NextFin News - The artificial-intelligence buildout has crossed a line that few investors saw coming: this year, spending on AI compute and data centers by the world's five largest cloud companies will exceed $690 billion, more than seven times what it was six years ago, and the money is no longer coming from their own cash registers. Instead, tech giants are borrowing and selling stock at a pace that pits them directly against governments in the same global pool of capital - a collision that has pushed the 10-year Treasury yield to 4.81%, lifted the term premium to its highest level in years, and forced the US Treasury to step in with emergency-style bond buybacks to keep borrowing costs from spiraling.
What began as a corporate capital-spending boom has become a test of how much duration the world's deepest, most liquid market can absorb without repricing everything else. The question is no longer whether AI is profitable; it is whether the bond market can fund both the AI revolution and the American deficit at the same time.
The Numbers: A Capital Wave That Doubled in Two Years
The scale of the shift is easiest to grasp in three figures. Combined capital expenditure at Alphabet, Amazon, Meta, Microsoft and Oracle ran at roughly $490 billion over the twelve months through May 2026, up from $95 billion in fiscal 2020. It is expected to top $690 billion in fiscal 2026 - growth of more than 80% year over year, the steepest single-year acceleration of the cycle - and climb past $900 billion by fiscal 2028. Counting finance leases and customer prepayments, calendar 2026 guidance points to close to $800 billion.
That trajectory is corroborated across independent estimates. A sell-side analyst survey compiled in early February projected combined capex at the five largest cloud providers rising from about $240 billion in 2024 to more than $580 billion in 2026 - an unprecedented two-year doubling. The Bank for International Settlements put the figure higher still, estimating the five largest hyperscalers alone will spend more than $1 trillion on AI-related capital expenditure from 2025 through 2026. Looking further out, equity analysts at Goldman Sachs expect large technology companies to commit $5.3 trillion in capital spending between 2025 and 2030, up from $4.5 trillion before first-quarter earnings.
The speed matters as much as the size. Infrastructure booms of this magnitude - canals, railroads, electrification, fiber - typically unfold over decades. This one is compressing into roughly five years, and it is arriving at the same moment sovereign borrowers are issuing debt at a post-financial-crisis pace. Central governments across the OECD sold a record $17 trillion of bonds in 2025 and are projected to reach around $18 trillion in 2026, according to the organization's Global Debt Report. The United States alone must refinance about $6.1 trillion of maturing debt over 2027-28, much of it at higher rates than the original issuance. Net interest on the federal debt is projected to reach $1.039 trillion in fiscal 2026, up 7% from the prior year and now exceeding what Washington spends on Medicare - the first time interest has overtaken the health program for seniors.
Two borrowers with deep pockets and inelastic demand curves are now showing up at the same window. One of them - the US Treasury - has the Federal Reserve's balance sheet as backstop; the other has earnings, but not enough.
The Funding Shift: From Self-Funded to the Capital Markets
For most of the past decade, big tech funded its own expansion. That era ended roughly 18 months ago. Incremental annual debt at the five hyperscalers rose from 9% of capital expenditure in fiscal 2024 to 32% over the latest twelve months through mid-2026, according to analysis of company filings. Equity, long absent from big-tech funding, has returned to the mix.
The signal moment came in June 2026, when Alphabet priced an $84.75 billion equity offering - the largest equity capital transaction ever for a listed company. Of that total, $44.75 billion, including a $10 billion private placement with Berkshire Hathaway, is earmarked for general corporate purposes such as AI capital expenditure; the remaining $40 billion is an at-the-market program covering employees' equity-award tax obligations. Oracle, meanwhile, has outlined $40 billion of combined debt and equity financing for fiscal 2027, which ends in May 2027.
"In the last 18 months, hyperscalers Alphabet, Amazon, Meta, Microsoft, and Oracle have moved from almost fully self-funded capex to raising external capital at scale."
The reason is arithmetic, not ambition. AI costs are front-loaded - chips, data centers, power infrastructure - while the revenue from the models running on that hardware is expected to arrive over a longer horizon. Fiscal 2026 free cash flow is expected to fall close to zero or turn negative for all five companies except Alphabet and Microsoft. When internal cash no longer covers the buildout, the capital markets become the funding source.
The race is also preemptive. Initial public offerings from Anthropic and OpenAI are anticipated in 2026 and 2027, which will ask the market to absorb another wave of AI equity. Alphabet's move confirms the urgency of locking in capital before those listings arrive. Some companies are also experimenting with leasing: SpaceX and, reportedly, Meta have leased data-center capacity to generate near-term returns on massive investments, though it remains unclear how easily leased GPUs and facilities could be reclaimed for internal use if internal AI demand accelerates.
