AI Is Winning the Race for Capital.
Who Pays The Price?
The AI boom has created a new kind of “crowding-out”
For most of economic history, “crowding-out” was a government problem.
In the 1980s, the debate was about Washington.
The US government was running large deficits. It needed more capital. Investors had only so much money they were willing to lend at the interest rates available at the time.
So, the government had to compete harder for it. Interest rates rose.
And everyone else who wanted to borrow, be it businesses, homebuyers, manufacturers, everyone had to compete against a borrower that could issue seemingly unlimited amounts of debt. That was the classic crowding-out argument.
Microsoft, Amazon, Alphabet, Meta, Oracle and the infrastructure ecosystem around them are committing extraordinary amounts of capital to data centres, GPUs, power generation, networking and cloud infrastructure.
Not all of it is being funded with cash.
Some comes through conventional corporate bonds. Some comes from banks and private credit. And increasingly, some comes through special-purpose vehicles (SPVs), joint ventures, project-finance structures and long-term leases. In simple terms, this allows the infrastructure to be financed by entities other than the technology company ultimately using it.
That distinction matters.
Consider Meta’s Hyperion data-centre campus in Louisiana.
Meta and Blue Owl created a joint venture to develop and own the project. Blue Owl managed funds hold 80%; Meta retains 20%. The parties expect roughly $27 billion of development costs. Meta will manage the development and use the infrastructure, while outside capital funds most of the vehicle.
Or CoreWeave.
In May, a dedicated subsidiary called CoreWeave Financing DDTL V LLC raised a $3.1 billion delayed-draw term loan backed by high-performance-computing infrastructure supporting customer contracts. The facility was rated below investment grade – Ba2 by Moody’s and BB+ by Fitch.
Or Stargate.
OpenAI and SoftBank each invested $500 million in SB Energy. OpenAI simultaneously agreed to lease 1.2 GW of data-centre capacity, while SB Energy separately secured $800 million of preferred equity from Ares.
The pattern is important:
The company legally borrowing the money and the business ultimately expected to generate the cash for its repayment are increasingly not the same entity.
The FT estimates that Meta, Oracle, xAI, CoreWeave and others have already shifted more than $120 billion of AI data-centre financing into off-balance-sheet structures – financing that does not sit directly as conventional debt on the technology company’s balance sheet.
Not:
“How much debt does Microsoft or Meta report?”
But:
“How much capital has been committed because investors ultimately believe an AI company or hyperscaler will generate enough cash to support it?”
AI Capital: By the Numbers
$121 billion: Bonds issued in 2025 by Alphabet, Amazon, Meta, Microsoft and Oracle – compared with an average of roughly $28 billion a year between 2020 and 2024.
$225 billion: Bond issuance estimated by S&P Global for hyperscalers and related entities, including Nvidia, by around mid-2026.
A separate Dealogic-based estimate covering Google, Amazon, Meta, Nvidia, Oracle and SpaceX puts issuance at approximately $240 bn through July 9, 2026.
$350 billion: Approximate bond issuance across 2025 & 2026 YTD, based on the S&P Global estimate – how rapidly AI-related demand for capital has accelerated.
Beyond the Balance Sheet
$1.65 trillion: Estimated off-balance-sheet obligations across Alphabet, Microsoft, Amazon, Meta and Oracle, according to Nikkei’s analysis of company filings.
That compares with approximately $1.35 trillion of obligations reflected on their balance sheets. The off-balance-sheet figure has grown roughly 8× in four years.
Importantly, the $1.65 trillion is not conventional debt. It primarily represents future leases, purchase commitments and contractual infrastructure obligations that do not yet appear as balance-sheet debt.
And the Number Gets Bigger
A Wall Street Journal analysis published this week estimates that nine major technology companies have more than $3 trillion of off-balance-sheet commitments, much of it associated with the AI infrastructure buildout.
If structural demand for capital pushes long-term US interest rates even modestly higher, the transmission travels far beyond Silicon Valley:
Higher Treasury yields
→ higher corporate borrowing costs
→ higher mortgage rates
→ higher discount rates, and pressure on equity valuations
→ investors demand higher returns from emerging markets
→ pressure on EM currencies and capital flows
→ higher domestic financing costs.
And that brings the story directly to India.
An Indian business may never buy an Nvidia GPU.
An Indian household may never use a US private-credit fund.
An Indian small-cap company may have absolutely nothing to do with artificial intelligence. Yet…
Yet all three ultimately operate in a financial system whose cost of capital is influenced by the US risk-free rate (the return available on US govt debt, against which other investments are often compared).
That can mean weaker FPI flows, INR pressure, higher bond yields and lower valuation multiples.
A government borrowing in its own currency generally has far greater ability to refinance its debt – to replace maturing debt with new borrowing.
A project SPV does not have the same flexibility.
A leveraged cloud provider does not.
A private-credit vehicle promising liquidity to its investors does not.
And a data centre built for today’s compute architecture may not retain the value investors assumed if tomorrow’s architecture changes.
This is where the parallel with the pre-2008 credit boom becomes uncomfortable.
2008 was not simply about bad mortgages.
It was about a financial system that became exceptionally good at turning underlying economic promises into apparently safe securities.
The current AI boom is nowhere close to the mortgage crisis in scale or banking-system leverage.
But the question rhymes:
Conclusion
Are investors underwriting the borrower – or underwriting the name standing somewhere behind the borrower?
That distinction rarely matters during a boom.
It matters enormously when refinancing begins.
Disclaimer: The above content is for knowledge purpose only and under no circumstances is the information therein to be used or considered as constituting an offer to buy or sell, or solicitation of any offer to buy or sell or recommendation to acquire or dispose of any security, commodity or investment or to engage in any other transaction.
