When AI Writes Code: Why the Simplicity of Vibe-Coding Is Deceptive
In 2025, anyone can build an app in ten minutes without writing a single line of code. Just open ChatGPT or Claude, describe what you want — a booking system, a game, a mortgage calculator — and get a ready-to-run program. No Python, no JavaScript, no experience required. This new phenomenon is called vibe-coding.
The Illusion of Democratized Creation
Vibe-coding lets you express the "vibe" of what you want to build, and AI turns that vision into code. It feels like a revolution — a true democratization of software development. Now a student can create a startup, a teacher can build a learning platform, and a local barista can launch a loyalty app without ever touching a keyboard of code. But beneath this optimism lies a serious problem: people are creating software in languages they don’t understand using tools they can’t control.The Invisible Filters Are Gone
Traditional development required learning the craft. It forced developers to absorb concepts like error handling, security, optimization, and architecture — the invisible filters that protected software from catastrophic failures. Vibe-coding erases those filters. Anyone can generate an app in minutes, but knowledge and discipline don’t appear automatically.The Black Box With a Personality
Every AI model has its own style of programming. GPT-4o prefers simple, pragmatic solutions. Claude Sonnet 4 tends toward architectural complexity. OpenCoder-8B creates rough drafts that rarely suit production. AI is not a neutral translator of ideas into code — it inherits biases, flaws, and bad habits from its training data. A vibe-coder has no way to see those biases and no tools to evaluate the quality of what AI produces.Three Critical Flaws of AI-Generated Code
In October 2025, the software-analysis firm Sonar published a report titled “Programming Personalities of Leading Language Models.” It evaluated six popular AIs — GPT-5, Claude Sonnet 4, Claude 3.7 Sonnet, GPT-4o, Llama 3.2 90B, and OpenCoder-8B — and found three universal weaknesses:- Insecure code patterns. AI models often reproduce SQL injections, XSS vulnerabilities, and unsafe password handling because such patterns exist in their training data.
- Lack of engineering discipline. They ignore edge cases, skip input validation, and omit exception handling. Code works only under ideal conditions.
- Technical debt generation. The output is hard to maintain, extend, or debug — a time bomb for any future developer.
The Danger of “Working” Code
A small-business owner asks AI to create a web app for customer orders. It works fine at first. After a month, pages load in 20 seconds. After two, a stray input crashes the system. After three, a data breach reveals a simple SQL injection. The AI-generated code was functional but not secure, not efficient, and not sustainable. For non-technical creators, “it runs” is mistaken for “it works.”Four Rules for Safe Vibe-Coding
Be specific. Describe validation, error handling, and security requirements explicitly. 
Use code-quality tools like Sonar to automatically scan for vulnerabilities. 
Limit vibe-coding to prototypes and non-critical apps. 
Retest after model updates — generation style can change overnight.The Transition Period
AI companies are actively investing in secure-coding training for their models. Future versions may indeed write robust, professional-grade software. But in 2025, we’re still in a transitional era — technology is good enough to feel magical but not reliable enough to trust blindly.Conclusion
Vibe-coding is a powerful tool, not a replacement for understanding. A hammer doesn’t make you a carpenter; a camera doesn’t make you a photographer. AI coding doesn’t make everyone a developer — it just opens the door for those willing to learn the rules. Its simplicity is seductive but dangerous if used without awareness. Understanding the limits of your AI is what turns vibe-coding from a risky experiment into a reliable innovation tool.Editorial Team — CoinBotLab