How to Detect AI Code Without Using Any Tools (2025 Edition)

Tools like Vibe Code Detector are great for instant analysis. But what happens when you're in a code review meeting? Or reading a pull request on your phone? Or interviewing a candidate who might be reciting ChatGPT output?

You need to develop your "AI Radar."

In this comprehensive guide, we'll teach you the manual heuristics—the mental shortcuts—that seasoned engineers use to spot machine-generated code instantly. No plugins required.

1. Low Naming Entropy

"Entropy" is a measure of randomness or surprise. Human naming has high entropy. We use domain slang, abbreviations, and sometimes weirdly specific terms based on the team's history.

AI naming has zero entropy. It always picks the most statistically probable name.

2. The Comment "Echo"

Humans write comments to explain why. AI writes comments to explain what. Because AI doesn't truly understand the "why" (the business context), it can only describe the syntax.

We call this the Echo Effect: the comment just echoes the code below it.

// Filter the users by age
const filteredUsers = users.filter(
  user => user.age > 18
);

// Sort the users by name
filteredUsers.sort((a, b) => 
  a.name.localeCompare(b.name)
);

// Legal requirement: 18+ only
const adults = users.filter(u => u.age > 18);

// Alphabetical for the dropdown UI
adults.sort((a, b) => 
  a.name.localeCompare(b.name)
);

3. Repetitive Logic Structures

AI models are completion engines. Once they start a pattern, they stick to it religiously. This leads to code that looks "copy-pasted" even if it wasn't.

Look for Structural Rhythm. If every function in a file has the exact same shape (Validate -> Process -> Return), it's a strong signal. Humans are messy; we inline things, we skip validation for internal helpers, we mix styles.

4. Overly-Perfect Consistency

This sounds counter-intuitive. Isn't consistency good? Yes, but inhuman consistency is suspicious.

• Spacing: Every object property is perfectly aligned.
• Ordering: Imports are perfectly alphabetical (without a linter).
• Completeness: Every single case in a switch statement is handled, even the impossible ones.

If the code looks like it was formatted by Prettier before it was even written, it's AI.

5. Common ChatGPT Phrases

LLMs have "verbal tics" that leak into their documentation and comments. If you see these phrases, raise a flag:

6. The "Happy Path" Bias

AI is optimistic. It writes code for the scenario where everything works. It often forgets:

• Network timeouts.
• Malformed JSON responses.
• Race conditions.
• Memory leaks in `useEffect`.

If the code handles the success case perfectly but has a generic `catch (e) { console.log(e) }` at the end, it's likely machine-generated.

Conclusion: Trust Your Gut

The best detector is your own intuition. If the code feels "soulless," "flat," or "tutorial-like," it probably is.

These manual heuristics are great for spot-checking. But for a comprehensive analysis of an entire repo or a large snippet, you need something faster.