AI-driven capital spending is reshaping global credit markets. Morgan Stanley estimates AI-related debt issuance has already crossed $236 billion in the first five months of 2026, four times last year’s pace, and is on track to nearly double by year-end. Here is everything Indian investors need to know.
Key Takeaways
- Morgan Stanley forecasts global AI-related debt issuance to reach ~$570 billion in 2026, up from ~$60 billion in the same period last year.
- As of May 31, 2026, AI-linked issuance stands at nearly $236 billion, a 4x surge year-on-year.
- Hyperscalers Alphabet, Amazon, Microsoft, Meta, and Oracle are projected to collectively spend ~$805 billion in capex in 2026, crossing $1.1 trillion in 2027.
- The tech sector now accounts for ~10% of the Bloomberg Investment Grade Corporate Bond Index, up from 9% in 2024.
- For Indian investors, this shift in global credit supply affects FII flows, bond yields, and broader emerging market risk appetite.
The Numbers: How Big Is the AI Debt Wave?
The scale of AI-related borrowing in 2026 has no historical precedent. Morgan Stanley expects $250 billion to $300 billion of debt issuance in 2026 from hyperscalers and related joint ventures alone, with total AI-linked global issuance, including chip firms, data centre project finance, and structured vehicles, forecast to approach $570 billion for the full year.
To put this in context: in 2025, the five major hyperscalers issued approximately $121 billion in U.S. corporate bonds, more than four times their 2020–2024 annual average of $28 billion.
By October 2025, the total amount of debt tied to AI had ballooned to $1.2 trillion, making it the largest segment in the investment-grade market, at 14% of the high-grade market, AI now surpasses U.S. banks as the largest sector in the JP Morgan U.S. Liquid Index.
AI-Related Global Debt Issuance: Year-on-Year Surge
| Period | Estimated AI Debt Issuance | YoY Change |
|---|---|---|
| Jan–May 2025 | ~$59 billion | Baseline |
| Jan–May 2026 | ~$236 billion | +4x (~300%) |
| Full Year 2025 (est.) | ~$200 billion | — |
| Full Year 2026 (forecast) | ~$570 billion | +185% |
| 2027 (projected) | $800 billion+ | Capex crosses $1 trillion |
Sources: Morgan Stanley, Bank of America, Economic Times. Data as of June 10, 2026.
Why Are Tech Giants Turning to Debt Now?
Tech companies have historically been self-funded, generating enormous free cash flows that covered most of their investment needs. That model is breaking down under the weight of AI infrastructure spending.
Large technology companies are likely to commit more than $1 trillion of spending in just the 2025–2026 period. Debt markets are expected to play a big role to cover roughly half of this, as mega-cap hyperscalers are highly rated in credit markets and can use their capital leverage to fund growth.
Morgan Stanley now expects Amazon, Alphabet, Meta, Microsoft, and Oracle to collectively spend about $805 billion in capital expenditures in 2026, up from a prior estimate of $765 billion. For 2027, projected spending has climbed to roughly $1.1 trillion.
Those same hyperscalers are expected to drive about 40% of total Russell 1000 cash capex over 2026 to 2028, representing more than $2 trillion in spending.
Hyperscaler Capex Projections (2026–2027)
| Company | 2026 Capex Estimate | Key AI Focus |
|---|---|---|
| Amazon (AWS) | $100 billion+ | Data centres, cloud AI |
| Microsoft (Azure) | $100 billion+ | OpenAI infra, Copilot |
| Alphabet (Google Cloud) | $75 billion+ | TPUs, Gemini infra |
| Meta Platforms | $65 billion+ | Llama, AI training clusters |
| Oracle | $50 billion+ | AI cloud expansion |
| Combined (2026) | ~$805 billion | — |
| Combined (2027) | ~$1.1 trillion | — |
Source: Morgan Stanley, May 2026.
How Is This Debt Being Structured?
The financing isn’t just plain vanilla corporate bonds. The full spectrum of credit markets, secured, unsecured, structured and securitized across both public and private realms, now plays a role in financing AI-related infrastructure.
Morgan Stanley advised Meta on the $27 billion structured JV for the U.S. AI data-centre campus in 2025.
Key debt structures being deployed include:
Investment-Grade Corporate Bonds: The dominant instrument. Wall Street estimates centre on $300 billion in AI-related IG supply for 2026, potentially delivering $360 billion in 10-year duration equivalents. This issuance is notably long-dated, reflecting the multi-decade useful life of data centres and associated infrastructure.
Convertible Bonds: Popular with smaller AI infrastructure players. Morgan Stanley expects about $20 billion of AI-related deals in leveraged finance markets in 2026, and JPMorgan projects $150 billion over the next five years.
Non-USD Issuance: Hyperscalers are now diversifying their investor base by issuing bonds in euros, yen, and sterling, broadening global demand and supply dynamics simultaneously.
