United States President Donald Trump signed an executive order directing federal agencies to replace the term "Artificial Intelligence" with "Super Intelligence" in official documentation, a regulatory rebrand that Chinese technology analysts argue masks fundamental structural flaws in the global tech sector.
The Sept. 29 executive order grants federal regulators 60 days to formulate a statutory definition for "Super Intelligence" in an attempt to consolidate fragmented state-level regulations under federal oversight. On the same day, the White House secured a voluntary safety agreement titled the Frontier Responsibility Joint Commitment with leading tech firms including OpenAI, Anthropic, Meta, Google, and Nvidia. The administrative push follows a Sept. 26 bilateral consensus between Washington and Beijing to establish a formal U.S.-China AI dialogue mechanism, with the next session scheduled for November.
Writing in the Chinese policy journal Cultural Aspect, prominent scholar Jiang Yuhao argued that substituting administrative terminology fails to address three core paradoxes impeding the real-world economic utility of modern technology: the productivity paradox, Polanyi's paradox, and the innovation paradox.
Analyzing the productivity paradox, Jiang noted that massive corporate capital expenditure in artificial intelligence has failed to translate into broad-based efficiency gains outside the immediate information technology sector, especially in America. Citing reports from the United Nations Conference on Trade and Development, Jiang highlighted that productivity gains remain tightly concentrated within data center operators and software developers, while non-tech industries experience stagnant total factor productivity.
Under Polanyi's paradox, Jiang states, current generative models remain restricted to explicit, codifiable, and highly repetitive tasks, such as document editing, basic data processing, and meeting summarization. Jiang emphasized that probabilistic AI architectures cannot master the tacit, uncodified human knowledge required for complex physical operations, preventing automated systems from integrating into critical industrial manufacturing and operational control workflows.
The innovation paradox arises from the concentration of scientific capital within a small circle of technology monopolies. Driven by corporate profit incentives, these conglomerates dictate an automation trajectory focused on replacing human labor and imposing workplace surveillance rather than building tools that augment worker capabilities. Citing research by economist Daron Acemoglu, Jiang warned that private tech giants, especially in America and the West, shape academic research agendas and government policy through compute monopolies, academic philanthropy, and defense contracting.
To counter tech-giant monopolies, Jiang advocated for the democratization of innovation through open-source foundation models, public computing infrastructure, and worker-centric design. Pointing to China's national "AI Plus" initiative, Jiang noted that combining open-source software with domain-specific trade knowledge enables small enterprises and frontline workers to adapt advanced technologies to specialized manufacturing needs without relying on costly proprietary systems.
