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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteCode that passes its tests can still be unfinished if you cannot explain what it does or why its approach fits the problem. Tejas Patil’s September 2, 2026, DEV Community post, “My Code Works. An AI Wrote It. Neither of Us Knows Why,” makes that distinction as personal advice: passing checks is not the same as understanding the implementation. The useful question is not whether AI wrote the code, but whether you can take responsibility for it.
What passing tests establish—and what they do not
A passing test suite establishes that the code met the conditions those tests checked. It does not, by itself, establish that the tests cover the important cases, that the implementation is secure, or that the change fits the surrounding system. That is the gap at the heart of Patil’s argument: a working result is not automatically a finished piece of work.
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This is not a claim that every developer must read and memorize every generated line. Patil’s proposed rule is narrower and more practical: explain aloud what the code does and why the chosen approach is appropriate. He puts it this way: “If I can’t explain why the code works, I haven’t finished. I’ve just stopped.” Treat that as a useful rule of thumb, not a validated universal measure of competence.
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Before merging or shipping a change, try to give a clear account of its behavior, assumptions, and consequences. The amount of scrutiny should match the task: a small, reversible change is not the same as a production-critical or security-sensitive one.
- State the behavior. Describe what the change does from the caller’s or user’s point of view, including what happens on errors and unusual inputs.
- Trace the important path. Follow the main data flow and identify the conditions, dependencies, and side effects that determine the result. You do not need to recite every line, but you should be able to explain the parts that matter.
- Explain the design choice. Say why this approach fits the existing code and constraints. If you cannot compare it with a plausible alternative, investigate before treating it as settled.
- Check what the tests actually cover. Know which expected behaviors are exercised and which remain unchecked. A green test run is evidence about those checks, not a guarantee about everything the software might do.
- Scale review to risk. Give extra attention to authentication, permissions, data handling, money movement, destructive operations, and production-critical paths. When the consequences of an error are serious, ask for another qualified review rather than relying on your own explanation alone.
AI use and productivity are different questions
How many developers use AI tools does not tell us whether those tools make every task faster, and a productivity result from one setting should not be applied to every developer or codebase.
Stack Overflow’s 2025 Developer Survey reported that 51% of professional developer respondents used AI tools daily; 46% of developers said they did not trust AI output accuracy. These are survey findings about reported use and trust, not measurements of code quality or task speed. See the Stack Overflow 2025 AI survey results.
A different kind of evidence comes from METR’s early-2025 randomized trial. In a study involving 16 experienced developers and 246 tasks in mature open-source repositories they already knew, tasks took 19% longer with AI tools. Participants nevertheless estimated after the study that AI had made them faster. This is a narrow result for that group, repository familiarity, task set, and period—not a general estimate of AI’s effect. Read METR’s account of the 2025 study.
Keep broad claims in perspective
Patil’s post is valuable as a reflection on responsibility, but some of its numerical claims should not be treated as established facts. The post does not identify primary sources for its claims about projected AI-generated code or the share of junior developers deploying code they do not fully understand. Its claim about a sustained decline in junior developer employment also lacks a verified labor-market series in the linked material. Without clear definitions and supporting primary sources, those figures do not establish how common these outcomes are.
That qualification does not weaken the practical point. Whether AI use is common, faster, or slower in a given context, the person responsible for a change still needs an adequate basis to review it. “I can explain the behavior and the choice” is a more useful standard for an individual change than an unsourced industry-wide percentage.
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