Don't Let Vibe Coding Become Technical Debt: How Developers Can Balance Speed and Responsibility

Everyone is using AI to write code now: a single line of natural language, and the model gives you a bunch of working implementations. That's what's called vibe coding.

It's genuinely comfortable, but once a project needs to go into production and someone has to take long-term responsibility for the system, the tension between vibe coding and engineering responsibility starts to surface.

1. The Real Benefits of Vibe Coding

Before we jump to criticism, it does have clear value:

• Faster start: no need to flip through documentation for half a day or start from an empty file. AI can help you generate pages, APIs, and sample code, making it great for prototyping and validating ideas.

• More direct learning: want to try a library, a framework, or a new API? Throw a few tasks at AI and see how it writes — it gives you a better feel than just reading docs.

• Efficient for small tools: internal scripts, simple automation, one-off pages — getting them done with vibe coding is highly cost-effective.

The problem isn't vibe itself; it's that many people conflate “it works” with “it's accountable”.

2. What Matters from an Engineering Perspective

Once you stand from an engineering perspective, your focus shifts:

• Systematic thinking: how modules are split, where boundaries lie, how data flows, and whether there's a way to investigate when something goes wrong.

• Maintainability: six months later when requirements change, can you avoid rewriting half of it? When a new developer joins, can they understand your current structure?

• Stability and reliability: can the system hold up under high concurrency, dirty data, or weird user behavior?

• Accountability: who is ultimately responsible for the system's delivery and online behavior — not just "the AI didn't write it well"?

These questions essentially ask: Are you the owner of the system, or just an AI user?

3. Speed vs. Maintainability: Where Does the Conflict Lie?

When we use vibe coding for business logic, several typical conflicts keep recurring:

• In the name of speed, structures are compressed into a bunch of "barely working" implementations: full of temporary checks, copy-paste, long functions that no one wants to touch.

• To meet deadlines, testing and monitoring become TODOs that "we'll get back to later" — only to discover edge cases were never considered after going live.

• For convenience in tweaking prompts, a lot of logic is hidden inside prompts, leaving code just as an output. This makes it hard for the team to have a unified understanding and review decisions.

There's one more thing many people don't realize at first: don't let AI change too much code at once.

In an ideal scenario, AI helps you fill in a function or optimize a piece of logic. But in practice, it can easily touch several files at once: routes, data structures, call chains, tests — all changed together.

If you habitually click "Accept All Changes," after a few rounds, your brain can't keep up with the evolution of the entire codebase — you only know "it works now," but not "what exactly changed."

The result is a role reversal:

• You slowly shift from being a "programmer" to a "tester and approver," only caring whether errors disappear and pages display correctly.

• AI becomes the real "code writer," freely playing with structure and naming in the codebase, while you neither have time nor the desire to dig deep.

Over the long run, you may lack the energy and motivation to care about whether the code is reasonable, whether the structure is rotten, or how deep the technical debt has become.

That's why, from an engineering perspective, you need to deliberately control the scope of AI's changes and set aside time to understand and review before each merge. Otherwise, you're just fully delegating engineering responsibility to a "colleague" who won't be on call for you.

In other words: Speed is an immediate benefit; maintenance cost is a slowly accumulating price.

If no one deliberately pulls back the engineering perspective, the project will go all the way down the path of "feeling great today, hurting later."

4. A Simple Balancing Framework: When to Vibe, When to Engineer?

To resolve this conflict, instead of hastily labeling vibe coding, set yourself a simple set of rules.

You can judge by two axes: cost of mistakes and time horizon.

Low cost of mistakes, short time horizon

Examples: personal prototypes, internal small tools, one-off pages, low-risk UIs. In these cases, you can vibe code boldly:

• Use AI to quickly generate implementations,

• Try several approaches,

• Add minimal engineering guardrails, like basic exception handling.

The focus here: fast validation, fast learning. Low cost of mistakes means engineering doesn't need to be heavy.

High cost of mistakes, long time horizon

Examples: payments, authentication, permissions, orders, transactions, risk control, long-term maintained core business systems. In these cases, you must switch back to engineering mode:

• Clearly define boundaries and module splits

• Diligently do testing, logging, monitoring, alerts

• Consciously review and refactor AI-generated code

The focus here: system safety, sustainable evolution. You can't say "let me just vibe it and see."

Keep this practical phrase in mind:

Vibe to discover and validate ideas; engineering to carry and protect those ideas.

As soon as a project transitions from "let's try" to "long-term responsibility," your working mode should switch from Vibe Coding to engineering mode.

5. What Does Engineering Responsibility Look Like in the AI Era?

Many people think "engineering responsibility" is an abstract term, but in the AI era, it can be broken down concretely:

• Stop pursuing "every line hand-written by me" and instead pursue "I have the final say on critical paths and key decisions": know where the system's core logic lies, and know where to look when something goes wrong.

• Treat AI as a "super intern," not a "scapegoat colleague": let it generate code, write tests, and give suggestions, but the final structure and behavior to merge must go through your understanding and judgment.

• Deliberately train your code aesthetics and system intuition: recognize what is technical debt and what is good structure, not just check if syntax is correct.

• Invest part of your energy into business understanding, architecture planning, and quality guardianship, rather than just staying at the level of "the person who writes code." These are the parts AI can't temporarily replace.

To compress into one sentence: AI can help you write code quickly, but it cannot bear engineering responsibility for you. You can enjoy speed, no problem, but you have to decide for yourself: which parts just need to be fast, and which parts must be solid.

6. A Checklist Developers Can Use Right Away

Finally, here's a handy daily checklist you can run through when using AI to write code:

• Is the cost of failure for this feature high? If so, don't rely solely on vibe.

• Will someone still maintain this logic in six months? If yes, start organizing it the engineering way from today.

• If the system goes down in production, can I explain its behavior? If not, it means I don't know it well enough yet.

• Am I using AI this time to quickly try something, or to go to production? The former can have more vibe, the latter must have more engineering.

With such self-checks, you won't find yourself — while "writing happily" — pushing yourself and your team into a situation where "maintenance hurts."