How do I know the code my AI assistant wrote is actually correct?
機会
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.
重要な理由
Automated correctness guarantees for AI-generated code are what turn coding assistants from speed tools into reliability tools.
機会をどう評価するか
Opportunity Scoreは測定値ではなく、私自身の見解です。どれほど痛みを伴うか、どれほど頻繁に影響を与えるか、そして今日時点で解決策がいかに少ないか。スコアが高いほど、構築する価値が高いと私は考えています。
それが現れたときにどれほどの痛みをもたらすか。
実際にどれほど頻繁に人々がそれに直面するか。
今日時点で、それに対する優れたツールがいかに少ないか。