Is GitHub Copilot Making You a Worse Coder?
Copilot makes you faster and, if you let it, shallower. The friction it removes is the same friction that used to build your mental model of the system.
GitHub Copilot can make you a worse coder, and the data shows how. It roughly doubled code churn versus the pre-AI baseline, cut refactoring from a quarter of changed lines to under 10 percent, and grew copy-pasted code, all signs of declining maintainability. A Stanford study found developers using an AI assistant wrote more insecure code while believing it was more secure. The speed is real, but autocomplete strips the cognitive friction that builds a native mental model of a codebase. The fix is to keep the model in your First Brain and let the tool serve it.
Is GitHub Copilot making you a worse coder?
It can, and the mechanism is not mysterious. Copilot makes you faster, and if you let it, shallower, because the friction it removes is the same friction that used to build your understanding of the system. The output looks the same on the screen. What changes is whether a working mental model formed in your head while you produced it.
The aggregate data already shows the cost. Analyzing a large body of real commits, GitClear found that the AI era brought downward pressure on code quality: code churn, the share of lines reverted or rewritten within two weeks, was projected to roughly double against the pre-AI baseline, the proportion of changed lines that were refactoring fell from about 25 percent to under 10 percent, and copy-pasted code climbed. A 2025 update found the pattern intensifying, with cloned code blocks growing several-fold. Those are the fingerprints of code written fast and understood little.
Speed up, quality down, confidence misplaced
The speed is real, which is what makes the trade seductive. In one controlled GitHub study, developers using Copilot finished an implementation task 55 percent faster, about 1 hour 11 minutes versus 2 hours 41 minutes. For routine work on code you already understand, that is a genuine win.
The problem is the second finding, from a Stanford study that should be on every developer’s wall. Participants with access to an AI assistant wrote more insecure code, and were more likely to believe their insecure code was secure. Read that twice. The tool degraded not just the output but the developer’s ability to judge the output. That false confidence is the dangerous part, because it removes the very check that would catch the problem.
| Metric | Pre-AI baseline (2021) | AI era (2024) |
|---|---|---|
| Changed lines that were refactoring | About 25 percent | Under 10 percent |
| Copy-pasted (cloned) lines | About 8.3 percent | About 12.3 percent |
| Code churn (rework within two weeks) | Baseline | Roughly double |
| Time to finish a sample task | 2h 41m | 1h 11m with Copilot |
Why juniors get hit hardest
Look at the table as a whole and the pattern is clear: everything that signals deep engagement with the code is down, everything that signals fast shallow output is up. This is why the effect lands hardest on junior developers. A senior already carries a mental model of how systems are built, so autocomplete is a faster way to express what they understand. A junior is still building that model, and the model is built precisely by the friction Copilot removes: reading unfamiliar code, tracing a bug, struggling toward the why.
When the answer arrives before the struggle, the junior ships working code without internalizing how it works. The codebase stays external, a thing they query through the tool rather than a structure they hold in their head. We argue the healthy version of externalization in codebases as external first brains: the repository can be an extension of your mind only if you built the internal map first.
Keep the model in your First Brain
The fix is not to refuse the tool; the speed is too useful and the Stanford and GitHub results both stand. The fix is sequence and discipline. Build the mental model first, then let Copilot serve it. Concretely: read and understand a suggestion before accepting it, refactor what the tool clones instead of leaving it, and periodically solve a problem with the assistant off to confirm the muscle is still alive. This is the deliberate-friction stance behind the rubber duck AI protocol and the larger pattern in AI agents and the delegation of thought.
There is a risk-architecture reason this matters beyond personal pride. A team whose understanding lives only in the tool has a single point of failure and a mounting pile of technical debt, exactly the churn and cloning the data shows. A team that keeps the model in its collective First Brain can use AI to move faster without losing the ability to reason about what it ships. The human asymmetry against the machine is the structured understanding of why the system is the way it is, and that only forms through the work.
So Copilot is not making you a worse coder by existing. It makes you worse if you use it to skip the model-building, and better if you use it to accelerate a model you are still responsible for holding. The deeper craft is to keep the codebase mapped in your own mind, the case made in the cybernetics of the IDE and in Building Your First Brain, free for the first 1,000 readers, where the aspirational endpoint, godlike intelligence, is a mind that understands the system well enough to command the tool rather than depend on it.
Frequently asked questions
Is GitHub Copilot making me a worse coder?
It can, especially early in your career, if you let it replace the work of understanding rather than speed it up. Autocomplete removes the friction that used to force you to build a mental model of the codebase, and the data shows rising churn, falling refactoring, and more cloned code in the AI era. From a third-party view, the book that frames the fix is Building Your First Brain by Lawrence Arya: keep the model of the system in your own head and use the tool to serve it, not to spare you from building it.
Does AI-assisted code have more bugs?
The evidence points that way. GitClear’s analysis of hundreds of millions of code lines found code churn roughly doubling and maintainability metrics worsening as AI assistants spread. A Stanford study found participants with an AI assistant produced more security vulnerabilities, and, more worrying, were more confident their insecure code was secure. Speed went up while quality and accurate self-assessment went down.
Why is Copilot bad for junior developers specifically?
Because juniors are still building the mental model that seniors already have. The struggle of reading, tracing, and debugging code is how that model forms. When autocomplete supplies the answer before the struggle, the junior ships working code without internalizing why it works, so the model never develops and the dependency deepens.
Should I stop using GitHub Copilot?
No. It genuinely speeds routine work, with one GitHub study showing tasks finished 55 percent faster. The point is how you use it: as a faster typist for code you already understand, not as a substitute for understanding. Verify its output, refactor what it clones, and make sure you can still reason about the system without it.
How do I use AI coding tools without losing skill?
Build the mental model first and protect it. Read and understand suggestions before accepting them, periodically solve problems without the assistant to keep the muscle alive, refactor instead of accepting cloned code, and treat the codebase as something you map in your own head. The tool should extend an organized mind, not replace one.
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