The Problem With “Vibe Coding” Nobody Talks About

AI has changed how quickly we can write software.

A few years ago, building a small application meant searching documentation, writing functions, debugging errors, reading Stack Overflow answers, and spending hours figuring out why something was not working.

Today, you can describe an idea in plain English and get working code in seconds.

You can ask an AI coding tool to build a dashboard, create an API, generate a database schema, fix an error, or add authentication.

It feels almost magical.

This is where the term vibe coding comes in.

You describe what you want, accept the generated code, run the application, and keep prompting until the result looks right.

For prototypes, experiments, and learning, this can be incredibly powerful.

But there is a problem that does not get enough attention:

Code that works is not necessarily code that you understand.

And that difference becomes very expensive when your project grows.

What Is Vibe Coding?

Vibe coding is a style of software development where you rely heavily on AI to generate code based on natural-language instructions instead of manually writing and understanding every part of the implementation.

A typical workflow looks something like this:

Idea
  ↓
Prompt AI
  ↓
AI generates code
  ↓
Run application
  ↓
Something breaks
  ↓
Prompt AI again
  ↓
AI fixes it
  ↓
Repeat

This workflow can produce surprisingly good results.

You can go from an empty folder to a functioning prototype in a fraction of the traditional development time.

The problem is not the speed.

The problem is what happens when you stop being able to explain why the code works.

The First Few Hours Feel Amazing

Imagine you want to build a simple task management application.

Instead of manually creating the project structure, components, API routes, database models, and styling, you tell an AI:

Build a task management application with user authentication, task creation, filtering, and a responsive dashboard.

A few prompts later, you have something that looks impressive.

The UI works.

Tasks can be created.

The database is connected.

Authentication appears to work.

You might think:

"I just built an application in one afternoon."

And technically, you did.

But now imagine that a user reports:

"Sometimes my tasks disappear after refreshing the page."

You open the code.

There are 25 files.

Three API routes.

Several hooks.

A database layer.

Authentication middleware.

Multiple state management patterns.

And a few hundred lines of code you did not personally write.

Suddenly, the speed advantage starts disappearing.

You now have to understand a system before you can safely modify it.

That is the part of vibe coding people often underestimate.

The Real Cost Is Not Writing Code

Traditional development has an obvious cost:

Writing code takes time.

AI-assisted development changes that equation.

Writing code becomes cheaper.

But other costs become more important.

These include:

• Understanding the architecture
• Reviewing generated code
• Debugging unexpected behavior
• Testing edge cases
• Maintaining dependencies
• Understanding security implications
• Refactoring messy implementations
• Explaining the system to other developers

In other words:

AI reduces the cost of producing code, but it does not eliminate the cost of understanding software.

That distinction is extremely important.

AI Can Generate Code You Cannot Maintain

One of the biggest dangers is accepting code simply because it works.

Consider a simple example.

You ask an AI to fetch users from an API.

It generates:

async function getUsers() {
  const response = await fetch("/api/users");
  const data = await response.json();
  return data;
}

Looks fine.

Now your application grows.

You need:

• Authentication
• Error handling
• Request cancellation
• Loading states
• Caching
• Retry logic
• Pagination
• Logging

The original implementation may still technically work, but it was never designed for the complexity you eventually added.

AI can help you solve each new problem.

But if you keep adding fixes without understanding the architecture, your project can slowly become a collection of patches.

Everything works.

Nothing is clean.

That is technical debt.

The "It Works" Trap

There is a dangerous psychological effect with AI coding tools.

When the application runs successfully, you receive immediate confirmation that your approach worked.

That encourages you to move forward.

But software has many layers of correctness.

Something can:

• Run successfully
• Produce the expected UI
• Pass a basic test
• Still contain a security vulnerability
• Still have poor performance
• Still have race conditions
• Still leak sensitive information
• Still fail under unusual inputs

For example, an AI might generate authentication code that works perfectly during your testing.

But if you do not understand sessions, cookies, token storage, CSRF, authorization, and access control, you may not notice that users can access resources they should not be allowed to access.

The application works.

The architecture does not.

AI Does Not Understand Your Product Like You Do

Another overlooked problem is context.

AI can understand the instructions you give it.

But your entire product contains context that may not be written in your prompt.

Why was a particular database structure chosen?

Why does one API behave differently from another?

Why is a certain validation rule required?

Why does a particular customer need a specific workflow?

Why was a seemingly unnecessary abstraction introduced?

A developer who has worked on a project for six months has accumulated knowledge that does not exist in the codebase alone.

If AI generates most of your implementation, you can accidentally create a system where the person maintaining it understands less about it than the model that generated individual pieces of it.

That is a serious maintainability problem.

The Biggest Skill You Need Is No Longer Typing

This does not mean developers should stop using AI.

Quite the opposite.

AI is becoming one of the most useful tools available to developers.

But the valuable skill is shifting.

Instead of asking:

"How quickly can I write this function?"

Developers increasingly need to ask:

"How do I know this implementation is correct?"

