How China’s affordable, accessible AI strategy is shaping global innovation
The narrative around “AI at all costs” seems to have lost some steam recently. Once heralded as an unstoppable force for growth, AI now faces critical questions about sustainability, particularly as funding models evolve and market sentiment shift.
One key indicator worth watching closely in comparison with the dot-com era is the source of funding for hyperscaler capital expenditure. Recent evidence shows that companies have started to rely on debt rather than free cash flows and earnings to finance their spending needs. This shift has contributed to a change in sentiment around an overheated AI boom and triggered a selloff in the latest U.S. equity market.
Meanwhile, closer to home, Asian stocks have also begun to consolidate after an extended rally, with the Hang Seng Tech Index retreating from a multi-year highs in October. Nonetheless, China’s distinct approach to AI, backed by strong policy support and a deliberate effort to balance competition with innovation, could drive Asia’s AI-driven rally further.
China isn’t chasing the same AI dream as the West. While U.S. firms focus on artificial general intelligence and high-performance models, China is taking a more pragmatic path, emphasizing efficiency, adoption, and ecosystem impact. The key question now is how China can balance intensifying competition with a more sustainable innovation trajectory.
Unlike the capital-intensive strategies of Western tech giants, Chinese firms are demonstrating that advanced AI can be built without top-tier hardware. Models like DeepSeek R1 and other open-source entrants reflect a shift toward cost-efficiency and accessibility. This has spurred a wave of open-source development among domestic players, prioritizing openness, replicability, and shared progress over pure proprietary control.
This pragmatic approach is shaped by necessity. Limited access to cutting-edge AI chips has prompted China to pursue a more commercially viable path focused on application-level success. Thus, a vertical strategy involving smaller models trained on proprietary data may be best suited for resource-constrained enterprises in China. Firms with domain-specific datasets, such as healthcare records or industrial logs, are well-positioned to train tailored models, build defensible moats, and eventually translate these capabilities into profitable and scalable offerings. This is an ongoing shift where China is moving away from pushing the limits of model size and focusing instead on developing practical applications built on existing architectures.
This shift toward practical applications also highlights the role of China’s open-source approach, which enables faster iteration through shared code, reduces entry barriers, and fosters a more collaborative innovation environment. By distributing development across a broader base of contributors, the ecosystem has become more resilient to external shocks and less reliant on any single chokepoint.
Looking ahead, China is well-positioned to lead in AI application development. The momentum sparked by DeepSeek’s breakthrough earlier this year has raised awareness and accelerated adoption on AI technologies. Major platforms or the so called “super apps” such as WeChat, Alipay, and Taobao are now integrating AI features into their existing ecosystems, leveraging proprietary data and user engagement to deliver highly personalized services. Generative AI usage has also surged in recent months, with the user base of Gen AI tools doubling since the start of this year1.
Practical use cases are emerging across China’ digital ecosystem. Take Alibaba’s recently launched Qwen, an AI-powered chatbot app designed to be a one-stop assistant for mobile users. Qwen will serve as an integrated personal AI shopping assistant on Taobao for a faster, more personalized and interactive experience. Alibaba also plans to integrate other key services such as maps, food delivery, ticket booking, and office tools into the platform.
The approach shows how Chinese firms are moving beyond traditional enterprise solutions and tapping into the fast-growing consumer AI segment.
Apart from these, AI adoption is also expanding across industries. In robotics, models are being used to train machines to perform tasks even in unfamiliar environments, a long-standing industry challenge. Chinese robots have already competed in marathons, sparred in boxing matches and performed folk dance routines.
In manufacturing, large language models (LLMs) are being embedded into advanced equipment such as smart connected vehicles and precision machine tools, while AI agents are increasingly deployed across the entire production lifecycle, from design and development to testing and implementation. Just look at the near fully automated factories producing electric vehicles around the clock without even requiring lighting.
In healthcare, authorities are advancing intelligent diagnostic tools and treatment assistance across primary-level medical institutions.
All of this reflects the government’s intention to make AI foundational and widely accessible, a goal reinforced by strong policy support. In August, the State Council’s AI+ Plan set out to integrate AI into six major sectors with a target of 90 percent adoption by 20302, while the latest Five-Year Plan strengthens this direction by designating AI as core infrastructure.
Even with a clear development strategy and strong policy support, the next challenge for China’s AI sector is finding a path that avoids the pitfalls of intensifying competition. Experience from industries such as autos, energy, and materials has already driven policy efforts to prioritize quality and innovation. Thus, a more sustainable path forward involves fostering innovation-driven growth, where development is shaped by differentiation, operational efficiency, and scalable impact rather than just price-based competition.
China’s ambition to position its open-source AI models as global benchmarks has also attracted attention from U.S. companies and policymakers. For domestic firms, international expansion is increasingly becoming a strategic priority, especially as many Chinese models already deliver comparable performance at lower cost. According to OpenCompass, while U.S. firms continue to lead in certain benchmarks, Chinese companies are rapidly narrowing the gap. Of the top 20 models, 10 are from China, including both established major players alongside emerging startups3.
With a well-calibrated approach that combines government support, open-source collaboration, and a balanced competitive landscape, China is building an AI ecosystem focused on scale, accessibility, and long-term integration across the economy. A common analogy is the rise of smartphones, which connected billions not because they were the most powerful, but because they were affordable and good enough. Similarly, China’s leaner models could have broad impact across industries, both domestically and globally.
Over the longer term, AI will continue to be a megaforce that drives both equity returns and broader economic growth. For global investors looking beyond the U.S. for opportunities to participate in the global AI transformation, China is emerging as a compelling destination with a potentially higher risk-to-reward profile and a more sustainable upside.
A version of this article appeared in the South China Morning Post, titled How China’s focus on affordable, accessible AI will shape the future,' published on November 23, 2025.
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