With the rise of generative language models (LLMs) like ChatGPT, Claude, Gemini, and Copilot, we are witnessing a new cycle in software engineering. These are not just tools that complete lines of code—they are platforms that interpret ideas, build functional blocks, adjust syntax, and optimize structures almost instantly. We are shifting from “design before code” to “idea-to-code.”
From intuition to deployment
What makes vibe coding so transformative is its ability to drastically reduce the gap between conception and execution. Instead of starting with a detailed architecture or extensive diagrams, the developer begins with a command, a sketch, or a question. The AI responds with a proposed solution. The process continues in rapid cycles of refinement and validation, with the human acting as editor and strategist.
This dynamic will automate repetitive development tasks such as boilerplate generation, unit testing, automation scripts, and basic refactoring. Developers can then focus on what truly matters: architecture, user experience, and solving strategic problems.
It’s no coincidence that small teams are achieving exponential results. Interviews published by accelerator Y Combinator show that startups with fewer than 10 people are reaching million-dollar revenues—something unthinkable just a few years ago. The combination of generative AI, cloud environments, and collaborative tools is leveling the playing field, enabling small teams to deliver value at global scale.
In large enterprises, this new paradigm is already a reality. During LlamaCon 2025—the first conference dedicated exclusively to developers and researchers working with Meta’s Llama AI models—Microsoft CEO Satya Nadella revealed:
Between 20% and 30% of the code in the company’s repositories is now AI-generated. This underscores how quickly AI-assisted development is being integrated into large-scale software engineering workflows."
The global market for generative AI software—including vibe coding, low-code, and no-code platforms—is projected to grow from approximately $196.63 billion in 2022 to nearly $1.81 trillion by 2030, with a compound annual growth rate (CAGR) of 37.3%, according to Grandview Research.
But it’s not all sunshine and rainbows
Still, the landscape is not without challenges. Vibe coding is not a magic shortcut to quality, nor is it a replacement for traditional engineering. It demands greater technical discernment, critical thinking, and awareness of architecture, version control, testing, security, and scalability.
When used irresponsibly, vibe coding can create technical debt that’s hard to resolve. Code that “works” is not necessarily sustainable code. That’s why the developer’s role as solution architect remains central. AI is powerful, but it still operates based on statistical patterns and needs context, clear goals, and human curation.
This is not a revolution that breaks from the past—it’s an evolution that expands the possibilities for those who already master the fundamentals of software engineering.
The developer as orchestrator
We are witnessing the emergence of a new developer profile: someone who blends product vision, technical skills, and mastery of AI tools. Someone capable of translating business problems into technical solutions, supported by generative assistants but without relinquishing technical responsibility. Instead of less code, we are moving toward better code—faster and with less friction.
Vibe coding doesn’t eliminate the complexity of development. But it gives us a new way to face it: with creativity, autonomy, and more powerful tools than ever. AI didn’t come to replace programmers. It came to change what it means to “develop.”
Tips to make the most of vibe coding:
Start with clarity
- Define your vision objectively
- Use tools like ChatGPT, Claude, or Gemini 2.5 Pro to organize ideas, set goals, and map out features
Choose the right tool for you
- Beginners: start with Bolt.new, Replit, or Lovable
- Experienced developers: opt for VS Code, Cursor, or Windsurf for more sophisticated workflows and advanced control
Create a project plan with AI
- Draft a README.md or project.md file in your project folder with:
- Description of what will be built
- Planned features
- Future ideas
- Work with AI to simplify this plan and develop it section by section
Write specific prompts
- Instruct the AI clearly
- Avoid vague phrases. Provide context, goals, and constraints whenever possible
Build in small blocks
- Break the project into smaller parts
- Implement, test, and commit block by block
- This approach makes debugging and continuous evolution easier
Test rigorously
- Never assume AI-generated code is perfect
- Apply unit tests, integration tests, and simulations in different environments
Use advanced vibe coding tools
- VS Code: AI integration, smart suggestions, and efficient debugging
- Cursor: ideal for exploring, editing, and generating code with continuous AI assistance
- Windsurf: focused on productivity with robust support for teams and automation
Technical care and best practices
- Avoid unnecessary abstractions; prefer simple, readable solutions
- Use global variables with caution
- Document and comment: even if AI generates the code, write comments and maintain clear documentation