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AI can produce code that looks finished and still leaves problems a reviewer must find. In my month of letting AI write code, the junior developers caught issues I missed. That experience is a personal account, not evidence that junior developers generally review better than senior ones. The broader lesson is about delegation: generated code needs someone who understands the surrounding system to inspect, test, and explain it.
What happened when I delegated the code
I asked AI to write code over the course of a month, then reviewed the results myself. I expected my experience to make that review straightforward. It didn’t: junior developers on the team noticed problems I had overlooked.
The important point is not that the code was obviously broken. Plausible-looking output can pass a quick read while still failing to fit the application’s assumptions or behavior. I was too ready to treat code that looked coherent as code that was correct.
This account describes what happened in my work; it does not establish how often AI-generated code fails, or that junior developers are better reviewers as a group. The studies available on AI-assisted development examine different tasks and outcomes, not the defects in this particular experience.
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Why a confident-looking answer still needs review
Code generation can reduce the effort of producing a first draft, but a draft is not a verdict. The tool does not take responsibility for whether a change fits the project’s architecture, handles edge cases, preserves existing behavior, or remains understandable to the team. Those questions require context beyond the generated snippet.
In Stack Overflow’s 2025 developer survey, 46% of respondents said they distrusted AI output accuracy, compared with 33% who trusted it. Respondents could select frustrations: 66% cited answers that were almost right, and 45% cited time-consuming debugging of AI-generated code. These are reported perceptions and experiences, not measurements of the defect rate in generated code. The survey drew more than 49,000 responses from 177 countries; 84% said they used or planned to use AI tools in development. Stack Overflow’s 2025 survey results show both adoption and continuing friction.
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That friction helps explain why another person can matter. In the same survey, 75% said they would ask another person when they did not trust an AI answer, and 61.3% said they wanted to fully understand their code. Those are survey respondents’ answers, not a universal workflow rule. But they point to a practical standard: accept help with drafting without surrendering the responsibility to understand the result.
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It depends on the task, the developer, the project, and the tool. A randomized METR trial offers a useful warning against assuming that faster code generation means faster work overall. Sixteen experienced open-source developers completed 246 tasks in mature projects they had worked on for an average of about five years. With early-2025 tools, including Cursor Pro and Claude 3.5/3.7 Sonnet, the developers took 19% longer on average when allowed to use AI. That is a result for this sample and setting, and it measures task completion time—not defect frequency or productivity for all developers. METR’s study summary describes the trial and its scope.
METR reports that participants expected AI to reduce completion time even though the measured result went the other way. The contrast is a reminder that time spent prompting, checking, correcting, and integrating output counts too. The trial used early-2025 tools; it should not be treated as a benchmark for every later assistant or every kind of software work.
There is also evidence pointing to benefits under different conditions. In a controlled study of a bounded task—writing API endpoints for a web server—GitHub reported improved code-quality scores for participants using Copilot. That vendor-published result concerns a particular task and outcome; it does not establish that the resulting code was universally maintainable, secure, or correct in a larger application. It does not contradict METR’s time result, because the studies measured different things in different settings. GitHub’s report on its code-quality study gives its task and findings.
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What the evidence says about security and workflow
Security claims need the same care as productivity claims. A 2025 ACM/SIGAPP paper on developers’ perceptions of generative AI and security reports that about a quarter of surveyed developers expressed confidence in AI-generated code. It also reports that experienced developers perceived a decline in secure-coding proficiency when using generative AI. This is perception evidence, not a direct audit establishing a universal vulnerability rate for AI-generated code. The ACM/SIGAPP paper, “My Code Is Less Secure with Gen AI,” describes the survey.
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How to review code written by AI
A review should test whether the change is right for the project, not merely whether the code is readable. The person accepting it should be able to explain what it does, why it belongs, and how its behavior has been checked.
- Check the change against the request. Read the diff and identify what changed, what did not, and whether the implementation adds behavior that was never requested.
- Trace the surrounding system. Inspect the callers, data flow, error handling, permissions, and conventions the code interacts with. A locally plausible change can still violate an application-level assumption.
- Test expected behavior and boundaries. Run the relevant existing tests, add tests for the intended behavior, and consider failure cases and edge conditions. Passing tests support a claim about the behavior they cover; they do not prove the whole change is correct.
- Check maintainability and security in context. Look for unnecessary complexity, unsafe handling of inputs or secrets, and assumptions that could expose data or weaken access controls. Escalate specialist concerns rather than treating a clean-looking diff as proof of safety.
- Ask the author to explain it. If the developer cannot explain the code and its trade-offs, the team has not yet established understanding. Revise it, investigate it, or decline to merge it.
This is not a case for assigning review by seniority alone. A reviewer who knows the affected code path, asks clear questions, and verifies behavior can catch issues regardless of title. In my case, the junior developers did that work—and my own review had not.
What I changed after the month
I stopped treating AI output as a completed task. It can still be useful for drafting or exploring an implementation, but I account for the cost of checking and integrating it instead of assuming that generation itself saves time. The relevant question is whether the tool helped with this task after review, not whether it produced code quickly.
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I also made review a condition of delegation rather than a final formality. If I cannot explain a generated change, trace its effects, and show what has been tested, I should not accept it just because it looks polished. The month’s most useful correction came from people who were willing to question the code—including the code I had already approved in my own head.
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