AI Spending Fuels Record Tech Bond Boom as Capex Heads Toward $1.2 Trillion

Artificial intelligence investment is driving a historic expansion in corporate borrowing as Alphabet, Amazon, Meta, Microsoft and Oracle prepare to increase combined capital expenditures from roughly $800 billion in 2026 to $1.2 trillion in 2027, forcing technology companies to rely increasingly on bond markets and alternative financing structures to fund data centers, chips and power infrastructure.

Global bond issuance by AI-linked companies has already surpassed $400 billion in 2026 and is running at an annualized pace above $500 billion, according to Institute of International Finance data cited by Yahoo Finance. Across the wider U.S. corporate market, companies issued $1.9 trillion of bonds through August, 30% more than during the comparable period in 2025.

The scale of the borrowing marks an important transition in the AI investment cycle. The largest technology companies entered the buildout with substantial cash generation and investment-grade balance sheets, but the capital required to expand computing capacity is increasingly too large to be funded solely from operating cash flow.

AI capex is moving beyond Big Tech balance sheets

Goldman Sachs Research expects the five hyperscalers’ capital expenditures to reach about $1.2 trillion in 2027 and $1.4 trillion in 2028. The bank estimates 2027 spending alone would require approximately $300 billion of annual AI-related revenue for the companies to break even on the investment. The Goldman Sachs analysis of global AI investment also cautions that conventional hyperscale capex estimates do not capture private companies, non-U.S. investment or the full range of infrastructure financing involved in the buildout.

That financing requirement is expanding the AI trade from an equity-market theme into a much broader credit-market event.

Amazon, Alphabet, Meta and Oracle issued approximately $194 billion of bonds through July 7, up 79% from roughly $108 billion during all of 2025, according to a Reuters analysis of LSEG data. Goldman Sachs expects the five major hyperscalers, including Microsoft, to issue about $250 billion this year and $400 billion in 2027.

The result is an unusually large volume of highly rated technology debt entering portfolios traditionally dominated by financial companies, industrial issuers and other investment-grade borrowers.

Bond investors are becoming more selective

The volume is beginning to affect pricing.

Reuters reported this month that spreads on AI-related bonds had widened to about 115 basis points, compared with roughly 78 basis points for the broader corporate market. Investors remain willing to finance AI infrastructure, but the prospect of repeated issuance is making some buyers more reluctant to commit capital without additional compensation.

That represents a significant change from the early phase of the AI boom, when the dominant technology companies could largely finance investment from enormous operating cash flows.

As capital expenditures rise, bond investors increasingly have to evaluate not simply the issuer’s current credit rating but the cumulative amount of borrowing required to sustain the AI expansion.

The question becomes particularly important when technology companies issue debt across multiple maturities. More supply can pressure the secondary-market value of existing bonds even when the issuer’s underlying creditworthiness remains strong.

Private capital is becoming part of the financing stack

Public bonds represent only one part of the funding required.

AI infrastructure is also drawing private credit, infrastructure equity, project finance and joint ventures as companies seek ways to expand computing capacity without putting every dollar of investment directly onto corporate balance sheets.

PE NEWSWIRE recently reported that Broadcom, Apollo and Blackstone launched a $35 billion AI infrastructure financing platform designed to finance more than 20 gigawatts of AI computing capacity through 2028.

The platform illustrates how private capital can complement conventional corporate borrowing. Large alternative managers can finance individual data centers, computing equipment and infrastructure projects while hyperscalers reserve their own balance sheets for other capital expenditures.

Institutional investors are participating directly as well. CPP Investments committed $1.75 billion this year to support EQT’s AI infrastructure strategy through data center operator EdgeConneX, as covered in PE NEWSWIRE’s report on the pension investor’s AI data center allocation.

The combination of bonds, private credit, infrastructure funds and project financing is creating a funding market that increasingly resembles the financing of energy and telecommunications infrastructure rather than a conventional technology capital-spending cycle.

AI is becoming a material share of capital markets

The scale is already visible across financing channels.

S&P Global Market Intelligence said technology companies accounted for about 27% of U.S. investment-grade corporate bond issuance in 2026, as well as 60% of convertible issuance, 50% of equity follow-ons and 20% of high-yield issuance. Hyperscaler AI-related capital expenditure has increased from about $80 billion in 2019 to more than $700 billion this year, according to S&P’s analysis.

The capital requirements extend well beyond servers.

AI data centers require electricity generation and transmission, cooling infrastructure, land, fiber networks and enormous volumes of semiconductors and memory. Each layer creates additional financing opportunities for infrastructure funds, private credit managers, banks and bond investors.

That broadening investment footprint helps explain why AI-related financing is becoming relevant to private-market portfolios even when the underlying borrower is a publicly traded technology company.

Credit markets face a trillion-dollar funding test

The next phase will test whether debt markets can absorb the AI buildout without materially increasing borrowing costs.

The five largest hyperscalers are among the strongest corporate borrowers in the world, but the amount of capital being raised is unusual even by investment-grade standards. More frequent issuance means investors can demand greater concessions, particularly when they expect another large transaction from the same borrower within months.

At the same time, alternative capital providers stand to benefit if companies increasingly move infrastructure assets into joint ventures, special-purpose vehicles and project-finance structures.

That makes the AI investment cycle relevant well beyond technology-focused equity investors.

For private credit and infrastructure managers, the opportunity lies in financing assets that sit underneath the hyperscalers: data centers, computing equipment, power generation and transmission capacity. For bond investors, the challenge is absorbing an unprecedented volume of technology debt while determining how much AI-related concentration is appropriate.

The AI gold rush is therefore becoming as much a capital-structure story as a technology story. With hyperscaler spending projected to reach $1.2 trillion next year, the central financing question is shifting from whether Big Tech can afford AI investment to how public and private capital markets will divide the cost.