Off-Balance Sheet Project Finance: Off-balance-sheet project finance structures backed by long-term leases are emerging as a key mechanism to fund massive data-centre projects without overwhelming corporate balance sheets.
AI Debt by Instrument Type (2026 Estimates)
| Instrument | Estimated Volume | Key Issuers |
|---|---|---|
| Investment-Grade Corporate Bonds | $250–$300 billion | Hyperscalers, JVs |
| Private Credit / Project Finance | $150–$200 billion | Data centre developers |
| Leveraged Finance | ~$20 billion | AI infrastructure firms |
| Convertible Bonds | $15–$25 billion | CoreWeave, smaller AI cos |
| Non-USD Bonds | $30–$50 billion | Alphabet, Microsoft |
Sources: Morgan Stanley, JPMorgan, Loomis Sayles. Data as of June 2026.
The Bigger Picture: $3 Trillion in Infrastructure Needed
The 2026 issuance surge is just the beginning. Morgan Stanley and Moody’s Ratings estimate at least $3 trillion of capital spending will be needed for data centres and related infrastructure in the coming years, with JPMorgan projecting more than $5 trillion once power generation is included.
The same group of companies is projected to spend around $4.1 trillion in capital expenditure between 2026 and 2030. If even half of these investments are financed through bonds, tech companies could account for a large share of global corporate bond issuance, making them some of the most influential issuers in international debt markets.
Estimates suggest around $300 billion in AI or data centre-related bond issuance over the next year alone and approximately $1.5 trillion over the next five years, which would make the AI-related segment a significant component of corporate bond indices, representing 15–20% of most indices, larger than the U.S. banks component in some cases.
What This Means for Indian Investors and FII Flows
While AI debt is primarily a U.S. and global phenomenon, the knock-on effects for Indian markets are real and worth tracking.
FII Allocation Dynamics: As AI bonds flood global IG indices with high-rated, attractive-yielding paper, institutional allocators globally face rebalancing pressures. Any rotation toward higher-yielding tech bonds could temporarily divert flows from emerging market debt, including Indian G-Secs and corporate bonds.
Risk-Appetite Signal: The sheer scale of AI capital deployment signals that institutional confidence in the technology growth cycle remains high. This is broadly positive for global risk appetite, and supports FII equity inflows into India’s tech-adjacent sectors.
Bond Supply and Yield Impact: Hyperscaler deals have been issued at a weighted average new issue concession of +12 basis points, compared to a market average of +2.5 basis points. This premium issuance pricing could exert mild upward pressure on global credit spreads if supply overwhelms demand, a scenario that RBI and SEBI bond desks will be watching.
Indian IT Sector Read-Across: Sustained hyperscaler capex directly benefits Indian IT services companies like TCS, Infosys, Wipro, and HCL Tech, which derive significant revenue from cloud migration, data engineering, and AI implementation services for these very clients.
India Market Implications at a Glance
| Factor | Impact on India | Direction |
|---|---|---|
| FII bond flows | Mild competition from AI IG bonds | Neutral to marginally negative |
| FII equity flows | Risk-on environment benefits India | Positive |
| Indian IT sector | Hyperscaler clients spend more | Positive |
| INR / USD | Dollar bond supply may keep DXY elevated | Watch |
| RBI policy | Global yield pressure from AI supply | Neutral |
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Risks: Is This Another Dot-Com?
Morgan Stanley itself has flagged this risk explicitly. Hyperscaler AI capex is “set to exceed dot-com era telecom capex in both magnitude and length.”
Morgan Stanley’s research suggests a 1% to 2% margin uplift from AI could support a massive investment base, but the bear case is that investment outpaces monetization for years.
S&P Global Ratings has also identified AI valuations and leveraged borrower refinancing needs as key risks to global credit market liquidity in 2026.
However, there is a critical structural difference from 2001: the companies doing the borrowing today are among the most profitable businesses in human history.
Corporate bond investors take comfort in the blue-chip status of the hyperscalers; the vast majority of the investment is supported by companies with very profitable existing lines of business that aren’t going away as they invest in this new growth area.
Bottom Line
The AI debt supercycle is not a niche credit story; it is reshaping the architecture of global bond markets. Worldwide spending on AI is forecast to reach $2.52 trillion in 2026, a 44% increase year-over-year, compared to a total of $1.6 trillion spent on AI between 2013 and 2024 combined.
With hyperscaler capex crossing $1 trillion in 2027 and debt financing covering roughly half of that, AI bonds will be a dominant force in global credit for years to come.
For Indian investors, the key watch points are FII equity flows (broadly positive), Indian IT sector order books, and any global credit spread widening that could affect domestic borrowing costs.
Disclaimer: This article is for informational purposes only and does not constitute investment advice. Please consult a SEBI-registered financial advisor before making any investment decisions.
