Imagine you could program an app by rubbing a magic lamp, telling a genie your requirements in plain and straightforward language, and the genie produces a functional app for you. While there are no magic lamps, AI coding assistants come very close to granting that wish–for better and for worse. With large language models (LLM), developers can input natural language prompts and generate code in any programming language. Andrej Karpathy, co-founder of OpenAI, coined the term vibe coding in 2025 to describe "fully giving in to the vibes, embracing exponentials and forgetting that the code even exists."

This new paradigm marks a shift from deliberate, line-by-line coding to a more fluid, intuitive collaboration between human intent and machine execution.

Vibe coding is not a replacement for developers but is a strategic enabler for faster digital transformation, increasing productivity and a cost-effective option for quicker-to-market tools. However, IT executives must combine governance with enablement to maximize the value while also controlling the risk that comes with vibe coding.

How does vibe coding work?

A developer begins by choosing an AI coding assistant and describing the kind of functionality or feature they want. Then AI responds with code suggestions, which the developer can review, accept or refine. From there, the developer continues to iterate, adding new features or adjusting by prompting the AI with specific instructions, creating a dynamic, conversational workflow.

Vibe coding vs. traditional programming

Traditionally, the process of programming has been very structured and methodical, whereas vibe coding describes a more creative, or flow-based approach. Here's a breakdown of how these approaches differ:

Benefits of vibe coding

Vibe coding offers several key benefits, especially for developers looking to move quickly from ideas to implementation and reduce repetitive tasks.

• Faster development. An experienced developer can use vibe coding to complete an app in mere hours as opposed to days or weeks of development time.
• Lower barrier to entry. The only language a developer needs to vibe-code is their own natural speaking language. Vibe coding enables developers to launch a functioning project without knowing how to code. AI can also be a potent tool for developers learning how to code or understanding how an app works.
• Prototyping. Vibe coding's speed enables development teams to create functional minimum viable products (MVPs) quickly. This makes vibe coding ideal for presenting a project to investors in the race to market. Plus, it enables faster functional iteration through experimentation.
• Hobby or internal projects. If public access or security is not a concern, vibe coding is ideal. Its speed and ease of use enable developers to solve problems and build resolutions quickly.
• Multimodal programming. Vibe coding expands code generation beyond typing into an integrated development environment (IDE), including speech-to-text prompting.
• Employee collaboration and productivity. Developers shift from writing code to reviewing and optimizing it. Other employees, such as analysts and product managers, can also provide input on the programming to enable cross-functional collaboration across business and IT departments.

Limitations of vibe coding

Does vibe coding sound too good to be true? It can be, depending on how it's used. What makes vibe coding a powerful tool for smaller applications and prototypes makes it a liability in larger codebases or where security is a priority.

• Mistakes and hallucinations. Code-generating AI is just as prone to hallucinating as any other popular AI tool. A study by several computer science researchers found that, on average, commercial AI models suggested software packages that did not exist 5.2% of the time. In comparison, that percentage leaped to 21.7% for open source models.
• Limited technical complexity. Each prompt given to an AI has a limited context window–analogous to memory–that includes a lot of data about your environment, like the contents of your open tabs. This provides context for the AI so it can make well-informed decisions. However, this context window comes in different sizes for different AI models, plus larger context sizes may affect the AI's performance. The more complex a project is, the more context the AI needs to understand the project.
• Difficult to debug and maintain. Accepting the AI code without reviewing can lead to creating a codebase where no human understands what the code does and why. If the AI introduces bugs that it cannot fix, then progress is completely blocked without a developer who can understand the output.
• Unoriginal. Coding AI trains on existing code examples and can only produce what it knows. It cannot come up with a revolutionary process or idea all by itself.

 Executives should treat vibe-generated code as a quick prototype. However, the program still needs to be reviewed. It must be reviewed for customer-facing services and checked for regulatory compliance if it involves other sensitive data.

Security concerns of vibe coding

A developer named Leo announced on X (formerly known as Twitter) that he had released an entirely vibe-coded SaaS application. In two days, his application was beset by hackers, and Leo posted that random problems were happening. Security issues abound when leaning so heavily on AI for an entire project. Here are a few of the reasons why:

• Vulnerabilities in the LLM or platform. Any software product that relies on external dependencies inherits potential vulnerabilities. AI coding platforms are no exception. Recently, security researchers discovered exposed API endpoints in the vibe coding platform, Base44, that enabled attackers to create new accounts to access private applications using a non-secret app_id value, bypassing all authentication mechanisms.
• Developer error. Vibe coding tools will produce precisely what the developer tells them to. If the developer does not include security practices in their prompts, the AI will not generate code adhering to best security practices.
• Data privacy. LLMs work by ingesting data to use as training data to improve the model. If the project involves sensitive data, such as payments, health records, proprietary code or business secrets, the AI tool must have strict data-isolation in place to prevent the AI from using protected information in other applications.

How to implement vibe coding

Considering its limitations, it's best to take care when integrating vibe coding into your project to make the most of it.

1. Plan your project. One trait that vibe coding and traditional programming share is that both are most effective when guided by a clear plan from the start. Determine what you want to build and break the steps down into easily ingestible portions. Keep in mind what security and code standards you wish to adopt for your project.
2. Decide your "vibe" strategy. True vibe-coding is defined by surrendering all decisions to AI. AI-assisted coding is a hybrid approach, where developers prompt the AI for code and then carefully examine the output before approval. Find the balance that serves your top priorities.
3. Choose an AI coding assistant. Not all models are built the same. Some specialize in code generation, while others can solve more complex problems. Different models have different policies regarding data isolation and privacy, as well as cost. Take care to choose the AI agent that would work best for your project.
4. Use source control. This is a good idea for any kind of coding, but it is especially vital for vibe coding. Create checkpoints for yourself when your project is in a good working state, so you can easily adjust as needed.
5. Iterate. Create one feature at a time and provide as much detail and context as you can with each prompt. Refine and refactor your code until it matches your vision.
6. Test. Make sure your project is working every step of the way. AI is excellent for generating automated tests, but make sure you also perform manual tests, including dependency validations and automated tests to block mergers with unknown/invalid packages.
7. Define guardrails. Be sure to have security reviews and coding standards in place. Vibe coding programs should still be reviewed for accuracy and compliance awareness, so approval workflows are necessary.