【By Observer Net, Ruan Jiaqi】
“The U.S. clearly has an advantage over China, but why isn't it using it?” Jonas Nahm, associate professor at the School of Advanced International Studies of Johns Hopkins University, is puzzled by this question.
As a core scholar in the White House economic team during the Biden administration, focusing on industrial policy and green economy, he was active in the media during Trump's second term, analyzing the gap between China and the U.S. in manufacturing, AI automation applications, and China's productivity advantages, advocating that the U.S. should accelerate automation and industrial upgrading to cope with competition.
On February 24 local time, Nahm published a long article in The New York Times, stating that he believes the core issue of the U.S. manufacturing productivity lagging behind China lies in: although the U.S. leads globally in AI research and development, it lags far behind China in converting technology into industrial productivity.
He explained that China has formed a significant advantage in output per capita and manufacturing scale through large-scale deployment of automation, robots, and AI real-time management of production; in contrast, the U.S. focuses too much on cutting-edge research and trade protectionism, neglecting the practical needs of digital transformation of traditional factories and workforce training.
The U.S. holds technological advantages but fails to implement them, relying solely on tariff measures for protectionism, completely missing the core issue, which has left this senior political economist and policy expert deeply anxious.
Nahm called on U.S. policymakers to change their approach, supporting the construction of digital infrastructure and the promotion of technological application, truly advancing a technology-driven re-industrialization strategy.
"The U.S. has always emphasized invention and innovation, but neglected application. But sometimes, technology must return to the front lines of production (technology needs to be treated as factory work) — this may not be attractive, even boring, but it is the core of enhancing competitiveness."
Jonas Nahm, video screenshot from Johns Hopkins University
Public information shows that Nahm served as a senior economist for industrial strategy at the Council of Economic Advisers in the Biden administration, and was one of the key advisors pushing its "revival of industrial policy" agenda, supporting government investments such as the Inflation Reduction Act (IRA) to revitalize American manufacturing, while emphasizing supply chain resilience and reducing reliance on China.
In his article, Nahm mentioned that re-industrialization is currently one of the few economic goals with bipartisan consensus in the U.S., with both the Biden and Trump administrations prioritizing the rebuilding of American manufacturing, but Washington generally attributes the competitiveness gap of American factories to so-called "unfair subsidies" and market distortions.
In his view, this judgment is completely off track, and the core issue facing the U.S. is not that. "The institutional forces driving rapid change in China cannot be ignored," he said.
Nahm cited the example of Tesla's Shanghai factory, whose output per person far exceeds that of its California factory, pointing out that the key difference lies in China having formed a production model centered on large-scale application of automation, robots, and AI, whereas the U.S. has neither achieved this nor acknowledged the reality, and has failed to convert its technological advantages into equivalent production efficiency through manufacturing layout.
He wrote that unlike the U.S., which confines AI to laboratory research, China has already integrated AI into factory production, deeply embedded in equipment operation, production scheduling, fault diagnosis, and other automation upgrade processes.
The application of diverse AI technologies significantly improves production efficiency: visual inspection can accurately remove defective products, intelligent scheduling automatically balances production, inventory, and logistics, and real-time data analysis can detect minor inefficiencies before they cause production line delays.
Today, China has built more than 30,000 smart factories, with more than half of the global new industrial robots added in 2024 being deployed in China; Hong Kong economists' research also shows that in multiple industries from steelmaking to shipbuilding, the output per worker in Chinese factories has already exceeded that of their U.S. counterparts.
The changes at the production front are particularly obvious. Nahm gave an example, stating that as of last year, the Ningbo factory of Zhike, a Chinese carmaker, had equipped over 800 robots and even tried using humanoid robots for tasks like material handling, assembly, and quality inspection.
Huawei's humanoid industrial robot officially starts working at the Ningbo Zhike 5G Smart Factory. People's Daily
He specifically explained that these robots do not follow fixed instructions, but instead adapt to the production line conditions through cameras, sensors, and AI, similar to how driver assistance systems adjust according to road conditions, enabling flexible responses to changes, safe collaboration with workers, and handling routine adjustments that would otherwise cause production halts, ultimately improving output per person and alleviating the shortage of skilled workers.
Such achievements are not limited to experimental stages. Nahm noticed that Midea Group's Jingzhou factory has adopted AI-driven control systems that coordinate robots, sensors, and production equipment. According to the company's manager, the system has reduced response time from hours to seconds; Xiaomi also claims that through smart manufacturing technology and 700+ robots, its Beijing factory can produce a car every 76 seconds on average.
Nahm emphasized that national strategy support is crucial. For over a decade, China has treated factory modernization as a national project, promoted by all levels of government, through financial support, unified industry standards, training, and building shared digital networks for small and medium enterprises, promoting intelligent transformation covering the entire industry chain.
On the other side of the ocean, however, the situation is different. Nahm pointed out that although the U.S. leads in AI frontier research and large language models (LLMs), the policy focus completely ignores industrial implementation. Even though companies like Ford are trying AI visual inspection applications, these are only scattered explorations.
A survey by the Manufacturing Leadership Council found that only 18% of surveyed manufacturers have a formal AI operational strategy, and two-thirds of companies find it difficult to scale up AI pilot projects into production.
The main obstacles come from implementation rather than technology itself, especially for small and medium enterprises: many factories lack complete data and still rely on manual records, making it impossible to support digital tools; three-quarters of companies struggle to connect old equipment to efficient systems; eight out of ten companies lack employees who can operate AI manufacturing tools; and more than half of companies believe the initial investment in AI projects is too high.
The article directly states that in the face of China's rising manufacturing, the U.S. policy still focuses on tariffs and trade restrictions, rather than improving factory efficiency. These measures seem tough, but they have little effect on narrowing the productivity gap; more paradoxically, U.S. manufacturers highly rely on imported robots and sensors, and tariff policies actually increase the cost of enterprise automation upgrades.
Nahm suggests that the U.S. government should reduce protectionism and instead support companies in implementing digital tools, promote the digital transformation of old equipment, upgrade factories to be compatible with sensors and analytical software, strengthen AI skill talent training, and build shared digital infrastructure at federal and local levels to help small and medium manufacturing enterprises apply advanced technologies.
"China is doing exactly that. Germany, Japan, South Korea, and other developed economies are also taking similar approaches," he wrote. "Technology must return to the front lines of production."
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Original: toutiao.com/article/7610652331440423476/
Statement: This article represents the views of the author and not necessarily those of the editorial board.