The combined debt of the Magnificent Seven technology companies has increased approximately 45% to $578 billion by mid-2026, raising questions about the financial risks associated with the artificial intelligence investment boom. According to a report by SBI Research, the borrowing of Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla has expanded significantly as technology companies invest in data centres, computing infrastructure and other AI-related projects.
SBI Research has also warned that rising US Treasury yields and higher borrowing costs could put pressure on the market valuations of these companies. Using an equity-duration approach, the report estimates that an increase in yields could potentially put between $1.67 trillion and $2 trillion of their combined market capitalisation at risk. The estimate is a sensitivity scenario, not a prediction that the companies will necessarily lose that amount in market value.
Magnificent Seven Debt Reaches $578 Billion
The Magnificent Seven collectively held approximately $578 billion in debt by mid-2026, compared with around $399 billion in 2025, according to SBI Research.
The combined figure is nearly 1.7 times the group’s debt level in 2023, highlighting the scale of borrowing undertaken by some of the world’s largest technology companies.
The seven companies are:
- Alphabet, Google’s parent company
- Amazon
- Apple
- Meta Platforms
- Microsoft
- Nvidia
- Tesla
These companies have become central to the global AI ecosystem. Their investments span semiconductor development, cloud computing, data centres, networking equipment, software infrastructure and AI models.
The growth in debt has attracted attention because the technology industry historically relied heavily on internally generated cash flows to finance expansion. Companies are increasingly combining their cash reserves with debt financing and other funding arrangements to support the scale of investment required for AI infrastructure.
| Metric | Reported figure |
|---|---|
| Magnificent Seven combined debt, mid-2026 | $578 billion |
| Combined debt in 2025 | $399 billion |
| Year-on-year increase | Approximately 45% |
| Estimated market capitalisation | Approximately $23 trillion |
| Potential market value at risk | $1.67 trillion–$2 trillion |
The figures are SBI Research estimates as reported by ANI. The published summary does not provide a company-by-company breakdown or a detailed definition of the debt measure used. Therefore, the $578 billion figure should be treated as the report’s aggregate estimate rather than a directly reconciled total from the seven companies’ individual financial statements.
AI Infrastructure Spending Is Driving Borrowing
The expansion of artificial intelligence is creating unprecedented demand for computing infrastructure.
Technology companies are investing heavily in facilities equipped with advanced processors, high-performance servers, cooling systems, networking equipment and electricity infrastructure.
Building these facilities requires substantial capital expenditure, often involving multiyear construction and equipment commitments.
The largest cloud providers are also competing to secure computing capacity before demand increases further. This has encouraged companies to invest in data centres and enter long-term agreements with infrastructure suppliers.
The Bank for International Settlements (BIS) has separately highlighted the scale of this investment cycle. Its October 2026 analysis estimated that capital expenditure by the five largest US hyperscalers could exceed $800 billion in 2026, equivalent to approximately 2.5% of US GDP.
The BIS also reported that gross bond issuance by this group had reached $222 billion by September 22, 2026.
These figures cover a different group of companies from the Magnificent Seven, so they should not be added to SBI’s debt estimate. However, they demonstrate the wider financing requirements emerging across the AI infrastructure industry.
Why AI companies need more capital
AI infrastructure investment involves several major expenses:
- Data centres: Construction of large facilities to accommodate computing equipment.
- GPUs and processors: Procurement of expensive AI chips and specialised hardware.
- Electricity: Long-term power supply arrangements and energy infrastructure.
- Networking: High-speed connections between processors and data-centre facilities.
- Cooling systems: Equipment required to manage the heat generated by high-performance computing.
- Cloud capacity: Long-term infrastructure commitments to meet customer demand.
The challenge is that these investments require substantial upfront spending, while the revenue generated from AI services may take years to fully materialise.
Rising US Treasury Yields Could Pressure Technology Valuations
SBI Research has identified rising US Treasury yields as a potential risk to the market valuations of the Magnificent Seven.
The relationship between interest rates and stock valuations is particularly important for companies whose market prices depend heavily on expected future earnings.
Investors generally value companies by estimating the present value of their future cash flows. When interest rates and required returns increase, those future earnings are discounted more heavily.
This can reduce the theoretical value investors are willing to pay for a company, even if its underlying business operations remain unchanged.
SBI Research used an equity-duration approach to estimate the potential sensitivity of the Magnificent Seven’s valuations to higher yields.
| Valuation indicator | SBI Research estimate |
|---|---|
| Combined market capitalisation | Approximately $23 trillion |
| Potential market value decline | $1.67 trillion–$2 trillion |
| Main risk factor | Higher US Treasury yields |
| Valuation mechanism | Higher discount rates applied to future earnings |
The estimated decline represents approximately 7%–9% of the group’s reported combined market value.
