What is Vibe Coding?

Vibe coding is a new, AI-powered approach to software development where you describe what you want to build in plain language, and artificial intelligence (usually large language models like ChatGPT or OpenAI Codex) generates the code for you. The term was introduced by computer scientist Andrej Karpathy in February 2025 and quickly gained traction in the tech world.

How Vibe Coding Works

Instead of manually writing code, you interact with an AI coding assistant by giving it prompts—simple, natural language instructions about your app or feature idea. The AI returns working code, which you can test, refine, or ask the AI to fix or improve. For example, you might say, “Build me a website where users can RSVP for an event,” and the AI will generate the HTML, JavaScript, and logic for you.

Key Advantages

• Accessibility: Anyone, even without coding experience, can create software by describing their needs, making development more inclusive.
• Speed: You can go from idea to prototype in minutes or hours, as the AI automates much of the setup and repetitive coding work.
• Creativity: By focusing on ideas and user experience rather than technical details, creators can experiment and innovate more freely.
• Efficiency: The AI handles boilerplate and standard code patterns, letting you concentrate on design and problem-solving.

Typical Workflow

1. Choose an AI coding tool (like Replit, Cursor, GitHub Copilot, or Hostinger Horizons).
2. Describe your requirements in a clear, goal-oriented prompt.
3. Review and test the AI-generated code.
4. Refine your prompt or ask the AI to fix issues as you iterate toward your desired outcome.

Use Cases

• Rapid prototyping and MVPs for startups or entrepreneurs.
• Personal projects, interactive art, or multimedia experiences.
• Small business tools or automations built without a developer.

Limitations

• Less control and flexibility compared to traditional coding; best for small-to-medium projects.
• Potential security and quality risks if AI-generated code isn’t reviewed by experienced developers.
• Scalability and performance may be limited for more complex or production-grade applications.

In Summary

Vibe coding shifts the developer’s role from manual coding to guiding, testing, and refining AI-generated code, making software creation faster, more accessible, and more creative—but still requiring human oversight for quality and security.

How does vibe coding compare to traditional coding in terms of maintainability

Maintainability in Vibe Coding

• AI-Generated Complexity: Vibe coding relies on AI to generate code based on natural language prompts. While this accelerates development, the resulting code often lacks consistent structure and clear documentation, making it harder to understand, debug, and update over time. Example: A startup using AI to generate a user authentication system may receive functional code quickly, but six months later when they need to add social sign-in options, the developers struggle to understand the original implementation’s structure and must spend significant time reverse-engineering the codebase before making changes.
• Technical Debt: AI-generated code can be messy, with redundant or inefficient logic, and may not follow established best practices. This increases technical debt and can make future modifications or scaling difficult. Use Case: An e-commerce website rapidly developed with vibe coding works well for the initial 500 users, but when traffic grows to 10,000 daily visitors, performance issues emerge from inefficient database queries and unoptimized rendering logic that weren’t apparent in the prototype phase.
• Limited Flexibility: Early architectural decisions made by the AI are often “locked in,” making it challenging to refactor or adapt the codebase as requirements evolve. Example: A team uses vibe coding to create a mobile app with a specific state management approach. As the app grows, they realize a different architecture would be more suitable, but refactoring proves nearly impossible without rewriting large portions of the codebase from scratch.
• Debugging Challenges: Since developers may not have written the original code, diagnosing bugs or adding features can be more difficult, especially as the project grows in size and complexity. Use Case: When a critical bug appears in production, engineers spend days rather than hours identifying the root cause because they need to understand the AI’s implementation choices across multiple interconnected components before they can safely fix the issue.
• AI Dependency: Maintenance depends on the capabilities and updates of the AI tool used. If the tool changes or is discontinued, maintaining the code can become even more problematic. Example: A company builds their entire backend using a specific AI coding assistant, only to find that a year later, the tool’s new version generates incompatible code patterns, making it difficult to maintain consistency across the codebase.

