Why Java Remains China’s Tech Staple and the Next Languages to Master

Java: Core Enterprise Language in China

The JetBrains 2025 Developer Ecosystem report shows that globally less than 30% of developers list Java as their primary language, while in China the proportion rises to 58% . This disparity is driven by the extensive use of the Spring ecosystem (including Spring Boot, Spring Cloud), together with RPC frameworks such as Dubbo and ORM tools like MyBatis, in core business systems:

E‑commerce platforms, payment gateways, transaction middle‑ends and membership services are built on these stacks.

Enterprise hiring favors Java because the ecosystem is mature, well‑documented and risk‑controlled.

Individual developers choose Java for the high volume of job opportunities and a relatively low learning curve.

These factors create a stable industry loop: once a large B2C platform adopts Java‑based infrastructure, replacing it becomes costly and risky, so the language remains “hard to replace”.

Python: Dominant in AI Application Development

Python ranks second in the report. Its dominance stems from the rapid growth of AI‑related workloads, where Python is the lingua franca for:

Data preprocessing and feature engineering.

Model inference and serving (e.g., using Flask, FastAPI, or TensorFlow Serving).

Tool‑chain integration and automation scripts.

Mastering Python is therefore essential for developers who want to participate in AI product development.

JavaScript: Full‑Stack Versatility

JavaScript holds the third position. With AI‑assisted coding blurring the line between front‑end and back‑end, full‑stack competence becomes increasingly valuable. JavaScript’s ecosystem (Node.js, React, Vue, etc.) enables developers to:

Write server‑side services that complement Java or Python back‑ends.

Build interactive UI components that integrate AI‑generated content.

Leverage a single language across the entire application stack, improving productivity.

Other languages can be learned as niche supplements when specific scenarios demand them.

AI Coding Assistants in the Chinese Market

Among AI‑driven code assistants, the following observations were made:

GitHub Copilot remains the market leader both globally and domestically.

Cursor enjoys a higher adoption rate among Chinese developers than the global average, despite a perceived higher price. Users report strong productivity gains.

Web‑based assistants (e.g., DeepSeek) have a sizable user base; DeepSeek’s usage share in China is comparable to GPT’s.

Gemini has low visibility in China, though the upcoming Gemini 3 may change this.

Relying solely on web‑based assistants limits efficiency because they lack persistent context and require frequent tab switching. Embedding AI directly into IDEs (e.g., via native plugins or AI‑native IDEs) can dramatically improve workflow speed and reduce overtime.

Practical Resource for AI‑Assisted Coding

Developers new to AI‑augmented development can start with the open‑source repository: https://github.com/liyupi/ai-guide This repo provides a free “Vibe Coding” tutorial that walks beginners through setting up AI assistants, configuring IDE plugins, and executing simple code‑generation tasks. The accompanying online reading site is: https://ai.codefather.cn/vibe Following the tutorial helps developers integrate AI into their daily workflow, moving from “copy‑paste” habits to a more proactive, context‑aware coding style.