Maybe Higher Interest Rates or High CAPE Ratios Will Not Derail AI Investment

The issue economists and financial analysts always face when attempting to assess the impact of higher interest rates on equity valuations in general or artificial intelligence in specific is that evaluations must be made on the assumption that “all other things remain equal,” which, of course, is rarely the case. Markets are dynamic, so, by definition, all else does not remain equal. 

Consider the expected impact of price-earnings ratios and higher real interest rates. 

The cyclically adjusted price-earnings ratio (CAPE) being at a historically-high level suggests lower returns from equity markets over the next decade or so and a possible "reversion to mean" for AI equity values.

But the high CAPE ratio does not mean artificial intelligence public equities are necessarily overvalued or in a “bubble.” It does mean that AI firms must grow into their valuations by continued high earnings growth. 

Observation	What CAPE suggests
U.S. equities are expensive relative to historical earnings	Strongly supported
Long-term expected real returns are probably lower than historical averages	Historically supported
AI stocks specifically are overvalued	Not established by CAPE
AI is a speculative bubble	CAPE cannot establish this
AI earnings expectations are unusually important to market valuation	Yes
A substantial earnings disappointment could have disproportionate market consequences	Yes
Higher interest rates would be especially consequential	Yes
AI productivity could eventually justify some of today's valuation	Possible
Today's valuation requires unusually strong future earnings growth	Yes

Is today's high CAPE a denominator problem or a numerator problem? The denominator problem happens if investors have simply bid up stocks too far relative to sustainable earnings.

The numerator problem happens if today's earnings substantially understate the future earnings power of the companies because AI is about to raise productivity and profits dramatically. Take your pick., 

The second possibility is the "this time is different" argument, an obvious red flag for most of us. Historically, that argument has sometimes been correct economically while still being wrong financially.

In the shorter term, investors will have to evaluate the likely impact of higher interest rates (the “real” rate being the thing that matters) on the AI ecosystem. 

The conventional wisdom is that higher real interest rates (nominal rate minus inflation) tend to slow economic growth. That should mean the AI ecosystem also grows more slowly, as higher financing costs reduce investment and therefore potentially future revenue growth.

Also, the present value of distant future cash flows falls when the discount rate rises. This can be particularly severe for AI companies because much of their expected value is based on future rather than current earnings.

Part of AI ecosystem	Likely effect of higher rates	Why
Hyperscaler AI capex	Moderate slowdown	Higher cost of capital raises the hurdle rate for marginal data centers, GPUs and power projects. But Microsoft, Alphabet, Amazon and Meta have enormous cash flows and strategic reasons to keep investing.
Frontier-model companies	Moderate-to-large pressure	Companies with large compute bills and limited current profits become more dependent on external capital. Higher rates make investors demand a clearer path to monetization.
AI startups	Large pressure	Valuations depend heavily on discounted future cash flows and access to venture capital. Higher rates particularly hurt companies whose revenues are distant or speculative.
Data-center developers	Moderate-to-large pressure	These are extraordinarily capital-intensive, long-duration projects. Financing costs directly affect project economics.
GPU/accelerator suppliers	Initially modest; eventually meaningful	Existing compute shortages and contractual commitments can insulate near-term demand. But slower infrastructure deployment eventually feeds back into equipment orders.
Power/electrical infrastructure	Moderate pressure	Projects with long lead times and large upfront investment become harder to finance, although genuine power scarcity can preserve demand for some projects.
Cloud AI services	Mixed	Higher rates can restrain customers' IT budgets, but AI can also be justified as a way to reduce labor costs or increase productivity.
Enterprise AI software	Mixed/slightly negative initially	Discretionary experiments are vulnerable, while applications with measurable ROI may actually become more attractive if companies are under pressure to improve productivity.
AI applications with little capex	Relatively resilient	Higher rates don't materially change their marginal cost of development or deployment.
AI infrastructure with long-term contracts	More resilient	Contracted cash flows can support project financing even when the general cost of capital rises.
Existing profitable technology companies	Relatively resilient	High cash generation makes them less dependent on external financing.

Also, it matters greatly “why” interest rates are climbing. If rates rise because the economy is strong and inflation remains persistent, that is a very different scenario from rates rising because of a recession. And, at the moment, U.S. gross domestic product is still growing, according to the Federal Reserve.

Higher rates “should” put pressure on some suppliers in the AI value chain, such as high-performance-compute-as-a-service suppliers. But some will argue that better capital investment discipline will result. 

Higher rates might even help suppliers of used graphics processor units, as such units might retain their value better.

Effect of higher rates	Likely impact on AI
Higher cost of debt	Negative for leveraged AI infrastructure
Higher required return on new projects	Negative for marginal data centers and GPU deployments
Lower equity valuations	Negative, particularly for startups dependent on new funding
More expensive private credit	Negative for neoclouds and infrastructure developers
Pressure on hyperscaler free cash flow	Negative, potentially causing capex discipline
Higher discount rate applied to future AI profits	Negative for valuations
Stronger incentive to monetize existing GPUs	Mixed/positive—could increase utilization
Higher hurdle rate for speculative projects	Potentially positive for industry discipline
Cash-rich hyperscalers' ability to self-finance	Mitigates the effect

As usual, higher borrowing costs will be a negative for startups and smaller firms more reliant on borrowed money. 

AI layer	Rate sensitivity
Nvidia/AMD-type highly profitable chip suppliers	Low–moderate
Hyperscalers	Moderate
Large profitable AI software companies	Moderate
AI infrastructure developers	High
Neoclouds	Very high
Frontier-model startups	High
Early-stage AI startups	Very high

Still, ceteris paribus (“all other things being equal”) rarely describes events in the real world. All other things will not remain equal. Higher interest rates “should” slow AI investment. But competitive pressures within the industry, evidence of value and financial impact, capital availability and all sorts of other potential macroeconomic influences are likely to have an effect as well.

