Physical AI is reshaping the global competitiveness of Chinese manufacturing: From being the world's factory, to the factory of factories, and now towards the development of industrial foundation models.

Summary of Key Points

The core message of this article is that physical AI is fundamentally reshaping Chinese manufacturing. It is driving China's transformation from being the “world’s factory” (exporting end products) to becoming a “factory of factories” (providing production capabilities) and a “source of industrial foundational models” (setting industry standards), ultimately redefining the global trade and manufacturing landscape. Physical AI does not merely enhance machine intelligence or automate certain processes; it enables factories to perceive their environment, make real-time adjustments, and continuously learn, thus creating new ways of organizing production. With its comprehensive range of industries, diverse application scenarios, and rapid coordination capabilities, China holds a unique advantage in this field. In the future, manufacturing competition will shift from a focus on labor costs to a battle for system capabilities.

Detailed Analysis

1. Physical AI: Not Just “Smarter Machines,” but the New “Brain” of Factories

What exactly is physical AI? Simply put, it involves integrating AI directly into robots, production lines, drones, and other physical devices, allowing them to perceive their surroundings, think independently, and act accordingly. For example, a robot can detect deviations in part placement during assembly and automatically adjust its movements; a production line can optimize its processes in real-time based on order changes.

Differences from Traditional Automation:

Traditional automation relies on fixed programs that repeat predefined actions (such as screwing screws along a set path), whereas physical AI uses multi-modal large models (combining vision, language, and motion) to learn and reduce the need for human intervention, making it more adaptable to complex situations.

Differences between China and the US in Physical AI:

• The US focuses on “model-driven” approaches, emphasizing large models, closed-source systems, and advanced computing power (e.g., Tesla’s vertical integration and computational dominance).
• China adopts a “scenario-driven” strategy, leveraging its extensive factory experience to quickly deploy and scale AI solutions. China accounts for 54% of the world’s industrial robot installations, creating a vast “AI testing ground” where models can be continuously refined in actual production.
• China also has a cost advantage, with sensor and servo motor prices being over 30% lower than those in the US, which facilitates the widespread adoption of physical AI in factories.

2. Chinese Manufacturing: Moving from Selling Products to Selling Capabilities

China used to be known as the “world’s factory,” mainly exporting consumer goods like clothing and smartphones. Now, it is transitioning to becoming a provider of production elements and system capabilities, such as high-precision components, automated equipment, and industrial control systems.

Example: Emerging manufacturing hubs like ASEAN, India, and Mexico undertake final assembly tasks (e.g., smartphone assembly), but they rely on China for critical components and equipment. Data shows that China’s exports of electronics and machinery increased by 21.8% in early 2026, reflecting a shift towards higher-value added products rather than low-cost manufacturing.

The Role of Intermediate Goods:

Even if direct trade between China and the US declines, China still plays a crucial role in global production through the supply of intermediate goods. For instance, more than half of the components in smartphones exported from Vietnam come from China. This interdependence makes China’s position in global manufacturing more stable and less replaceable.

3. Production and Organization: From Rigid Processes to Flexible Collaboration

Physical AI is transforming how factories operate and are organized:

• Production: Moving from rigid automation to “embodied intelligence.” Traditional production lines were designed to produce a single product; with physical AI, they can quickly switch between different products (e.g., producing cars with various configurations). Industrial foundational models (IFMs) are key, as they can be applied across different scenarios without reprogramming.
• Organization: The traditional hierarchical structure is being replaced by human-machine collaboration. AI processes data in real-time, allowing frontline workers to adjust production schedules directly. Job boundaries are becoming blurred, and workers are shifting from operating machines to monitoring and optimizing AI systems.
• ESG (Environmental, Social, Governance) Considerations: Physical AI can improve energy efficiency (e.g., by adjusting equipment power levels) and reduce carbon emissions. It also replaces dangerous or repetitive tasks, enhancing production safety.

4. The Global Manufacturing Landscape: Which Countries Will Gain the Upper Hand?

The article uses a four-quadrant model based on automation maturity and the degree of integration with China’s industrial chain to describe different global manufacturing pathways:

• China’s Path: High levels of automation and integration with Chinese industries, with local factories serving as benchmarks and ASEAN becoming advanced manufacturing hubs that rely on Chinese equipment and systems.
• Europe and the US: Focusing on high automation but with lower integration, particularly in sectors like semiconductors and defense, where supply chain security is prioritized through policy subsidies.
• Southeast Asia: Starting with low levels of automation but gradually upgrading to higher levels by adopting Chinese equipment and physical AI (e.g., some Vietnamese factories are approaching China’s standards).
• Africa: Lacking advanced automation and integration, Africa relies on Chinese basic equipment but faces challenges due to infrastructure and institutional constraints.

Future Trends:

Within 30 years, the focus of manufacturing will shift from labor costs to system capabilities. From 2025 to 2035, AI will enhance human productivity; from 2035 to 2045, systems will play a central role in production integration; and from 2045 to 2055, manufacturing locations will be determined by technology (computing power, algorithms) and infrastructure (electricity, networks).

5. Business Opportunities for Companies

In the era of physical AI, the competition lies not in whether a company uses AI but in its ability to define new industry standards:

• From Product Selling to Standard Output: Creating foundational industry models and control protocols to ensure that upstream and downstream companies adhere to these standards (e.g., becoming the “operating system” for the industry).
• Reorganizing Human-Machine Relationships: Designing roles that facilitate collaboration between humans and machines (e.g., algorithm optimizers, system coordinators) and building unified platforms to integrate data from various devices.
• Building Resilience: Preparing for potential disruptions such as computational power outages and energy fluctuations by deploying local computing resources and distributing production across multiple regions.

Conclusion

Physical AI is not just about improving existing manufacturing processes; it aims to establish a new industrial model centered around equipment, data, and algorithms. The country that successfully develops this model will dominate the next generation of global manufacturing.