S&P 500’s Record High Conceals Hidden Risks: AI Computing Power Costs Are Quietly Undermining the Bull Market’s Foundation

The record-setting rally in U.S. stocks is facing a structurally underestimated market threat—the sharp rise in AI computing power costs, which is eroding the profitability of hyperscale cloud computing companies and raising deep doubts about whether massive AI spending can be justified by sufficient demand.

The growth rate of data center costs has obviously outpaced revenue growth. Microsoft attributed $25 billion of its 2026 capital expenditures to higher component prices, and Meta CEO Mark Zuckerberg expressed similar concerns when raising the spending range mid-year. In the more than $700 billion of AI infrastructure construction this year, an increasingly larger proportion is being swallowed by rising input costs.

Optimistic AI narratives were once the core driver for the S&P 500 repeatedly hitting historic highs. However, as demand prospects become uncertain and free cash flow continues to shrink, the foundation of this logic is loosening.

According to Nomura Equities, 10 stocks contributed 69% of the S&P 500 index's nearly 20% gain from its yearly low; Goldman Sachs estimates that AI-related investments will drive about 40% of overall per-share earnings growth for the index in 2026. Once AI momentum fades, the market will face a vacuum lacking profit support.

Surging Memory Costs: Computing Power Expansion Faces Price Bottlenecks

The main pressure point for rising AI infrastructure costs lies in memory. According to SemiAnalysis, the proportion of hyperscale cloud companies' capital spending on memory has jumped from 8% in 2023 to nearly a third, driven by the fact that each GPU generation requires more DRAM, making the cost curve increasingly steep.

Supply-demand imbalances have significantly pushed up memory chip prices. Micron pointed out that robust demand means some customers can only get 50% of their needed supply. This has enabled the company to raise DRAM prices by about 60% and NAND prices by about 70%.

Power supply shortages further exacerbate expansion bottlenecks. According to RBC research, grid access approvals currently require about 55 months, whereas data center construction only takes about two years. Entering 2026, Microsoft had $80 billion in unfulfilled Azure orders due to GPUs idling while awaiting power access. Power provider PJM stated that, due to data center load growth, capacity electricity prices have surged more than 10-fold in two years.

Free Cash Flow Depleted: Hyperscale Firms Turn to Debt-Fueled Expansion

The rapid expansion of capital spending is compressing the financial space of hyperscale cloud companies. Pimco estimates that capital spending will absorb 94% of these firms' operating cash flows over the next two years, with free cash flow greatly diminished.

This fundamentally changes these companies' market risk profile. Once a stable source of stock buybacks, hyperscale firms now rely on credit markets to finance expenditures. This not only tightens financial conditions, but also means that current stock valuation multiples do not fully reflect this risk shift.

For now, strong cloud and AI revenue growth partly masks these pressures, but whether this concealment can persist depends on whether demand can keep pace with supply-side expansion.

Doubtful AI Demand Outlook: Monetization Gap Hard to Bridge

The market’s assumption of "unlimited" AI demand is facing increasing skepticism. Gartner predicts that by the end of 2027, over 40% of intelligent agent AI projects will be cancelled due to rising costs, unclear ROI, and insufficient risk controls. S&P Global surveys show that in the 12 months ending last October, 42% of companies abandoned most AI project plans before going into production.

The scale of the monetization gap is equally alarming. Bain estimates that current data center construction requires $2 trillion in annual revenues by 2030 to break even, while three years after ChatGPT was launched, AI revenue remains at only about $20 billion. Meanwhile, Forrester forecasts that companies will postpone a quarter of planned AI spending until 2027, and less than a third can link AI investments with actual profit improvements.

Moreover, AI platforms such as OpenAI and Anthropic face IPO pressures, which will force them to gradually abandon their earlier strategy of acquiring users below cost, and instead focus on profitability. This means a considerable part of current AI computing power demand may disappear accordingly.

Overvalued Market: Extremely Limited Margin for Error

Current market valuations allow almost no earnings surprises. The S&P 500 is priced at about 21 times forward earnings, above the long-term average. According to Allianz research, the AI sector’s overall valuation is close to 25 times EV/EBITDA, surpassing even the telecom sector at the peak of the 2000 internet bubble.

Under this valuation backdrop, earnings downgrades will have an amplified effect through compressed multiples. Take the "Magnificent Seven" for example: if their per-share earnings decrease by 10%, coupled with a downward revaluation of P/E multiples, this would significantly drag down the index overall.

Triggering this risk doesn’t require a systemic crisis—just one major hyperscale company lowering its 2027 capital expenditure guidance due to weak demand would be enough to rapidly unravel the current AI trading logic.

Notably, there are signs of sector leadership rotation within the market, with the equal-weighted S&P 500 recently outperforming the market-cap-weighted index. But if AI momentum fades before other sectors show notable profit growth, the entire index risks lacking a profitability floor. This outlook is reinforcing the necessity to hedge stock returns ahead of time.

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