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I let an AI coding assistant write much of my code for a month. The work often looked convincing, but the junior developers reviewing it caught problems I had missed. The month taught me that generating code is not the same as understanding whether it fits the system: someone still has to inspect the changes, test them in context, and explain why they are safe to keep.
What happened when I delegated code to AI
I went into the experiment expecting a straightforward trade: describe what I wanted, accept a plausible implementation, and spend less time writing routine code. Instead, the assistant could produce code that looked finished while leaving me with a different job: determining whether it actually belonged in the application.
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The most revealing failures were not necessarily spectacular. A change could appear reasonable when read on its own and still be wrong for the surrounding code, assumptions, or behavior. I did not always spot that mismatch on my first pass. The junior developers who reviewed the work did.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThat is the personal lesson, not evidence that junior developers are generally better reviewers than senior developers. A reviewer who knows the relevant code path, asks what a change affects, and checks the behavior can catch a plausible mistake regardless of seniority. My own review was not enough simply because I had written the prompts or approved the generated code.
Why plausible code still needs a human review
Code generation answers a local request; production code has to work within a larger system. A snippet can satisfy the prompt’s visible requirement while missing an assumption elsewhere in the application. Passing a narrow test also does not, by itself, establish that a change is maintainable, secure, or correct across the full set of use cases.
Review is therefore not just proofreading syntax. The reviewer needs to understand the intended behavior, inspect how the change interacts with existing code, and decide whether the tests cover the risks. If the person accepting a change cannot explain what it does and why it is safe, the fact that an assistant produced it is not a substitute for that understanding.
Does AI coding save developers time?
There is no single productivity result that applies to every developer, tool, and task. One useful counterexample to automatic speedup is a randomized controlled trial by METR: 16 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, available, they took 19% longer on average. That is a result about task-completion time in this particular sample and setting—not a universal slowdown, a defect rate, or a benchmark for every later tool. METR’s study summary also reports that participants expected AI to reduce completion time, illustrating how perceived usefulness and measured speed can diverge.
Other evidence measures different things and points to different outcomes. In a bounded API-endpoint task, GitHub’s vendor-published controlled study reported improved code-quality scores for participants using Copilot. That result matters, but it does not contradict METR’s timing result: the studies used different participants, tasks, tools, and outcome measures. A quality score on a bounded task is not the same as time saved in a mature repository or proof of whole-application correctness. GitHub’s report describes its study and scope.
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Together, the studies argue for a task-specific judgment rather than a blanket claim that AI either makes developers faster or fails to do so. The tool may help on one bounded task and add work on another, especially if reviewing or debugging its output takes longer than writing the change directly.
What developers report about trust and debugging
Stack Overflow’s 2025 developer survey found that 46% of respondents distrusted AI output accuracy, compared with 33% who trusted it. Respondents also cited AI answers that were almost right as a frustration (66%) and time-consuming debugging of AI-generated code (45%). These are survey responses about experience and trust, not results from an audit measuring how often generated code contains defects. The survey received more than 49,000 responses from 177 countries; its findings describe respondents, not a census of all professional developers. Stack Overflow’s survey results provide the detail.
The same organization reported that 75% of surveyed respondents would ask another person when they did not trust an AI answer, and 61.3% wanted to fully understand their code. Those answers fit the central point of my month: human review is not an optional layer to bolt on after generation. It is part of deciding whether generated work can be trusted in context. Stack Overflow’s survey announcement gives the broader survey figures.
Security concerns need careful interpretation
A 2025 ACM/SIGAPP paper, My Code Is Less Secure with Gen AI, reports developers’ perceptions of AI and secure coding. About a quarter of the surveyed developers expressed confidence in AI-generated code, and experienced developers perceived a decline in secure-coding proficiency when using generative AI. This is evidence about reported confidence and perceptions; it does not establish a universal vulnerability rate or directly prove how often generated code is insecure. The paper’s summary sets out that scope.
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That distinction matters in practice. A reviewer should treat security as a question to investigate in the actual change—not infer safety from fluent explanations, confidence scores, or the mere presence of tests. The evidence here supports vigilance, not a numerical claim about the security of every AI-generated patch.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to review code written by an assistant
My mistake was treating a plausible answer as if it were nearly a verified change. A more reliable handoff keeps the human responsible for intent and validation:
- Define the behavior first. State what should change and what must remain unchanged before asking for implementation. A clear requirement gives the author and reviewer something concrete to check.
- Inspect the complete change. Read the diff, not just the assistant’s summary. Check the touched files and the surrounding code for assumptions or side effects that the prompt may not have captured.
- Ask for an explanation, then verify it. Have the author or assistant explain important decisions and edge cases. Treat that explanation as a review aid, not proof; confirm it against the code and application behavior.
- Run relevant tests and add missing ones. Use tests that exercise the intended behavior and plausible failure cases. A passing test suite is useful evidence for what it covers, not a guarantee about untested behavior.
- Use a second reviewer when uncertainty remains. A colleague can bring knowledge of project conventions or affected code paths that the person prompting the assistant—or the assistant itself—may lack. Review quality depends on context and attention, not job title alone.
- Keep only code you can own. Before merging, make sure the responsible developer can describe the change and its risks well enough to maintain it later.
What the evidence can—and cannot—say
Developer surveys help explain how people experience AI tools, but they do not measure the defect rate of generated code. Controlled studies can measure a defined task or outcome, but their results remain tied to that setup. METR’s task-time result, GitHub’s bounded quality result, and survey responses about trust are complementary context, not interchangeable verdicts. A Microsoft Research survey of 791 developers likewise examined desired AI support and concerns about practical utility and reliability; it does not verify the bugs in my account. Microsoft Research’s study describes that survey scope.
JetBrains Research surveyed 481 programmers about activities such as feature implementation, test writing, bug triage, refactoring, and natural-language work, along with reasons developers may avoid assistants, including trust, policy, and limited project context. That provides workflow context, not proof that a specific assistant caused a specific bug. JetBrains Research’s study overview explains its focus.
The studies do not independently establish that I used a particular tool for a month or that junior developers caught the particular problems I missed. Those are claims of the first-person account, not conclusions from these studies. Their broader lesson is narrower and more useful: AI can assist with code, but speed and quality depend on the task, and generated work still needs people who can review it in its real context.
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