AI-Driven Debt Surge: Goldman Sachs and Morgan Stanley Analyze Borrowing Trends

The AI boom is increasingly fueled by debt, with new analyses from Goldman Sachs and Morgan Stanley highlighting a dramatic rise in AI-related borrowing. Goldman Sachs estimates that $489 billion in AI-related debt has been issued so far in 2026, surpassing its previous forecast for the year. Meanwhile, Morgan Stanley projects that global AI-related debt issuance could reach nearly $570 billion by year-end.

The driving force behind this surge is the hyperscalers—large tech firms investing heavily in data centers, semiconductors, and power infrastructure. Morgan Stanley anticipates that these companies will need to spend over $1 trillion on capital expenditures by 2027.

Notably, the trend is also evident in riskier credit markets. Goldman Sachs reported that AI-linked leveraged finance issuance has reached $88 billion year-to-date in 2026, a significant increase from $20 billion in the same period last year. High-yield AI infrastructure supply has also surged, totaling $40 billion in 2026, compared to just $12 billion for all of 2025.

As leverage among hyperscalers has doubled from approximately 0.9x to 1.8x in just six months, concerns are growing about the sustainability of this debt-driven growth. Both banks noted a shift in investor sentiment, with a growing preference for investment-grade offerings, creating a bifurcated credit landscape.

In Asia, banks have also ramped up lending to AI-related projects, with Taiwanese lenders extending a record NT$4 trillion (around $126 billion) in loans in the first seven months of 2026. This trend reflects a broader increase in AI supply chain financing across the Asia-Pacific region.

As the AI sector continues to expand, the implications of rising leverage and concentrated lending are critical for investors and regulators alike, particularly in regions heavily invested in AI infrastructure.