Confidently Wrong — When the AI Writes Plausible Nonsense

Crashing code is a gift. It tells you something is wrong. The dangerous bug is the one where everything runs fine and the answer is wrong. AI is exceptionally good at producing this class of bug because it optimizes for plausibility. Plausibility is not correctness.

The five disguises of plausible nonsense

Why AI produces these specifically

• Loops, conditionals, and aggregations are the highest-density code patterns in training data — many subtly wrong examples exist
• The model copies idioms from one language into another (Python's `len() - 1` confusion came from C-style loops)
• It defaults to the most common pattern, not the most correct one for your context
• Edge cases (empty inputs, single-element arrays) appear less in training than the common case

The three-input verification rule

For any nontrivial AI-generated function, run it on three inputs before trusting it: the happy path, the edge case (empty/null/zero), and a weird case (wrong type, very large, unicode). If you cannot quickly construct three inputs, the function is too vague to test and probably wrong somewhere.

# AI gave you this function for computing percentiles def percentile(values, p): values = sorted(values) k = (len(values) - 1) * p / 100 return values[int(k)] # Three-input verification: print(percentile([1, 2, 3, 4, 5], 50)) # 3 — looks right print(percentile([1, 2, 3, 4, 5], 100)) # 5 — looks right print(percentile([], 50)) # IndexError — broken # The model never tested empty input. Almost no AI-written # function does on the first pass. This is the most common bug class.

Differential testing — your secret weapon

If you don't trust the AI-generated implementation, write a slower obviously-correct version and compare. Run both on a hundred random inputs. If they ever disagree, the AI version is wrong. This catches off-by-ones and edge-case omissions almost every time.

import random def percentile_oracle(values, p): """Slow, obviously correct, used only as ground truth.""" values = sorted(values) n = len(values) if n == 0: return None idx = max(0, min(n - 1, int(round(p / 100 * (n - 1))))) return values[idx] for _ in range(1000): vals = [random.random() for _ in range(random.randint(1, 50))] p = random.uniform(0, 100) if percentile(vals, p) != percentile_oracle(vals, p): print("DISAGREE:", vals, p) break

Ask the AI to attack itself

# After the AI writes a function, immediately: "Now act as a hostile reviewer. Find three inputs where this function behaves wrong, dangerously, or surprisingly. For each, show the input, the actual output, and the expected output." # About 60% of the time, the model finds its own bug. # The other 40%, the bugs it lists ARE bugs but it didn't realize it wrote them.

“The bug that compiles is worse than the bug that crashes.”

The big idea: AI optimizes for code that looks right, not code that is right. Build a habit of three-input verification, adversarial self-review, and differential testing for anything important. The compiled-and-running bug is the one that ships, and the one that hurts most.