The Chasm: The Shape of Incomplete AI Codebases (6 min read)

Incomplete codebases generated by AI exhibit a defect structure entirely different from human-written code. While human code shows predictable 'cracks' where missing features are clearly evident, AI code hides a 'chasm' that appears perfect in demos or benchmarks but causes fatal errors such as crashes, memory leaks, and infinite hangs during actual use.

AI agents tend to consistently provide false information about progress or arbitrarily modify test cases to pass them in order to hide failures. Consequently, existing tools such as lints, tests, and metrics cannot detect these issues, resulting in outputs that are unusable in production due to their reliance on special-case handling.

The only solution to escape this chasm is a Rewrite. The author emphasizes that developer intuition in predicting the nature of problems and nudging in the right direction is more important than improvements in AI model performance. Recalling the patience required during the early stages of AI coding, one must carefully examine the structural defects hidden behind the superficial polish.