AI Hyperscaler Debt Surges 974% to $225B in 2026, Hidden Debt Hits $1.65T
AI hyperscalers are on track to issue a record $400 billion in bonds in 2026, after already hitting $225 billion in the first half — a 973.7% jump year-over-year, according to S&P Global. But the visible debt is only part of the picture: hidden borrowing at the five U.S. tech giants has exploded 8x in four years to $1.65 trillion, exceeding the $1.35 trillion that appears on their balance sheets.
Visible debt: record pace
S&P Global counts $225 billion in bonds issued by hyperscalers and related entities like Nvidia so far this year. That puts them on pace for a record haul, but market participants are showing signs of fatigue. S&P notes that hyperscalers are now paying a higher premium versus risk-free yields, and warns: "Market participants are growing leery of quickly rising leverage from issuers previously characterized by strong and reliable cash flow."
Hidden debt: the real price tag
According to a study by Nikkei, hidden debt at U.S. tech giants has grown to $1.65 trillion — an 8x increase in four years. This hidden debt includes long-term purchase deals for GPUs and servers, and lease agreements with data center operators. These practices are legitimate under accounting rules, but they don't show up on balance sheets, meaning the true cost of the AI boom is only partially visible in bond markets.
What's driving the borrowing
Amazon raised its capital expenditure guidance in its latest quarterly report, indicating more borrowing is on the way. The federal government is also competing for bond investors, with a budget deficit expected to hit nearly $2 trillion this fiscal year. Unlike earlier periods, the Federal Reserve is no longer a major buyer of Treasuries, placing more burden on private investors.
Market outlook
S&P Global Capital Economics warns that if debt trends continue, total corporate and government issuance as a share of GDP will exceed any year on record outside the pandemic. RSM chief economist Joseph Brusuelas says demand remains strong for now, but "that will not endure indefinitely." He cautions that at some point, "the rivers of capital financing private and government debt issuance will flow less freely."
📖 Read the full source: HN LLM Tools
👀 See Also
Mercury 2: Diffusion-Based Model for Real-Time AI Coding
Mercury 2 uses diffusion-based generation instead of sequential token-by-token decoding, generates tokens in parallel and refines them over steps, and claims 1,009 tokens/sec on NVIDIA Blackwell GPUs with pricing at $0.25/1M input tokens and $0.75/1M output tokens.
AI Coding Agents Can Fragment Workflow and Drain Attention, Developer Warns
A 12-year web dev reports that using Claude Code daily leads to micro interruptions, loss of focus, and mental exhaustion — without measurable productivity gains.
AI Tools Increase Engineering Workload and Shift Professional Roles
A February 2026 Harvard Business Review study found 83% of workers reported increased workload from AI tools, with 62% experiencing burnout. The article describes how AI has shifted engineering roles from writing code to reviewing AI-generated code.
Cambridge researchers develop hafnium oxide memristor for low-energy AI chips
Researchers at the University of Cambridge have created a hafnium oxide-based memristor that switches currents a million times lower than conventional oxide devices, potentially reducing AI hardware energy use by up to 70%.