Maintainability in Traditional Coding

• Structured and Standardized: Traditional coding emphasizes clean architecture, consistent patterns, and thorough documentation. This makes codebases easier to understand, debug, and extend—even years after initial development. Use Case: A finance application written with traditional coding practices can be seamlessly maintained by new team members who join years after the original developers have left, thanks to clear documentation, consistent naming conventions, and adherence to established design patterns.
• Comprehensive Testing: Developers typically implement robust testing frameworks (unit, integration, end-to-end), ensuring that code changes do not introduce regressions and making long-term maintenance more reliable. Example: When implementing a major new feature in a healthcare system, developers can confidently make substantial changes because a comprehensive test suite immediately identifies any unintended side effects in critical patient-facing functions.
• Full Control: Human developers can refactor, optimize, and restructure code as requirements change, ensuring the codebase remains healthy and adaptable. Use Case: A team building a data visualization platform can gradually transition from a monolithic architecture to microservices over several months without disrupting existing functionality, thanks to careful planning and incremental refactoring.
• Scalability: Traditional coding practices are better suited for large, complex, or mission-critical systems that require ongoing support and evolution. Example: A banking system designed with traditional coding practices can scale from handling thousands of transactions daily to millions while maintaining strict security and compliance requirements, with infrastructure and code that evolve together through carefully planned iterations.

Real-World Application Scenarios

Startup MVP Development

Vibe Coding Approach: A solo founder with limited technical expertise uses AI coding tools to build a functional marketplace app in just two weeks. The MVP successfully demonstrates the concept to investors and early users. However, after securing funding and hiring developers, the team discovers they need to rebuild the application almost entirely to support the planned growth.

Traditional Coding Approach: A technical founder spends six weeks building a marketplace MVP with clean architecture. The development takes longer initially, but when funding arrives, new developers can quickly understand the codebase and extend it. Within three months, they’ve added advanced features while maintaining the original core functionality.

Enterprise System Integration

Vibe Coding Approach: A midsize company uses AI to rapidly generate API integrations between their CRM and a new inventory management system. The integration works initially, but becomes increasingly unstable as both systems receive updates. After six months, data inconsistencies force them to hire consultants to rewrite the integration properly.

Traditional Coding Approach: An enterprise team plans and implements API integrations following established patterns with comprehensive error handling, logging, and monitoring. The integration takes longer to build initially but remains stable through multiple system updates and scales smoothly as transaction volumes increase.

Summary Table

Aspect	Vibe Coding	Traditional Coding
Speed	Very fast for prototypes and MVPs	Slower, but more deliberate
Maintainability	Challenging for large/long-term projects	High—built for stability and longevity
Flexibility	Limited by AI decisions and tool capabilities	Full control—easy to refactor and scale
Debugging	Often harder, as code may be unclear or messy	Easier, with clear logic and documentation
Best For	Prototypes, simple apps, rapid iteration	Enterprise, mission-critical applications

Hybrid Approaches Emerging

Many teams are now adopting hybrid approaches that leverage the strengths of both methodologies:

1. Prototype with Vibe, Refine with Traditional: Using AI for rapid prototyping, then having experienced developers refactor critical components with proper architecture.
2. AI for Boilerplate, Human for Core Logic: Letting AI handle repetitive standard code (like form validation), while humans focus on business logic and architecture decisions.
3. Traditional Framework, Vibe Implementation: Establishing a well-designed architecture and component structure traditionally, then using AI to accelerate implementation of individual components within those constraints.

Verdict

Traditional coding clearly wins for long-term maintainability and quality.

Vibe coding is excellent for rapid prototyping and lowering the barrier to entry, but it often results in code that is harder to maintain, debug, and scale over time. For projects where stability, security, and future-proofing are priorities, traditional coding remains the superior approach.

The ideal approach may vary based on project requirements, team expertise, and business constraints. Critical systems with long lifespans benefit most from traditional methods, while time-to-market experiments may justify accepting the technical debt that comes with vibe coding.