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Vibe Coding: Useful Shortcut or Technical Debt Generator?

Recently I have been thinking a lot about a pattern I see in my own workflow and across engineering teams: vibe coding.

It feels magical; tell an AI what you want, get runnable code instantly. But as with any abstraction shift, it comes with a set of risks we should acknowledge.

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What Is “Vibe Coding”?

Vibe coding is when you skip writing code directly and instead describe what you want to an LLM, which then generates the code for you.

The emphasis is on outcome rather than craftsmanship. It’s the same trajectory we’ve seen before, like accountants going from calculators to Excel. Work gets faster, but the skill shifts to understanding the structure behind the automation.

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Where Vibe Coding Helps

I’m not anti–vibe coding. It’s incredibly useful in the right contexts.

One-off or Throwaway Tasks

Sometimes it’s not worth building a full, well-structured project:

• Quick data cleanup
• Ad-hoc CSV transforms
• Script that runs once and never again

Rapid Simple Web Apps

I’ve used vibe coding to spin up:

• My wedding invitation app
• A blog prototype

For quick prototypes where speed matters more than code quality, AI feels like a superpower.

Lightweight Data Cleaning

For one-time HubSpot cleanup and upload tasks, it saved hours. I didn’t need perfect architecture, just correct output.

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Where It Starts to Hurt

The problems begin when vibe-coded output becomes part of something that will survive longer than a sprint.

Here’s what I worry about:

1. Inconsistent Structure

LLMs don’t care about your conventions. You can end up with five styles of naming, mixed patterns, and accidental architecture that’s painful to extend.

2. Missing Safety and Quality

Vibe-coded scripts usually lack:

• tests
• error handling
• type safety
• clear boundaries
• validation

Great for demos, dangerous for systems.

4. Code You Can’t Debug Later

If you didn’t design it, it’s harder to fix. A “quick script” can evolve into a critical pipeline that no one fully understands.

5. Overconfidence

The biggest trap is thinking “it ran once, so it’s correct.” In data engineering especially, silent failures are more damaging than loud ones.

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A Better Balance

The lesson isn’t “don’t use AI.” It’s used intentionally.

• Use vibe coding for speed when the code is temporary.
• Use it as a collaborator (helpers, regex, boilerplate) on long-lived projects.
• Don’t let generated code become production infrastructure without human design behind it.

Just like Excel didn’t make accountants obsolete—vibe coding doesn’t replace engineering. It shifts the skill from typing code to verifying, reasoning about, and maintaining what’s generated.