A government work report, summarizing achievements in 2025, emphatically stated that "domestic large language models are leading the global open-source ecosystem." Early in 2025, DeepSeek emerged, demonstrating to the world the vigorous power of open source in stimulating AI innovation and the vast potential of China's AI industry through its cost-effectiveness and open-source nature. By early 2026, OpenRouter, a global large model aggregation and routing platform for AI application developers, showed that the usage volume of Chinese large models had surpassed that of American models for two consecutive weeks. This year marked a breakthrough for domestic models, transforming AI from a specialized tool into a universal assistant. The rise of AI Agents disrupted the market, moving AI from laboratories into countless industries. Behind these changes, open source played a crucial role in disseminating technology. It not only spurred overall prosperity in related industrial chains like infrastructure, chips, and energy but also accelerated the process of AI application, turning it into a shared public infrastructure.

Jim Zemlin, Executive Director and CEO of the Linux Foundation, commented, "Behind the current flourishing AI innovation, open source is not merely a contributing factor but the core driving force." Open source has been instrumental in bringing Chinese large models to the global stage. It is now widely considered an essential requirement. Huang Tiejun, Chairman of the Beijing Academy of Artificial Intelligence, asserted, "Complete open source is the necessary path to advance artificial intelligence." Around the Spring Festival this year, companies like Alibaba and Stepfun released multiple open-source models in quick succession, attracting international attention. Tesla founder Elon Musk praised the newly open-sourced Qwen model on social media, calling it "impressive." NVIDIA CEO Jensen Huang also publicly acknowledged the significant role of Chinese models in the open-source ecosystem during CES 2026, specifically mentioning models like Kimi K2, DeepSeek V3.2, and Qwen.

Chinese open-source large language models have performed excellently in various benchmarks, demonstrating superior performance surpassing leading closed-source models in certain scenarios. Last year, the download volume of Alibaba's Qwen series of open-source models surpassed Meta's Llama models globally, claiming the top spot. Chinese open-source models dominated leaderboards; in February, all ten top-ranked models on the major open-source model leaderboard released by Hugging Face were either Chinese models or derivatives of Chinese models. Many international companies are adopting Chinese open-source models as a critical foundation for AI implementation. Research indicates that 80% of the open-source large models used by American developers originate from China. For instance, Amazon announced that its newly developed embodied AI model would be based on Qwen3.0, incorporating DeepSeek's distillation technology. The CEO of Airbnb stated publicly that their customer service system relies heavily on Chinese models, praising them as "better and cheaper than OpenAI."

This phenomenon confirms the innovative vitality and rapid iteration speed within China's AI technology layer, with a steady improvement in the degree of independent control over core technologies. Open source has provided China with an opportunity for rapid advancement on the international stage. At the national level, it is now regarded as a strategic project requiring systematic promotion. After being included in the 14th Five-Year Plan five years ago, "open source" was once again written into the outline of the 15th Five-Year Plan. During this year's National People's Congress sessions, the government work report explicitly proposed to "support the construction of AI open-source communities and promote a thriving open-source ecosystem."

As open source becomes an industry consensus, the focus of competition is subtly shifting. A significant change is that companies are moving from merely releasing models to actively managing ecosystems, where open-source projects, communities, licenses, contributions, and applications are interdependent and co-evolve. Those who can attract more developers and accumulate more application scenarios are more likely to gain an advantage in the next phase. In this process, AI Agents have become a crucial vehicle for amplifying the value of open source. This year, the government work report for the first time proposed to "create new forms of the intelligent economy" and included AI Agents as a key promotion direction. Compared to traditional AI, Agents are characterized by autonomy, planning capabilities, and execution power, enabling them to perform a series of complex tasks from content creation and customer service to data analysis and operational management. Consequently, they are seen as a key driver for revolutionizing productivity and lifestyles.

The open-source approach significantly lowers the barrier to using AI Agents. Individual developers and small-to-medium enterprises no longer need to build complex systems from scratch; they can quickly deploy and adapt solutions to build Agent applications tailored to specific needs and possessing practical production capabilities, making the concept of a "one-person team" a reality. The entire AI industry has keenly observed this trend. Internationally, NVIDIA launched the Nemotron 3 Super open-source model, designed specifically for large-scale Agent operation, increasing Agent AI throughput fivefold. Several domestic open-source models have also proactively adapted to the development needs of Agents, with targeted capability development. For example, Alibaba open-sourced Qwen3-Coder-Next, specifically designed for programming Agents and local development; Moonshot AI released its new-generation trillion-parameter open-source multimodal large model Kimi K2.5, pioneering Agent cluster scheduling capabilities; and Zhipu AI open-sourced its GLM-4.5 model, claiming the first native integration of reasoning, coding, and Agent capabilities within a single model to meet the complex demands of future Agent applications. It is evident that AI's role is shifting towards that of collaborative "digital employees," and capability building around this emerging form will become the next variable in open-source competition.

Open source is making AI as accessible as water and electricity, bringing the possibility of "ubiquitous intelligence available anytime, anywhere, to everyone." However, the proliferation of the open-source path also brings a series of potential challenges to the forefront. "Homogenization" is a common issue faced by open-source models. Currently, most mainstream models essentially follow the core Transformer design. As model iterations become increasingly frequent, the gap in general capabilities is narrowing, with different vendors' models routinely taking turns topping performance charts. Industry experts suggest that what will truly differentiate players in the future is deep industry understanding and implementation capability, not just the model itself.

"How to monetize" is another challenge for companies. While open source brings broader ecosystem influence, it inherently involves high investment with low, or even no, direct revenue, making it difficult to generate profits in the short term. Currently, most vendors primarily rely on customized deployment and value-added services for revenue, with overall profitable business models not yet fully established. Security concerns are equally important. There are long-standing worries within the industry about the potential misuse of open-source models. The rapid proliferation of open-source products also places new demands on users' security awareness. Recently, the popular open-source Agent application OpenClaw led to risk advisories from official cybersecurity platforms highlighting a series of high-risk security vulnerabilities and associated risks.

It has been suggested that a full-process security review mechanism for open-source technology should be established, involving regular security testing and vulnerability assessments for core open-source projects. Clear accountability for security should be defined to guard against cybersecurity risks and technology leakage, ensuring national digital security. Simultaneously, exploring diversified open-source business models is crucial, promoting deep integration between open-source projects and market demands. Through technical services, customized development, and ecosystem partnerships, developers and companies can achieve value returns within the open-source ecosystem.

Furthermore, deepening open-source governance and standardization布局 is essential. Efforts should focus on leading the development of industry standards in areas like data compliance and model evaluation, actively integrating into the international open-source governance system. Leveraging China's vast market and diverse application scenarios, domestic practices can be transformed into international rules, continuously enhancing influence within the global open-source ecosystem.