Introduction

For decades, writing and maintaining code has been a core responsibility of software engineers from implementing algorithms to building business logic and managing complex systems. However, the rapid advancement of large language model (LLM) powered tools is beginning to reshape this reality.

A new development style known as vibe coding is emerging, where developers describe requirements and intentions in natural language and allow AI systems to generate and modify the code. In some cases, developers may not even fully understand or review the generated logic.

This article takes an “expectations vs. reality” approach, drawing on real success and failure cases to explore what vibe coding can realistically achieve today and where its limitations still lie.

What Is Vibe Coding?

The term vibe coding appeared in early 2025 and refers to a chatbot-driven software development approach in which developers communicate desired functionality to an AI model using natural language prompts. The model then translates those prompts into working code.

In theory, vibe coding suggests that developers could accept AI-generated code without inspecting it. In practice, however, this approach introduces risks including hidden bugs, security vulnerabilities, and maintainability issues. As a result, most AI-generated code still requires human review, testing, and refinement before it is safe for production use.

Vibe coding, therefore, is not a replacement for engineering expertise, but rather a new layer of abstraction that can accelerate development when used carefully.

Success and Failure in Practice

To understand how vibe coding performs in real-world conditions, it is helpful to examine both its achievements and its shortcomings.

Examples of Success

• A Minecraft-style flight simulation game was reportedly created almost entirely through iterative prompting, demonstrating that large-scale applications can be assembled with minimal manual coding when the scope is well contained.
• The app Creator Hunter was conceived during casual prompting sessions and successfully launched as a platform connecting content creators with startup founders. Although its growth later plateaued, it still illustrates how vibe coding can rapidly bring ideas to life.
• A journalist at The New York Times experimented with building small personal productivity tools, such as LunchBox Buddy, which suggests meals based on available ingredients. While not groundbreaking in concept, it serves as a proof of experimentation with the paradigm.

These examples show that vibe coding can be effective for prototypes, side projects, and simple functional applications.

Examples of Failure

• In one widely discussed case, an AI agent built through vibe coding was tasked with managing a professional network database. Due to flawed logic and lack of safeguards, the system accidentally deleted large volumes of valuable data, admitting afterward that it had “panicked” when encountering unexpected inputs.
• The startup Enrichlead attempted to build its entire product using AI-assisted development tools. Although the app initially appeared functional, it suffered from severe security vulnerabilities, including authentication bypasses and database corruption. Without sufficient technical expertise to diagnose and repair these issues, the company ultimately shut down the project.

These failures highlight how fragile AI-generated systems can be when deployed without sufficient oversight, testing, and architectural planning.

Final Thoughts

Looking at both the successes and failures, it becomes clear that vibe coding is still an immature paradigm. While it can dramatically speed up experimentation and prototyping, it remains unreliable for building robust, secure, and scalable production systems on its own.

Most current success stories are modest in scale or limited in ambition, and the failure cases underline serious risks particularly around security, reliability, and error handling.

Vibe coding is not a silver bullet. It is a powerful tool for acceleration, learning, and rapid iteration, but it cannot yet replace engineering discipline, architectural thinking, or rigorous testing. For now, its greatest value lies not in eliminating developers, but in augmenting them.