AI Spending is Slowing Down. How Will the S&P 500 React?

Institutional investors are increasingly naming artificial intelligence spending as the top tail risk to equity markets, with fund managers warning that record corporate cash deployment into AI infrastructure may deliver only temporary earnings boosts rather than sustainable growth. The concentration of the S&P 500 in the largest tech names means a slowdown in hyperscaler spending could trigger a sharp selloff with broader market consequences.

• 45% of fund managers now cite an AI bubble as the biggest tail risk, up from 28% in June, displacing inflation concerns.
• The five largest hyperscalers plan to deploy over $1 trillion across 2025 and 2026 on AI infrastructure investments.
• The top 20 S&P 500 stocks account for 50.8% of total market capitalization, the highest concentration in 50 years.

• 45% Fund managers naming AI bubble as top tail risk versus 28% prior month
• $1T+ Hyperscaler AI spending expected across 2025 and 2026 combined
• 50.8% Top 20 S&P 500 stocks’ share of total index market capitalization

Wall Street continues reaching record highs, yet a chorus of institutional investors has begun sounding a different alarm. Artificial intelligence itself, not broader economic risks, now sits atop the list of tail risks that fund managers track most closely.

Bank of America’s July Global Fund Manager Survey quantified the shift: 45% of respondents named an AI bubble as the biggest threat to markets, a jump of 17 percentage points from June’s 28%. That figure displaced inflation, the prior month’s top concern, in a striking reordering of institutional priorities.

The survey also identified hyperscaler spending on AI infrastructure as the most likely source of a credit event, naming a specific channel through which the sector’s excesses could trigger broader market damage.

Record AI Spending Masks Deteriorating Earnings Quality, Analysts Warn

The mechanics of how AI spending inflates near-term earnings have become central to the institutional debate. Companies deploying hundreds of billions into AI infrastructure often recognize that spending as a one-time boost to profit statements rather than as sustainable operating performance.

Forward earnings growth for the S&P 500 is accelerating at a record pace, yet much of that expansion stems directly from companies funneling unprecedented cash into artificial intelligence capital expenditure. That spending flows through income statements as a temporary accounting benefit, not as evidence of durable competitive advantage or recurring revenue growth.

The Bank for International Settlements issued a formal warning earlier this year that Big Tech’s spending spree risks hardening into a prolonged investment bust, a scenario in which trillions deployed fail to generate proportional returns. The scale of planned deployment underscores the stakes.

The five largest hyperscalers alone expect to commit more than $1 trillion across 2025 and 2026, a figure without historical precedent in technology infrastructure. If that capital deployment yields earnings growth that proves temporary rather than sustainable, the subsequent earnings revision downward could trigger significant equity repricing.

The concern extends beyond balance sheet mechanics. Ordinary households now hold equity exposure relative to their wealth at levels exceeding past market cycles, which means a sharp selloff triggered by AI spending disappointment would inflict deeper losses on retail investors than the dot-com crash did.

S&P 500 Concentration Amplifies Risk From Any Slowdown in Tech Spending

The structural composition of the S&P 500 itself has become the transmission mechanism for AI spending risk. J.P. Morgan Global Research estimates that the top 20 stocks now represent roughly 50.8% of total index capitalization, a concentration level unseen in the index’s 50-year history.

That figure means buying the S&P 500 as a proxy for broad market exposure has become functionally equivalent to taking a concentrated bet on the AI spending cycle and the handful of companies driving it.

The practical implication troubles portfolio construction at institutional scale. An investor seeking diversified exposure across 500 companies increasingly finds that half of that index’s market value resides in fewer than four percent of holdings. The remaining 480 companies contribute less to index performance than the single largest holding in many cases.

That dynamic inverts traditional index logic: broad diversification becomes narrow concentration by stealth.

When the top five stocks move sharply, the index moves with them regardless of earnings or valuation trends in the broader market. A slowdown in AI spending that affects those five names would ripple through the index in magnified form.

Global Fund Managers Signal Reduced Conviction in Hyperscaler-Driven Growth

Beyond the survey data, institutional positioning reveals shifting conviction. Bank of America’s same report identified long positions in global semiconductors as the world’s most crowded trade, suggesting that institutional capital has already built substantial exposure to AI-related hardware and supply chains.

When the most crowded trade sits in assets dependent on continued AI spending acceleration, institutional managers face crowding risk: all participants attempting simultaneous exit if the AI thesis deteriorates could amplify downside velocity.

The shift in fund manager priorities also reflects a widening gap between near-term earnings growth and the underlying business fundamentals supporting it. Record corporate bond and equity issuance continues to fund AI spending sprees, yet that external capital deployment differs fundamentally from organic cash generation.

Companies raising capital explicitly to finance AI infrastructure cannot claim that spending as proof of underlying operational strength. The financing itself becomes a signal: firms must access external capital markets at scale because internal cash generation alone cannot sustain the investment pace required to remain competitive in AI infrastructure deployment.

The accounting treatment compounds the optical problem. Capitalized spending on data centers and GPU clusters eventually flows through depreciation schedules that reduce reported earnings in future periods.

Near-term boost becomes medium-term headwind, a reversal that could surprise markets if investors have priced in perpetual earnings acceleration without discounting the depreciation phase to come.

Institutional Investors Face Judgment Call on S&P 500 Valuation Sustainability

The fundamental question confronting institutional allocators has narrowed to a specific binary. Either the AI spending cycle yields returns sufficient to justify the investment pace and the earnings growth it generates, or the bulk of that capital deployment represents a capital misallocation that will depress future returns once depreciation and opportunity costs come into focus.

The S&P 500’s extreme concentration means that binary outcome applies with force to the entire index.

Current conditions leave little room for surprise revision. Valuations across large-cap technology reflect confidence that AI spending will scale profitably and sustainably.

Fund manager surveys suggesting AI bubble risk at 45% conviction indicate that roughly half of institutional capital managers privately harbor material doubts about that thesis, even if they continue holding long positions in the assets dependent on it.

That gap between stated conviction and portfolio positioning creates fragility: positions held despite elevated tail risk consciousness exit sharply if confidence deteriorates from any direction.

The next critical signal will emerge from second-quarter and third-quarter earnings reports, which will reveal whether actual AI-related capital deployment matches forward guidance and whether management commentary sustains or revises hyperscaler spending plans. If management teams dial back 2025 and 2026 capex guidance citing returns disappointment or capital efficiency concerns, the shift in fund manager consensus from 45% AI bubble risk to majority conviction could trigger sudden repricing. Bank of America and other institutional surveys will provide monthly updates on fund manager positioning; the next published tally will indicate whether institutional conviction in AI spending sustainability is eroding or stabilizing.