Vibe coding is transforming software development by letting developers use AI to generate code through conversation, fundamentally changing the way software is built. Coined by Andrej Karpathy in February 2025, vibe coding allows developers to describe their requirements in natural language, which AI models then translate into working code.

What is Vibe Coding

Vibe coding is a new, AI-powered approach to software development where developers write code by describing what they want in natural language, and an AI agent (like GitHub Copilot, Claude, or Cursor) generates the code for them.

In Simple Terms:

It’s like saying: “Build a login screen with Google OAuth and passwordless email auth”

And the AI writes the code, sets up the UI, and maybe even deploys it — all through a conversational process.

The Rise of Vibe Coding and Agentic AI

Vibe Coding = building software by conversing with AI agents (like Claude 4, Copilot, Cursor).
Agentic AI can reason, act, and interact with systems to complete tasks autonomously.
VSCode Agent Mode and GitHub Copilot (with Claude Sonnet 4) are leading this movement.
Gartner named Agentic AI the top tech trend of 2025.

Referencs: Suggestions for Successful Agentic Development

The Mechanics of Vibe Coding

Vibe coding simplifies the coding process with a conversational approach. Developers follow these steps:

Describe their requirements in plain language, akin to explaining concepts to a colleague.
Review AI-generated options, often multiple implementations.
Provide feedback on the AI’s output, identifying what works and what doesn’t.
Refine the output through iterative conversation until the code meets specifications.

This technique turns coding into a dialogue with AI, enhancing productivity significantly. Tools like GitHub Copilot, Cursor, and Replit are pivotal in facilitating this new workflow.

Key Features of Vibe Coding

Aspect	Description
Conversational	Developers talk to AI in natural language — like giving instructions to a teammate.
Agentic AI	The AI doesn’t just autocomplete — it reasons, iterates, and can even run/test/debug code.
Iterative	You guide the AI by refining the output until it meets your needs.
Speed-focused	Helps build MVPs, UI flows, or backend logic much faster than traditional hand-coding.
Tools like	Cursor, Copilot, Replit Ghostwriter, Claude Code, Windsurf, DevIn, and Sourcegraph Cody.

Difference Between Traditional Coding and Vibe Coding

While traditional coding and vibe coding both aim to build functional software, the way developers interact with the tools and logic is fundamentally different. Below is a breakdown of how these two paradigms compare:

Aspect	Traditional Coding	Vibe Coding
Interaction Model	Developers manually write code line-by-line in a text editor or IDE	Developers describe functionality in plain language; AI generates code through a conversation
Tooling	Requires knowledge of specific programming languages, frameworks, and syntax	Uses AI coding assistants like GitHub Copilot, Claude, Cursor, and Replit
Speed	Slower—especially in early development or prototyping phases	Rapid MVP and feature delivery by skipping boilerplate and scaffolding
Learning Curve	Steeper; beginners must learn to code from scratch	Lower barrier to entry; ideal for those who can describe logic clearly but lack deep coding experience
Code Ownership	Full control over every line, with high precision	AI takes first pass; developers act more like reviewers, editors, and system designers
Error Handling	Debugging done manually with logs, breakpoints, etc.	Some tools support auto-debugging or error correction via conversational refinement
Best Use Cases	Mission-critical systems, low-level programming, custom algorithms	UI/UX development, CRUD apps, integrations, internal tools, rapid experiments
Collaboration Model	Developer-centric; collaboration often requires shared codebases	More fluid; teams can align faster around feature specs while AI does the heavy lifting

Why Companies Are Adopting Vibe Coding

Data from Y Combinator indicates that a quarter of their Winter 2025 cohort has codebases that are 95% AI-generated. This shift allows startups to operate with fewer engineers—10 engineers can now accomplish the work that once required 50-100. Garry Tan, Y Combinator’s CEO, highlighted this transformation, noting substantial efficiency gains. Companies leveraging vibe coding report completing in weeks what traditionally took months.

Where Vibe Coding Works Best

Rapid prototyping
UI/UX development
Repetitive backend tasks
Solo projects or early-stage startups
Code scaffolding and refactoring

Essential Skills for Vibe Coders

Success in this new coding paradigm requires a unique skill set:

Clear Communication: Articulating technical requirements effectively.
Pattern Recognition: Identifying when AI-generated code requires adjustments.
Systems Thinking: Understanding how various components interact.
UX Intuition: Prioritizing user experience in final products.

Karpathy emphasizes that while technical knowledge remains valuable, it is more crucial to recognize good code than to write every line independently.

When Vibe Coding Excels and Its Limitations

Vibe coding is particularly effective for:

Rapid Prototyping: Quickly developing minimum viable products (MVPs).
User Interfaces: Designing responsive and modern front-end applications.
Standard Features: Implementing common functionalities like authentication or payment processing.
Solo Projects: Allowing individual developers to achieve what typically requires teams.

However, challenges arise in areas such as:

Complex Algorithms: Where mathematical precision is critical.
Mission-Critical Systems: Like healthcare or financial applications where bugs can have severe repercussions.
Security-Sensitive Components: Where AI could inadvertently introduce vulnerabilities.
Legacy System Integration: Where undocumented code behaviors pose challenges for AI.

A cloud architect’s experience highlights the risks: an AI-generated infrastructure code for Azure missed crucial security configurations, resulting in significant issues post-deployment.

Career Implications in the Vibe Coding Era

Developers must adapt their strategies based on experience level:

Junior Developers:

Utilize tools like GitHub Copilot to enhance learning.
Focus on understanding AI-generated code rather than just using it.
Participate in AI developer communities on platforms like Discord and LinkedIn.

Mid-Level Developers:

Integrate AI into existing workflows while maintaining traditional skills.
Develop expertise in evaluating and refining AI outputs.
Use AI to automate tedious tasks, allowing time for creative endeavors.

Senior Developers and Tech Leads:

Create strategies for incorporating vibe coding into team processes.
Establish review protocols for AI-generated code.
Focus on system design skills that AI struggles with, ensuring effective team training in prompt engineering.

A balanced approach is crucial, as one developer noted, “Our team still writes traditional code when it makes sense, but we can now build 5x faster by knowing when and how to leverage AI.”

The Future of Software Development

The emergence of vibe coding signals a shift towards a more conversational form of programming. While this trend can democratize coding, it also raises questions about the future of software engineering roles. The ability to articulate requirements and manage AI outputs will become increasingly critical.

Vibe coding does not eliminate the need for skilled developers; instead, it reshapes their roles. As companies embrace these changes, SSOJet’s API-first platform offers secure SSO and user management solutions tailored for enterprise clients, featuring directory sync, SAML, OIDC, and magic link authentication. Explore our services to enhance your development processes effectively.

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Vibe Coding: Build Software by AI