Credit markets are beginning to price the divergence. On July 9, S&P downgraded Oracle to BBB-, one notch above junk, from BBB, citing surging capital expenditure, negative free cash flow and customer concentration. Moody's carries Oracle at Baa2 with a negative outlook, one notch above S&P's new rating; a matching downgrade would likely pressure bond prices and widen credit-default swaps just as Oracle plans to raise further funding. The other hyperscalers retain substantial headroom before any downgrade, but the same themes - customer concentration in OpenAI and Anthropic, and declining free cash flow - are visible across the group.
The Transmission Mechanism: How Corporate Bonds Move Treasury Yields
The first-order effect of AI financing is obvious: more corporate bonds. The second-order effect is what is rattling the Treasury market, and it runs through duration supply.
When a hyperscaler issues a 10-year or 30-year bond to fund a data center, it is not just borrowing dollars - it is selling long-duration risk to the same investor base that buys long-dated Treasuries: pension funds, insurance companies, sovereign wealth funds, and bond mutual funds. Those investors have finite risk budgets. Every incremental dollar of 30-year corporate paper they absorb is a dollar they cannot put into 30-year government bonds unless they expand their balance sheets or accept more duration risk overall.
Researchers at the Federal Reserve Bank of Dallas estimated in February that Wall Street's centered forecast for AI-related investment-grade issuance is about $300 billion in 2026, which could translate into new duration supply of as much as $360 billion in 10-year equivalents over the year - roughly one-eighth of the duration supply coming from the US Treasury. That is not large enough to break the market on its own, but it is large enough to matter at the margin, and margins are where term premium is set. The OECD, in the same report that flagged record sovereign borrowing, separately estimated that nine major AI players will issue $1.2 trillion of corporate bonds over 2026-2030 to fund capital expenditure - a persistent supply overhang, not a one-quarter spike.
The term premium - the extra compensation investors demand for holding long-term bonds instead of rolling short-term ones - has turned into an uncertainty tax. It turned positive in 2024 and has risen through 2026, with another jump this year, according to Federal Reserve Bank of New York estimates cited by asset managers. As of August 17, the 10-year Treasury yield stood at 4.81%, its highest level since late 2023, of which 1.37 percentage points was term premium - the highest decomposition reading in the Federal Reserve Bank of San Francisco's series. Only 3.43 percentage points reflected the average expected overnight rate over the next decade. In other words, more than a quarter of the 10-year yield is now a fear premium, not a rate forecast. By August 25 the yield had eased to 4.67% after the Treasury's intervention, but the term premium had not surrendered its gains.
The market's reaction in August made the mechanism visible. Market participants described a buyers' strike in the 10-to-30-year segment that had taken hold since late June. On August 19, the Treasury Department announced it would at least double the maximum size of its bond buyback operations, from $2 billion to at least $4 billion, targeting the 10-to-20-year and 20-to-30-year segments. Yields fell sharply on the announcement. A sovereign borrower resorting to market operations to cap its own borrowing costs is a tell: the marginal buyer has become harder to find.
There is a third-order channel that is less discussed but more dangerous. Higher term premium on Treasuries becomes the benchmark for everything else. Mortgage rates, corporate investment-grade spreads, municipal borrowing costs, and the discount rates used to value the very AI projects driving the issuance all reprice off the long end. The AI buildout, in effect, raises its own cost of capital as it progresses - a self-limiting feedback loop that does not appear in any company's guidance.
Is This Cyclical or Structural? The Call
Here the analysis must separate two forces that are easy to blend and costly to confuse.
The cyclical leg is real and potentially sharp. If AI monetization disappoints - if enterprise software revenue fails to convert into the margin expansion that guidance assumes - free cash flow will recover more slowly than planned, leverage will stay elevated, and the hyperscalers will be forced to slow spending. Capital-expenditure cycles in technology have historically overshot and corrected: the fiber-optic buildout of the late 1990s, the smartphone capex wave of the early 2010s, and the cloud data-center expansion of 2017-19 all saw growth rates peak and then mean-revert. On that read, the bond-market pressure is a cyclical supply shock that fades as capex growth normalizes from its fiscal 2026 peak.
But the structural leg is what makes this episode different, and it is the stronger force. Three things have changed at the regime level. First, the borrower base has permanently widened: a handful of technology platforms now have capital budgets comparable to mid-sized sovereigns, and their demand for long-duration funding is tied to a multi-decade compute buildout, not a single product cycle. Second, the supply side of the bond market has structurally less shock absorption than before the pandemic - bank balance-sheet constraints, tighter capital rules, and a smaller cohort of natural long-duration buyers mean the marginal dollar of duration supply moves yields more than it used to. Third, the sovereign side is not going anywhere: the US fiscal deficit and the global refinancing wall are not cyclical fluctuations but a persistent feature of the 2020s.
