Is China Building the Future of AI Governance Through Open-Source Modeling?
China’s rapid advancements in artificial intelligence (AI) are significantly influenced by major tech companies such as Alibaba, Baidu, Tencent, and iFlytek. A pivotal aspect of this development is a strong emphasis on open-source collaboration, which is reshaping the landscape of AI governance.
Open-Source Models Leading the Charge
Models like Alibaba’s Qwen 3 series and Qwen 2.5 are designed on open frameworks that encourage developer contributions and integration across various platforms. These models are competitive against prominent versions like GPT-4 Turbo and have earned the title of open-source king, making significant contributions to the global open-source AI ecosystem.
Baidu’s ERNIE series, which includes the widely adopted ERNIE Bot, and Tencent’s Hunyuan model also thrive within this ecosystem. They benefit from an environment where research institutions, startups, and industry players openly share tools, datasets, and model architectures. Similarly, iFlytek’s Spark 4.0 Turbo has shown remarkable benchmarks, exemplifying the success of this multistakeholder, open innovation strategy.
A Decentralized Approach to AI Development
In contrast to the more closed and proprietary models seen in the United States, China leverages state support and open-source infrastructure to accelerate collective progress. This strategy allows companies to build, iterate, and deploy foundation models at scale while fostering a uniquely domestic AI ecosystem. This progression not only signifies China’s growth in AI capabilities but also underscores Beijing’s ambition to shape the future of global AI governance.
Rather than retaliating against U.S. attempts to block access to critical technologies through export control measures, China is adopting a decentralized approach that aims to secure its industrial base over the long term. This strategic shift toward open-source AI development aligns with China’s guerrilla economic strategy, which seeks to exploit weaknesses in global supply chains, strengthen ties with the Global South, and present its domestic innovations as viable alternatives to Western technologies.
Positioning AI as a National Priority
China’s framing of AI as a critical national priority is not only about enhancing national competitiveness but also about showcasing a thriving private sector operating under state control. The evolving nature of China-U.S. AI competition revolves around which countries will lead the next wave of AI innovation and how different global powers will respond.
Strengthening this policy position is China’s advocacy for its open-source model as an ideological tool to challenge the dominance of Western technology. President Xi Jinping has stated that AI development “should not be a game of rich countries,” highlighting the need for inclusive AI governance. China has consistently raised this issue at global platforms like the United Nations through initiatives like the AI Capacity-Building Action Plan and the U.N. AI resolution.
Scaling Alternatives and Global Influence
China’s increasing focus on open-source AI enables it to develop alternatives that are less dependent on Western supply chains and licensing regimes. This strategy not only enhances technological resilience amid export controls but also positions China as a credible actor in promoting alternative norms and frameworks for global AI governance.
China’s AI diplomacy complements its homegrown technology development model, potentially undermining the existing influence of Western norms. The reliance of the United States on closed-source AI models may limit its ability to lead global conversations on responsible AI development.
Future Leadership in AI Governance
This evolving contest indicates that neither the Chinese nor the U.S. model is absolute. Future leadership in AI governance may hinge on each country’s capacity to adapt and reconcile these competing paradigms. As both countries reinforce their existing power structures while striving to uphold their ideological principles, a truly global AI development framework must prioritize shared governance, responsible and equitable access, and multilateral cooperation.
