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AI

How do I know the code my AI assistant wrote is actually correct?

85

Opportunity

LLMs produce code that passes unit tests but satisfies no formal properties. A model can construct tests that pass by construction, not because the logic is right. Formal verification tools exist but require writing specifications in theorem-prover syntax, which almost no working developer does. Recent benchmarks show that frontier models achieve only 3.2 percent success on end-to-end verifiable code generation, meaning the gap between plausible-looking code and proven-correct code is nearly entirely open. Teams shipping AI-written code into production are making a bet on test coverage that the models themselves can game.

Why it matters

Automated correctness guarantees for AI-generated code are what turn coding assistants from speed tools into reliability tools.

How I score the opportunity

The Opportunity Score is my own read, not a measurement: how much it hurts, how often it bites, and how little exists to solve it today. Higher means I think it is more worth building.

Severity8/10

How much pain it causes when it shows up.

Frequency9/10

How often people actually run into it.

Whitespace8/10

How little good tooling exists for it today.

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