By Jacques Bughin
China’s AI strategy reshapes competition far beyond foundation models and chatbots. As Jacques Bughin explains, managers must look at scale, integration and deployment rather than raw model power. Understanding these dynamics helps decision makers assess risk, opportunity and partnership in a market treating AI as national infrastructure, not isolated innovation.
Understanding China’s position in the AI race requires stepping away from Western assumptions that the contest is mainly about foundation models. In China, the model is only the starting layer. The real competitive engine lies upstream in cloud architecture and downstream in national-scale deployment. A manager evaluating partnership, competition, or opportunities in China must grasp ten essential realities that define the Chinese AI trajectory. Each is rooted in evidence, company cases, and the way the Chinese market actually operates.
When a manager evaluates the efficiency of a Chinese platform, it is the integration, not the model, that explains the leap in adoption.
The first reality is that China is not building AI as a loose collection of apps and platforms but as an integrated technology stack. This stack connects foundation models such as Alibaba’s Qwen or ByteDance’s Doubao with cloud providers including Alibaba Cloud, Tencent Cloud, and Baidu’s AI Cloud, and links further downstream to workflows running inside DingTalk, WeChat Work, Alipay, Meituan, and entire municipal service systems. This means that AI deployment in China is fast, uniform, and often invisible to the user. When a manager evaluates the efficiency of a Chinese platform, it is the integration, not the model, that explains the leap in adoption.
A second reality is that China builds for scale from day one. DingTalk, with hundreds of millions of users, deploys more workplace agents in a month than most Western enterprise SaaS firms deploy in a year. These agents are not demos but operational capabilities handling HR approvals, procurement flows, financial checks, travel validations, and compliance steps. This scale acts as an engine for rapid iteration, meaning China’s agentic systems evolve through millions of real-world feedback loops per day. Managers must understand that this scale advantage compresses innovation cycles dramatically.
A third truth is that China’s digital ecosystems are structurally unified. A Western manager is accustomed to siloed systems: ERP, HRIS, CRM, ticketing, payments, messaging. In China, the same firm may run daily operations, messaging, approvals, payments, forms, file storage, customer interactions, and analytics all inside a single super-app environment. DingTalk for enterprises, WeChat Work for SMEs, and increasingly ByteDance’s Feishu enable agentic automation with almost no integration overhead. This is why multi-agent workflows already appear in logistics, commerce, and city services: the ecosystem makes agent-to-agent coordination genuinely feasible.
A fourth factor is that Chinese firms prioritize multimodal and real-time agents rather than text-only assistants. ByteDance’s Doubao is optimized for video, image, and real-time signals because Douyin, TikTok’s sister platform, runs on real-time multimodal behavior. Baidu’s models focus on real-time reasoning because Apollo, its autonomous driving system, requires agents to coordinate across perception, planning, and fleet routing within milliseconds. Chinese AI strategy is shaped by sectors where real-time autonomy matters: retail operations, logistics, mobility, and urban services.
A fifth insight is that multi-agent systems are far more advanced in China than in Europe or the United States. In Baidu Apollo taxis, dozens of agents operate simultaneously: a perception agent, a prediction agent, short-horizon and long-horizon planning agents, and a fleet coordination agent. In ByteDance’s e-commerce engine, pricing agents, advertising agents, inventory agents, and logistics agents work in asynchronous negotiation loops to optimize conversion and cost. Tencent’s financial platforms use evaluator agents to monitor fraud-detection agents. These systems are operational, not prototypes. A manager analyzing competition should understand that China is not theorizing about multi-agent AI; it is deploying it.
A sixth reality is that China’s industrial and manufacturing base is uniquely suited to agentic automation. Firms like Haier, Midea, Geely, BYD, and CATL already run digitalized factories with IoT, MES systems, centralized scheduling, and real-time data visibility. This foundation enables agentic systems to take over scheduling, quality control, machine setup, procurement coordination, and energy optimization. Siemens and ABB operate globally and are strong in Europe, but China is deploying at a faster internal velocity because the country has more greenfield plants and fewer legacy integration obstacles.
A seventh point is that regulatory structures in China support rapid iteration of enterprise AI. China’s AI regulations emphasize platform accountability rather than restrictive usage controls. For enterprise AI, this means firms can deploy agentic systems across workflows without facing the friction of overlapping data, privacy, or compliance requirements found in Europe. Chinese privacy law is real, but enforcement patterns focus on misuse and societal harm, not innovation. Managers should understand that regulatory speed is part of China’s competitive advantage.
An eighth truth is that Chinese consumer behavior accelerates agent adoption. Chinese users are accustomed to automation, from mobile payments to autonomous delivery robots. This cultural readiness dramatically reduces the adoption friction for AI-driven services. It is no accident that Meituan deploys hundreds of autonomous delivery units or that JD.com uses intelligent warehouses with agents coordinating robots. The population accepts and expects automation. This allows Chinese companies to deploy agentic systems at a depth Western companies cannot match without cultural change.
A ninth insight is that China’s mobile-first economy forces AI companies to optimize for inference efficiency, not model size. Chinese AI firms build leaner, faster reasoning models such as Qwen-1.5B, Doubao Lite, and Tencent’s small Hunyuan variants because these models run directly on smartphones, point-of-sale terminals, and industrial devices. Managers who believe the Chinese AI race is about parameter counts misunderstand the real technology direction: China’s competitive edge lies in low-latency, cost-efficient, highly-deployed agent models.
China is building not a collection of AI tools but a national operating system for agentic intelligence.
A final and crucial point is that China’s AI strategy is not simply technical; it is geopolitical. Every deployment of an agentic workflow inside DingTalk, every multi-agent system inside a factory, every city adopting Baidu’s fleet-level autonomy, and every ByteDance agent operating cross-border commerce strengthens China’s position in global value chains. Managers must realize that AI in China is tied to industrial policy, national competitiveness, and economic sovereignty. When a Chinese firm deploys an AI agent, it is not merely automating a task; it is reinforcing the country’s position in global supply chains.
Together, these ten elements form a picture of a deeply coordinated AI economy. China does not win because it trains bigger models. It wins because it deploys agents deeper inside digital ecosystems, industrial infrastructure, and consumer environments.
Western managers must stop evaluating China through a generative AI lens and start evaluating it through the lens of agentic automation. China is building not a collection of AI tools but a national operating system for agentic intelligence.
Jacques Bughin
