𝗜 𝗔𝘂𝗱𝗶𝘁𝗲𝗱 𝗠𝘆 𝗧𝗲𝗮𝗺’𝘀 𝗔𝗜 𝗖𝗼𝗱𝗲. 𝗛𝗲𝗿𝗲 𝗜𝘀 𝗪𝗵𝗮𝘁 𝗪𝗲 𝗙𝗼𝘂𝗻𝗱.
My team used AI to build code at record speed. We shipped features in one third of the time. Our velocity looked great. Our test coverage hit 91%.
Then we hit a wall.
We faced production bugs that were hard to fix. A simple refactor took four weeks instead of four days. A new hire told me the code was clean but impossible to understand.
We spent three weeks auditing the codebase. We found technical debt that no scanner could catch. The debt was architectural. It was behavioral.
AI tools solve the immediate problem in your prompt. They optimize for the local task. They do not understand the whole system. They do not know which services you plan to delete soon. They do not know about your long-term data models.
The result is code that is locally correct but globally fragile.
We found four specific patterns:
- Hidden Edge Cases AI writes code that passes the tests you give it. It is not good at writing tests for its own mistakes.
- The fix: An engineer must explain the code to a colleague without looking at it. If they cannot explain it, they cannot merge it.
- Test Coverage Theater AI generates tests that cover the code that exists. It does not write tests for how the system should actually behave.
- The fix: Every AI test suite must pass an adversarial review. A second engineer must try to break the code.
- Invisible Coupling AI adds dependencies to solve a prompt quickly. It might weave notification logic into your billing or user modules. This makes it impossible to separate services later.
- The fix: A senior engineer must approve any new dependency introduced by AI.
- Shallow Error Handling AI often writes error blocks that look complete but fail to handle real system failures.
- The fix: We use a change test. We measure how many files break when we make one small change. High impact means high coupling.
AI is not the enemy. You must treat AI like a junior engineer. You must provide guidance, set expectations, and use your judgment to override the output.
The tools are excellent at tasks. They are not excellent at the job.
Optional learning community: https://t.me/GyaanSetuAi