China’s overcapacity and the future of AI dominance

Debates over the artificial intelligence rivalry between the United States and China often revolve around headline metrics: whose large language models perform better on benchmarks, who controls the most advanced semiconductor fabrication nodes, and who is attracting the most elite AI researchers. While these questions matter, they are increasingly insufficient for understanding where the real contest is headed. As artificial intelligence moves beyond chatbots and image generators into vehicles, factories, logistics networks and urban infrastructure, the decisive factor is no longer model elegance but deployment at scale. Viewed through this lens, China possesses a powerful and underappreciated advantage – one rooted in a phenomenon long considered a structural flaw: overcapacity.

For decades, economists have criticized China’s growth model for encouraging excessive investment, redundant industrial capacity and low returns on capital. Local officials have been incentivized to build factories, industrial parks and infrastructure regardless of demand, supported by state banks, local government financing vehicles and industrial policy mandates. This system has produced steel mills without buyers, solar panel factories operating at razor-thin margins and shipyards competing for limited global orders. In conventional economic terms, this is wasteful and distortionary.

Yet in the age of embodied and infrastructure-based AI, the same logic that generates overcapacity can become a strategic asset.

The future of artificial intelligence is increasingly physical. AI systems are being embedded into cars, robots, drones, manufacturing equipment and energy grids. These applications require far more than powerful algorithms. They demand cheap and reliable hardware, dense deployment across millions of devices, constant software updates informed by real-world data, and extensive supporting infrastructure – from data centers and power systems to smart roads and communications networks.

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