IMF warns AI share-price correction could destabilise global markets

13 October 2026 — The International Monetary Fund has warned that an AI share-price correction could destabilise global markets, with seven big technology stocks accounting for more than 35% of the S&P 500, up from 12% a decade ago, the Guardian reported.

Seven technology stocks exceed 35% of the S&P 500

The IMF’s twice-yearly Global Financial Stability Report identifies an AI correction as one of three prominent risks, alongside higher government borrowing costs and the threat of a major cyber-attack. It warns that falling AI-related asset prices could spread beyond the US. Asian markets would be most exposed outside the US, while investors in the UK and euro area could be affected through their US holdings.

The seven stocks are Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla. Their combined market capitalisation has risen by more than 50% over two years. The Guardian reported that the IMF sees weaker AI investment or profitability as a possible trigger for a correction, with borrowing, concentrated holdings and financing arrangements capable of amplifying it.

A separate IMF study by Yingyuan Chen, Deepali Gautam and Jason Wu examined a broader set of companies across the AI supply chain. Firms in its AI ecosystem analysis accounted for approximately $21 trillion of the roughly $36 trillion increase in S&P 500 market capitalisation since late 2022, Devdiscourse reported.

An earlier AI Affairs report covered Kristalina Georgieva’s warning that an AI earnings shortfall could cause a market shock. The Global Financial Stability Report also identifies borrowing by AI companies, hedge fund investment and retail investors borrowing to buy AI shares as potential sources of forced selling during a downturn.

Fourteen US deals link equity and revenue commitments

Across 14 major US financing deals, the separate IMF study counted approximately $300 billion of direct equity investment and $1.4 trillion in commitments for future revenue, averaging five years in duration, Devdiscourse reported. Nine of those arrangements subsequently attracted more than $650 billion in additional debt financing.

In the arrangements described by the researchers, a technology company can invest in an AI developer while also purchasing computing services or infrastructure from it. That connects the investment to expected sales between companies in the same sector. The study says weaker-than-expected AI revenue could leave companies struggling to honour contractual commitments and cause losses across interconnected businesses.

The Guardian reported that the IMF identified transactions between hyperscalers and weaker AI firms worth $300 billion over three years, supplemented by $650 billion in debt and associated with $1.4 trillion in future financial commitments. The fund warned that a reassessment of AI investment and profitability could reduce spending, depress share prices and tighten financial conditions beyond the sector.

Neocloud leverage exceeds 600% in IMF study

The separate study assessed 74 public and private AI supply-chain companies. Large hyperscalers and semiconductor developers had operating margins approaching 30%, while smaller cloud providers focused on graphics processing units — known as neoclouds — had leverage exceeding 600%, negative profitability and capital expenditure intensity above 300%, Devdiscourse reported.

The study also put the collective valuations of the three largest private foundation-model companies at more than $2 trillion as of August 2026, against combined revenue of less than $75 billion. Among the AI-related companies the researchers examined, corporate bond issuance exceeded $530 billion since 2023, including approximately $405 billion since January 2025.

Chip accounting is another pressure point in the study’s scenario analysis. Major hyperscalers generally assume equipment lifespans averaging seven years. Reducing the assumed lifespan to three years could lower their operating margins by more than nine percentage points. Under the modelled financing conditions, hyperscaler debt could rise from approximately $800 billion to more than $1 trillion.