Correctness Isn’t Competence
AI-generated code risks becoming a liability in production unless we demand efficiency and quality.
Correctness isn’t competence. And in the age of AI-generated code, that distinction matters more than ever.
“Models achieving high correctness scores do not necessarily produce efficient algorithms or maintainable code.” — COMPASS benchmark authors
Passing test cases isn’t enough. If an LLM gives you working code that runs in exponential time or collapses into a tangle of nested logic, you’ve just inherited a problem disguised as a solution.
Why it matters
The COMPASS benchmark — built from 50 real Codility programming challenges and 393,000 human submissions — tested top models on three axes: correctness, efficiency, and code quality.
The results show what many developers already suspect: passing tests is only part of the story. O4-Mini-High was the most consistent across all three measures. Gemini 2.5 Pro produced clean code but sometimes at the cost of efficiency. Claude Opus 4 often wrote structurally sound code but stumbled badly on efficiency and consistency.
The deeper lesson is that code quality is largely independent of correctness and efficiency. A model can pass every test case while still producing code riddled with deep nesting, “bumpy” logic, and high complexity — the classic symptoms of technical debt. Poor-quality code isn’t just ugly; it creates real costs in maintenance, onboarding, and debugging that compound over time.
In production, that can mean code that crawls under load, outages when edge cases hit, or months of costly refactoring just to make the codebase sustainable.
Takeaway
We need to stop treating LLM correctness as competence. AI-generated code demands more scrutiny, not less. If organizations push unexamined model outputs into production, they’re not just accelerating delivery, they’re accelerating liability.
There’s now a market for cleaning up the inefficiency and mess left behind.
I’ve even seen it firsthand: a local coding-shop entrepreneur told me they’re launching a practice dedicated to repairing generated code. There’s now a market for cleaning up the inefficiency and mess left behind — a market created by AI itself.
The measure of readiness isn’t "does it run once?" It’s "does it scale, can we maintain it, and will it last?"