Introduction to Vibe Coding

A new term is rapidly spreading in the tech community—Vibe Coding. The concept is simple: instead of writing code line by line, you describe your requirements to an AI, which generates, debugs, and even deploys the code for you. This sounds like science fiction? In the Winter 2025 batch of Y Combinator, 25% of startups had over 95% of their code generated by AI. This is not a trend; it’s reality. If you want to experience the practical performance of models like ChatGPT, Claude, and DeepSeek in programming scenarios, Kula (t.kulaai.cn) offers a multi-model aggregation platform for direct comparison of various models in code generation, debugging, and architecture design.

According to a recent report from CITIC Securities, Vibe Coding is defined as a “paradigm revolution in programming for the AI era,” predicting that it will create 3 million related jobs by 2030. Currently, 92% of American developers use AI coding tools daily, with AI generating 41% of code globally. Software development is undergoing the most profound paradigm shift since the birth of the internet.

Evolution of AI Programming Tools

The evolution of AI programming tools can be divided into three stages:

Stage One: Code Completion (2021-2023). GitHub Copilot pioneered this category. The core model is that you write a line, and AI completes it, essentially an advanced version of autocomplete. During this stage, AI programming assistants functioned more like a “super search engine,” retrieving the most likely next line from a vast pool of open-source code.

Stage Two: Conversational Programming (2024-2025). The rise of Cursor marked a turning point. Developers can converse with AI, describing their needs, and the AI generates code after understanding the entire project context. The Composer mode allows AI to modify multiple files simultaneously, while the Codebase index helps it understand project structure. AI evolved from a “completion tool” to a “pair programming partner.”

Stage Three: Intent Programming (2026 to present). The emergence of Claude Code has completely rewritten the rules. Developers no longer need to understand every detail of the code; they only need to describe “what result they want,” and the AI autonomously completes the entire process of requirement analysis, architecture design, code writing, testing, and bug fixing. This is Vibe Coding—humans are responsible for “intent,” while AI handles “implementation.”

The essential difference among these three stages is that the distance between humans and code is increasing, while the distance to “problem-solving” is decreasing.

Competition Among Four Major Tools

The current competitive landscape of AI programming tools can be summarized with four players, each occupying a different ecological niche:

GitHub Copilot: Efficiency Player. Relying on OpenAI’s model and GitHub’s code ecosystem, Copilot remains the fastest for daily code completion. Its $10/month personal plan is affordable, and deep integration with VS Code and JetBrains makes it a standard for teams. However, Copilot’s limitations are evident—it has limited context understanding and struggles with complex logic in large projects.

Cursor: Project-Level Operator. Cursor’s core competitiveness lies in its “global understanding.” The Codebase index allows it to comprehend the entire project structure, while the Composer mode enables cross-file collaborative modifications. When you need to refactor a legacy project with 500 files, Cursor’s value becomes apparent. However, its pricing of $20-40/month and the migration cost of an independent IDE make it more suitable for medium to large teams.

Claude Code: Deep Reasoning Expert. Claude Code is not a plugin but an AI agent that can run autonomously in the terminal. It can read and write files, execute shell commands, run tests, analyze error logs, and self-correct. For complex tasks like “help me identify performance bottlenecks in this microservice architecture and provide optimization solutions,” Claude Code’s deep reasoning capabilities are unmatched by other tools. The downside is its slower response time and high usage costs based on tokens.

Domestic and Open Source Alternatives: Windsurf / Cline / Cursor. In China, Tencent’s CodeBuddy extends AI capabilities from programming to project management and enterprise office scenarios, while Baidu’s Comate and Alibaba’s Tongyi Lingma continue to optimize in the Chinese programming context. The open-source community’s Cline offers developers a fully controllable AI programming agent solution.

These tools do not replace each other but form a “tool matrix”: use Copilot for daily completion, Cursor for project refactoring, Claude Code for deep challenges, and self-developed solutions for enterprise integration. The smartest developers are already using a mix of tools, switching based on task complexity.

AI Agents: From Writing Code to Autonomously Completing Projects

If Vibe Coding changes the way “humans write code,” AI agents are changing how “humans manage projects.”

The traditional development process involves: product managers writing PRDs → engineers breaking down tasks → writing code → testing → deployment → operations. Each step requires human intervention and coordination. The vision for AI agents is that you provide a high-level goal—such as “build a customer service system that supports real-time chat”—and the agent autonomously completes the entire process from technology selection, architecture design, code writing, testing, deployment, to operations.