The verdict: this is a structural shift with a cyclical overlay. The competition for capital between AI builders and governments will not revert on its own; it is the new baseline for the long end of the yield curve. What can revert is the intensity - the fiscal 2026 peak growth rate of more than 80% will not repeat, and a monetization disappointment would ease near-term issuance pressure. Investors should treat the direction of term premium as structurally higher, and the year-to-year swings as cyclical noise around that higher floor.
The Counter-Thesis: It Is Not AI, It Is the Fed
The strongest challenge to this reading comes from PIMCO, which argues that AI financing needs do not override the cyclical drivers of yields. Its decomposition is specific: since February 27, the last business day before the Iran conflict began, the 10-year Treasury yield has risen roughly 51 basis points, of which 38 basis points reflects shifting rate expectations and only 13 basis points a higher term premium. On that view, the bond market is reacting to the Federal Reserve's policy path, inflation persistence, and the usual business-cycle forces - not to hyperscaler balance sheets. Blaming AI issuance, in this framing, mistakes correlation for causation.
The counter-thesis has force. Monetary policy expectations are the dominant driver of yields over most horizons, and the Fed kept its policy rate at 3.50%-3.75% at its June meeting while nine of 18 officials penciled in at least one rate hike for 2026, lifting the median projection to 3.8% from 3.4% in March. If the Fed is perceived as behind the curve on inflation, rate expectations will carry yields higher regardless of how many data centers Amazon builds.
But the counter-thesis answers the wrong question. Nobody claims AI issuance is the sole or even the largest driver of the level of yields. The claim is narrower and more testable: AI supply is a marginal but persistent upward pressure on the term premium specifically - the component that has risen while rate expectations have been comparatively contained. That is why the falsifying signal matters. If the term premium falls back toward its pre-2024 range as AI issuance normalizes and Treasury supply stabilizes, the cyclical view wins and this article's structural call is wrong. If instead the term premium stays elevated or grinds higher even as the Fed holds steady and issuance growth slows, the supply-and-structure argument is confirmed.
What to Watch: The Signals That Decide the Trade
The forward picture splits cleanly by horizon.
In the short term - the next two to four quarters - watch issuance and the Treasury's reaction function. The key metric is quarterly AI-related investment-grade supply versus the Dallas Fed's $300 billion annual estimate; a sustained run above that pace keeps pressure on the long end. Equally important is whether the Treasury continues to use buybacks and maturity-tilt adjustments to manage borrowing costs. The August 19 intervention set a precedent; its repetition would confirm that policymakers now see the long bond as a policy variable.
In the medium term - fiscal 2027 and 2028 - the decisive variable is monetization. Watch hyperscaler free cash flow: if it recovers toward guidance, external financing needs fall and duration supply eases. If it stays near zero or negative, the funding shift becomes permanent and credit spreads widen, starting with the most exposed names. Oracle's July downgrade is the canary; a Moody's follow-through would be the confirmation. Also watch the IPOs of Anthropic and OpenAI, expected in 2026 and 2027 - a third equity supply wave hitting a market already digesting $84.75 billion of Alphabet paper.
In the long term - the rest of the decade - the structural call hinges on the term premium. A base case of 4.5%-5.0% on the 10-year Treasury with a 100-150 basis point term premium is consistent with dual sovereign-and-corporate duration demand. An upside case for yields - 5.5% or higher - requires either a fiscal deterioration that overwhelms the Fed's ability to intermediate, or an AI capex path that exceeds even the $5.3 trillion Goldman Sachs forecast. A downside case - the 10-year back toward 3.5%-4.0% - requires either a sharp AI monetization miss that forces capex cuts, or a recession that sends investors back into Treasuries despite the supply.
One quantifiable threshold settles the debate: if the 10-year term premium prints at or above 1.50% for two consecutive quarters while the Fed's expected policy path is unchanged, the structural competition-for-capital thesis is confirmed. If it falls below 1.00% as issuance normalizes, the cyclical view prevails.
The bond market has spent two decades treating technology as a disinflationary force that lowered the cost of capital. The irony of the AI boom is that funding it may do the opposite - turning the companies that built the cheapest capital in history into the borrowers bidding it up, while governments that never stopped borrowing find themselves sharing the window with a new kind of sovereign-sized competitor.
Explore more exclusive insights at nextfin.ai.
