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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsIt is a plausible risk, not an established outcome: available evidence shows AI investment adding to global borrowing demand, but does not show that AI debt has directly displaced emerging-market sovereign borrowers.
What “crowding out” could mean
The concern is that companies financing AI infrastructure could compete with governments and other borrowers for investors’ money. If that demand contributes to higher yields or tighter financial conditions, countries that already depend on external financing could face a more difficult or expensive time raising funds. That is a conditional transmission mechanism—not proof that AI borrowing has caused emerging-market borrowing costs to rise.
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There is also a separate, better-documented use of the term. When a government borrows more in its own country, domestic banks may buy more government bonds and make fewer loans to businesses. That can constrain private investment even without any change in global bond-market access. The global competition hypothesis and domestic bank-lending channel should not be treated as the same finding.
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How large is AI-related borrowing?
In its April 6, 2026 Global Financial Stability Report, the International Monetary Fund estimated that AI-related capital expenditure would total $3.4 trillion through 2029. The IMF also reported that hyperscalers had raised more than $100 billion in bond financing since January 2025. These figures indicate the scale of investment and bond financing; the capital-expenditure figure is an estimate, not a tally of completed spending.
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The IMF described investor appetite for hyperscaler investment-grade debt as healthy and financial-stability risks as contained at the time of publication. It also flagged possible future balance-sheet pressures, reliance on multiple financing channels, and uncertainty about returns if technology becomes obsolete more quickly than expected. In a July 28, 2026 bulletin, Bank for International Settlements authors described the boom as a large, increasingly debt-financed investment surge, while noting that its effects vary across countries. Strong demand at one point in time does not establish that financing conditions will remain benign—or that borrowers elsewhere have already been displaced.
Why emerging-market borrowers may be exposed
Many emerging markets and developing economies (EMDEs) already contend with costly financing, external refinancing needs and pressure from debt service. The IMF’s February 20, 2025 report on EMDE financing challenges warned that many face high financing costs, large external refinancing needs and declining net external flows. Payments on existing debt can leave governments less room for development and social spending, regardless of what is happening in AI finance.
The scale of those existing pressures is visible in World Bank figures. Low- and middle-income countries (LMICs) paid $741 billion more in external-debt principal and interest than they received in new financing over 2022–2024, and their external debt stock reached $8.9 trillion in 2024, according to the World Bank’s 2025 International Debt Report. These are broad LMIC aggregates; they do not describe every emerging economy or measure an effect caused by AI borrowing.
Access to financing has not moved in only one direction. The same report said net debt inflows to LMICs rose to roughly $210 billion in 2024. Bonds and multilateral institutions supplied most long-term net inflows, even as some private and bilateral sources contracted. Net inflows are a flow measure; they do not cancel out the larger principal and interest paid over the three-year period.
Which financing channels are vulnerable to a shock?
Emerging-market portfolio debt liabilities average about 15 percent of GDP, and nonbanks provide 80 percent of that capital, the IMF reported in its April 2026 analysis of nonbank investment. The IMF found that portfolio debt outflows respond to volatility. Funds and hedge funds can react more sharply to market swings than some longer-horizon investors, making the investor mix relevant when conditions turn adverse.
This helps explain why a broader repricing or risk-off episode could matter to some borrowers. But it does not demonstrate that hyperscaler bond issuance triggers such an episode, or that AI borrowing has caused portfolio outflows from emerging markets. A claim of direct displacement would require evidence connecting AI-related issuance to changes in sovereign issuance, yields or capital flows.
Domestic borrowing can squeeze businesses through a different route
In 62 of 86 countries with available data, domestic debt increased between 2023 and 2024, the World Bank reported in its 2025 debt report. It warned that when governments borrow more locally, banks may hold more sovereign bonds instead of lending to businesses. The result can be less credit for private investment within a country—a domestic crowding-out risk, not evidence that global AI borrowers have shut sovereign issuers out of international markets.
The World Bank’s June 2026 Global Economic Prospects also warned that higher EMDE government debt may push up interest rates and crowd out resources for public investment and development priorities. It said uncertainty over how the AI boom might affect inflation complicates policymaking. These concerns involve government-debt burdens and domestic macroeconomic pressures; they do not establish that AI companies have taken financing away from EM issuers. Shorter domestic debt maturities can add another strain by increasing refinancing and rollover risk.
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What evidence would show that AI is actually crowding out sovereigns?
A convincing test would need to separate AI’s contribution from other market forces and track the specific channels through which sovereign borrowers raise funds. Relevant evidence would include:
- Issuance: whether sovereign borrowing falls or becomes more expensive as AI-related corporate bond issuance rises, accounting for broader changes in market demand.
- Yields and financing conditions: whether borrowing costs change in a way that can be linked to AI financing rather than to other causes.
- Flows and investor mix: whether foreign investors redirect funds from sovereign debt, and whether outflows are concentrated among more volatility-sensitive investors.
- Repayment and maturity schedules: whether external refinancing needs or short maturities make particular borrowers more exposed to a change in financing conditions.
Debt stocks, gross issuance, net flows and debt-service payments answer different questions. A large debt stock does not by itself show that a country has lost market access; nor does one year of positive net inflows establish that refinancing is easy. Comparisons also need to distinguish sovereign bonds, domestic bank borrowing, external bank loans, nonbank portfolio funding, private credit and multilateral finance, which carry different risks and costs.
What to conclude now
AI’s expanding, partly debt-financed investment adds to global demand for capital, while many EMDEs enter this period with high financing costs, external repayment needs and sensitivity to volatile portfolio flows. That combination makes competition for funding a reasonable concern to monitor. The evidence available as of the cited 2026 reports does not show that AI borrowing has directly crowded emerging-market sovereigns out of global markets, that hyperscaler borrowing specifically raised their funding costs, or that an AI debt bubble has burst.
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