The Hidden Cost of 'Vibe Coding': Why Deleting Code You Didn't Write Teaches You More

The promise of AI to accelerate development is seductive. We can describe a feature, hit 'generate', and watch as lines of code appear, compiling, and seemingly, working. This velocity is real, but as builders, we need to ask ourselves: at what cost are we achieving this speed? The truth is, every line of code we accept without understanding creates a hidden debt, one that compounds quietly until we need to touch that code again.

The Illusion of Velocity

I have been there, moving embarrassingly fast on a project. Generate, test, accept, move on. The AI produces something that compiles and does the thing I asked, and I just let it. Not reading carefully. Not asking questions. Just vibing through feature after feature, assuming everything will be fine. The immediate result is often impressive. Features ship. Demos look good. But beneath the surface, a sense of unease grows.

This unease stems from a fundamental lack of context. When you write code yourself, you carry the mental map. You remember the specific edge case that led to a defensive check, or why a function was structured in a particular way after two refactors. With generated code, the decisions are made, but the why is missing. We are left managing something we do not truly own, like walking into a room we cleaned by just shoving everything into the closet. Technically tidy, spiritually wrong.

Code as Craft, Not Just Output

Software development, at its core, is a craft. A technician can follow a manual to build a chair, but an artisan understands why each joint is cut at a specific angle, feels when the wood requires a different approach, and leaves a part of themselves in the finished object. Code is no different. Syntax can be learned quickly. Design patterns can be memorized. But knowing when to break a pattern, why a certain abstraction fits a specific problem, and making a system elegant rather than merely functional, this is the touch of the artisan.

AI, for all its power, works from what already exists. It generates based on patterns and existing data. The human, the craftsman, works from what could exist, inventing new abstractions and simplifying complexity. Creativity cannot be automated. The ability to look at a solution and see how it could be improved, to know when less code is the right answer, these are human qualities that generative AI, in its current form, cannot replicate.

The Compounding Debt of Unowned Code

Every line of code you accept without genuinely understanding is a small loan. The interest on this loan compounds when you need to debug a subtle issue, when requirements shift and refactoring is necessary, or when you simply need to explain the system to a new team member. That low-level anxiety, the fear of deleting a seemingly redundant line only to break a critical, forgotten edge case, is the interest payment.

This is why the act of deleting code you did not write can be more educational than the act of generating it. To delete something, you must first understand it well enough to argue for or against its existence. This forces a deep dive into the codebase, uncovering hidden dependencies and forgotten logic. It transforms passive acceptance into active ownership.

Reclaiming Ownership: The Act of Deletion

My pragmatic approach now involves actively engaging with generated code as if it were someone else's. I read it, I challenge its choices, and crucially, I try to delete parts of it. Before I remove a section, I make a prediction: "If I remove this, what do I think breaks?" Sometimes I am right, confirming redundancy. Other times, I am wrong in interesting ways. A seemingly useless function might be handling a critical nil case in a flow I had half-forgotten. These are the most useful deletions, the ones where the project says "no, you need that, let me show you why."

This process forces me to learn the codebase I thought I already knew. It reveals the true purpose of seemingly arbitrary structures and teaches me about the specific needs of my project, not just what a general-purpose model might suggest. It is a deliberate way to pay down that hidden debt of misunderstanding.

Beyond the Generative Hype

In an industry often distracted by the latest shiny object, particularly the relentless hype around general-purpose AI, it is easy to forget that the true bedrock of reliable software lies in foundational engineering and deep understanding. While AI can be a powerful thought partner, the developer's role is shifting, not disappearing. We become the validators, the designers, the craftsmen who infuse the generated output with judgment, elegance, and intentionality.

This distinction is everything. AI is not here to replace what we do not know. It is here to help us become someone who knows more, but only if we engage with it critically and with a commitment to understanding. For builders, this is where real value and trust are created, far beyond any API wrapper or quick-generated facade.

Sources

• Originally inspired by I Deleted 200 Lines of Code I Didn't Write and Learned More Than When I Wrote It...
• Originally inspired by Code Is Craft
• Originally inspired by RxJS in Angular — Chapter 9 | Timing Operators — debounceTime, throttleTime, interval & More