The AI boom is coming to bond markets - should investors care?

The physical buildout of AI – the data centres, the power connections and the networks that make it work – costs enormous sums, and a growing share of that bill is being funded with borrowed money. That turns AI into a story for bond investors too.

From cash to credit

The scale is hard to overstate. CreditSights estimates the five biggest US tech companies investing in AI ('hyperscalers') – Amazon, Microsoft, Alphabet, Meta and Oracle – are on course to spend in the region of $600 billion on infrastructure this year, the bulk of it AI-related. Until recently, these companies funded that growth largely from their own pockets. That has changed. Data-centre-related debt issuance roughly doubled to around $180 billion in 2025, according to S&P Global Market Intelligence. The hyperscalers alone sold $121 billion of bonds, more than four times the five-year average [1]. Alphabet (owner of Google) even issued a rare 100-year sterling-denominated bond [2] – a reminder that this is a UK story too, not just an American one.

For bondholders, this is both an opportunity and a change in the rules. For years, the market treated these AA and A-rated companies as 'cash-plus' – rock-solid balance sheets that barely needed to borrow. Now they are issuing debt on a scale that forces investors to think harder about a company's ability to repay its debts, the sheer volume of new supply of bonds, and the eventual profits on all that spending.

Chart 1: Big-five hyperscaler capex ($bn); 2026 projected. Bond issuance averaged roughly $28bn a year before 2025. Sources: company filings, BofA Securities and analyst estimates.

What bond investors actually worry about

Buying a data-centre bond is not the same as buying a share from a technology company. Own the shares and you are betting on the chance that AI transforms profits. Own the bond and your best case is that you collect your regular monthly or quarterly payments and get your money back. You take the risk without the upside, so the questions you ask are different:

These are less familiar risks – whether the project is completed on time and on budget, the quality of the tenant and the strength of the lease agreements underpinning the debt. They are closer to the questions you would ask about a toll road or a power station than about a typical corporate borrower. That is the key point: much of this is infrastructure-style debt, where the money comes from long-dated contracts and physical assets rather than a company’s day-to-day operations.

Public and private – two doors into the same theme

There are two main routes into this market, and active investment managers use both. The first is the public bond market, where the hyperscalers’ investment-grade bonds have become one of the largest single sources of new supply. The second is private credit: increasingly, data centres are financed through dedicated investment vehicles that raise money from private lenders and insurers, secured against the buildings themselves and the contracts with the technology giants that use them.

At Aberdeen, we invest in both publicly traded bonds and private credit, and we think that breadth matters. Being able to compare a stock-market-listed hyperscaler bond with a privately financed data-centre structure – and judge which offers better value for the risk taken – is exactly the kind of decision active management is for. A passive bond fund typically owns more of whoever issues the most debt; that is not the same as owning the right debt.

Chart 2: Estimated share of each credit market exposed to data-centre and software risk (higher-case estimate). Source: Aberdeen Global Macro Research, Q2 2026 scenario analysis (data: UBS); illustrative of a downside scenario.

Where AI meets our own process

There is a neat symmetry here: the same technology reshaping the bond market is also changing how we analyse it. We use AI tools to sift far more research and market data than any team could read unaided, to identify patterns worth a closer look and, just as importantly, to challenge our own thinking – testing whether an investment idea stands up to the evidence. Used well, it does not replace judgement; it frees our analysts to spend more of their time on the decisions that genuinely add value.

In practice, it earns its keep on the everyday jobs: scanning for bonds that look cheaper or more expensive than they should be before the stock market opens; pressure-testing a broker’s pitch against our own numbers in minutes rather than hours; pulling risk and positioning across dozens of portfolios on demand; and drafting the first cut of routine research documents. In effect, it does much of the looking backwards – combing through the history and the data – so our analysts can spend more of their time looking forwards. The judgement stays with us; the legwork just gets faster.

Final thoughts…

AI is no longer only an equity story. As the build-out is increasingly financed with debt, it is opening up fresh opportunities – and fresh risks – for bond investors. The winners will not simply be those with the most AI exposure, but those who can tell a well-structured, well-secured bond from a fragile one. That is a job for careful, active analysis – and, increasingly, for the smart use of technology on both sides of the trade.

[1] https://introl.com/blog/data-center-deals-61-billion-record-december-2025

[2] https://www.cnbc.com/2026/02/23/big-techs-ai-bond-binge-shatters-unspoken-contract-with-investors.html