China’s strategy in artificial intelligence (AI) is evolving beyond mere foundation models and chatbots, presenting a unique competitive landscape that challenges traditional Western perspectives. According to Jacques Bughin, CEO of MachaonAdvisory and a former senior partner at McKinsey, managers must shift their focus from raw model power to considerations of scale, integration, and deployment when navigating this burgeoning market. Understanding these dynamics is crucial for assessing risks, opportunities, and partnerships in a nation where AI is treated as national infrastructure rather than isolated innovation.
To comprehend China’s position in the AI race, it is essential to recognize that the model is merely the starting layer of a much deeper system. The real competitive engine in China lies in upstream cloud architecture and downstream national-scale deployment. For managers evaluating partnerships or competition, there are ten essential realities that shape the Chinese AI trajectory. These insights are grounded in evidence and reflect how the Chinese market operates.
The first reality is that China is constructing AI as an integrated technology stack rather than a collection of disparate apps and platforms. This stack seamlessly connects foundation models—such as Alibaba’s Qwen and ByteDance’s Doubao—with cloud providers like Alibaba Cloud, Tencent Cloud, and Baidu’s AI Cloud. Further downstream, these models link to workflows running inside popular applications such as DingTalk, WeChat Work, and Alipay. This intricate integration fosters fast, uniform deployment, often invisible to the end user. When managers assess the efficiency of a Chinese platform, it is this integration that explains the notable leap in adoption.
Secondly, Chinese firms build for scale from inception. For instance, DingTalk, which boasts hundreds of millions of users, deploys more workplace agents in a month than most Western enterprise SaaS companies manage in a year. These agents are operational tools, handling tasks such as HR approvals and procurement flows. The scale not only drives rapid iteration but also compresses innovation cycles significantly, making it imperative for managers to recognize this advantage.
The third truth is that China’s digital ecosystems are structurally unified, contrasting sharply with the siloed systems typically found in the West. In China, a single company might oversee daily operations, messaging, approvals, payments, and analytics within one super-app environment. Platforms like DingTalk for enterprises and WeChat Work for SMEs enable agentic automation with minimal integration overhead, allowing for seamless multi-agent workflows across various sectors, including logistics and urban services.
Another distinction lies in the focus on multimodal and real-time agents in China. Unlike the text-only assistants prevalent in the West, models such as ByteDance’s Doubao are optimized for video and image processing, essential in dynamic environments such as retail and autonomous vehicles. Chinese AI strategy is thus heavily influenced by sectors where real-time responses are critical.
Moreover, multi-agent systems in China are operationally more advanced than those in Europe or the U.S. In Baidu’s Apollo autonomous taxis, various agents work simultaneously to optimize performance. Similarly, ByteDance’s e-commerce platform employs multiple agents in negotiating pricing and logistics, demonstrating a practical deployment of these systems rather than mere prototypes.
The sixth insight involves China’s industrial base, which is particularly conducive to agentic automation. Companies like Haier, Midea, and Geely already operate digitalized factories, allowing for the efficient implementation of agentic systems that oversee scheduling and quality control. The abundance of greenfield plants in China enables a faster deployment velocity compared to global counterparts.
Regulatory structures also play a crucial role in supporting the rapid iteration of enterprise AI. Chinese regulations emphasize platform accountability, enabling companies to roll out agentic systems without facing the compliance hurdles typical in Europe. While privacy laws exist, enforcement tends to focus on misuse rather than stifling innovation, which serves as a competitive advantage.
Chinese consumer behavior further accelerates agent adoption, with users accustomed to automation in services ranging from mobile payments to delivery robots. This cultural readiness significantly reduces friction for AI-driven services, facilitating extensive deployment of agent systems, as evidenced by companies like Meituan and JD.com.
Additionally, China’s mobile-first economy compels AI companies to prioritize inference efficiency over model size. By developing leaner, faster reasoning models, Chinese firms are able to optimize for low-latency applications across devices, from smartphones to industrial machines. This focus indicates that the competitive edge lies not in sheer model size but in the deployment of efficient agent models.
Ultimately, China is building a national operating system for agentic intelligence rather than merely a collection of AI tools. Each deployment of an agentic workflow contributes to reinforcing the country’s position within global supply chains, illustrating that China’s AI strategy is fundamentally intertwined with its geopolitical ambitions and industrial policy.
In conclusion, these ten elements paint a comprehensive picture of a deeply coordinated AI economy in China. As the nation continues to advance its AI capabilities, Western managers must recalibrate their assessments to recognize the strategic importance of agentic automation over generative AI models.
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