A developer might spend hours implementing a feature, fixing edge cases, writing tests, and preparing a pull request.
Today, tools such as Claude Code, GitHub Copilot, Cursor, Codex, and other coding agents can generate surprisingly large changes in minutes.
What happens when we can generate code faster than we can understand, review, and verify it?
I think this is becoming one of the biggest bottlenecks in AI-assisted software development.
GitHub has been expanding Copilot Code Review so AI can participate more deeply in the review process.
Recent updates allow Copilot Code Review to use additional tools for analysis, including running builds, tests, and targeted checks.
GitHub has also experimented with an ensemble of specialized agents during code review.
In its reported experiments, GitHub said the approach increased the rate at which developers addressed high-severity review comments by 47%.
The human developer can slowly become the least informed person in the workflow.
You might approve a pull request containing code you didn't write, tests you didn't design, and a review generated by another AI system.
If the original requirement was misunderstood, the implementation and tests can agree with each other while still being wrong.
AI can verify that code matches its interpretation without proving that its interpretation matches reality.
Traditional technical debt often comes from developers knowingly taking shortcuts.
Who understands the system deeply enough to debug the interaction between those pieces?
Sonar describes it as the rework and risk created when AI-generated code arrives faster than teams can properly verify, understand, and maintain it.
tickets completed pull requests merged features shipped lines changed deployment frequency
That becomes increasingly difficult when agents can generate huge changes quickly.