The U.S. government needs trillions of dollars to finance deficits. AI companies need trillions more to build data centers, power systems and computing infrastructure.
Both ultimately depend on the same thing: investors willing to provide capital.
That does not mean there is a fixed pile of money that can suddenly run out. Global capital markets can expand, foreign investors can buy more bonds, and higher yields can attract new demand.
But when governments and corporations issue unusually large amounts of debt at the same time, borrowers may have to offer investors better returns.
What Crowding Out Actually Means
Traditional crowding out happens when government borrowing pushes up interest rates and makes private borrowing more expensive.
Suppose Treasury needs to sell hundreds of billions of dollars of additional bonds.
Investors now have more government securities to choose from. To persuade them to absorb that supply, Treasury yields may need to rise.
Corporate bonds are usually priced relative to those yields.
If a 10-year Treasury yields 4%, a highly rated company might borrow at 4.8%. If the Treasury yield rises to 5%, the same corporate spread would imply roughly 5.8%.
That extra percentage point can matter enormously when companies are borrowing tens of billions of dollars.
The dynamic is particularly important for artificial intelligence because the industry is unusually capital intensive. Data centers require GPUs, networking equipment, electricity, cooling, land and long-lived infrastructure.
Our guide to AI financing shows how the buildout increasingly relies on bonds, private credit, leases and project finance rather than corporate cash alone.
AI Companies Are Becoming Huge Borrowers
The first phase of the AI boom was largely financed from Big Tech’s enormous cash flows.
That is changing.
Reuters reported in August that Amazon, Alphabet, Meta and Oracle have dramatically increased bond issuance as AI-related capital expenditure accelerates. Hyperscaler spending could reach roughly $250 billion financed through bond markets in 2026 and potentially $400 billion in 2027, depending on how spending and cash flow evolve.
T. Rowe Price estimates data-center capital expenditure could reach about $5.5 trillion between 2026 and 2030, requiring funding from investment-grade bonds, private credit, securitized markets and other debt channels.
The pressure is already visible in corporate finance.
AI spending at the largest technology companies has climbed so quickly that free cash flow is becoming a more important investor concern. Coinpaper’s guide to whether AI spending is paying off shows why strong earnings do not automatically mean enough cash remains to fund future infrastructure internally.
| Borrower | Why it needs capital |
|---|---|
| U.S. Treasury | Deficits, refinancing existing debt |
| Hyperscalers | AI servers and data centers |
| Utilities | New generation and grid infrastructure |
| Data-center developers | Construction and power connections |
| AI cloud companies | GPUs and expansion |
The Government Is Borrowing Heavily Too
At the same time, Washington is running a deficit of roughly 6% of GDP, according to recent Reuters analysis.
Higher spending, weaker corporate-tax receipts and elevated interest costs mean Treasury must continue issuing large quantities of debt. Reuters also notes that rapid AI investment can temporarily reduce tax receipts because companies deduct large capital expenditures more quickly.
So AI creates an unusual feedback loop.
The technology boom requires enormous private investment, while tax incentives supporting that investment can initially reduce government revenue—potentially increasing Treasury borrowing at the same time.
The competition is not limited to U.S. markets either. U.S. hyperscalers are increasingly issuing euro-denominated bonds, raising concern that American technology companies could compete with European governments and companies for regional investor demand.
Why Investors Should Care
Crowding out does not mean AI projects suddenly stop getting financed.
It means the price of capital can rise.
Higher Treasury yields increase corporate borrowing costs, make safer government bonds more competitive with stocks and lower the present value investors assign to future technology profits.
That relationship is why higher Treasury yields can hit AI companies twice: their infrastructure becomes more expensive to finance while their equity valuations can fall at the same time.
The outcome ultimately depends on what the AI investment produces.
If AI generates large productivity gains, higher tax revenue and strong corporate profits, the economy may comfortably absorb both government and private borrowing.
If the returns disappoint, investors could become much more selective.
The real question is therefore not whether there is enough money in the world.
It is what yield investors will demand before they agree to finance both Washington’s deficits and one of the largest corporate investment booms in history.