So interest rate increases might not have the slowing impact one might otherwise expect. Nor might the impact of a high CAPE necessarily cause the bursting of the "AI bubble." There are simply too many moving parts.

Nvidia Asset-Backed "Securitization" Moves

Nvidia is working with six private equity and financial entities to create a financing mechanism for servers that essentially aims to turn hardware capex into infrastructure. 

Critics will say it is a sign of excess in the artificial intelligence market. Optimists might say the move makes Nvidia-powered AI infrastructure an investable asset class, allowing financing to be made by institutional investors.

Nvidia signed memorandums of understanding with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. 

The partners will assemble capital pools at rates Nvidia characterized as attractive, with intended beneficiaries spanning frontier AI labs, enterprises, and cloud providers. 

The commercial aim is to enable compute buyers to obtain capacity without showing that “capex” on balance sheets, much as an airline leases aircraft rather than buying planes.

In other words, the mechanism shifts server and compute hardware depreciation schedules to longer-lived categories similar to commercial real estate or toll roads. 

Essentially, the effort aims to effectively securitize compute, functionally if not in a textbook form. 

“Securitization” means a special purpose vehicle pools financial assets such as loans, leases, or receivables, then issues notes whose repayment comes principally from those pooled cash flows. 

There are other similar forms that accomplish the same ends, if not using precisely the same means. 

Structure	What investors finance	Is it securitization?
Equipment-secured loan	GPU servers and sometimes customer contracts	No; it is secured/private credit
Direct infrastructure or project loan	Data center, power, cooling, and network assets	No; it is project finance
SPV ownership plus lease	SPV owns GPUs/data center and leases compute capacity to an operator	Not automatically
Sale-leaseback	A sponsor sells assets to an SPV and leases them back	Not automatically
Asset-Backed Securities (ABS) and Commercial Mortgage-Backed Securities (CMBS)	Pool of GPU leases, compute receivables, data-center loans, or tenant leases	Yes, or close economic analogy

The model resembles aircraft-lease or equipment forms of asset-backed securities, where: a bankruptcy-remote vehicle owns equipment and receives contractual lease or service payments. 

Equipment ABS also have been used to finance shipping, and rail assets as well. 

That, in turn, will help customers access scarce compute at scale by moving such compute capabilities off the balance sheet, in principle alleviating investor concern about the timing of AI capital expenditure and near-term financial returns.

AI Infra Financing Gets Creative

Financing of AI infrastructure has evolved into a complex, multi-layered financial architecture that extends well beyond traditional corporate balance sheets. 

External financing structures include:

• Strategic partnerships: Frontier model labs and hyperscalers are forming partnerships for regional development, power infrastructure, and equity contributions

• Public sector and sovereign support

• Captive markets: In some instances, state-owned enterprises or governments direct domestic demand toward local chip manufacturers.

Financing Model	Description	Example / Context	Source
Structured/Off-Balance Sheet	Using infrastructure funds and private credit to distribute risk across a layered set of claims.	General industry shift toward using private credit and structured vehicles to fund data center buildouts.	BIS
Community-First Partnerships	Joint commitments between developers and providers to share infrastructure costs and regional responsibilities.	Microsoft's "Community-First AI Infrastructure" plan and OpenAI's "Stargate Community" initiative.	HKS
National Sovereign Investment	Coordinating investments in data, compute, and algorithms through sovereign-backed frameworks.	Frameworks for "AI Triads" in low-to-middle-income countries using structured funding tranches.	Oxford
Captive Market Funding	Generating revenues through domestic mandated demand to fund internal R&D cycles.	Huawei’s AI chip revenue generation within the Chinese domestic ecosystem.	Bruegel

In many instances, the intent is to reduce capital investment requirements by moving to off balance sheet vehicles or “compute as payment” arrangements.

Hyperscaler	Model Supplier	Deal Type / Structure	Estimated Value / Capacity	Source
Google	Anthropic	Multi-year compute commitment + Equity investment	Up to $40B investment; 3.5GW TPU capacity (via Broadcom)	Silicon Republic
Amazon (AWS)	Anthropic	Compute credit + Equity investment	Up to $25B total commitment; multi-year cloud compute	Silicon Republic
Microsoft	OpenAI	Exclusive cloud provider + Multi-stage capital injection	~$10B+ in multi-year funding; 49% profit stake	Aranca
Meta	N/A (Self-build)	Structured finance (SPV) for data center buildout	~$30B "Hyperion" SPV (Blue Owl Capital led)	SoftwareSeni
Google/Anthropic	SpaceX	Compute infrastructure delivery contracts	Potentially >$70B over multi-year term	AA

As seen with Meta’s "Hyperion" transaction, hyperscalers are increasingly utilizing Special Purpose Vehicles (SPVs) and partnerships with private credit firms (e.g., Blue Owl Capital) to fund massive data center buildouts. This allows the companies to offload the capital intensity of the physical build while retaining operational control and capacity priority.

In many of these deals, "compute" has become a literal form of payment. The Google-Anthropic and Amazon-Anthropic deals are not merely cash-for-equity; they are deeply intertwined with multi-gigawatt (GW) capacity commitments and customized hardware access (such as Google’s TPUs).

Financing is no longer focused just on chips. The capital is increasingly directed toward the "AI Triad"—the integration of compute, dedicated energy infrastructure, and data center physical shells. This is evidenced by the trend of co-locating data centers with renewable energy sources and the invocation of national defense acts (as seen in the U.S. in early 2026) to prioritize grid expansion for AI.