Why Check AI Generated Code

When a developer writes code, it goes through code review. A colleague reads it, questions assumptions, catches mistakes. When an AI writes code, that review step often disappears. The code goes straight from generation to production, and nobody checks what's actually in it.

AI Code Is Not Reviewed Code

Traditional development has natural checkpoints: pull requests, pair programming, team code reviews. These processes exist because humans make mistakes, and other humans catch them.

AI-generated code bypasses these checkpoints. A developer prompts an AI, gets a response, and if it works -- it ships. The volume of AI-generated code makes manual review impractical. A single developer using AI tools can produce more code in a day than a team could review in a week.

This isn't a theoretical concern. It's the reality of how AI coding tools are used: fast generation, minimal review, rapid deployment.

Common AI Code Mistakes

AI coding assistants make specific, predictable categories of mistakes:

Security vulnerabilities. AI frequently embeds secrets directly in source code -- API keys, database passwords, JWT secrets. It uses eval() for dynamic code execution when safer alternatives exist. It generates placeholder credentials like password123 that look like they're meant to be replaced but often aren't.

Hallucinated packages. AI suggests importing packages that don't exist on npm. These aren't typos -- they're plausible-sounding package names that the AI generates from its training data. A developer who installs them gets an error at best, or a malicious typosquatting package at worst.

Dead code patterns. Empty function bodies, catch blocks that do nothing, parameters that are declared but never used. AI generates these as scaffolding but never fills them in, leaving code that looks complete but isn't.

Fake implementations. Functions marked async that never await anything. Error handlers that log but don't recover. Configuration options that reference nonexistent settings. The code structure is correct, but the substance is missing.

Research Backs This Up

Studies show that AI-generated code has measurably higher cognitive complexity than human-written code -- 39% higher on average. Higher complexity means more potential for bugs, harder maintenance, and greater risk in production.

This isn't because AI is bad at coding. It's because AI optimizes for generating plausible code, not maintainable code. It doesn't refactor. It doesn't simplify. It produces whatever pattern matches its training data, even when a simpler approach would work better.

Automated Checking at Scale

Manual code review doesn't scale to the volume of AI-generated code. But automated checking does.

Static analysis can scan thousands of files in seconds, applying consistent rules across every line of code. It doesn't get fatigued. It doesn't miss patterns because it's reviewing the tenth file in a row. It checks every function, every import, every configuration value.

LintMyAI applies 28 rules specifically designed for AI-generated code patterns:

npx lintmyai .

It runs in CI pipelines, catches issues before they reach production, and provides clear explanations of what's wrong and why it matters.

The Cost of Not Checking

Every unchecked AI-generated file is a potential security incident, a future debugging session, or a piece of technical debt that compounds over time. The cost of checking is one command. The cost of not checking is unpredictable.

Automated code checking isn't about distrusting AI -- it's about applying the same quality standards to AI-generated code that we've always applied to human-written code. Code review exists for a reason. When AI writes the code, automated tools need to fill the review gap.

Get started with LintMyAI and add automated AI code checking to your workflow in under 60 seconds.