Currently, this vision is far from fully realized, but progress is faster than most people expected. Claude Code can autonomously complete medium-complexity functional development tasks, and OpenAI’s Codex Agent performs well in independent project builds. The promotion of the MCP (Model Context Protocol) standardizes collaboration among different AI tools, allowing developers to combine various AI capabilities like building blocks.

Open-source frameworks like openclaw further lower the barriers to building AI agents. From automating office processes to intelligent customer service systems, from code review robots to autonomous operation agents, AI agents are moving from the lab to production environments. Gartner predicts that by 2028, at least 15% of daily work will be completed by AI agents.

Redefining the Role of Developers

This is the most sensitive and important topic: Will AI replace programmers?

The answer is: AI will not replace “programmers,” but it will replace “programmers who only write code.”

Consider the current capabilities of AI programming tools. They excel at generating boilerplate code, implementing common algorithms, writing unit tests, identifying known bug patterns, and refactoring regular code. These tasks are primarily the domain of junior developers.

What they struggle with are: understanding ambiguous business requirements and making reasonable technical decisions, weighing multiple constraints, designing long-term evolving system architectures, tackling unprecedented technical challenges, and communicating and coordinating with people. These are the core values of senior developers.

This means the competency model for developers is undergoing a fundamental transformation:

• From “coding ability” to “problem definition ability”—you don’t need to write every line of code, but you need to accurately describe what you want and judge whether the AI’s output is correct.
• From “single skill” to “AI tool orchestration ability”—knowing which tasks to assign to which tools and how to combine multiple tools to form an efficient workflow.
• From “code craftsman” to “system architect”—higher-level design decisions and a global perspective become more valuable.
• From “independent development” to “human-machine collaboration”—learning to pair program with AI and become a manager of AI teams.

The AIGC industry white paper introduces an interesting concept: “Context Engineering”—the ability to transform implicit knowledge within enterprises into structured context understandable by AI will become one of the core competencies of the future. Those who excel at this will be the most scarce “translators” in the AI era.

Beyond Programming: AI Tools Reshaping All Knowledge Work

The evolution of AI programming tools is, in fact, a microcosm of the broader trend of AI empowering knowledge work.

In the AI search domain, tools like Perplexity and DeepResearch are changing how people access information—from “keyword matching + manual filtering” to “natural language questioning + structured answers,” search engines are being replaced by “answer engines.” Over 60% of brand marketing leaders are unsure whether their products will appear in AI search results, and GEO (generative search engine optimization) is becoming the new battleground for marketing.

In the AI dialogue model field, models like ChatGPT, Claude, Gemini, DeepSeek, Tongyi Qianwen, and Kimi continue to enhance their capabilities in understanding complex instructions, generating high-quality text, and performing deep reasoning. The entry of new players like Xiaomi’s MiMo and Meituan’s LongCat signifies that large models have spread from pure AI companies to the entire tech industry.

In the AI content production sector, tools for AI novels, scripts, images, and music are maturing, allowing one person to accomplish what previously required a team for content creation.

These changes point to a trend: AI is evolving from a “tool” to “infrastructure,” much like electricity and the internet, no longer an optional means of efficiency enhancement but a fundamental prerequisite for work methods.

Who Will Be Eliminated and Who Can Seize Opportunities

Finally, some hard truths.

For individual developers, refusing to use AI programming tools is not “upholding tradition” but “actively giving up competitiveness.” With 92% of American developers using them daily, if you don’t, your productivity will lag significantly. However, blindly trusting AI-generated code and deploying it without review is also dangerous. AI-generated code may contain security vulnerabilities, logical errors, and performance issues; human judgment and review capabilities remain the last line of defense.

For entrepreneurs, the current window of opportunity lies in the deep integration of “AI + vertical scenarios.” The competition among general AI programming tools is fierce, but in specific industries (like financial compliance code, medical data analysis, game development), AI programming solutions combined with domain knowledge remain a blue ocean.

For technical team managers, the key decision is not “whether to use AI” but “how to establish workflows and quality control systems for the AI era.” Code reviews, security audits, and architectural decisions cannot only not be replaced by AI, but they also become more critical due to AI’s extensive involvement.

Vibe Coding is not the end; it is the beginning. In the next two to three years, we are likely to see AI agents evolve from “assisting programming” to “autonomously completing projects,” AI search evolve from “replacing search engines” to “replacing most research work,” and AI content tools evolve from “assisting creation” to “autonomous creation.”

Developers will not disappear, but the definition of “developer” will be completely rewritten. Those who adapt to this change will find their productivity amplified tenfold. Those who resist this change will find themselves left behind by the times.

The choice is in everyone’s hands.