Technology companies are issuing large amounts of debt to help fund AI infrastructure, but the available reporting does not verify that Rishi Sunak said they would outpace the US in issuing long-term debt. Nor does it establish that comparison: the reports track corporate borrowing, not a like-for-like measure of US government long-term debt issuance.
What the bond figures show
The scale of corporate borrowing has grown, but the totals depend on which companies, financing types and dates are counted. In a 2025 report based on Reuters calculations, CNA said Alphabet, Meta, Oracle and Amazon had issued nearly $90 billion in public bonds since September. The report attributed $25 billion to Alphabet, $30 billion to Meta, $18 billion to Oracle and $15 billion to Amazon. Microsoft was identified as the fifth company in the group, but had not tapped the debt market in recent weeks.
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That public-bond tally is narrower than a separate estimate from BofA Securities analysts. CNA and The Economic Times reported that the analysts put hyperscaler debt issuance above $120 billion that year when a separate $27 billion Meta financing deal was included. They compared it with an average of $28 billion over the preceding five years. The broader figure includes financing beyond the four-company public-bond tally, so the two should not be treated as competing estimates of the same measure.
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Why technology companies are borrowing
AI infrastructure requires investment in data centers, chips, networks, storage, land and power. Companies can fund that build-out with operating cash flow, bonds, private credit and other forms of financing; bond sales are one part of a much larger capital-spending picture.
The Economic Times, in an article updated November 21, 2025, reported Sage Advisory forecasts for AI capital expenditure to rise from more than $200 billion in 2024 to just under $400 billion in 2025, then reach $600 billion in 2027. Sage also projected $100 billion of net debt issuance in 2026. Those were forecasts, not confirmed outcomes. They describe a possible funding trajectory, not debt already issued.
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Market participants have described why bonds may be needed as investment scales. Brij Khurana, a portfolio manager at Wellington Management Company, told CNA: “You have all these hyperscaler issuance coming out, and I think the market woke up to the fact that it’s not going to be private credit markets that are going to fund AI, it’s not going to be free cash flow. It’s going to have to come from the public bond markets,” Larry Hatheway, global investment strategist for Franklin Templeton Institute, said doubts about AI spending had emerged in recent weeks, including questions about firms’ ability to finance it through debt. These are judgments about funding needs and market sentiment, not proof that any issuer cannot pay its obligations.
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Not automatically. CNA reported that investors still viewed the companies as lightly leveraged relative to their scale. But the issuers do not have identical balance sheets or cash generation. TrendForce DataTrack’s July 2026 analysis described Oracle as more leveraged and under greater cash-flow pressure than several peers, while characterizing Alphabet and Microsoft as having stronger cash flow and relatively low leverage. Those are DataTrack’s assessments, not a uniform rating of the sector.
To judge an individual company’s capacity to borrow, investors need to consider more than the face value of new bonds. Relevant questions include how much cash and operating cash flow it has, how much it is spending, how much debt it already carries, when repayments are due, and whether future AI-related revenue can justify the investment. Gross debt and net debt are not interchangeable measures.
What could constrain borrowing and investment
A company may be willing to spend and able to borrow while still facing limits on how quickly it can raise money at acceptable rates. Investors must absorb the volume of new bonds, and they may demand higher yields or a premium for longer maturities. TrendForce DataTrack said in July 2026 that new supply was beginning to test investor absorption, with pressure more concentrated in longer maturities. It also reported that financing remained available, even as supply, duration and pricing hurdles increased.
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That matters for the AI build-out because financing costs and investor demand can affect the timing and economics of projects. Goldman Sachs analysts, quoted by CNA, said: “Supply bottlenecks or investor appetite are more likely to act as constraints on near-term capex than cash flows or balance sheet capacity.” This is an assessment of potential near-term constraints, not a claim that a particular company has been shut out of bond markets.
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Keep bond issuance separate from leases and forecasts
TrendForce DataTrack put future lease commitments disclosed by several large cloud and data-center-related companies at approximately $850 billion as of the end of the first quarter of 2026. That figure is not a total of bonds issued or cash already borrowed. The commitments can cover offices, warehouses and other facilities, not only AI infrastructure, and some agreements may have termination provisions. Calling all of them “AI debt” would overstate what the figure measures.
Likewise, capital expenditure is spending on assets, net debt issuance is a financing measure, and bond issuance records a particular type of borrowing. A forecast for spending cannot be added to a bond tally as though both measured debt. Each number needs its own period, company group and definition.
What the Sunak headline does—and does not—establish
The 2025 and 2026 reports cited above document substantial technology-company borrowing and investment; they do not attribute a forecast about outpacing US long-term debt issuance to Rishi Sunak. The available coverage does not establish the wording, date or setting of such a statement, and it does not provide a direct comparison with US government issuance. Without a primary record such as a speech, interview or transcript, the attribution should be treated as unverified rather than repeated as fact.
To test the comparison itself, a reader would need a defined period and consistent measures: for example, corporate bonds issued by a named group over a specified interval versus US government debt issued over the same interval, with long-term maturities identified in both sets. The figures reported by CNA, The Economic Times and TrendForce DataTrack do not provide that comparison.
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