Finally, you can ask your leadership to give you time to pay back technical debt. And AI adoption is the reason why.
I have been seeing a pattern where leadership buys Copilot/Cursor licenses and expects immediate 10x gains, but the engineering team struggles to adopt them.
The thesis of this article is that AI acts as a throughput multiplier. If your codebase is clean (SOLID, DRY, explicit interfaces), AI accelerates you. If your codebase is spaghetti or relies on "tribal knowledge" (implicit context), AI just generates bugs faster than you can fix them.
I argue that "clean code" is no longer an aesthetic preference but a hard requirement for AI enablement, because AI agents effectively have no long-term memory of your project's history.
Curious if others are seeing this friction between "AI expectations" and "Legacy Code reality"?
yshrestha•1h ago
Finally, you can ask your leadership to give you time to pay back technical debt. And AI adoption is the reason why.
I have been seeing a pattern where leadership buys Copilot/Cursor licenses and expects immediate 10x gains, but the engineering team struggles to adopt them.
The thesis of this article is that AI acts as a throughput multiplier. If your codebase is clean (SOLID, DRY, explicit interfaces), AI accelerates you. If your codebase is spaghetti or relies on "tribal knowledge" (implicit context), AI just generates bugs faster than you can fix them.
I argue that "clean code" is no longer an aesthetic preference but a hard requirement for AI enablement, because AI agents effectively have no long-term memory of your project's history.
Curious if others are seeing this friction between "AI expectations" and "Legacy Code reality"?