The Vibe Coding Bubble Is Starting to Burst

AI can generate an application in hours. The harder question is whether you can debug it, maintain it, secure it, and persuade anyone to use it.

Image: The Vibe Coding Bubble Is Starting to Burst

You should learn how to work with AI coding tools now — not because they are about to replace every developer, but because they are changing what developers are expected to do.

Writing code is becoming cheaper.

Understanding systems is not.

That difference matters more than most demos admit.

I have watched an AI assistant create an authentication flow, connect a database, generate API routes, and build the frontend around them in one session. The application looked nearly complete.

Then I tested the refresh-token flow.

The assistant had stored the token insecurely, duplicated validation logic across multiple routes, and created an edge case where expired sessions produced an endless retry loop.

The code existed. The system did not really work.

That is the uncomfortable truth behind vibe coding: generating software and engineering software are not the same activity.

The Demo Creates the Wrong Mental Model

A typical AI coding demo follows a clean path:

  1. Describe an application.
  2. Let an agent generate the files.
  3. Run the project.
  4. Celebrate when the landing page appears.

This is useful, but it hides most of the work.

A real product also needs:

  • Input validation
  • Authentication and authorization
  • Error handling
  • Database migrations
  • Logging and monitoring
  • Rate limiting
  • Accessible interfaces
  • Deployment configuration
  • Maintenance after release

The first version is often the easiest part.

Most tutorials stop when the application runs locally. Production starts where the tutorial ends.

That is why arguments about whether vibe coding is “alive” or “dead” miss the more useful question:

Which parts of software development can safely be delegated, and which parts still require judgement?

AI Can Make You Faster — and Still Slow Down the Project

Research on AI-assisted development does not produce one universal answer.

In a controlled GitHub Copilot experiment, developers completed a small JavaScript task considerably faster with AI assistance. But a later METR study found that experienced open-source developers working inside familiar, mature repositories took 19% longer when AI tools were allowed — even though they believed the tools had made them faster.

That sounds contradictory. It isn’t.

AI performs well when:

  • The task is clearly defined.
  • The surrounding code is limited.
  • Success can be checked quickly.
  • Common patterns already exist in its training data.

It becomes less reliable when:

  • The repository has years of undocumented decisions.
  • Several services depend on one change.
  • Business rules contain exceptions.
  • Incorrect code appears plausible.
  • Verification takes longer than generation.

Generating ten files in two minutes feels productive. Reading those files, reconstructing their assumptions, and repairing hidden mistakes may take longer than writing a smaller solution yourself.

The surprising part is that speed of output can reduce speed of delivery.

The Difference Between Vibe Coding and AI-Assisted Engineering

Consider an Express route generated from a simple prompt:

app.get("/api/users/:id", async (req, res) => {
 const user = await User.findById(req.params.id);
 res.json(user);
});

It looks reasonable. It may even pass the first manual test.

But it has several unanswered questions:

  • What happens when the ID is malformed?
  • Should every authenticated user access every profile?
  • What response is returned when no user exists?
  • Could sensitive fields be exposed?
  • How are database failures recorded?

A safer version makes those decisions visible:

app.get("/api/users/:id", requireAuth, async (req, res, next) => {
 try {
 const { id } = req.params;
 if (!mongoose.isValidObjectId(id)) {
 return res.status(400).json({ message: "Invalid user ID" });
 }
 if (req.user.id !== id && req.user.role !== "admin") {
 return res.status(403).json({ message: "Access denied" });
 }
 const user = await User.findById(id).select("-passwordHash");
 if (!user) {
 return res.status(404).json({ message: "User not found" });
 }
 return res.status(200).json({ user });
 } catch (error) {
 next(error);
 }
});

The value of the second version is not its length. It is the reasoning captured inside it.

AI may help produce that reasoning, but only when the developer knows which questions to ask.

Where AI Coding Tools Actually Help

After using these tools across full-stack and AI projects, I find them most valuable as accelerators for bounded work.

Image — Where AI Coding Tools Actually Help

A practical workflow looks less impressive than an autonomous-agent demo, but it is usually safer:

  1. Define the behaviour yourself.
  2. Ask AI for a narrow implementation.
  3. Review the proposed approach before accepting code.
  4. Run tests and inspect failure paths.
  5. Check security and data-handling assumptions.
  6. Commit a small, understandable change.
  7. Measure whether the feature solves the actual problem.

This keeps the developer in control of the system rather than merely approving generated changes.

The Real Product Problem Has Nothing to Do With Code

AI has reduced the cost of creating software. It has not created demand for that software.

A polished dashboard can still solve nothing.

A technically impressive SaaS product can still have no distribution, no differentiation, and no reason for users to return. Faster development sometimes makes this worse because teams invest less time validating the problem before building the solution.

Before opening an AI coding agent, answer three questions:

  • Who experiences this problem repeatedly?
  • How are they solving it now?
  • Why would they switch to this product?

If those answers are weak, generating the application faster only helps you reach the wrong destination sooner.

What Changed for Me

Earlier, I measured AI coding tools by how many lines they could produce.

Now I measure them by how much verified work they remove.

That distinction changed my workflow.

I use AI freely for scaffolding, test cases, documentation, migrations, debugging hypotheses, and exploring unfamiliar libraries. I become much more deliberate around authentication, payments, permissions, destructive database operations, and architectural decisions.

The unexpected lesson was not that AI writes bad code. Often, it writes perfectly acceptable code.

The problem is that acceptable code placed inside the wrong system is still wrong.

The Skill Developers Need Next

Vibe coding is unlikely to disappear. The term may fade, pricing may change, and today’s tools will be replaced by stronger ones. OpenAI, for example, now meters additional Codex usage through token-based credits, showing how access and economics can evolve even while the underlying capability remains available.

The durable skill is not memorizing syntax.

It is being able to:

  • Break vague requirements into testable behaviour.
  • Recognize risky assumptions.
  • Read generated code critically.
  • Debug across system boundaries.
  • Decide what should not be automated.
  • Build something people genuinely need.

Use AI to reduce mechanical work. Let it propose solutions. Let it challenge your first approach.

But keep ownership of the decisions.

Because the developer who can generate the most code will not necessarily win.

The developer who can determine which code should exist — and prove that it works — will.

From Dev Simplified

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