What Is Vibe Coding? The Pros and Cons of Writing Code with AI

What Is Vibe Coding?

Lately, the term "Vibe Coding" has been popping up more and more in tech circles. Put simply, it refers to a whole new way of programming: instead of typing out code line by line, you tell the AI what you want in natural language—English, Hindi, or whatever language you're comfortable with—and the AI generates the complete code.

The concept was introduced by Andrej Karpathy, a former co-founder of OpenAI. According to a YouTube creator's short video, the core of Vibe Coding is "you describe the requirements, the AI writes the code," freeing developers from syntax details.

From the perspective of technological evolution, this concept didn't appear out of nowhere. Large language models have continuously improved their capabilities on code generation tasks, turning "programming by talking" from a joke into a practical workflow. Karpathy's framing resonated because it precisely captures what many developers are already doing.

Andrej Karpathy is a major figure in deep learning, having served as Tesla's AI Director and as a founding member of OpenAI, with long-standing work in computer vision and autonomous driving. After leaving OpenAI in 2023, he focused on personal projects and educational content, and he has a large technical audience on social media. Because of this, new concepts he introduces tend to spread quickly through the developer community. The word "Vibe" originally means "atmosphere or feeling," and here it suggests that developers rely more on intuition and natural language descriptions to drive development rather than carefully crafting syntax character by character—which is one reason the naming has sparked discussion.

How Does Vibe Coding Work?

The actual workflow isn't complicated. Open a tool like Cursor, Claude, or ChatGPT, enter a prompt such as "build me a to-do app with dark mode," and the AI outputs runnable code.

According to the video, these tools are particularly well-suited for prototyping, personal side projects, and hackathons—scenarios that share a common trait: speed matters far more than code robustness. You can see something working within minutes instead of spending days setting up scaffolding.

This approach lowers the barrier to entry for programming, allowing non-professional developers to quickly validate ideas. For experienced developers, it acts like an on-demand pair programmer, saving a great deal of time on documentation lookups and boilerplate code.

Cursor, mentioned in the article, is an AI code editor built on a fork of VS Code that supports chatting with the model directly inside the editor to generate and modify code; Claude is a large language model from Anthropic, and products like Claude Code also support code generation and engineering tasks; ChatGPT is OpenAI's general-purpose conversational product. What these tools share is a foundation in "code large models"—models trained on massive amounts of public code and documentation with code completion and generation capabilities. They typically improve the accuracy of generated code through prompt engineering and context injection (such as referencing project files), but output quality still depends heavily on the clarity of prompts and the completeness of project context.

Where Are the Risks?

The problems explicitly pointed out in the video are worth heeding: if you don't understand the code the AI generates, you can't debug it. This is the core weakness of Vibe Coding.

Specifically, the risks cluster in several areas. Security vulnerabilities are one—the AI may generate code with injection risks or permission issues, and you won't notice because you can't read it. Code structure can also become messy: it works in the short term but is hard to maintain in the long term. A more realistic dilemma: when the AI gets stuck, you get stuck with it, because you don't have enough knowledge to figure out where the problem lies.

The video's view is straightforward: using Vibe Coding in production is risky. Prototypes and experiments can be used boldly, but for systems that actually go live, someone needs to be accountable for every line of code.

The "injection risks" mentioned in the article usually refer to security vulnerabilities such as SQL injection and command injection: if AI-generated code directly concatenates user input into database queries or system commands, attackers could exploit it to read or tamper with data or even take control of the server. Permission issues include APIs lacking authentication and sensitive information hardcoded in the code. These vulnerabilities often don't cause immediate errors but lie dormant until they're exploited after launch, making them especially dangerous for developers who don't understand the code. In addition, AI-generated code may introduce outdated third-party dependencies or ones with known vulnerabilities, which is another area requiring manual review.

The Right Way to Use It

So how should you use it? The video's advice is: learn the fundamentals well, and treat AI as an assistant, not as your boss.

This positioning is crucial. Developers who understand basic concepts can judge whether the output is reasonable, identify potential problems, and step in to correct things when the AI makes mistakes. According to the video, developers who both understand the fundamentals and know how to use AI can be many times more efficient than those writing code purely by hand—because the AI handles repetitive work while humans make architectural decisions and quality checks.

Viewed from another angle, Vibe Coding doesn't make programming fundamentals less important; instead, it makes judgment more valuable. The barrier to writing code is lowering, but the ability to understand code, evaluate code, and be accountable for code remains a scarce resource.

Conclusion

Vibe Coding is a real and useful way of working, especially suited for rapid validation and low-risk scenarios. Its value lies in acceleration, not in replacing understanding. Treat it as an assistant and you gain efficiency; treat it as your boss and you take on the risk of not understanding what you're shipping.

One-line Summary

Vibe Coding is a new way to let AI write code using natural language—great for rapid prototyping but risky in production.

Paragraph Summary

The article introduces Vibe Coding, a concept coined by former OpenAI co-founder Andrej Karpathy: developers describe requirements in natural language, and AI generates complete code. With tools like Cursor, Claude, and ChatGPT, prototypes, side projects, and hackathon entries can be built quickly. This approach lowers the programming barrier and significantly speeds up development, but the core risk is that if developers don't understand the generated code, they can't debug it and may miss security vulnerabilities and structural problems—making it unsuitable for direct use in production. The article advises developers to build strong fundamentals, treat AI as an assistant rather than a boss, and keep humans responsible for architectural decisions and quality control, balancing efficiency with code quality.