AI Infrastructure Cost-Cutting Identified as Top 2026 Risk; 100bps FOMC Rate Cut Forecast

Dale Smothers, speaking on the Schwab Network on January 25, 2026, articulated what he considers the most significant market risk for the coming year: a potential retrenchment in corporate AI infrastructure spending [1]. His analysis centers on the possibility that businesses may discover their actual AI computing requirements fall substantially short of the ambitious infrastructure buildouts currently budgeted across the technology sector. This thesis carries substantial implications for major technology companies that have positioned themselves as hyperscale AI infrastructure providers and could face revenue pressure if enterprise demand normalizes.

The timing of Smothers’ warning is particularly notable given the substantial capital commitments made by Big Tech firms during 2024 and 2025. Companies including Microsoft, Amazon, Google, and Meta have announced multi-year infrastructure investment programs totaling hundreds of billions of dollars specifically earmarked for AI data center expansion and associated computational capacity [2]. Smothers’ concern suggests these spending programs may face revision as enterprises complete their initial AI deployment waves and assess actual return on investment.

S&P Global Ratings has independently identified similar risks in their January 2026 analysis titled “Where Are AI Investment Risks Hiding?” [3]. Their research highlights multiple catalysts that could trigger AI spending cuts, including enterprise adoption lag as organizations struggle to integrate AI capabilities into existing workflows, and algorithmic efficiency breakthroughs that reduce the computational intensity required for AI model operation. The institutional convergence of this perspective from both sell-side analysts and credit ratings agencies suggests the concern transcends any single viewpoint and represents a broadly recognized market risk factor.

The S&P Global analysis specifically flags debt-fueled capital expenditures in the AI sector as a credit risk concern, noting that companies financing substantial infrastructure investments through borrowing could face heightened vulnerability if revenue expectations fail to materialize [3]. This adds a financial stability dimension to Smothers’ earnings-focused thesis, suggesting the risk extends beyond top-line growth deceleration to potential balance sheet stress.

Smothers’ accompanying prediction of 100 basis points in Federal Reserve interest rate cuts represents a significant policy expectation that would substantially alter the macroeconomic environment [1]. This forecast is more aggressive than some consensus projections and suggests the Federal Reserve may respond to slowing economic growth with meaningful monetary accommodation. The interconnection between AI infrastructure spending and monetary policy creates a complex feedback loop: rate cuts could theoretically support continued infrastructure investment by reducing financing costs, but Smothers’ expectation of cuts also implies underlying economic weakness that could validate broader recession concerns.

Market data indicates relatively stable indices with minor volatility in early January 2026 trading, suggesting investors are processing these risk factors without significant panic response [0]. The NASDAQ’s approximately 8.47% gain during this period reflects elevated expectations for technology sector performance that could prove vulnerable to downward revision if AI spending concerns materialize more concretely [0].

Recent market behavior has shown what some analysts describe as a “Great Rotation” from large-cap technology stocks toward small-cap and value segments [2]. This rotation could accelerate significantly if AI infrastructure spending concerns become more pronounced, as investors seek reduced exposure to capital-intensive technology names while maintaining equity market participation through more diversified or defensive positioning. The rotation dynamic suggests Smothers’ warning may already be partially reflected in current market structure, though the extent of potential further rotation depends on the actual trajectory of AI spending decisions by major corporations.

The root of Smothers’ concern lies in the gap between projected AI infrastructure requirements and actual enterprise deployment outcomes. Organizations across industries have engaged in substantial AI pilot programs and proof-of-concept deployments, but the translation from experimental use to production-scale infrastructure has proven more challenging than some initial projections suggested. This adoption lag could result in enterprises scaling back or delaying planned capacity expansions, creating a demand shock for hyperscalers that have invested heavily in anticipation of continued growth.

Not all major technology companies face equal exposure to this risk. Companies with diversified revenue streams across enterprise software, cloud services, and consumer products may prove more resilient than those more heavily concentrated in AI infrastructure services. Similarly, companies with established enterprise relationships and recurring revenue models may experience less severe impacts than those more dependent on new AI-specific infrastructure contracts.

The analysis identifies several interconnected risk factors warranting attention. First, AI spending correction risk reflects the legitimate concern that enterprise AI adoption rates may not match current infrastructure buildout trajectories, potentially leaving significant capacity underutilized and triggering spending program revisions. Second, Big Tech earnings vulnerability poses a threat to companies with significant AI revenue dependencies, particularly those whose stock valuations incorporate aggressive growth assumptions that may prove optimistic. Third, rate sensitivity creates dual-sided risk, as a 100bps Federal Reserve rate cut could benefit rate-sensitive sectors while simultaneously signaling economic weakness that validates broader recession concerns.

Despite the risk factors, certain opportunities emerge from this environment. The potential rate cut trajectory could benefit interest-rate-sensitive sectors including real estate, utilities, and small-cap equities, potentially offering diversification benefits for portfolios seeking reduced technology concentration. Additionally, the rotation toward value segments could create relative outperformance opportunities for investors who accurately anticipate the shift and position accordingly.

Investors should prioritize monitoring Big Tech first-quarter 2026 earnings guidance for any changes to AI capital expenditure projections, Federal Reserve communications for signals on the rate trajectory, enterprise AI adoption metrics and return on investment developments, and S&P Global credit risk assessments for AI-related sectors. These monitoring priorities provide early indicators of how the AI spending thesis is evolving and whether Smothers’ risk scenario is materializing.

The analysis synthesizes multiple perspectives on 2026 AI infrastructure spending risk, with Dale Smothers’ Schwab Network commentary representing one analytical viewpoint that aligns with broader institutional concerns. Market indices demonstrate relative stability with minor volatility, suggesting orderly processing of these risk factors rather than panic-driven repricing. The convergence of Smothers’ thesis with S&P Global’s credit risk analysis and Forbes’ coverage of AI data center investment flows creates a coherent narrative of growing institutional concern about AI spending sustainability. Investors should maintain awareness of these risks while recognizing that the actual trajectory of AI infrastructure spending will depend on enterprise adoption outcomes that remain uncertain.

[0] Ginlix Analytical Database – Market Indices Data (S&P 500, NASDAQ, Dow Jones, Russell 2000, January 2026 trading data)

[1] Schwab Network – “Smothers: AI Buildout Biggest 2026 Risk, FOMC to Cut Interest Rates 100bps”
URL: https://www.youtube.com/watch?v=H59VDJPvcqg
Published: 2026-01-25

[2] Forbes – “Whether To Follow $602 Billion Flowing To AI Data Centers In 2026”
URL: https://www.forbes.com/sites/petercohan/2026/01/23/whether-to-follow-602-billion-flowing-to-ai-data-centers-in-2026/
Published: 2026-01-23

[3] S&P Global Ratings – “Where Are AI Investment Risks Hiding?”
URL: https://www.spglobal.com/ratings/en/regulatory/article/where-are-ai-investment-risks-hiding-s101665242
Published: January 2026