The Dilemma of Venture Capital: Triple Challenges of Capital, Financing, and Relationships in the AI Boom

Oracle’s recent sharp decline in stock price has sparked deep market reflection. This is not just a company issue but a signal that the entire AI venture capital logic is quietly shifting. Investors are beginning to realize that simply telling stories of growing capital expenditure is no longer enough to sustain cloud valuation dreams; they need to see real business returns. So, what exactly is the market worried about? From the perspectives of capital returns, financing difficulties, and corporate interconnectedness, AI venture capital is facing unprecedented challenges.

Can Massive Capital Investment Bring Real Returns?

The most notable feature of this AI wave is the unprecedented increase in capital spending by tech companies. This shift is pushing U.S. tech firms from traditional “asset-light” models toward more capital-intensive “asset-heavy” structures. According to FactSet data, the five major hyperscalers have cumulatively spent $357.2 billion on AI-related areas over the past four quarters, with market expectations that this figure will surpass $500 billion by 2026.

These numbers seem enormous, but the deeper issue lies in uncertain returns. From a cash flow perspective, these five companies allocate about 60% of their free cash flow to AI capital expenditures. Among them, Oracle’s capital spending accounts for 582% of its operating cash flow, meaning its free cash flow can no longer cover its investment needs—relying on external financing to sustain such investment pace.

Market concerns about AI investment ROI are intensifying. Although AI is widely regarded as the most promising future technology direction, its commercialization path remains blurry, and profit models are still being explored. This raises a fundamental question: how much actual profit can these huge investments eventually generate?

From an economic standpoint, capital investments typically follow the law of diminishing marginal returns. As investment scales up, the marginal efficiency of AI investments is likely to decline. Worryingly, however, the costs of AI investments have not decreased—in fact, since 2023, prices for computers and information processing equipment have continued to rise, contrasting sharply with the declining prices of capital goods during the 1990s internet boom. This phenomenon reveals a stark reality: current AI investments have not yet entered the economies of scale phase but are instead caught in a “diseconomies of scale” trap.

This shift in perception directly impacts stock valuations. If investors previously had overly optimistic return expectations, those expectations are now adjusting. Oracle’s stock decline is a direct reflection of this adjustment. It marks the end of an era—one where merely announcing increased capital expenditure could excite the market. Now, investors want tangible, visible business returns, not endless capital commitments.

Deteriorating Financing Environment and Repricing of Corporate Debt Risks

Where does the funding for large-scale investments come from? If internal cash flows are insufficient, external financing becomes necessary. The availability and cost of financing depend on financial markets’ confidence in a company’s debt repayment ability and operational prospects—that is, credit conditions. Once market confidence wavers, credit tightens, and companies face higher borrowing costs, threatening the sustainability of their investment plans.

Oracle faces such a dilemma. Its massive capital expenditure plans heavily rely on external financing. According to recent financial reports, Oracle’s cash burn this quarter has increased further, with free cash flow falling to -$10 billion. From the balance sheet perspective, as of May 2025 (fiscal year 2025), Oracle’s earnings (before interest, taxes, depreciation, and amortization) totaled $28.9 billion, while net debt stood at $97.7 billion. The huge gap between earnings and debt is glaring.

This mismatch is prompting the market to reprice Oracle’s debt risk. A key indicator is the credit default swap (CDS) spread, which has been rising over the past few months, surpassing 140 basis points and reaching the highest levels since the 2008 financial crisis. This indicates that lenders are increasingly worried about Oracle’s credit risk and are demanding higher default protection. Consequently, Oracle’s future financing difficulty and costs are likely to rise significantly.

Oracle is not an isolated case. Other AI-related companies face similar issues. For example, cloud infrastructure provider CoreWeave, in November 2024, had to cut its full-year revenue guidance due to delays in customer contract fulfillment. Following a sharp stock decline, the company announced plans to issue $2 billion in convertible bonds to raise funds. This series of actions further heightened market concerns about its financing pressures, with its stock falling sharply again. Since November, CoreWeave’s stock has declined by 37%. Meanwhile, its bond CDS spread has surged from below 400 basis points to around 773 basis points, indicating worsening credit conditions and refinancing challenges.

Tech Giants’ Interconnectedness and Systemic Risks

A distinctive feature of this AI wave is that traditional tech giants are gradually taking on the roles of venture capitalists, becoming major backers of startups. This model has advantages—enhanced industry synergy, collective strength, and reduced information asymmetry—but also introduces new vulnerabilities: complex investment and financing relationships among companies, where risks can quickly propagate through supply and capital chains, creating systemic contagion.

Currently, Nvidia, OpenAI, Oracle, and others have established deep cooperation networks covering investments, cloud services, chip deployment, and joint R&D. These companies are “interlinked,” forming a tightly connected web. Specifically: Nvidia has agreed to invest up to $100 billion in OpenAI, procure $6.3 billion worth of cloud services from CoreWeave, and invest $5 billion in Intel with plans for joint chip R&D. OpenAI has signed a $300 billion cloud computing partnership with Oracle, plans to pay up to $22.4 billion to CoreWeave, and deploy billions of dollars worth of AMD chips.

Market fears that if any one of these companies faces investment failures or liquidity issues, the negative impact could quickly spread to others. Oracle’s recent stock adjustment already provides an answer—other related stocks have also weakened. Even well-performing companies like Broadcom have seen their stock impacted. This phenomenon underscores that the market is re-evaluating the systemic risks embedded in the “grouping” strategy among current AI firms.

Deep Impact of AI Logic Shift on the U.S. Economy

In 2025, the U.S. economy shows resilience, largely thanks to expansion in AI-related fixed asset investments and the wealth effects they generate. Estimates suggest that in the first half of 2025, AI contributed about 0.7 percentage points to the year-over-year growth of U.S. real GDP, accounting for roughly one-third of overall growth. In other words, without AI, the endogenous growth momentum of traditional industries is insufficient, and overall economic performance is not as strong as the headline figures suggest.

Looking ahead to 2026, if doubts about the returns on AI capital expenditures grow and financing constraints tighten for related companies, a realistic scenario is that growth in AI-related fixed asset investments will slow significantly. This risk cannot be offset by loose monetary policy, as the core constraint for AI is not high financing costs but the uncertainty of investment returns. Additionally, tariffs imposed by the Trump administration have increased supply costs, directly or indirectly raising AI capital goods prices. These supply-side constraints are not easily alleviated by Federal Reserve easing.

The wealth effects from AI are also worth noting. Moody’s research shows that nearly half of U.S. consumer spending is contributed by the top 10% income group, which holds about 87% of U.S. stocks. Over recent years, this wealthy class has benefited from significant capital market returns. If a market correction weakens this wealth effect, consumer spending could face downward pressure.

Meanwhile, the U.S. labor market shows signs of ongoing weakness, with uncertain employment prospects dampening consumer confidence. As the economy enters a late-cycle phase, subdued consumption demand often becomes prominent. The current “K-shaped” consumption pattern—where high-income groups and ordinary consumers diverge more—may be signaling similar underlying issues. Continuous monitoring is essential.

In summary, the AI venture capital landscape is shifting from optimism to rationality. Slowing capital expenditure growth, tightening financing conditions, and rising systemic interconnectedness are intertwined challenges reshaping the entire investment logic. This is not just short-term market volatility but a profound reflection on the sustainability of AI investments.