Encyclopedia

Vibe coding is an approach to software development that leverages artificial intelligence (AI) tools to create digital products through natural language prompts rather than traditional manual coding. The term was coined by OpenAI co-founder Andrej Karpathy in early 2025, quickly becoming a significant trend in Silicon Valley and the broader tech industry.

Overview

Vibe coding represents a paradigm shift in software development by emphasizing the "vibe" or overall concept of the software rather than the code itself. In this approach, developers communicate with AI tools using simple text prompts or verbal descriptions, allowing the AI to generate, refine, and debug code with minimal human intervention. A distinctive aspect of vibe coding is that human developers often evaluate the code's functionality through testing rather than reading and comprehending every line of code.

As Karpathy described it, vibe coding is "where you fully give in to the vibes, embrace exponentials, and forget that the code even exists." This approach enables people with limited or no coding knowledge to create applications, websites, and other digital products through AI assistance.

History

The concept of vibe coding emerged in early 2025 when Andrej Karpathy coined the term in a post on the social media platform X (formerly Twitter). While AI-assisted coding had been growing in popularity for several years prior, Karpathy's framing of "vibe coding" as a distinct methodology catalyzed widespread adoption and discussion in the tech industry.

According to reports from Y Combinator, approximately 25% of their startups in 2025 were relying on AI for 95% of their codebases. Google also reported that around 25% of its new code was AI-generated during this period.

Methodology

The vibe coding process typically follows these steps:

1. Concept Description: The developer describes their desired product or feature in natural language.
2. AI Code Generation: AI tools generate code based on the description, often creating a complete prototype.
3. Feedback and Iteration: The developer reviews the result and provides feedback for refinement, often without reading the underlying code.
4. Testing and Validation: The AI runs tests to verify functionality, with the developer evaluating the product based on its behavior rather than code inspection.
5. Refinement: Through multiple iterations of feedback and modification, the product is refined until it meets requirements.

This methodology allows rapid prototyping and development, with iterations that might have taken days or weeks being completed in minutes or hours.

Tools and Platforms

Several tools and platforms have emerged to support vibe coding:

• Cursor: An AI-powered code editor
• Bolt: A browser-based platform for developing applications through natural language
• Claude: An AI assistant with advanced coding capabilities
• Windsurf: A desktop-based AI code editor designed for advanced coding tasks
• Cline: A collaborative AI coder

Business Implications

Vibe coding has several significant implications for businesses:

• Development Speed: Projects that previously required large teams can be completed by smaller teams in less time. According to Garry Tan, CEO of Y Combinator, "A team of just 10 vibe coders can easily be on their way to building the next multi-million dollar start-up... That work otherwise might have taken 50 or 100 engineers."
• Democratization: The barrier to entry for software development has been lowered, allowing entrepreneurs without traditional coding backgrounds to create technology startups.
• Code Volume: The approach tends to generate significantly more code than traditional methods, raising questions about storage, versioning, and intellectual property management.
• Quality Concerns: Critics have raised questions about code quality, security, and technical debt in systems where humans may not fully understand the generated code.

Skills and Education

The rise of vibe coding is reshaping the skills required in software development:

• Prompt Engineering: The ability to craft effective prompts has become a crucial skill.
• Systems Thinking: Understanding complex systems and their behaviors, rather than implementation details, has grown in importance.
• Code Reading: While less code writing may be needed, understanding and evaluating code written by others (or AI) remains valuable.

Jules White, professor of computer science at Vanderbilt University, described the impact on computer science education: "It's the most exciting time in computer science because all the computer science, in many ways that we've been doing for the past decades, is dead."

Best Practices

Industry experts recommend several best practices for effective vibe coding:

1. Starting with templates: Using pre-built templates as a foundation for new projects
2. Embracing experimentation: Leveraging the speed of vibe coding to try multiple approaches
3. Prioritizing security: Taking special care with sensitive data and security-critical applications
4. Staying updated: Keeping abreast of rapidly evolving tools and techniques

Successful vibe coding requires a balance between rapid iteration and thoughtful architecture, with developers needing to understand when to leverage AI assistance versus when to apply traditional development approaches. The methodology works particularly well for prototyping and initial development phases, though many organizations are now successfully using it throughout the entire development lifecycle.

Criticisms and Concerns

Despite its advantages, vibe coding has faced several criticisms:

• Code Quality: Concerns about inefficiency, security vulnerabilities, and maintainability in AI-generated code
• Technical Debt: Worries that the approach may lead to increasing technical debt over time
• Skill Development: Questions about how future software engineers will develop deep technical understanding if they rely heavily on AI
• Comprehension Limits: Concerns about deploying production code that no human fully understands

Companies like TurinTech have emerged to address these concerns, developing tools that can optimize and validate AI-generated code to improve performance, security, and scalability.

Notable Examples

One widely cited example of successful vibe coding is fly.pieter.com, an in-browser multiplayer flight simulator created by Pieter Levels. According to reports, the game was initially developed in just 30 minutes using AI tools and was generating approximately $50,000 per month in revenue by March 2025.

Other examples include chemical reaction simulators, business applications, and custom websites created through iterative prompting rather than traditional coding.