China’s New AI Path: From Policy and Compute to the Consumer “Entrance War” and a List of Popular Tools

• The 2025 Government Work Report includes “AI Plus” as a key priority, explicitly supporting the broad application of large models and the development of intelligent terminals and smart manufacturing equipment.
• With advanced chips constrained externally, China is placing more emphasis on an engineering-led approach that is deployable, operable, and scalable, while accelerating domestic compute systems (for example, Huawei Ascend and SuperPod-style clusters).
• On the consumer side, the market is entering an “entrance war”: AI assistants are no longer just for chat, but are becoming workflow entrances for search, reading, writing, and creation, with some products reaching tens of millions to over a hundred million monthly active users.

1. Why China’s AI now looks more like an industry strategy than a lab experiment

China’s AI momentum is moving from a “model release wave” to large-scale application diffusion. Policy signals have clearly placed AI at the center of industrial upgrading. In the 2025 Government Work Report, “AI Plus” is proposed, emphasizing the combination of digital technologies with manufacturing and market strengths, supporting the broad application of large models, and promoting next-generation intelligent terminals and smart manufacturing equipment. The message is straightforward: AI is not just a showcase of R&D成果, but something expected to be usable, affordable, and sustainable in real operations.

2. The rulebook: advancing development and governance at the same time

China has also formed an early regulatory framework for public-facing generative AI. The Interim Measures for the Management of Generative AI Services focus on “promoting healthy development and regulating application,” and define compliance expectations for providing generative AI services to the public (such as scope, responsibilities, and content governance). This directly shapes product direction: enterprise adoption tends to prioritize controllability, risk governance processes, and deployable models such as private deployments, industry customization, and data isolation.

3. Compute and chips: an engineering breakthrough under constraints

Advanced semiconductor export controls are a key background factor for understanding China’s AI trajectory. Related U.S. rules have aimed to restrict China’s ability to obtain or produce advanced semiconductors for high-end computing; analysis from U.S. policy and research institutions has also summarized how these controls evolved and how China may still obtain some capabilities through supply-chain gaps. Under these conditions, a common strategy in China is “system-level capability”: scaling total compute through large interconnected clusters rather than relying only on the peak performance of a single chip.

4. A domestic compute stack and “supernode” thinking

A concrete example is Huawei’s Ascend roadmap and its large-scale cluster strategy. Public reporting and Huawei’s own materials describe plans around Ascend generations and “SuperPod / supernode” style clusters that can interconnect thousands (and potentially more) of chips. This approach highlights a system engineering focus: interconnect, memory bandwidth, scheduling, and software stack become as important as raw chip specifications. It also explains why “cost-effective models” and “low-cost inference” are particularly valued: when single-chip limits are harder to overcome, efficiency and scalable deployment become the competitive core.

5. Industrial rollout: AI becomes workflow components, not a single tool

A key feature of China’s AI rollout is the shift toward practical, repeatable workflow modules rather than standalone chat experiences. Common adoption patterns include search and reading summarization, extracting information from research reports and announcements, customer service and operations analytics, document generation and review, retrieval-augmented knowledge assistants (RAG), quality inspection, predictive maintenance, and more. These applications may not look flashy, but they can save time, reduce cost, and improve conversion efficiency—then iterate quickly across large numbers of enterprise scenarios.

6. The consumer entrance war: whoever owns high-frequency entry points wins growth

On mobile, AI competition looks like an “entrance” battle rather than a single-feature race. AI assistants are turning into daily entry points for search, reading, writing, and task execution. Market reports have suggested that while the user base is large, competition is intense and retention is becoming the decisive factor—many AI-native apps experience churn as the market moves from “who launched first” to “who can keep users.”

7. A list of popular China AI tools in the market (consumer-focused)

1. Doubao (ByteDance)

• Positioning: a general AI assistant and content-creation/workflow entrance (text, voice, multi-scenario tasks).
• Market signal: often cited as a leading consumer AI app in China, with top-tier MAU.

2. DeepSeek

• Positioning: a general assistant plus a model ecosystem (including a strong presence around open models and reasoning).
• Market signal: frequently discussed as a representative competitor in China’s AI app landscape.

3.Yuanbao (Tencent)

• Positioning: a “general-purpose assistant” built on Tencent’s Hunyuan model stack, with tight integration into Tencent’s broader ecosystem (writing, translation, search, reading summaries, etc.).
• Market signal: widely tracked among the top tier of consumer AI apps.

4. Quark (Alibaba)

• Positioning: evolved from a browser/search entrance into Alibaba’s main consumer AI platform; positioned around integrating assistant capabilities into everyday browsing and search workflows.
• Market signal: often referenced as Alibaba’s key To-C AI platform push.

5. Kimi (Moonshot AI)

• Positioning: known for long-text handling, reading, and research-oriented tasks.
• Market signal: a recognized consumer AI brand, with competitive pressure highlighting how intense the entrance war has become.

6. Wenxiaoyan (Baidu; formerly ERNIE Bot / “Wenxin Yiyan”)

• Positioning: differentiated as more of a “search assistant,” emphasizing information discovery and integration.
• Market signal: reflects Baidu’s attempt to stand out in a crowded assistant market.

7. iFLYTEK Spark (Xinghuo / Spark)

• Positioning: strong in speech-related capabilities and education/office scenarios; more solution-oriented in many deployments.
• Market signal: often listed as a major player beyond the very top consumer-traffic leaders.

8. China vs. the U.S.: China is more “deployment-diffusion,” the U.S. more “frontier-breakthrough”

A simplified but useful comparison is that the U.S. often prioritizes frontier capability and research breakthroughs first, then productizes; China more often productizes quickly into high-frequency entrances and industrial workflows, prioritizing deployment, cost, integration, and operability—especially under hardware constraints. This does not mean China is not pursuing frontier research, but its most visible competitive advantages often show up in scaled deployment rather than benchmark leadership alone.

9. Global governance narrative: competing not only on products, but on rules

Alongside deployment, China is also active in AI governance narratives and international coordination. Related international-facing documents and conference initiatives have presented themes such as “AI for good,” safety and controllability, fairness and inclusion, and openness and cooperation. This suggests that China’s AI strategy is not only about building products and models, but also about influencing the broader governance framework around AI adoption.

Conclusion

China’s AI trajectory is best understood as “system-level diffusion”: policy encouragement of application, enterprises turning models into deployable workflow components, and consumer-side entrance wars that rapidly scale users and retention. In this playbook, benchmark leadership is not the only story—deployment capability, cost structure, governance mechanisms, and ecosystem integration increasingly define real competitiveness.