Concerns are mounting over the rapid growth and potential volatility of the artificial intelligence (AI) sector, as the United States and China engage in an intense competition to lead the industry. The contrasting strategies of the two countries create differing risks for global markets and economies.

American AI firms have attracted substantial investment, driving market valuations to unprecedented levels. Nvidia, the largest company by market capitalization and a key supplier of AI chips, is now valued at approximately $5 trillion—more than ten times its worth four years ago. Its recent second-quarter sales reached $96 billion, with projections indicating 70 percent growth over the next year. Anthropic, a startup behind the AI model Claude, is expected to go public later this year with an estimated valuation near $2 trillion, having grown its revenues to around $65 billion since 2022. For comparison, last year Britain’s largest banking firm reported revenues of $71 billion.

Despite impressive revenues, industry experts highlight the escalating capital requirements for AI companies. The sector is forecasted to invest $7 trillion in data centers by 2030, a figure that surpasses available internal funds, indicating a likely increase in borrowing. This intensifying investment demands continually growing revenues to satisfy investors’ expectations. Some analysts caution that growth may be reaching a plateau as companies hesitate to pay premium prices for leading American AI models, raising questions about the sustainability of the current boom.

In contrast, China’s AI approach focuses on delivering more affordable alternatives. Chinese firms typically spend less than a tenth of the amount that U.S. companies allocate to data centers, enabling them to offer AI services at lower prices. For example, Moonshot AI’s Kimi K3 model performs at about 95 percent of the capability of Anthropic’s Claude Fable 5 but at roughly one-third the cost. Chinese models also tend to be more open, often hosted on third-party servers and customizable to user needs. This openness, combined with temporary U.S. government restrictions that limit access to advanced American AI models by foreign users, has contributed to China surpassing the U.S. in overall AI usage volume this year, even though U.S. firms continue to generate higher revenue per customer.

The global AI landscape also includes players beyond the U.S. and China, such as companies in France, Canada, and formerly independent firms like the UK’s DeepMind, now a Google subsidiary. These competitors often adopt more open models, which may help drive prices down as competition increases.

While broader access and lower costs benefit consumers, these trends pose challenges for investors. If leading AI firms are unable to maintain significant pricing power, their ability to generate returns sufficient to justify current valuations could diminish, potentially triggering sharp declines in stock prices. Given that AI-related stocks constitute a significant portion of major market indices like the S&P 500, such a downturn could have widespread consequences, particularly amid concerns over government debt levels and overall financial market stability.

Historical parallels have been drawn to previous technological investment bubbles, such as those surrounding the railway industry in the 19th century, which ended in financial crises and significant investor losses in both Britain and the U.S. Observers hope that, regardless of market fluctuations, the underlying advances made by AI will have lasting positive impacts akin to those brought about by transformative technologies of the past.