AI financing boom intensifies credit risk concerns; why hasn't the surge in US Treasury yields widened corporate bond spreads?

Notice that although US Treasury yields have climbed sharply recently, the investment-grade corporate bond market has not simultaneously released an obvious credit risk alarm. The 10-year US Treasury yield rose from 3.96% in February 2026 to 5.31% in early October, at one point touching its highest level since 2002, but over the same period the credit spread of investment-grade corporate bonds relative to US Treasuries fell rather than rose. This divergence shows that bond investors are not yet broadly demanding that companies pay higher additional compensation for credit risk. However, against the backdrop of continued expansion of artificial intelligence (AI) infrastructure construction and rising financing needs among technology companies, credit spreads running at low levels do not mean that corporate financing risk has disappeared. As investment in AI data centers, chips, and supporting infrastructure expands, more and more technology companies are beginning to use bonds, bank loans, and other financing arrangements to support capital expenditures. When a high interest rate environment overlaps with the funding needs of AI investment, whether companies can convert huge capital expenditures into sufficient cash flow will become an important variable affecting future credit risk. Why have corporate bond spreads narrowed even as US Treasury yields have climbed sharply? Judging from recent market data, the rise in US Treasury yields has not triggered a simultaneous widening of credit spreads on investment-grade corporate bonds. ICE data show that the option-adjusted spread (OAS) of the US investment-grade corporate bond index fell from 86 basis points in February 2026 to 84 basis points in September, and further declined to 82 basis points on October 8. At the same time, the 10-year US Treasury yield rose to 5.31% in early October and at one point touched an even higher level. The two sets of data show a clear divergence: as an important benchmark for corporate financing pricing, US Treasury yields have risen substantially, but the additional credit risk premium that investment-grade companies need to pay relative to US Treasuries has declined somewhat. To understand this phenomenon, it is first necessary to distinguish between market interest rates and credit spreads. The financing cost of corporate bonds is usually determined jointly by a benchmark rate and a credit spread. A rise in US Treasury yields means that the benchmark financing cost companies face when issuing new debt increases; the credit spread reflects the additional compensation investors require for holding corporate bonds rather than comparable US Treasuries, covering expected default losses, liquidity, and other risk factors. Therefore, a narrowing credit spread does not mean that corporate financing costs have fallen. Suppose US Treasury yields rise by 100 basis points while corporate bond credit spreads narrow by 20 basis points; then, all else being equal, the yield on newly issued corporate bonds would still rise by about 80 basis points. In other words, the recent performance of corporate bond spreads shows that investors have not demanded that investment-grade companies pay more credit risk compensation, but this cannot offset the financing cost pressure brought by rising benchmark rates. So why have credit spreads not widened along with the continued rise in US Treasury yields? One explanation is that rising interest rates do not necessarily mean economic conditions are deteriorating. If the rise in yields reflects improved economic growth expectations, strong investment activity, or higher inflation expectations, corporate earnings and debt-servicing capacity may still be resilient. Especially in an environment of relatively steady economic growth, investors do not necessarily need to sharply increase risk compensation for corporate bonds. Inflation may also play a role. For companies with fixed-rate debt, if revenue and nominal cash flows can rise with the price level, the real repayment burden of existing debt may be somewhat reduced. However, this effect is not inevitable: if costs rise faster than revenue, or if high inflation forces interest rates to remain elevated for a long time, companies' financial pressure may still increase. In addition, corporate debt usually has different maturities, and not all debt is repriced immediately when market interest rates change. For fixed-rate bonds already issued, the coupon rate will not automatically increase because US Treasury yields rise. Therefore, the impact of changes in market interest rates on a company's overall interest expense often emerges gradually as debt matures and is refinanced. The AI infrastructure financing wave is changing companies' funding needs If the traditional investment-grade corporate bond market has not yet clearly raised credit risk premiums, then the financing needs brought by AI infrastructure construction are adding new variables to the future credit market. As large technology companies accelerate the construction of AI data centers, purchase high-performance chips, and expand cloud computing capacity, AI investment is no longer just research and development expenses or software development spending, but increasingly involves land, electricity, servers, networking equipment, and long-term infrastructure construction. These projects often require large-scale, sustained capital investment, and the capital recovery period may also be long. Some media, citing Morgan Stanley data, reported that technology companies' AI-related debt financing in 2026 is estimated to reach $466 billion, significantly higher than $101 billion in 2025. At the same time, AI-related debt financing activity cooled after a previous sharp expansion: related debt issuance in September 2026 fell to about $23 billion, less than half of August; the US investment-grade market also saw no AI-related bond issuance that month, while from January to August cumulative AI-related bond issuance in that market reached $306 billion. These figures reflect the deepening impact of AI infrastructure construction on the capital markets, and also show that investors are beginning to pay closer attention to financing scale, project returns, and potential leverage risks. Technology giants were previously able to rely more on operating cash flow to support cloud computing infrastructure construction, but as the scale of AI investment expands rapidly, even large companies with abundant cash flow may need to use bonds and other financing tools more heavily. For AI companies and related projects that have not yet formed a stable profit model, the importance of external financing is even more prominent. On October 12, Reuters reported that Tencent plans to raise at least $4.5 billion through US dollar and offshore