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.

Private Equity Impact on Child Care

Parents looking for childcare in the United States know how expensive it can be. 

A study authored by Jessica Brown of the University of South Carolina and Chris Herbst of Arizona State University finds no evidence that private equity is the primary reason child care is unaffordable, though an investigation by the U.S. Congress has been underway in 2026 and at least some legislation to regulate PE investments in childcare have been proposed. 

By some estimates eight of the 10 largest childcare providers now are owned by PE firms. 

A study of PE-owned childcare operations in the Netherlands found higher prices (three- to four-percent) but also fewer regulatory infractions, which some will argue suggests higher quality. 

That study also found that PE-owned facilities do not set the pricing strategies for other providers.

 

But private equity investments in childcare are likely to remain an issue, as is the case with PE ownership of other assets with a “social” character, such as health care or veterinary services.

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.

Virtually Nobody Believes in Completely-Unfettered Free Markets: the Issue is When to Intervene

There are lots of reasons why private equity investment in healthcare, child care, nursing homes or veterinary medicine is not much different than private equity in any other industry. PE normally looks for investment opportunities in industries and firms that are fragmented, mismanaged in some way, with room to grow and often featuring steady cash flow. 

The question is whether the financial structure and investment horizon of private equity ownership are well suited to organizations whose primary outputs include public goods such as health, safety, education, or care for vulnerable populations.

This seems to be another instance where we might encounter the idea of free markets needing a bit of management.

In such firms and industries, the concern is that firms may reduce staff, lower safety standards, or limit care to boost profit. 

Also, prices often rise for patients, students, or tenants after a buyout.

In other cases, the concern is a loss of local focus, as decisions move from local leaders to distant corporate offices.

Nursing homes and low-income clinics often face high risks of neglect because heavy debt loads push cash flow to debt service rather than customer care. 

So hospitals and nursing homes see debates over staffing levels and patient outcomes.

In housing markets rent increases and tenant displacement are issues. In education or child care, issues often include firms prioritizing fees over learning tools.

One study spanning eight countries, but 85 percent focused on the United States. Of the 55 cases, nursing homes were the most commonly studied healthcare setting. The analysis included:

• Nursing homes (17)

• Hospitals (9)

• dermatology (9)

• ophthalmology (7)

• multiple specialties or general physician groups (5)

• urology (4)

• gastroenterology (3)

• orthopedics (3)

• surgical centers (2)

• Fertility (2)

• obstetrics and gynecology (2)

• Anesthesia (1)

• hospice care (1)

• oral or maxillofacial surgery (1)

• Otolaryngology (1)

• plastics (1). 

As you might expect, given PE outcomes in other industries, “PE ownership was most consistently associated with increases in costs to patients or payers,” the study found.

“Additionally, PE ownership was associated with mixed to harmful impacts on quality,” the report says. In some instances, PE ownership was associated with reduced nurse staffing levels or a shift towards lower nursing skill mix.

“Health outcomes showed both beneficial and harmful results, as did costs to operators, but the volume of studies for these outcomes was too low for conclusive interpretation,” the authors conclude.

source: Burch et al 

“No consistently beneficial impacts of PE ownership were identified,” the study suggests. 

That is not to argue for barring PE involvement, but many observers might agree some limits might be desirable.

Private equity typically seeks to create value over a relatively short investment horizon (often three to seven years). 

The concern might be whether some of these financial tools create incentives that conflict with long-term service quality in some industries perceived to have social value with a public character. 

The PE playbook is fairly clear: restructure a business to create higher marketplace value. 

PE practice	Potential business benefit	Possible social concern
Reduce labor costs	Higher margins	Lower staffing ratios
Replace senior staff	Lower payroll	Loss of experience
Centralize purchasing	Lower costs	Lower flexibility or quality
Increase debt	Higher investor returns	Less financial resilience
Sale-leaseback of real estate	Unlock capital	Higher fixed operating costs
Roll-up acquisitions	Economies of scale	Reduced local competition
Aggressive billing	Higher revenue	Higher costs for patients or insurers
Short holding period	Faster capital recycling	Less investment in long-term quality

The key issue is that many quality investments—training, staffing, preventive maintenance, or workforce retention—generate returns over many years, while PE investors often realize returns much sooner.

Among all sectors, nursing homes have been studied most extensively, and there is some evidence of less-desirable outcomes. 

Multiple peer-reviewed studies have found associations between PE ownership and:

• higher hospitalization rates

• higher emergency department use

• increased deficiencies cited by regulators

• reduced staffing levels or changes in staffing mix

• higher mortality in some studies.

At the same time, some studies found little change in certain clinical processes, and a minority found no measurable decline in quality. Overall, recent systematic reviews conclude that the balance of evidence points toward mixed but generally less favorable quality outcomes after PE acquisition. (BMJ)

This does not mean every PE-owned nursing home performs poorly. Rather, ownership structure appears to influence average outcomes across large samples.

Evidence remains mixed across specialties, but systematic reviews generally find that PE ownership is often associated with higher costs, while quality effects vary by sector and study.