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The Rise of Cursor

· 4 min read
tanzhuo

Cursor is probably one of the fastest-growing developer tools of the past two years. This post traces its trajectory: starting from traditional IDEs, looking at what this AI-native editor actually did—and what problems it faces.

Background: IDEs as Programming Tools​

For a long time, software development relied mainly on traditional IDEs like Visual Studio Code, IntelliJ IDEA, and Eclipse. Their core capabilities were code editing, syntax highlighting, autocompletion, and debugging tools—but the actual code logic still had to be written by the programmer.

It wasn't until large language models (LLMs) arrived that software development entered the era of AI programming. The earliest wave of AI coding tools included GitHub Copilot and Tabnine, which mainly tackled code completion. Cursor's goal, however, was never simple completion. It was:

Build an AI-native development environment.

The Birth of Cursor​

Cursor is an AI-native code editor (AI-Native IDE) developed by Anysphere. The company was founded in 2022 by four MIT graduates:

  • Michael Truell
  • Aman Sanger
  • Sualeh Asif
  • Arvid Lunnemark

Cursor was officially released in 2023. Technically, it is:

An AI IDE built as a fork of VS Code

On top of the VS Code foundation it added a large set of AI features: AI code generation, AI debugging, AI refactoring, AI codebase queries. Cursor's goal was crystal clear:

Let developers write code directly through natural language.

Cursor's Core Technical Design​

Cursor's design philosophy centers on three key points.

1) AI Code Generation​

Cursor generates code through large language models. A developer can simply type "implement a Redis distributed lock," and Cursor will generate the code, comments, and tests. This is completely different from traditional IDE code completion:

  • Traditional IDE: the developer writes code, the IDE assists with completion
  • Cursor: the developer describes the requirement, the AI generates the code

This mode of working has come to be known as Vibe Coding.

2) Codebase Understanding​

One of Cursor's core capabilities is understanding the entire code repository. It builds vector indexes of the code, along with file relationships and dependencies, so a developer can ask "where is the user login logic in this project?" and Cursor will locate the relevant code. This is similar to a RAG + code embedding approach.

3) AI Code Modification​

Cursor supports a capability called Smart Rewrite. For example, given "make this module asynchronous," Cursor can automatically modify multiple files, adjust functions, and update call sites. This is nearly impossible in a traditional IDE.

Cursor's Rapid Growth​

Cursor's growth has been astonishing. Less than two years after release:

  • ARR exceeded $300 million
  • Funding raised exceeded $1 billion
  • Company valuation approached $30 billion

Cursor became one of the fastest-growing AI SaaS products ever. Many companies started using it—Nvidia, Stripe, Spotify—with Nvidia having over 30,000 engineers writing code with Cursor.

Cursor's Technical Challenges​

Despite Cursor's popularity, academic research has pointed out some issues: AI coding assistants speed up development, but may increase code complexity and the number of warnings.

One research paper noted:

After adopting AI coding assistants, project velocity improves in the short term, but code complexity rises as well.

Another study found:

AI code assistants are easily influenced by faulty code in their context, leading them to generate defective code.

Which is to say, AI programming still requires developers to review and stay in control.

Cursor's Future Direction​

Cursor's vision is aggressive: software development may eventually become "natural language → software system." The development workflow could turn into:

Requirements
↓
AI generates code
↓
AI generates tests
↓
AI deploys automatically

In other words, software development may shift from "writing code" to "describing requirements." Cursor has even articulated a vision:

One day, developers will only need to write 50 lines of pseudocode to generate 2,000 lines of complete code.

Wrapping Up​

By forking VS Code, Cursor quickly acquired a mature editor foundation, then built LLM capabilities into code generation, repository understanding, and cross-file modification—hitting the AI programming boom at exactly the right moment. The staggering growth numbers show the approach works, but the research is a reminder that AI-generated code brings new problems like rising complexity. No matter how powerful the tool, the developer remains ultimately responsible for code quality. That's unlikely to change for the foreseeable future.

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