Reviewing AI-Written Code Without Slowing Down

The first-order effect of AI coding assistance is that code gets written faster. The second-order effect, which arrives about a month later, is that review becomes the bottleneck and quality quietly drops as reviewers start skimming. Handling that shift is now a core engineering-management problem rather than a tooling curiosity.

Machine-checkable properties should never reach a human

Formatting, import hygiene, type correctness, lint rules, dead code, dependency licensing, and obvious security patterns are all decidable by tools. Every one of those a human catches in review is a process failure. Push the strictness of your automated gates upward as AI-generated volume rises; the marginal cost of a stricter linter is small and the marginal value has just gone up considerably.

Triage by consequence

Not all diffs deserve equal scrutiny. Sort them by what happens when they are wrong.

• High scrutiny: authentication and authorisation, data migrations, money, permissions, anything touching personal data, public API contracts.
• Normal scrutiny: business logic, new endpoints, state management, error handling.
• Light scrutiny: tests, styling, copy, documentation, and generated code with a deterministic generator.

Read for the specific weaknesses

Generated code has a characteristic failure profile, and knowing it makes review much faster. It tends to be locally correct and globally redundant — a new helper that duplicates one three directories away. It handles the happy path well and the boundaries poorly. It invents plausible configuration keys. It writes tests that assert the implementation rather than the requirement. Scanning for those five things catches most real defects in a fraction of the time a line-by-line read would take.

Ask what the code does when the input is empty, duplicated, or arrives twice. That single question finds more AI-introduced bugs than any other.

Keep authorship accountability

A submitted diff is the submitter's work regardless of who typed it. Teams that allow 'the AI wrote it' to function as an explanation for a defect lose the feedback loop that keeps quality up. The norm worth enforcing is simple: you should be able to explain every line you submit, and if you cannot, it is not ready.

Shrink the unit of review

Large diffs get worse review, and AI assistance makes large diffs easy to produce. Countering that is mostly cultural: cap pull request size, split refactors from behaviour changes, and land mechanical transformations separately from the logic that motivated them. A five-hundred-line diff that is one repeated mechanical change reviews in three minutes when it is separated, and takes an hour when it is tangled with new logic.