How Much AI Risk Is Already In Your Portfolio?
The IMF has warned that debt-financed AI investment and rising government borrowing costs could expose financial markets to a sharp correction. For wealthy families, the concern extends beyond technology shares to private credit, infrastructure and other investments tied to the same AI spending cycle.
The International Monetary Fund has warned that the rapid expansion of artificial intelligence is creating risks across global financial markets, as technology companies borrow more to finance infrastructure and investors increase their exposure to businesses expected to benefit from AI.
In its October 2026 Global Financial Stability Report, published during the IMF and World Bank annual meetings in Bangkok, the Fund identified a potential correction in AI-related investments, rising government bond yields and cyberattacks as three threats to financial stability.
Markets have remained resilient, supported by corporate earnings and optimism about AI-driven growth. The IMF nevertheless warned that weaker investment or disappointing profits could trigger a correction, with financial leverage increasing the potential damage.
For family offices and private investors, the concern extends beyond a fall in technology shares. AI investment now reaches corporate bonds, private credit, infrastructure funds and commercial property. A portfolio can hold several asset classes yet remain heavily dependent on the same companies continuing to spend.
AI Investment Is Increasing The Demand For Borrowed Capital
The AI expansion requires substantial investment in data centres, semiconductors, power infrastructure and communications networks. Technology companies are committing capital to facilities that may take years to generate the earnings investors expect.
Large technology groups can finance much of their spending through operating cash flow, but the scale of construction is also attracting banks, bond investors, private credit funds and infrastructure managers.
The IMF examined this development in its April 2026 financial stability report, which identified data centres as an increasingly important part of commercial property finance.
Since 2018, data-centre securitisation has reached $46 billion, with 70% issued through asset-backed securities and 30% through commercial mortgage-backed securities. The IMF cited projections that issuance could reach $150 billion by 2028.
These financing arrangements allow developers to raise capital against data-centre assets and future income. They also create new exposure for investors who may not regard their holdings as technology investments.
A private credit fund financing data-centre construction, for example, may depend on the same expansion plans that support valuations of listed technology companies. An infrastructure fund may rely on long-term leases with a small number of major cloud providers.
The income structures differ, but the underlying demand often comes from the same businesses.
Technology Shares Are Only The Most Visible Exposure
AI-related companies have become an increasingly important part of global equity markets, particularly in the United States.
The seven largest US technology groups commonly known as the Magnificent Seven account for more than 35% of the S&P 500, compared with approximately 12% a decade ago, according to figures highlighted in coverage of the IMF’s latest report.
Their growing index weight means investors can accumulate considerable technology exposure through ordinary US equity funds, even without buying individual AI shares.
The exposure may increase further when a portfolio also holds global equity funds, specialist technology funds or actively managed strategies that invest in the same companies.
For a family office, the relevant calculation is therefore not simply how much capital it has allocated to technology equities. It is how much of its total portfolio depends on the earnings and investment plans of a relatively small group of companies.
An investor holding US equities, global equities and a dedicated technology strategy may own the same companies through several different mandates.
A decline in these companies’ valuations could affect all three allocations at once.
Private Credit Adds Another Layer Of Risk
Private credit has become an important source of financing for businesses and infrastructure projects that require substantial capital but do not rely exclusively on traditional bank lending.
In May 2026, the Financial Stability Board estimated the global private credit market at between $1.5 trillion and $2 trillion.
The sector allows institutional investors and wealthy families to earn interest by lending directly or through managed funds. However, private loans are generally less liquid than publicly traded bonds, and their valuations often depend on periodic assessments rather than continuous market pricing.
The IMF’s October report warned that higher leverage and growing liquidity pressures could restrict private credit lending during a market downturn.
For AI infrastructure, refinancing deserves close attention.
A data-centre developer may borrow to construct a facility and expect to refinance the debt once it secures tenants and begins generating income. If borrowing costs rise, construction takes longer or demand falls short of expectations, the developer may struggle to refinance on the original terms.
Lenders could then face weaker cash flows, falling collateral values or pressure to extend existing loans.
The risk becomes more difficult to assess when private credit funds hold several loans connected to the same group of technology companies or infrastructure projects.
A family office may receive regular interest payments from a private credit allocation while its underlying borrowers remain exposed to the investment decisions of the same AI businesses represented in its equity portfolio.
Data Centres Carry Property And Operating Risks
Investors have increasingly treated data centres as an attractive infrastructure investment because of demand for computing capacity and cloud services.