renminbi bonds. The company's capital expenditure in the first half of 2026 more than doubled year-on-year to RMB 96.3 billion, reflecting that large technology companies are increasing AI-related investment. However, the financing plan at that time still depended on market conditions and investor demand and could not yet be regarded as a completed issuance. This also means that the impact of AI investment is extending from technology companies' capital expenditure budgets to the bond market. As financing scale expands, investors not only need to assess companies' existing balance sheets, but must also judge whether future investment can generate enough revenue and free cash flow to cover continuously increasing funding needs. The risk is not just how much is borrowed, but whether investment returns can be realized The credit risk that may arise from the expansion of AI financing is first reflected in the mismatch between capital expenditures and cash flow. Data center construction requires large upfront investment, and servers and AI chips also need continuous upgrading, but whether the related investment can generate sufficient returns depends on factors such as the commercialization progress of AI applications, customer demand, computing power utilization, and service pricing. If the pace of corporate investment continues to exceed the pace of operating cash flow growth, companies may need to fill the funding gap through new debt, equity financing, or other financing arrangements. Second, some AI infrastructure investments involve complex leasing arrangements, project financing, and long-term procurement commitments. Looking only at the debt figures on a company's balance sheet may not be enough to fully measure its future funding obligations. Regarding the potential leverage of AI hyperscale cloud service providers, Meta's future lease commitments amount to $347 billion, while its balance sheet debt is about $84 billion. At the same time, sell-side analysts expect the company's capital expenditures through 2028 may exceed $500 billion. Lease commitments are not equivalent to debt already recorded on the balance sheet, and future capital expenditure forecasts are not equivalent to actual financing needs. But these figures remind investors that when assessing the financial risk of AI infrastructure construction, in addition to on-book borrowings, attention also needs to be paid to lease obligations, long-term contract commitments, and future cash outlays. Third, high interest rates may cause previously manageable financing pressure to gradually emerge. For companies that have already issued fixed-rate bonds, rising market interest rates will not immediately change their coupon interest, but as old debt matures, new debt is issued, or loans are repriced, financing costs may gradually rise. If companies are still at the peak of capital expenditure at that time, or if cash flow generated by AI projects is lower than expected, debt-servicing capacity may come under pressure. More noteworthy is that companies at different points in the AI industry chain have close funding and business ties. Chip suppliers, cloud computing service providers, data center operators, and AI model developers may be interconnected through procurement contracts, cloud service agreements, leasing, and financing arrangements. Once some projects are delayed, customer demand falls short of expectations, or financing conditions tighten, pressure may transmit along the industry chain. However, this does not mean that the expansion of AI investment will inevitably lead to a credit crisis. Large technology companies usually have stronger cash flow, financing channels, and market position, and can buffer funding pressure to a certain extent. The key is that investors need to distinguish between companies with abundant cash flow and support from mature businesses, and projects that rely on continued external financing and still face significant uncertainty in realizing revenue. Low credit spreads do not mean the market has ruled out AI risk Judging from current investment-grade corporate bond spreads, the market has not yet broadly demanded that companies pay more credit risk compensation. But this indicator reflects overall market pricing and cannot fully present the risks faced by all companies, industries, and financing structures. On the one hand, the investment-grade corporate bond index is composed of different issuers, and the overall spread may mask changes in individual companies' credit conditions. On the other hand, credit risk usually does not immediately show up in default data simply because market interest rates rise. Companies may first respond to pressure through cash reserves, cutting other expenditures, or adjusting capital expenditures, and only later gradually face risks of higher refinancing costs or credit rating downgrades. In addition, the risk performance of investment-grade bonds and high-yield bonds may also differ. Companies with weaker credit quality, unstable cash flow, or higher leverage are usually more vulnerable to tightening financing conditions. Therefore, to judge whether AI financing is becoming a source of risk in the credit market, one cannot look only at overall investment-grade corporate bond spreads; it is also necessary to observe spread changes among issuers with different ratings, debt issuance conditions, financing needs, and corporate cash flow performance. For AI-related companies, indicators worth closely tracking going forward include: whether capital expenditure growth continues to outpace operating cash flow growth, whether new debt and lease commitments increase significantly, whether AI-related revenue can cover infrastructure investment, and whether bond issuance requires offering higher yields to attract investors. If in the future AI investment returns fall short of expectations, financing needs continue to climb, or corporate bond issuance conditions deteriorate, credit spreads may widen again. At that point, the pressure companies face will no longer be just the higher benchmark financing cost brought by rising US Treasury yields, but may also include higher additional financing costs caused by investors demanding greater risk compensation. Ultimately, US Treasury yields and corporate bond credit spreads reflect different levels of risk. The current low level of investment-grade corporate bond spreads shows that the market has not yet broadly raised its pricing of corporate credit risk; but as the scale of AI infrastructure investment expands, the relationship among debt, lease commitments, and future cash flow will become more important. For bond investors, what truly warrants vigilance is not simply the growth in financing scale, but whether companies can, in a high interest rate environment, convert increasing AI capital expenditures into sustained cash flow sufficient to support debt repayment and reinvestment.