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.

In other words, the winning AI ecosystem rests on three pillars: mass-produced hardware, iterative software learning and physical infrastructure. China’s political economy, uniquely capable of mobilizing capital and scaling production even in the absence of immediate profitability, aligns closely with these requirements.

The electric vehicle sector offers the clearest illustration of how overcapacity translates into AI advantage. Autonomous driving and advanced driver-assistance systems (ADAS) cannot be perfected in laboratories alone. They depend on massive volumes of real-world driving data collected across diverse environments, weather conditions and traffic scenarios. This requires a large installed base of modern vehicles equipped with sensors, computing power and connectivity.

China has built such a base at unparalleled scale. More than 60 percent of EVs sold domestically now include some form of driver-assistance technology, often bundled at little or no additional cost. Fierce competition among manufacturers – itself a product of overcapacity – has driven prices down and accelerated adoption. As a result, millions of vehicles are continuously collecting data on road conditions, human driving behavior and system failures.

Each car effectively functions as a mobile AI laboratory. The more vehicles on the road, the faster algorithms improve. Overcapacity, by pushing down prices and encouraging rapid market saturation, subsidizes this learning process in a way that market-driven systems struggle to replicate.

China’s advantage does not stop at vehicles themselves. The state is simultaneously building the infrastructure to support AI-driven mobility, often ahead of demonstrated demand. The “vehicle-road-cloud” strategy exemplifies this approach. Under this model, cars are integrated into a broader digital ecosystem featuring dense 5G networks, smart roads equipped with cameras and sensors, high-definition mapping systems and centralized cloud platforms that coordinate traffic and update software.

Pilot zones in cities such as Beijing, Shanghai and Shenzhen allow autonomous driving technologies to be tested under relaxed regulatory frameworks. While critics argue that much of this infrastructure may be underutilized in the short term, its existence creates a ready-made environment for rapid AI deployment once technologies mature.

A similar pattern is emerging in China’s push to develop what officials call the “low-altitude economy” – the commercialization of airspace below roughly one kilometer. This includes delivery drones, agricultural drones and, eventually, flying taxis. Local governments across the country are racing to establish drone industrial parks, offering subsidies, tax breaks and procurement guarantees to attract firms.

This competition has once again led to falling prices. DJI, which already dominates the global civilian drone market, has cut domestic prices significantly, further expanding adoption. Lower costs have enabled widespread use in logistics and agriculture. Food delivery platform Meituan has completed hundreds of thousands of drone deliveries, while agricultural drones now spray a substantial share of China’s farmland.

Each deployment generates operational data that improves navigation, obstacle avoidance and coordination algorithms. As with EVs, scale begets learning, and learning reinforces scale.

China’s approach to industrial robotics reinforces the same dynamic. Backed by national industrial policy and local subsidies, robot manufacturing has expanded rapidly. Chinese factories install roughly half of all industrial robots deployed worldwide each year, with domestic firms supplying a growing share of these machines at lower prices than foreign competitors.

This mass adoption accelerates the development of robotics AI by exposing systems to countless real-world manufacturing scenarios. Errors, inefficiencies and edge cases are identified and corrected more quickly when tens of thousands of robots are operating simultaneously across diverse industries.

None of this means China leads in every dimension of AI. The United States remains ahead in frontier model research, advanced semiconductor design and elite AI talent. However, the nature of the competition is shifting. The next phase of AI development will be defined less by abstract benchmarks and more by integration into daily life – transportation, logistics, manufacturing, agriculture and urban management.

China’s willingness to tolerate overcapacity, thin margins and duplicated investment enables it to build the physical substrate for this transformation at unprecedented speed. What appears inefficient from a macroeconomic perspective can be highly effective as a strategy for technological diffusion and learning.

For American policymakers, this shift poses a strategic challenge. A narrow focus on winning the race for better chips and models risks overlooking the importance of deployment infrastructure and embodied AI. Market-driven systems excel at innovation but often struggle with large-scale, coordinated investment in unproven technologies.

If the US fails to address this imbalance, it may retain leadership in AI research while ceding ground in the applications that ultimately shape economic productivity and global influence. The contest over artificial intelligence is no longer just about who builds the smartest algorithms, but about who embeds intelligence most deeply into the physical world.

In that race, China’s much-criticized overcapacity may prove to be one of its greatest strengths.

Please follow Blitz on Google News Channel