The US increases AI investment, why is the capital market divided

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On October 17, at the 2026 Autumn Meetings of the International Monetary Fund (IMF) and the World Bank Group, Clark, co-founder of the American technology company Anthropic, stated that artificial intelligence (AI) capabilities are rapidly improving, and according to its internal model estimates, U.S. GDP by 2030 may grow by 50% driven by AI. But faced with such an attractive expectation, the U.S. capital market shows two different sentiments: on one hand, it continues to increase industrial investment; on the other, there are concerns about overheated investment and market bubbles.

Are investment returns overestimated?

In recent years, U.S. AI investment has maintained strong growth momentum. Statistics show that the capital expenditure of five hyperscale cloud providers this year is expected to approach $800 billion. Behind this is the market's high expectation for AI development prospects, believing that AI is creating a huge brand-new market. The bullish side believes that the current large-scale investment is a necessary layout to seize future industrial advantages. The U.S. asset management company BlackRock still lists AI as one of its most favored investment themes, believing that the computing facilities and R&D systems currently being built do indeed take a long time to pay back, but may become an important foundation for future economic growth.

IMF Managing Director Georgieva said at a meeting held in New York on September 20 that although the world economy is more resilient than many people expected, it still faces risks brought by stubborn inflation, rising debt service costs and overheated AI investment. Xinhua News Agency

But the other side believes that this continuously increasing investment is overdrafting the future in advance. Ray Dalio, founder of the U.S. investment company Bridgewater, called the AI boom a "classic bubble," believing that large-scale borrowing combined with rising interest rates is approaching the conditions for a bubble burst. Bank of America's October global fund manager survey showed that nearly half of respondents believe a bubble will appear next year. Some companies have already borne higher debt costs to seize the computing power market. The computing power leasing company CoreWeave's net interest expense in the second quarter of this year reached $640 million, about a quarter of its revenue. Credit market forecast data show that CoreWeave's probability of default in the next five years is as high as 63%, and its stock price has also recently suffered a sharp setback.

Although market judgments are clearly divided, fund managers' net overweight in technology stocks still rose to the highest level since August 2020. This seemingly contradictory phenomenon reflects that investors are both worried that AI is being overpursued and unwilling to miss the growth dividends brought by this round of technological change. Neither of the two diametrically opposed judgments denies AI's technological prospects; rather, they focus on whether the current large investment and high valuations can be supported by real returns. Understanding the U.S. AI boom still requires judgment from two dimensions: industrial supply and end-user applications.

Overcapacity may emerge in the future

From the perspective of short-term supply, U.S. computing infrastructure remains tight, existing construction projects are supported by actual demand, and a large amount of new capacity has locked in customers in advance before it is completed. A report by the real estate services firm CBRE shows that in the first half of this year, supply in major North American data center markets increased by 33.7% year on year, but the vacancy rate was as low as 1.4%, and 80.4% of capacity under construction had already obtained pre-lease commitments.

But the current computing power shortage and future overcapacity are not contradictory; the two may appear in the same cycle. As a heavy-asset project, data center construction has a cycle of several years and cannot turn around at any time as market demand changes. The Bank for International Settlements pointed out in its "Annual Economic Report 2026" that precisely because there are bottlenecks in electricity, advanced chips and grid equipment, companies, in order to avoid falling behind, have rushed to lock in forward capacity in advance through long-term contracts.

Furthermore, forward orders signed by companies may be decoupled from real future demand. Some forward demand has already cooled. A report by the market monitoring agency of PJM, the largest regional grid in the United States, said that about 42% of the previously forecast new electricity load from data centers has been canceled or postponed. In addition, the large-scale expansion of U.S. computing infrastructure is also constrained by social costs and local policies. In February this year, hundreds of people gathered outside San Marcos City Hall in Texas to protest a proposed $1.5 billion data center. Residents expressed opposition at the hearing, mainly worrying about the project's water consumption, electricity use and long-term environmental pressure. The city council subsequently voted to reject the land-use zoning change required for the project. This is not an isolated case. New York State also announced in July that it would suspend the issuance of state environmental permits for the construction of data centers with electricity capacity of 50 megawatts or more.

