At the AI Investment Summit held in Beijing on September 16, themed around "Certainty Opportunities in the AI Infrastructure Era," Liu Yuhui, a council member of the China Chief Economist Forum, delivered a stark assessment of the U.S. artificial intelligence sector. He argued that while the American AI industry stands at a critical juncture in its march toward the technological singularity, the immense capital requirements of this push, combined with the current high-interest-rate dollar environment, have created a fierce competition for financial resources between two "capital-devouring giants." This dynamic, he cautioned, could trigger external shocks that spread risk across global markets if any disruption occurs.

From a technological evolution standpoint, Liu explained that the global AI industry is largely driven by the United States, advancing through large language models, chains of thought, agentic systems, and continuous learning toward a self-iterating singularity. Should this threshold be successfully crossed, the path to Artificial General Intelligence (AGI) would be significantly shortened. He noted that models like GPT-6 and Anthropic's next-generation systems are already showing signs of self-iteration, raising the potential risk of AI gradually slipping beyond human control. This, he suggested, is why some prominent figures in the tech industry have recently called for a measured slowdown in the development of advanced models.

In Liu's view, the race to breach the singularity is the most capital-intensive phase of AI development, carrying profound strategic implications. Policymakers are eager to achieve a technological breakthrough, as success could reshape the competitive landscape among major powers. However, the U.S. currently faces an environment where dollar-cycle funding costs are at a premium, with two major high-consumption entities vying for resources simultaneously. On one hand, the U.S. government is grappling with soaring fiscal deficits and an ever-expanding debt burden; on the other, AI giants require massive capital inflows to sustain their breakthrough efforts.

Citing foreign media reports, Liu pointed out that beyond their on-balance-sheet debt, the nine leading U.S. AI companies have accumulated substantial off-balance-sheet hidden liabilities over the past year. Some of these financing tactics bear a striking resemblance to the Wall Street financial engineering seen during the 2007-2008 crisis. He observed that these firms have largely exhausted their internal cash flows and are now reliant on external funding to keep their technological pursuits alive. Despite the rapid rise in debt levels, Wall Street's confidence in the AI breakthrough remains undiminished, with U.S. tech stocks maintaining elevated valuations in the high-rate environment. The technological reserves embedded in next-generation hardware products from companies like Nvidia provide the industrial foundation for this market optimism.

Liu also flagged the inherent risks in this trajectory. Should the breakthrough falter, the resulting debt-related pressures could transmit shocks to global markets. He noted that the current AI gap between China and the U.S. is temporarily narrowing, primarily because the U.S. is still grappling with pre-singularity challenges. If the U.S. successfully crosses the singularity threshold, however, the technological divide between the two countries could widen once again.

He further emphasized that global markets are now watching the U.S. AI campaign with bated breath. Western observers are banking on the success of the technological push, while some market narratives are bracing for a potential explosion of the super debt cycle. Liu underscored that these scenarios remain speculative and await real-world validation. Both the prospect of an industrial breakthrough and the onset of a debt crisis carry meaningful probabilities, making them an unpredictable external variable for global equity markets in the current climate.

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