Vibe Coding and the MVP Trap: why speed without engineering is a liability

In the last year, LinkedIn has been flooded with posts celebrating lightning-fast MVPs.

“We built our MVP in a weekend.”

“Two days with AI and the product is live.”

“No engineers needed, just prompts.”

From the outside, it looks impressive. Screenshots are polished. Demos work, at least once. For a non-technical founder, it feels like the rules of the game have changed.

But behind many of these MVPs is a growing and dangerous pattern: vibe coding.

Here I will explain why vibe coding creates fragile products, why it fails founders at the worst possible moment, and why the right path is not less engineering, but engineering first, accelerated by AI, not replaced by it.

The observations and principles below are grounded in real-world engineering practice and industry experience.

What is Vibe Coding?

Vibe coding is not “using AI.”

Vibe coding is building software based on surface-level success signals:

• Code is generated rapidly by AI

• Little or no understanding of how it works

• No meaningful review, testing, or architecture

• Decisions are made based on whether “it seems to work.”

The main problem is not speed. It is the absence of responsibility.

In vibe coding, the product works until it doesn’t. And when it breaks, nobody knows why.

Why vibe-coded MVPs look successful at first.

Vibe-coded MVPs often pass early demos because AI is very good at producing the first 70% of a system:

• UI scaffolding

• CRUD APIs

• Basic integrations

• Happy-path workflows

This creates a powerful illusion:

• Founders feel progress

• Stakeholders see motion

• Investors see something tangible

But software is not judged by its first demo. It is judged by what happens next.

Where Vibe Coding breaks down (and it always does).

The real cost of vibe coding shows up when the MVP meets reality.

1. No one owns the system.

In real engineering teams, someone is accountable for:

• Architecture

• Trade-offs

• Long-term consequences

In vibe coding, responsibility is outsourced to the AI.

But AI cannot:

• Understand business risk

• Anticipate future scale

• Defend technical decisions

• Debug complex failures

When something breaks, there is no owner, only prompts.

2. The product cannot evolve.

MVPs are not meant to be finished. They are meant to change.

Vibe-coded systems are brittle because:

• Components are tightly coupled

• Assumptions are implicit, not documented

• Architecture is accidental, not intentional

The moment you need:

• A second customer type

• A new pricing model

• A different integration

• Real analytics or security

…the system fights back.

What felt fast becomes painfully slow.

3. Debugging becomes impossible.

Debugging is not guessing. It is reasoning.

To debug, someone must understand:

• Control flow

• Data boundaries

• Failure modes

• System behavior under stress

Vibe coding skips the learning phase where this understanding is built.

The result:

• Bugs that “disappear” when re-tested

• Fixes that break unrelated features

• Endless patching instead of resolution

Many vibe-coded MVPs quietly die here.

4. Technical debt accumulates at warp speed.

AI accelerates development, but it also accelerates bad decisions.

Without:

• Standards

• Reviews

• Architectural intent

• Strong testing discipline

Technical debt compounds faster than in traditional development. Ironically, the faster you build without engineering rigor, the sooner you hit a wall.

The fundamental role of AI in MVP development.

The problem is not AI. The problem is how it is used. Experienced engineering teams already use AI successfully in three roles.

1. AI as a drafter.

AI produces an initial version. A human engineer reviews, corrects, and adapts it.

2. AI as a partner.

AI assists with boilerplate, autocomplete, and routine tasks. The engineer leads design and decisions.

3. AI as a validator.

AI reviews code for obvious issues. Humans make final calls.

In all three cases, accountability stays with the programmer. This is the critical difference between engineering and vibe coding.

Why being a programmer comes first.

For non-technical founders, this point is essential:

”AI does not remove the need for programmers. It raises the bar for what programmers are responsible for.”

A real programmer contributes value that AI cannot:

• System thinking

• Architectural foresight

• Risk anticipation

• Trade-off evaluation

• Long-term maintainability

AI removes routine, not judgment.

An MVP built without engineering judgment is not a shortcut; it is a deferred failure.

The “70% Problem” and why it matters to founders.

AI can often get you about 70% of the way toward a working product.

The remaining 30% includes:

• Edge cases

• Performance constraints

• Security concerns

• Data integrity

• Scalability

• Operational stability

This last 30% is where products succeed or fail.

Vibe coding ignores it. Engineering exists because of it.

What a healthy AI-accelerated MVP process looks like.

For founders, the takeaway is not “avoid AI.” It is crucial to choose the right kind of speed.

A healthy process looks like this:

Engineering-first mindset

• Clear ownership

• Intentional architecture

• Explicit trade-offs

AI used to remove friction

• Faster scaffolding

• Less boilerplate

• Quicker iteration

Strong feedback loops

• Code reviews

• Testing

• Debugging discipline

Focus on change, not just launch

• MVPs are starting points, not endpoints

This approach is slower on day one, and dramatically faster over months.

Why this matters more than ever.

As AI lowers the barrier to starting, it raises the cost of finishing.

Founders who mistake demos for products will:

• Burn engineering goodwill

• Accumulate invisible risk

• Struggle to scale or pivot

• Lose credibility with serious investors

Founders who pair real engineering leadership with AI acceleration will:

• Move faster and safer

• Build systems that evolve

• Turn MVPs into real businesses

Final thought.

Vibe coding is seductive because it feels like progress.

Engineering is more challenging because it entails responsibility.

AI does not change this truth; it amplifies it.

The winners in the AI era will not be those who generate code the fastest, but those who understand what they are building, why it works, and how it will fail, and use AI to move faster without losing control.

That is how MVPs survive their first demo. And how startups survive their first year.