User perception returns to rationality

From the end-market perspective, AI is being widely used in areas such as software development, office collaboration and customer service. More and more enterprises are willing to pay for AI productivity, and the revenue of leading model companies is also growing rapidly. Reports say that OpenAI's annualized revenue this year is expected to exceed $50 billion. Its research on individual ChatGPT users shows that six months after registration, users' average daily message volume is about 50% higher than in the first month.

But as model applications penetrate traditional workflows, enterprises' attitudes are also becoming increasingly rational. An EY survey of U.S. executives shows that 82% are concerned about token usage and related costs. A S&P Global survey shows that the proportion of enterprises "abandoning model projects midway" has doubled compared with the previous count. As model efficiency improves and market competition intensifies, model invocation prices continue to fall, and technological progress has instead narrowed profit margins to some extent. The well-known venture capital firm a16z estimates that driven by algorithm optimization and the popularization of open-source models, the inference price of large models with equivalent performance falls by about 90% per year on average, increasing pricing pressure on model service providers and also affecting the profit margins of all links in the industrial chain.

On July 23, 2025, the White House released an artificial intelligence (AI) action plan, listing dozens of federal policy actions the Trump administration will take in the future. Some media analyses believe that this move means the Trump administration is rescinding multiple policies from the Biden administration period and relaxing regulatory measures on AI development. Xinhua News Agency

The U.S. public's risk perception of AI is also shifting. The "Future Risks Report" released by AXA and Ipsos shows that in the ranking of risks in the next five to ten years by surveyed experts, AI and big data risks surpassed climate change to rank first. In the latest tracking survey by the Pew Research Center, 52% of U.S. adults feel "more concerned than excited" about the use of models in daily life, and as many as 71% of respondents expect AI to reduce jobs in the next two decades. A national poll by Quinnipiac University further shows that 74% of Americans do not trust the leadership of model companies, 77% believe that the development of powerful model systems should be paused or slowed until safety is assessed, and 86% support enforcing independent safety standards even if it slows down R&D. This widespread mentality in American society provides a foundation for groups that insist on "bearishness" and "deceleration."

Judgment requires multidimensional observation

But the Trump administration, which adheres to the "race" logic, has always clearly suppressed all "deceleration" arguments. In the face of the "deceleration theory" recently called for by Anthropic CEO Dario Amodei and others, the Trump administration gave a diametrically opposite tough response, firmly anchoring the policy focus on increasing investment and winning the artificial intelligence race. Trump himself alleged that "the so-called artificial intelligence doomsday warnings are an exaggerated hoax." David Sacks, the presidential science and technology adviser who once served as the White House artificial intelligence "czar," was even the first to publicly mock leading companies for seeking "regulatory capture" and industry monopoly in the name of safety.

This "race" logic has driven a series of policy trajectories of the Trump administration since 2025. On one hand, the White House is clearing regulatory barriers, accelerating data center and electricity approvals, and promoting full-stack exports of U.S. artificial intelligence technology through "Winning the Race: America's AI Action Plan"; on the other hand, it has launched the "National AI Legislative Framework," urging Congress to replace fragmented restrictive bills in various states with unified standards and accelerate the establishment of a nationally unified federal AI policy framework with the least burden, striving to lock in America's absolute dominance through comprehensive deregulation combined with accelerated infrastructure construction.

Therefore, the judgment on the U.S. artificial intelligence boom or bubble cannot rely solely on observing a single dimension, but requires deeper and longer-term observation of the tensions triggered by the U.S. market, society and political system in the face of artificial intelligence.

(Yao Xu is Secretary-General of the Global AI Innovation Governance Center and Associate Researcher at the Fudan University Development Institute; Guo Haobo is a Research Assistant at the Global AI Innovation Governance Center)

(Article source: Shangguan News) Middle of the main text Embedded advertisement