U.S. Tech Giants' Stock Prices Swing Wildly: AI Investment Return Concerns Reflect Economic Risks

US tech giants have recently disclosed their third-quarter earnings one after another. Companies such as Google's parent company Alphabet and Amazon saw their stock prices swing sharply because their quarterly results or fourth-quarter forecasts fell short of expectations. This has exposed the risks hidden beneath the US artificial intelligence (AI) investment boom: as AI businesses expand rapidly, massive capital expenditures are testing companies' cash flow, financing capacity and return on investment. Analysts believe that as data center construction drives growth in financing demand for corporate bonds, private credit and other instruments, the impact of the AI investment boom is extending from corporate operations to financial markets and the real economy. Once investment returns cannot cover financing costs, companies may face heavier debt burdens, asset impairment and financing contraction, and transmit these pressures outward through financial market volatility. Return on investment becomes a hidden concern On October 28, Google's parent company Alphabet released its third-quarter earnings, showing that Google Cloud revenue grew at a record pace, but the cash consumed by AI infrastructure investment exceeded the cash gains created by business growth. Alphabet's stock price fell more than 12% on the 29th, the largest single-day drop in its history, wiping about $500 billion off its market value. Amazon's earnings also showed revenue growth close to the fastest level in five years, but because capital expenditure exceeded expectations and cash flow remained under pressure, its stock price fell more than 6% in after-hours trading after the earnings release on the 29th. Meta Platforms' third-quarter revenue grew year on year, but high costs and expenses caused operating profit to fall 37%, free cash flow was negative $2.12 billion, and massive cash consumption still overshadowed the business highlights. Reuters analysis believes that investors are scrutinizing tech companies' performance more strictly and want to see more evidence that the growth brought by these investments can fund the next round of capital expenditure. Market research firm Gartner predicted in October that by 2030, AI data centers will need to generate a cumulative $8.5 trillion in revenue to cover capital expenditure and achieve the preset rate of return, but revenue over the same period is expected not to exceed $3.6 trillion. Market analyst David Morrison pointed out that AI infrastructure construction is accompanied by massive debt financing, and rising financing costs may erode future investment returns, while the timing and scale of realizing those returns remain highly uncertain. This has intensified market concerns about companies overinvesting and some computing power being underutilized. Transmission of financing risks is a hidden danger As financing channels for AI investment become increasingly diverse, financial risks are also extending from companies' own balance sheets to broader financial markets. Stijn Van Nieuwerburgh, a professor at Columbia Business School, pointed out that AI infrastructure investment is enormous in scale and its financing structure is increasingly complex. Although financing capacity has increased, potential liabilities and the difficulty of identifying risks have also increased. Research released by the Federal Reserve Bank of Kansas City in October shows that AI data center financing has already involved multiple markets, including investment-grade bonds, high-yield bonds, private credit, real estate investment trusts and asset-backed securities. As of the second quarter of 2026, the issuance of AI-related investment-grade bonds during the year had reached $330 billion, equivalent to 10 times the full-year issuance in 2023. Financing relationships among chip suppliers, data center operators and investment institutions are becoming increasingly close, forming complex funding links and making it easier for risks to spread across institutions. Former US Treasury Secretary Robert Rubin warned that the procurement commitments of some large AI companies are interlinked with supplier financing, and suppliers have even borrowed based on these commitments. Once the relevant companies fail to honor them, a chain reaction may be triggered. Changes in interest rates may further amplify financing pressure. Reuters recently reported that after expectations of rising interest rates strengthened, the credit default swap (CDS) spreads of AI infrastructure companies such as Oracle and CoreWeave rose significantly, reflecting investors' concerns about companies' debt-servicing capacity. Research by the Federal Reserve Bank of Dallas points out that expanding financing demand for AI data centers may increase the supply of long-term corporate bonds, change the structure of the fixed-income market and crowd out financing space for financial issuers. If financing costs continue to rise while investment returns fall short of expectations, companies' debt-servicing and subsequent financing pressure may further increase. Concentration of growth momentum exacerbates risks Third-quarter economic data released in the United States on October 29 show that increased corporate AI investment was an important factor driving growth. However, as economic growth becomes increasingly dependent on AI investment, the US economy faces structural risks such as concentrated growth momentum, misallocation of resources and rising uncertainty over investment returns. Kyle Moore, chief economist at the Century Foundation, pointed out that the benefits of US economic growth are unevenly distributed, with growth largely concentrated in AI infrastructure construction, while ordinary workers' wage growth has not kept pace with price increases. With the large-scale expansion of data centers, demand for resources such as chips, electricity, land and specialized construction capacity is growing rapidly. A 2026 report by the International Energy Agency pointed out that electricity, grid access, advanced chip manufacturing capacity and capital are becoming important constraints on the AI industrial chain. Competition for resources may also push up costs in other industries. Estimates by the Federal Reserve Bank of Dallas show that data center construction raised average US wholesale electricity prices by 6.9% in 2025, with an increase of 15% in northern Virginia. A 2026 study by the Massachusetts Institute of Technology also found that after data centers entered relevant markets, local average retail electricity prices rose 2.7%. The cost of grid expansion may be passed on to other businesses and residents. For the US economy, the key to AI investment lies not only in the scale of input, but also in whether it can be translated into broad productivity gains. If infrastructure expansion continues to outpace actual demand and commercial returns, the growth momentum currently driven by investment may weaken, and risks such as resource misallocation and insufficient investment returns may accumulate.