Yet ownership of a physical asset does not remove commercial risk.
Data-centre developers need reliable electricity, suitable land, cooling systems, network connections and tenants prepared to sign long-term agreements. Delays in obtaining grid connections can postpone construction and revenue.
In April, the IMF reported that power connection waiting times in some markets had reached between two and seven years.
Technology also creates a risk that traditional property investors may be less familiar with. Computing equipment can become obsolete relatively quickly, while advances in chip design or cooling technology may change the economics of existing facilities.
A long-term lease with a financially strong tenant can provide predictable income, but investors still need to examine the tenant’s creditworthiness, the remaining lease term and the costs of maintaining or upgrading the property.
Speculative developments carry greater uncertainty because their financial performance depends on securing customers after construction.
For investors, projected demand for AI computing is not the same as contracted revenue, and a completed facility is not necessarily a profitable one.
Higher Government Bond Yields Could Increase The Pressure
The IMF’s latest warning also covers government borrowing and rising sovereign bond yields.
High public debt, persistent inflation pressures and substantial bond issuance are putting pressure on financing conditions. Private companies are competing for capital at the same time that governments need to refinance and expand their borrowing.
When government bond yields rise, investors generally demand higher returns from other assets.
Companies issuing new debt may face higher interest costs, while existing bonds with lower coupons can lose market value. Infrastructure and property investments also become more expensive to finance.
This matters for AI projects because many require substantial spending before they begin producing revenue.
A data centre financed when borrowing costs were low may deliver a weaker return if its debt must be refinanced at a significantly higher rate. Investors purchasing the same project at an elevated valuation may find that future income no longer justifies the price.
Higher yields can also affect the equity market. When investors can earn more from government bonds, they may become less willing to pay high valuations for companies whose expected profits lie several years in the future.
An AI investment slowdown and rising yields could therefore reinforce each other, putting pressure on both the valuation of technology companies and the financing of the infrastructure they use.
What Family Offices Should Examine Across Their Portfolios
The IMF is not predicting an immediate collapse in AI-related markets. Its warning concerns the losses that could follow if investment and earnings fail to meet expectations, particularly where borrowing increases exposure.
For wealthy families, assessing that risk requires looking beyond the labels used in portfolio reports.
A conventional asset-allocation statement may show holdings in listed equities, corporate bonds, private credit and real assets. Each category can contain investments linked to the same AI expansion.
Family offices should therefore ask their managers to identify the companies, customers and financing arrangements that drive returns across those holdings.
Equity exposure: Review direct technology shares alongside the holdings of global and US equity funds. Overlapping positions may produce a larger concentration than the headline allocations suggest.
Credit exposure: Identify loans and bonds issued by data-centre developers, cloud providers, technology companies and infrastructure businesses. Examine maturity dates, interest coverage and refinancing requirements.
Infrastructure exposure: Assess contracted revenue, tenant concentration, construction commitments and dependence on electricity supply. Funds with similar investment strategies may hold assets serving the same customers.
Liquidity: Compare the ability to sell investments with the time needed to meet family expenses, capital calls and other financial commitments. Private funds may restrict redemptions just when markets are under pressure.
Portfolio stress testing: Estimate the effect of a simultaneous decline in technology valuations, higher borrowing costs and weaker private-market asset values. This provides a clearer view of potential losses than examining each asset class separately.
These reviews do not require families to abandon AI-related investments. They help establish whether the expected returns justify the amount of capital exposed to the same economic developments.
The Investment Opportunity Remains, But Financing Matters
Artificial intelligence continues to create demand for computing infrastructure, electricity, software and specialist services. Companies that turn this demand into sustainable earnings may offer attractive long-term investment opportunities.
The question is how much investors are paying for those earnings and how much borrowing is required to achieve them.
A technology company financing investment from established cash flows faces different pressures from a developer dependent on repeated rounds of external financing. An infrastructure project with contracted customers and manageable debt also has a different risk profile from one built ahead of confirmed demand.
These financial characteristics matter more as the investment cycle matures and investors begin expecting returns on the capital already committed.
The IMF’s warning makes portfolio concentration a more pressing issue for families with investments across public and private markets.
AI exposure can appear in several places on a balance sheet, from shares in the world’s largest technology companies to loans secured against data-centre assets. If the same spending cycle weakens, those investments may lose value together.
The priority is to measure how much capital depends on continued AI investment, how much of that exposure is financed by debt and whether sufficient liquidity remains available if markets turn.
