Cloud computing companies reporting third quarter earnings face scrutiny over 2026 capital spending plans amid concerns that artificial intelligence infrastructure investment could damage non-technology sectors by diverting capital away from traditional industries. The "crowding out" theory suggests massive AI spending growth could starve other economic sectors of investment, hurting competitiveness and innovation outside technology.
Key Takeaways
- Top cloud providers plan nearly $400 billion AI infrastructure spending in 2025, with growth moderating to 19% in 2026 from 54% in 2025
- Crowding out theory suggests concentrated AI investment could starve non-technology sectors of capital, similar to telecom boom effects on manufacturing
- Some economists dismiss concerns, noting cloud giants self-fund infrastructure with cash flow rather than competing for external capital
- Companies adding debt for AI data center expansion face risks if overbuilding occurs, though analysts suggest impact would remain contained
- Accounting rules create ongoing depreciation expenses from current infrastructure spending, potentially constraining future profitability even with revenue growth
Major cloud computing providers lead AI data center spending, joined by technology companies expanding infrastructure. AI infrastructure partners and specialized cloud providers renting GPU-equipped servers represent a wave of new entrants pursuing opportunities in AI computing capacity. This concentration of capital raises questions about broader economic impact.
The Crowding Out Theory
Some economists and market strategists dismiss concerns that AI infrastructure spending will damage other economic sectors. One prominent economist rejects the crowding out thesis, noting that large federal deficits haven't prevented technology sector expansion and suggesting AI investment doesn't represent zero-sum competition for capital.
This perspective argues that cloud giants self-fund massive AI data center buildouts with internally generated cash flow. If they didn't invest in AI infrastructure, these companies would likely deploy capital for stock buybacks or acquisitions—neither of which would benefit the broader economy as much as infrastructure investment.
However, some companies are adding debt to fund data center expansion. Critics compare debt securitization of AI-related loans to housing boom patterns from the early 2000s, suggesting similar systemic risks could emerge.
If AI infrastructure overbuilding occurs, the largest cloud providers will likely scale back spending given their strong financial positions. However, companies that borrowed heavily to expand capacity could face significant difficulties. One economist noted this scenario differs from historical credit market disruptions, suggesting the impact would remain contained rather than creating systemic crisis.
Historical Parallels to Telecom Boom
Some venture capitalists and investment professionals draw parallels between current AI investment patterns and the dot-com crash. One managing partner argues that massive capital spending concentrated in narrow economic sectors during the 1990s diverted investment away from manufacturing.
This capital diversion starved small manufacturers of funding and increased their cost of capital, requiring higher margins to remain viable. Simultaneously, trade policy changes and tariff reductions made competing against international manufacturers more difficult. The combination of rising domestic capital costs and increased international competition contributed to manufacturing job losses during that period.
The same dynamics may be occurring now, according to this analysis. Large private equity firms face strong incentives to allocate capital exclusively toward AI data centers rather than other sectors. This creates similar capital diversion effects, potentially disadvantaging non-technology industries requiring investment for competitiveness and innovation.
Capital Spending Trajectory and 2026 Outlook
In 2025, spending by top cloud computing firms is expected to approach $400 billion. Estimates vary based on which companies analysts include and how they define cloud infrastructure spending.
For 2026, Wall Street analysts expect AI capital spending growth to moderate significantly. Predictions suggest the largest cloud providers will increase capital spending by 19% in 2026 versus 54% growth in 2025. The largest providers are expected to taper spending most dramatically.
Investors will scrutinize companies' capital expenditure forecasts and guidance during third quarter earnings calls. Major technology companies are scheduled to release quarterly results at month-end, providing the first comprehensive view of 2026 investment plans.
Accounting Considerations for Infrastructure Investment
Accounting rules create complexity for evaluating AI infrastructure spending sustainability and impact on profitability.
Remaining performance obligation represents total revenue companies expect to recognize from customer contracts not yet fulfilled. Cloud firms can only recognize revenue as they deliver services, creating timing gaps between contract signing and revenue realization. This metric provides visibility into future revenue but doesn't reflect current financial performance.
Depreciation expenses represent growing concerns for technology companies. Cloud providers purchase data centers, servers, storage devices, and networking equipment treated as long-term assets on balance sheets. As AI infrastructure useful life declines, depreciation expenses increase and impact profit margins.
This accounting treatment means that capital spending concentrated in short periods creates ongoing expense recognition spread over asset lifespans. Companies making aggressive AI infrastructure investments today will carry depreciation expenses for years, potentially constraining future profitability even if revenue growth materializes as projected.
Implications for Supply Chain and Enterprise Technology
The AI infrastructure investment debate carries implications beyond financial markets:
Capital availability for enterprise technology. If crowding out effects materialize, enterprises in non-technology sectors may face higher capital costs for their own digital transformation initiatives. Supply chain technology investments could become more expensive as capital flows preferentially toward AI infrastructure.
Competitive dynamics shift. Organizations with strong cash generation can self-fund AI capabilities, while those requiring external capital face disadvantages. This creates growing capability gaps between well-capitalized enterprises and smaller competitors.
Vendor ecosystem concentration. Massive infrastructure spending by largest cloud providers reinforces their market position, potentially reducing competitive alternatives for enterprises evaluating AI deployment options.
Economic resilience questions. If AI infrastructure investment diverts capital from manufacturing, logistics infrastructure, or other physical economy sectors, long-term competitiveness could suffer even as AI capabilities advance.
The debate ultimately centers on whether AI infrastructure spending represents productive investment generating broad economic returns or speculative excess that will correct sharply, creating collateral damage across sectors dependent on stable capital markets.
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