What “vibe coding” actually is

Vibe coding is the practice of generating large amounts of code by prompting AI tools and accepting the output with minimal reading, design, or verification. It feels magical. Features appear in minutes. Demos look polished. The dopamine hit is real.

Source: https://x.com/adnaannasir_/status/2095368979309310090/photo/1

For throw away prototypes, personal scripts, or early exploration, it works brilliantly. The cost of testing an idea has collapsed. That part is genuinely good.

The trouble starts when people treat the same approach as a complete substitute for software engineering.

The issues that keep showing up

Once a vibe-coded project leaves the demo environment and meets real users, real data, and real attackers, the same failure patterns appear again and again:

Security holes that demos never reveal.

AI frequently produces code that looks finished but is insecure. Hardcoded secrets in the frontend. Authentication checks only on the client. Missing authorization. Weak input validation. These aren’t rare edge cases, analyses of deployed vibe-coded apps keep finding them at high rates.

Architecture that no one designed.

Features get bolted on one prompt at a time. Structure emerges by accident. The result is often inconsistent, tightly coupled, and difficult to reason about. After a few weeks the codebase outgrows anyone’s mental model including the person who “wrote” it and the AI that generated most of it.

Almost no real testing or error handling.

Models optimize for the happy path. Edge cases, race conditions, network failures, and bad data are frequently ignored unless you explicitly demand them. Every change becomes a coin flip.

Debugging that turns into archaeology.

The initial speed is intoxicating. Then a stubborn bug appears and the hangover begins. Because the code was never deeply read or understood, fixing one problem often creates several others. What felt like 5x productivity can evaporate once the real investigation starts.

AI reaches for packages liberally. Apps accumulate unnecessary libraries, outdated components, and sometimes hallucinated ones. Maintenance cost and attack surface grow quietly.

Scalability surprises.

What works with a handful of test users can collapse under modest real traffic because of missing indexes, N+1 queries, or other patterns the model never considered.

These problems are not mysterious. They all stem from the same root: accepting large volumes of generated code without the traditional disciplines of design, review, testing, and ownership.

Why so many people still miss it

Web development is the most visible and accessible part of software. It is also the area where current AI tools perform best on common patterns. When people see rapid progress there, some conclude that software engineering itself is “solved.”

It isn’t. Web apps are a subset. Distributed systems, performance-critical code, security-sensitive systems, long-lived products that must be maintained by teams — these still demand understanding that pure vibe coding does not produce.

The meme is not anti-AI. It is a warning against confusing speed of generation with depth of engineering.

A clearer way forward

AI coding tools are powerful. Used well, they make strong engineers faster and more productive. Used as a way to skip fundamentals, they produce fragile systems that look finished until they aren’t.

The practical middle path is straightforward:

• Use AI heavily for exploration and first drafts.
• Keep a short design or specification before big changes.
• Read the code you accept.
• Demand tests and review the diffs.
• Treat “it works once” as the starting point, not the finish line.

Vibe coding isn’t going away, and it shouldn’t. The issue in plain sight is the growing pile of beautiful, broken software that results when people forget the difference between generating code and engineering systems that last.

The fundamentals still matter. The only question is whether we learn that the easy way or the expensive way.