt. AI behaves like an army of talented junior developers: fast, eager, and confident, but missing the architectural judgment and security instinct that experience buys. The code looks professional; it simply carries habits that cost you later.
You do not need to read code; you need to know the failure modes
This is the same literacy gap I wrote about in the difference between having AI tools and having AI skills. Knowing what to trust is a skill, and for a leader, it shows up as knowing what to ask.
What follows is the engineer's quiet knowledge, translated: ten patterns Ox Security documented, what each costs the business, and one question to ask in your next sprint review. You will not sound like an engineer. You will sound like a leader who knows the soft spots.
The 10 AI code anti-patterns, in plain language
You do not need to ask all ten in one meeting. Pick the two or three that map to your product's risk and ask those consistently. The point is not interrogation; it is showing the team that someone is paying attention to the part that does not show up in the demo.
1. Comments Everywhere (in 90 to 100% of AI code)
AI floods code with explanatory comments. It feels helpful, but it buries the actual logic under noise, and reviewers start skimming instead of reading. The risk is quiet: real problems hide in plain sight because nobody wants to wade through the clutter.
Ask in sprint review: "When we review AI-assisted code, are we reading the logic, or skimming past the comments?"
2. By-The-Book Fixation (in 80 to 90%)
AI applies textbook patterns rigidly, even when they do not fit your situation. You get solutions shaped to a generic example instead of your real constraints, which means more code than the problem needs and slower delivery.
Ask: "Is this shaped to our problem, or to a textbook version of the problem?"
3. Over-Specification (in 80 to 90%)
AI tends to write hyper-specific, single-use code instead of reusable pieces. Every new variation then needs brand-new code, and your maintenance cost compounds quietly over months.
Ask: "If we needed this same capability elsewhere, could we reuse this, or would we rebuild it?"
4. Avoidance of Refactors (in 80 to 90%)
AI adds new working code but rarely improves the structure around it. Functionality grows; cleanup never happens. That is technical debt, the silent tax that makes every future change slower and costlier.
Ask: "When we add AI-assisted features, are we ever tidying up, or only ever piling on?"
5. Bugs Deja-Vu (in 70 to 80%)
Because AI repeats patterns it has seen, it tends to reproduce the same bug in several places at once. You fix it in one spot, and it resurfaces in three others you did not know about. Rework multiplies.
Ask: "When we fix an AI-introduced bug, do we check whether the same mistake was copied elsewhere?"
6. "Worked on My Machine" Syndrome (in 60 to 70%)
AI often writes code that runs fine in a clean setup but ignores the messy realities of production. The failure shows up later, under real load or real configuration, usually as an incident at an inconvenient hour.
Ask: "Has this been validated somewhere close to production, or only where it was written?"
7. Return of Monoliths (in 40 to 50%)
AI defaults to tightly coupled, all-in-one structures, quietly reversing years of work toward modular systems. The consequence is that a change in one corner can break another, and teams lose the ability to work on pieces independently.
Ask: "Is this making our system easier or harder to change one piece at a time?"
8. Fake Test Coverage (in 40 to 50%)
AI can write tests that lift your coverage numbers without actually checking whether the logic is right. You get a dashboard that says ninety percent covered while the real risks go untested. False confidence is worse than none, because it stops people from looking.
Ask: "Do our tests prove the behavior is correct, or just prove the code ran?"
9. Vanilla Style (in 40 to 50%)
AI often rebuilds functionality from scratch instead of using established, well-maintained libraries. That means more code you have to own, more places for security holes, and you give up the free security patches that trusted libraries receive.
Ask: "Did we rebuild something a proven library already does, and does better?"
10. Phantom Bugs (in 20 to 30%)
AI sometimes over-engineers for edge cases that will almost never happen, adding complexity and wasting resources. The cost is slower performance, higher spend, and a codebase more complicated than your product needs.
Ask: "Is this complexity solving a real problem we have, or an imaginary one?"
Common traps that make this worse
The first trap is treating the demo as proof. A working demo tells you the happy path runs. It tells you nothing about the patterns above. Ask one question about what is underneath, and you change what the team prepares for.
The second is asking all ten questions as a checklist and turning the sprint review into an audit. That kills the conversation and trains the team to get defensive. Pick the two or three that match your real risk, ask them consistently, and let the team experience it as attention rather than suspicion.
The third is assuming this is the engineers' problem to solve alone. The velocity that creates this risk was almost certainly approved at the leadership level. If your governance has not kept pace with how quickly AI is now shipping code, that gap warrants a candid conversation with your team, and it pairs naturally with knowing which governance protects you and which just slows you down.
Try this next week
Pick your next sprint review or technical conversation and choose just three of the ten questions above. I would start with Fake Test Coverage, "Worked on My Machine" Syndrome, and Vanilla Style, because those three pose the greatest security and reliability risks with the least technical explanation.
Ask them about any work that involved AI. Do not grade the answers in the room; just listen for whether they are crisp or fuzzy. Crisp answers mean the team is managing the risk. Fuzzy answers are not a failure; they are your next coaching conversation and the place to decide together who owns the review of AI output before it ships.
Once you know which patterns your team is and is not managing, you can name a clear owner for reviewing AI-assisted work. That is where real accountability starts.
If you want a structured way to build this fluency specifically for product roles, our AI for Product Owners micro-credential walks through exactly this: what to ask, what to trust, and what to redesign, using real product artifacts rather than toy examples.