However, the actual effect would depend on how much Treasury yields increase, the duration of future earnings, company-specific growth expectations and broader investor sentiment.
The estimate does not establish that all seven companies face the same level of financial risk.
Off-Balance-Sheet Commitments Add Another Layer of Risk
SBI Research has also drawn attention to substantial off-balance-sheet commitments made by technology companies.
These include obligations related to infrastructure purchases, leases, equipment and long-term investment arrangements.
Such commitments are important because a company’s reported financial debt does not always capture the full economic obligations associated with expanding its operations.
For example, a technology company may sign a long-term agreement to purchase computing capacity from a data-centre operator rather than finance the entire facility directly.
The arrangement may reduce the immediate need to borrow on the company’s own balance sheet, but it can still create significant future payment obligations.
The BIS has highlighted similar financing structures involving separate infrastructure companies and special-purpose vehicles. These entities may borrow money to build data centres while larger technology companies provide leases or long-term purchase commitments.
This structure can distribute financing across different entities, but it also creates connections between technology companies, infrastructure operators, banks and investors.
If demand for AI computing grows more slowly than expected, some infrastructure operators could face difficulty generating sufficient revenue to meet their debt obligations.
Anthropic’s Potential Listing Could Test AI Investment Expectations
SBI Research also pointed to Anthropic’s expected entry into public equity markets as an important development for the AI industry.
The report referred to a potential listing around November 12, 2026, with a targeted valuation of $1.5 trillion to $2 trillion.
Such a listing, if it proceeds on the reported terms, would give public-market investors an opportunity to assess the economics of a major AI developer.
The central question is whether AI companies can generate enough recurring revenue to justify the cost of developing and operating advanced models.
AI businesses face substantial expenses related to computing infrastructure, model training, research, talent acquisition and electricity consumption.
A public-market valuation would therefore be assessed against several factors:
- Growth in enterprise AI subscriptions.
- Revenue generated by commercial AI applications.
- Computing and infrastructure expenses.
- Cash burn and financing requirements.
- Ability to convert AI adoption into sustainable profits.
The proposed listing and valuation remain prospective developments, not a completed transaction. Its timing and final valuation could change.
Rising Bond Yields Also Increase the Federal Reserve’s Unrealised Losses
SBI Research has also examined the impact of rising Treasury yields on the US Federal Reserve’s holdings of government securities.
According to the report, unrealised losses on the Federal Reserve’s Treasury holdings increased by $26.3 billion between December 2025 and June 2026.
| Federal Reserve Treasury holdings | Amount |
|---|---|
| Unrealised losses, December 2025 | $535.3 billion |
| Unrealised losses, June 2026 | $561.5 billion |
| Increase | $26.3 billion |
| US 10-year Treasury yield, December 2025 | 4.18% |
| US 10-year Treasury yield, June 2026 | 4.44% |
When market interest rates rise, the prices of existing fixed-rate bonds generally decline because newly issued securities offer higher yields.
This reduces the market value of older Treasury securities held by investors, including central banks.
However, unrealised losses are not the same as realised losses. The Federal Reserve’s accounting treatment and ability to hold securities until maturity mean that a decline in market value does not automatically translate into an immediate cash loss.
SBI Research also clarified that the decline in the Federal Reserve’s total assets largely reflected its quantitative tightening programme, rather than an automatic write-down caused by higher yields.
The Bigger Picture
The rise in borrowing among major technology companies reflects a structural change in how the AI industry is being financed. Building large-scale AI infrastructure requires substantial capital, and even companies with significant cash reserves are increasingly relying on debt and long-term contractual commitments to support expansion.
The main risk is not simply the amount of debt accumulated. It is whether future AI revenues and productivity gains will be sufficient to justify the investment. Higher interest rates could increase financing expenses and reduce the present value of expected earnings, while weaker-than-expected demand could create difficulties for infrastructure operators carrying large financial obligations. At the same time, the Magnificent Seven companies have different business models, cash positions and borrowing requirements, so their exposure should not be treated as identical.
Looking Ahead
Investors will closely monitor the next financial results from Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla for changes in debt, capital expenditure, cash flow and AI-related revenue. The relationship between infrastructure spending and commercial returns will become increasingly important as companies move from building computing capacity towards monetising AI services.
The direction of US Treasury yields will also remain a major factor for technology valuations. If borrowing costs rise while AI infrastructure spending continues to expand, investors may demand stronger evidence of future profitability. The potential Anthropic listing could provide another reference point for how public markets value AI businesses, although its final terms and financial disclosures will determine how useful that comparison becomes.
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