Hyperscalers' Debt Binge Could Cut Emerging Markets Off From Capital

Borrowing for AI infrastructure has reached a scale at which tech giants and states are drawing on the same investors. In fall 2025, Meta issued roughly $30 billion in bonds, Oracle raised about $18 billion, and the structure around the Hyperion campus involving Blue Owl was valued at around $27 billion. In an article dated October 13, 2026, Bloomberg Tech describes the next stage: hyperscalers and governments are now competing directly for capital, and developing countries are at the greatest risk. Bloomberg's publicly available teaser gives no new figures, so what follows covers the mechanics of the process rather than fresh statistics.

Where the money comes from

Until recently, Microsoft, Alphabet, Amazon and Meta built data centers out of operating cash flow. Now capital expenditures are growing faster than it is comfortable to fund them from internal resources. The companies are turning to the bond market, using project finance and moving facilities onto the balance sheets of special-purpose structures. For an investor this looks attractive: the rating is high, and the paper is tied to a tangible physical asset.

The problem is that the pool of buyers for this kind of debt is limited. Insurers, pension funds and large asset managers hold limits per issuer, sector and currency. Every large hyperscaler issue takes up part of those limits.

Why emerging markets suffer first

Developing-economy states raise money through Eurobonds and loans from international banks. Their investor base is narrower and more sensitive to market swings. If the corporate bonds of tech giants offer comparable yield at lower risk, part of the capital moves there. The country is left to pay more or postpone its issue.

The paradox is that many of these countries want to attract data centers, chips and cloud capacity themselves. But they will have to finance the build-out of power generation, grids and workforce training on harsher terms.

The channels of pressure look like this:

• a growing supply of high-grade corporate bonds from tech giants;
• institutional investors' technology-sector limits filling up;
• wider spreads on sovereign issues from lower-rated countries;
• costlier borrowing for infrastructure unrelated to AI: roads, power grids, ports;
• off-balance-sheet structures that hide the sector's real debt load.

The last point matters most. When part of the obligations sits in joint ventures and leasing arrangements, the market sees only part of the risk. If conditions deteriorate, a repricing could be abrupt, and both groups of borrowers would suffer.

What market participants should do

Governments of developing countries should plan issues with a margin on timing and should not count on the window being open at any given moment. Diversifying toward regional development banks and long-term investors reduces dependence on a single channel. Where a country expects data centers, it makes sense to require investors to take part in power and grid infrastructure, not just to collect tax breaks.

Companies and startups in these regions need to build more expensive local capital into their models and account for currency mismatch risk between dollar-denominated cloud contracts and local-currency revenue.

Investors would do well to track the sector's total debt load, including off-balance-sheet obligations, and to compare tech-giant spreads with sovereign bonds of similar maturity. That gap will be the first to show where capital is getting cheaper and where it is getting more expensive.