I think the term "vibe coding" has no universally accepted definition but if you mean "coding without any prior coding experience" then the answer is no. Any leadership that know even the slightest about software know that AI tooling is on a spectrum. Vibe coding is at one end and tools like Claude Code and Cursor are on the other.
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"?
> …but the engineering team struggles to adopt them…
I have no way of knowing the answer to this question I wonder about: Does leadership consider AI adoption as being synonymous with vibe coding?
My knowledge of vibe coding is informed more or less completely by this one video I discovered this past summer [1]
The approach to coding I'm seeing in that video is impressive! No question! But it's also what I call the epitome of tech debt multiplying.
If vibe coding is what leadership expects the engineering team to be adopting, then there's a saying that goes, "Be careful what you ask for…"
[1] https://www.youtube.com/live/Pv5DU1nwp6U?si=4ic-HQvHWmVTyFIA
I think the term "vibe coding" has no universally accepted definition but if you mean "coding without any prior coding experience" then the answer is no. Any leadership that know even the slightest about software know that AI tooling is on a spectrum. Vibe coding is at one end and tools like Claude Code and Cursor are on the other.
Author here.
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"?