To The Moon

The AI computing infrastructure investment wave is confronting increasingly sharp contradictions: strong computing demand expectations driven by the rapid expansion of AI agents reinforce the need for companies to lock in computing resources on a large scale ahead of time, while energy inflation and the Federal Reserve's tightening expectations are simultaneously pushing up the funding costs of those investments.

In early Asian trading on September 28, after Trump rejected Iran's latest proposal to reopen the Strait of Hormuz, Brent crude rose 1.4% to $105.80 per barrel, and US Treasuries once again suffered heavy selling: the two-year yield, sensitive to policy rates, rose 5 basis points to 4.90%, while the 10-year Treasury yield rose 4 basis points, climbing above 5.20% for the second time since last Thursday.

In early Asian hours, Japanese and Australian government bonds also continued to come under heavy pressure, tracking US Treasuries.

For AI infrastructure-related companies such as Oracle, Amazon, Google, and CoreWeave that are borrowing to expand AI data centers, this means new financing requires paying higher interest and other funding costs; for equity investors, it means future earnings must continue to be tested by higher discount rates and higher financing costs.

Whether AI computing demand can keep growing, and how much cash return that growth ultimately leaves for shareholders, are becoming two separate questions that need to be answered independently.

The direct catalyst for this round of global bond market adjustment is the market's reassessment of when energy supply will recover and how much more the Federal Reserve needs to raise rates. In addition, for the 10-year and longer-dated US Treasury curve, a more critical structural force comes from "rising fiscal deficits + AI debt issuance" competing for the global pool of duration bond capital.

Understanding the 10-year US Treasury, the "anchor of global asset pricing," requires distinguishing the expected path of future short-term rates from the term premium required to hold long-term bonds.

On September 16, the Federal Reserve raised the policy rate by 25 basis points to 3.75%-4.00%. Expectations of further rate hikes under the inflation backdrop will transmit to long bonds through the future rate path; beyond the AI debt issuance frenzy and US Treasury fiscal expansion both competing for the global capital pool, fiscal financing, inflation uncertainty, and the supply-demand structure of long-term bonds are all factors affecting the extra compensation investors demand.

In Japan, financial markets are also losing the buffer once provided by the past low-rate environment. Recent quotes show that Japan's 10-year government bond yield reached 3.115% on September 25, a high since 1996; 20-year and 30-year yields rose to 3.900% and 4.130%, respectively, on September 24.

On September 18, the Bank of Japan decided to raise its policy rate from about 1.00% to about 1.25%, and explicitly stated that high oil prices, a weak yen, and AI-related demand are pushing up corporate costs, with some pressure beginning to pass through to consumer prices, while wage pass-through and inflation expectations are also strengthening.

After Trump rejected Iran's proposal, oil prices rose and bond selling returned. On Monday, US Treasuries came under selling pressure again.

Previously, US President Donald Trump rejected Iran's latest proposal to reopen the Strait of Hormuz, driving international oil prices higher, with Brent crude rising more than 2% early in the session to approach $107, intensifying global inflation concerns.

The rate-sensitive US two-year Treasury yield rose 5 basis points to 4.90%, while the 10-year Treasury yield rose 4 basis points to 5.20%.

Japanese and Australian sovereign bond prices also fell.

Last week, against the backdrop of hawkish comments from Federal Reserve officials, US Treasury yields across maturities surged to multi-year highs. Monday's Treasury selloff followed that move.

As shown in the chart above, the US 10-year Treasury yield is hovering near its highest level since 2007.

In early Asian trading, Brent crude rose 1.4% to $105.80 per barrel. Iran previously said it would not relax the conditions for reopening the Strait of Hormuz, once again increasing pressure on the Federal Reserve to curb inflation through rate hikes.

According to Axios, Trump said that although he rejected Tehran's latest proposal, he expects negotiations to resume this week.

Damien McColough, head of fixed income research at Westpac, said: "The Federal Reserve's continued hawkish signals and oil prices holding above $100 are key factors driving the bond market weaker."

Meanwhile, US Treasury Secretary Scott Bessent urged Federal Reserve policymakers to remain "open-minded" on interest rates, arguing that productivity gains from artificial intelligence and deregulation will help control US inflation.

The "yield backlash" of the AI boom: the stronger AI computing demand becomes, the more expansion requires a clear funding calculation. AI financing expansion has extended from investment-grade tech giants to record high-yield bond deals.

On September 24, SoftBank set the terms for new bonds totaling $10 billion and 1 billion euros, about $11.1 billion combined, which media widely described as the largest high-yield corporate bond transaction on record; according to SoftBank's announcement, the scheduled issuance date is September 29.

The 7.5-year dollar bond carries a coupon of 9.75%, and the proceeds include the final $10 billion payment for an additional investment in OpenAI.

This deal shows that AI financing channels remain open, but the price required to obtain huge amounts of capital is already quite high.

Large technology companies are also expanding financing scale and extending funding maturities.

Google parent Alphabet completed a $25 billion dollar bond issuance on August 10, with the longest tranche maturing in 2066; Amazon raised 4.25 billion pounds, about $5.76 billion, on September 9 through its first sterling bond transaction.

More noteworthy is the supply outlook: Goldman Sachs forecasts show that total debt issuance by hyperscale cloud providers is expected to reach a record $420 billion in 2027, 60% higher than the estimated 2026 level.

Financing pressure is coming simultaneously from benchmark rates and credit spreads. Market data on September 22 showed that spreads for AI-related issuers were about 115 basis points, higher than the 78 basis points for the broad investment-grade market.

Institutions interviewed emphasized that the extra compensation involves not only credit judgments but also continuously increasing bond supply, uncertainty over issuance pace, and portfolio concentration limits; these spreads cannot be directly equated with default probabilities.

For new dollar fixed-rate financing, the cost is usually composed of the benchmark yield for the corresponding maturity, typically closely linked to the 10-year US Treasury yield known as the "anchor of global asset pricing," plus credit and liquidity compensation.

When both parts rise at the same time, even if companies can still finance smoothly, their project returns may be compressed.

To some extent, this contradiction can be summarized as the "long-dated US Treasury yield curve backlash" of the AI boom: to realize future productivity gains, companies first expand demand for chips, power, construction, and financing; if short-term demand growth outpaces supply expansion, it may increase cost and inflation pressure, making financing conditions tighter.

Bessent stressed that AI and deregulation can raise productivity and help control inflation, a logic focused on long-term improvement in supply capacity; model analysis by researchers at the St. Louis Fed suggests that even if productivity gains have not yet materialized, optimistic expectations for future growth may first stimulate current demand and inflation.

The two judgments involve different time horizons: long-term cost-reduction potential cannot automatically offset the long-term financing cost pressure and resource constraints during the construction period.

From the perspective of AI infrastructure project finance, what truly deserves attention is how much room remains between return on investment and cost of capital, and whether cash flow can keep up with the debt repayment schedule.