That requires stronger fundamentals.

You need to understand:

• Data structures
• APIs
• Databases
• Networking
• Authentication
• Security
• Testing
• System design
• Git
• Debugging
• Performance
• Software architecture

You do not need to manually type every line.

But you should be capable of reading the important lines.

Use AI as a Multiplier, Not a Replacement

The best approach I have found is to treat AI like an extremely fast junior developer.

Give it a task.

Let it produce a solution.

Then review the solution.

Ask questions.

Test it.

Improve it.

This creates a very different workflow.

Instead of:

Prompt → Copy → Run → Repeat

Try:

Understand
   ↓
Plan
   ↓
Prompt
   ↓
Generate
   ↓
Review
   ↓
Test
   ↓
Understand
   ↓
Ship

The extra steps may feel slower.

In reality, they often make development faster because you reduce the number of problems that appear later.

A Better Vibe Coding Workflow

If you want to use AI heavily, you do not need to abandon the approach.

You need guardrails.

1. Start With Architecture

Before asking AI to generate hundreds of lines of code, define the basic architecture.

For example:

Frontend
   ↓
API Layer
   ↓
Business Logic
   ↓
Database

Know what each layer is responsible for.

Do not let the AI randomly decide your entire architecture through a series of disconnected prompts.

2. Ask for Small Changes

Instead of:

Build my entire application.

Try:

Create the authentication API endpoint.

Then:

Explain the implementation.

Then:

Add validation and error handling.

Then:

Write tests for this endpoint.

Small changes are easier to review and debug.

3. Ask AI to Explain Its Code

After generating an important component, ask:

Explain this code line by line and identify potential problems.

This turns AI from a code generator into a learning tool.

You may discover assumptions you would otherwise miss.

4. Test More Than the Happy Path

Do not test only:

User enters valid data → application works.

Also test:

• Empty input
• Invalid input
• Missing authentication
• Duplicate data
• Large inputs
• Network failures
• Unauthorized requests
• Unexpected API responses
• Concurrent requests

AI can generate these tests for you.

Use that advantage.

5. Review Security-Sensitive Code Manually

Be especially careful with:

• Authentication
• Authorization
• Payments
• File uploads
• Database queries
• Password handling
• API keys
• User permissions
• Personal data

Do not blindly trust generated code in these areas.

The Best Developers May Become Better With AI

There is an interesting future hiding inside all of this.

AI does not necessarily make experienced developers less valuable.

It can make them significantly more productive.

A developer who understands architecture can ask AI to generate an implementation quickly.

A developer who understands security can identify dangerous generated code.

A developer who understands databases can recognize inefficient queries.

A developer who understands testing can ask AI to generate meaningful test cases.

A developer who understands debugging can give AI better context.

The difference is not simply access to AI.

The difference is technical judgment.

AI gives you more output.

Your knowledge determines whether that output is useful.

When Vibe Coding Makes Perfect Sense

Vibe coding is not bad.

There are many situations where it is excellent.

Use it for:

Prototypes

You want to test an idea quickly.

Perfect use case.

Learning

You want to understand how a technology works.
https://goodoff.co/
Ask AI to build something and explain every part.

Small Personal Projects

If you are building a weekend project that does not handle sensitive information, moving quickly can be more important than creating perfect architecture.

Boilerplate

AI is excellent at repetitive code.

Let it handle the boring parts.

Experiments

Want to test an API or framework?

Generate a small prototype.

The problem begins when you treat a prototype as production-ready software simply because it works.

The Rule I Would Follow

Here is a simple rule for AI-assisted development:

If AI writes it, you should be able to explain it.

Not necessarily every line.

But you should understand the architecture, data flow, dependencies, security implications, and important decisions.

If you cannot explain how your application works without asking the same AI that built it, you have a problem.

That does not mean you failed.

It means you have reached the point where learning needs to catch up with implementation.

Vibe Coding Is Not the Enemy

The real problem is not vibe coding.

The problem is vibe coding without understanding.

AI has made software development dramatically more accessible.

Someone who struggled to write their first API can now build a functional application.

Someone with an idea can create a prototype without spending weeks learning every framework.

That is a good thing.

But accessibility should not be confused with mastery.

The ability to generate software is becoming easier.

The ability to build software that remains secure, understandable, scalable, and maintainable is still difficult.

And that is where developers should focus.

Use AI.

Generate the boilerplate.

Build prototypes quickly.

Automate repetitive work.

Ask AI to explain unfamiliar concepts.

But keep learning the fundamentals.

Because the future of programming may involve writing less code.

It will not involve understanding less software.

Final Thought

The most dangerous sentence in AI-assisted development is probably:

"I don't know how it works, but it works."

That sentence is fine for a five-minute experiment.

It is dangerous for production software.

Vibe coding gives developers something incredibly valuable: speed.

The developers who benefit the most will be the ones who combine that speed with technical judgment.

Let AI handle more of the typing.

Keep ownership of the thinking.

That is the difference between using AI to build software and letting AI build software you eventually cannot maintain.