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.

What Ails U.S. Student Math and Reading Skills?

Some might claim educational technology is to blame for declining U.S. student capabilities in math and reading. That probably is not the most-common explanation, however. 

U.S. student performance in reading and mathematics has been tracked primarily through the National Assessment of Educational Progress (NAEP). Overall, scores showed steady improvements from the 1970s through the early 2010s, peaking around 2013. 

Since then, performance has stagnated or declined, with sharp drops during the COVID-19 pandemic (2019–2022) due to school disruptions. Test scores indicate high school scores for the 2024 graduating classes have dropped. 

Many reasons have been advanced, but some might point to a couple of decades of declining scores. 

Post-pandemic recovery has been uneven: math has shown slight rebounds in some grades, while reading continues to decline. 

These trends are more pronounced among lower-performing students, widening achievement gaps. By 2024–2025, 12th-grade scores reached historic lows, with reading 10 points below 1992 levels and math at its lowest since 2005.

Study/Author(s)	Year	Key Proposed Reasons
Scammacca et al	2019	Initial proficiency levels create Matthew effects (higher initial skills lead to faster growth); demographic factors (socioeconomic status, ethnicity) influence starting scores and growth rates; grade-level differences affect trajectories, with lower performers growing slower over time.
Wyckoff (Annenberg Working Paper)	2025	Pre-2013 gains slowed due to policy shifts (reduced accountability post-NCLB); Great Recession funding cuts; Common Core disruptions; rising smartphone/social media use; demographic shifts (more English learners); pandemic accelerated existing declines, especially for low performers.
Malkus (AEI Report)	2025	Similar to Wyckoff: accountability rollback, funding reductions, smartphone proliferation, and out-of-school factors (student well-being) explain stagnation since 2013; pandemic worsened trends but isn't the sole cause; factors outside school (e.g., screens) play a major role.
DeBord (Dissertation)	2026	Post-pandemic: classroom factors (rebuilding routines, teacher adaptability); school collaboration; family/district supports; persistent gaps in foundational knowledge, reduced struggle tolerance, technology habits, and socioemotional needs hinder recovery.
NWEA Brief	2021	COVID-specific: remote learning disruptions led to below-pre-pandemic gains; greater impacts on math than reading; inequities in access (devices, support) affected lower performers more; recovery requires addressing missed foundations.

But there may be lots of other cumulative reasons, including social promotion (advancing students to the next grade despite inadequate performance to maintain age-appropriate grouping) and efforts to avoid stigmatizing learners (through reduced retention or labeling to prevent emotional harm), may contribute to declining academic achievement in U.S. students. 

These practices are often adopted to mitigate short-term social-emotional risks like low self-esteem or dropout likelihood from grade retention, but research suggests they can exacerbate long-term performance issues by allowing students to advance without mastering foundational skills, leading to compounded learning gaps, disengagement, and lower scores on assessments like NAEP.

This is particularly evident among lower-performing students, where declines have been steepest since the early 2010s. It’s complicated.

Disintermediation, Again

Disintermediation, the removal of elements in a value chain particularly related to distribution, was a primary effect of the internet. 

Artificial intelligence should cause even more disintermediation, as processes and value chain roles dependent on information asymmetry are removed. 

source: WallStreetMojo

Worse, some might fear, there might be no natural restorative mechanism similar to the boom-bust; recession-recovery; supply-demand cycles we commonly see in economies. 

Instead, in a worst-case scenario, AI continually depresses consumer spending (which represents about 70 percent of all economic activity) as AI leads to layoffs, which leads to less consumer spending, which increases the necessity of relying on AI to protect firm profit margins. 

Industry	How AI Removes Friction/Asymmetry	Expected Impact
Real Estate Brokerage	AI agents instantly access MLS data, decades of transaction records, valuations, and matching—eliminating agents' knowledge advantage and buyer/seller search friction.	Commissions drop sharply (e.g., from 5-6% to under 1%); many deals close agent-free or via AI "agent-on-agent"; widespread disintermediation and value loss for traditional firms.
Insurance Brokerage/Underwriting	AI enables instant policy comparison, re-shopping, risk prediction via vast data, and fraud detection—eroding inertia-based renewals and broker expertise.	15-20% premium loss from passive renewals; brokerage spreads compress; shift to direct/AI-driven models; broker selloffs (e.g., triggered by tools like Insurify).
Wealth Management / Financial Advisory	AI delivers personalized portfolio advice, tax strategies, and real-time market analysis—democratizing what once required expensive human experts and proprietary insights.	Erosion of 1% AUM or high advisory fees for basic services; "basic financial advisory" faces collapse; robo-advisors and AI tools dominate routine work.
Legal Services (Routine)	AI automates contract drafting, precedent research, due diligence, and basic navigation of laws—reducing asymmetry between lawyers and clients on standard matters.	Commoditization of routine work; reduced demand for junior/entry-level roles; faster/cheaper services pressure billable hours and firm margins.
Travel Booking Platforms	AI agents autonomously assemble custom itineraries (flights, hotels, etc.) faster/cheaper than platforms, bypassing search friction and default inertia.	Margin compression for intermediaries; habitual booking models disrupted; platforms lose value as direct AI routing prevails.
Logistics / Freight Brokerage	AI optimizes routes, matches shippers/carriers directly, and forecasts with superior data—eliminating broker coordination friction and information edges.	Broker fees collapse; rapid selloffs (e.g., truck brokers like RXO); shift to automated direct platforms.
Consulting / Professional Advisory Services	AI handles research, data analysis, and initial recommendations—eroding human expertise moats and proprietary knowledge asymmetry.	Reduced fees for routine/cognitive tasks; white-collar headcount cuts; productivity gains but revenue destruction for traditional models.
Ratings Agencies / Index Providers / Credit Checks	AI aggregates and analyzes public/proprietary data at scale—undermining exclusive research advantages and manual verification friction.	Proprietary edges erode; commoditization of ratings/indexing; lower barriers and pricing pressure.

Payments also are an obvious place to look for changes, though the changes might come from use of blockchain-based payment processors offering stablecoins.

source: Citrini Research