Most software engineers think AI coding makes them faster.

They’re wrong…

AI coding increases output.

But it also increases issues…

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According to CodeRabbit report, AI co-authored pull requests created 1.7x more issues than human-only pull requests.

Yet AI-generated code often looks correct at a glance.

Why?

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Because AI optimizes for surface-level correctness, not deep project context.

So AI coding is neither “good” nor “bad”.

It ‘amplifies’ patterns (including the wrong ones).

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Here’s what engineers should focus on with AI coding (according to CodeRabbit report):

1 Logic and correctness

↳ Logic and correctness issues were 75% more common in AI co-authored PRs.

↳ Algorithm and business logic errors appeared 2.25x more often.

↳ Error and exception-handling gaps were 2x higher.

↳ Null-pointer risks, misconfigurations, dependency ordering, and concurrency mistakes all showed large increases.

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2 Code quality and maintainability

↳ Readability issues were over 3x higher in AI PRs.

↳ Formatting problems appeared 2.66x more often.

↳ Naming inconsistencies were nearly 2x higher.

↳ Unused or redundant code increased as well.

These issues don’t always break production immediately.

But they slow reviews and compound technical debt.

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3 Security risks

↳ Security findings were about 1.5x higher overall in AI PRs.

↳ Improper password handling appeared 2x more often.

↳ Insecure references, injection risks, and insecure deserialization also increased.

None of these are new vulnerabilities.

They just appear more frequently with AI assistance.

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4 Performance inefficiencies

↳ Performance issues were rare.

↳ But excessive I/O operations were 8x more common in AI-authored PRs.

This shows AI’s tendency to favor clarity over efficiency unless told otherwise.

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5 Review workload and variance

↳ At the 90th percentile, AI PRs had 2x more issues than human PRs.

↳ This creates “busy” reviews that slow pipelines and raise defect risk.

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So how do you scale safely with AI coding?

You don’t remove reviews… Instead you strengthen them.

↳ Provide upfront project context and constraints.

↳ Enforce formatting, naming, and structure with CI policies.

↳ Add safety rails for error handling, nullability, and control flow.

↳ Codify security defaults instead of relying on AI suggestions.

↳ Use AI-aware code reviews to catch AI-specific failure modes.

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The teams that benefit most from AI aren’t writing more code.

They’re catching the right issues earlier.

How do you achieve quality with AI coding?

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💾 Save this for later and restack to help others become good at AI coding.