CAPE is an Issue, But How Much?

Nobody can know for certain--beyond the fact that U.S. financial markets are in historically above-average valuation levels--what could happen next. 

Some rationally expect a reversion to mean, which will mean lower valuations.

Others just as rationally argue that above-average valuations can persist for some time, and that a correction is not in store. AI might be among the reasons, if it changes growth expectations.

source:  Ark Invest

Consider the Cyclically Adjusted Price-to-Earnings Ratio (CAPE), widely considered a valuable long-term valuation metric. The current CAPE is high, suggesting caution and a likely correction to lower levels.  

But many analysts believe that changes in accounting rules since the early 2000s make the standard version look artificially high relative to its historical average. Adjusted, it might still be high, but not at internet bubble levels. 

The CAPE is calculated as the current S&P 500 Price divided by the average of 10 years of inflation-adjusted earnings.

The issue is that the denominator uses reported GAAP earnings, and those earnings have become more conservative over time, leading to a boost in CAPE that make comparisons with past levels misleading, the argument goes. 

Key accounting changes include: 

• Goodwill impairment rules (FAS 142, adopted in 2001)

• Large acquisition write-downs now hit earnings immediately.

• Before 2001, many such costs were spread over decades.

• This depresses modern earnings compared with earlier periods.

• Mark-to-market accounting

•  lk;juring crises, companies must recognize large non-cash losses.

• These can sharply reduce earnings even if long-term economics are less affected.

• One-time charges

• Restructuring costs and impairments are recognized more aggressively.

The result is that the denominator in today’s CAPE is lower than it would have been under earlier accounting rules, making the ratio appear higher.

Economist Jeremy Siegel argues for using National Income and Product Accounts instead of GAAP earnings, to better normalize over time. 

The standard CAPE can overstate market valuation materially because recent earnings include unusually large accounting write-downs by roughly 10 percent to 25 percent.

Others argue for using operating earnings rather than reported earnings, which also can adjust earnings by 15 percent.

Estimated Distortion	Standard CAPE 38	Adjusted CAPE
10%	38.0	34.2
15%	38.0	32.3
20%	38.0	30.4
25%	38.0	28.5

Using such methods, the market still appears expensive, but less so than it might appear. 

Other issues:

• Lower interest rates over long periods

• Higher profit margins

• Global diversification of large U.S. firms

• Greater use of stock buybacks instead of dividends

• Stronger institutional ownership and retirement savings flows.

These factors may justify a structurally higher "normal" CAPE than the 19th- and 20th-century average.

So some will argue a practical adjustment for accounting changes is to reduce the published Shiller P/E by 10 percent to 25 percent.

This suggests the market may still be richly valued, but not as dramatically overvalued as the unadjusted Shiller P/E implies.

It is a useful gauge of long-term valuation, but it is not a short-term market timing tool, as history shows that markets can continue to rise for years, even when the CAPE ratio is well above its historical average.

Several forces can keep markets rising despite expensive valuations:

• Earnings continue to grow

• Corporate profits may rise fast enough to justify higher prices

• Investor optimism and momentum

• Strong sentiment can sustain elevated valuations for extended periods

• Low interest rates

• When bond yields are low, investors are willing to pay more for equities

• New technologies can create expectations of stronger future growth

• Retirement contributions, buybacks, and institutional inflows can support prices.

Period	Approximate CAPE at Start	Years Until Major Peak	Additional Market Gain After CAPE Became Elevated	What Happened
1925–1929	25–32	4 years	+150% to +200%	Roaring Twenties speculation pushed valuations higher before the 1929 crash
1995–2000	25–44	5 years	+200% to +250%	Dot-com bubble drove extraordinary gains
2017–2021	30–38	4 years	+80% to +120%	Continued growth in Apple Inc., Microsoft Corporation, NVIDIA Corporation and other large-cap firms
2023–2026	Mid-30s (approx.)	Ongoing	Still developing	Strong enthusiasm around artificial intelligence and large technology firms

The point is that valuation is a poor short-term timing tool:

• A CAPE above average tells you expected long-term returns may be lower, but it does not predict when prices will stop rising

• Markets can stay expensive for years

• If profits rise rapidly, high valuations can become more sustainable

• Structural changes matter (lower inflation, global market reach, and dominant technology companies may justify higher valuation ranges than in earlier eras).

We still have to make our own choices about timing, though!

Akamai CDN Deal with Anthropic Shows Changing Value of CDNs

Anthropic's $1.8 billion, seven-year agreement to use the Akamai network shows the new uses of edge computing for frontier language models.

You might recall that in early April 2026, the launch of Claude Managed Agents caused Fastly, Akamai and Cloudflare share prices to plummet, as investors apparently viewed Claude Managed Agents as a direct infrastructure competitor to these platforms.

• Fastly plunged 18 percent

• Akamai sunk 13 percent

• Cloudflare collapsed by 11 percent. 

As has been the case for enterprise software as a service, security stocks and then content delivery networks, investors worried about the substitution effects. 

Some of us might already guess that the fears, while rational, are likely overdone. Perhaps the main damage is the resetting of  valuation multiples back to prior levels.

Software Segment	New "Floor" P/E	Historical Context
Mega-Cap SaaS (Microsoft, SAP)	28x – 32x	Historic: 35x+
High-Growth / "Rule of 40" (ServiceNow, CrowdStrike)	45x – 55x	Historic: 80x – 100x+
Mature / Cyclical Enterprise (Salesforce, Oracle)	18x – 24x	Historic: 25x – 30x
Infrastructure / Dev Ops (Datadog, Snowflake)	50x – 60x	Historic: 100x+
Mid-Market / "Broken" SaaS	12x – 16x	Historic: 25x

To be sure, low latency remains the paramount value, as has been the case for content providers for decades. But where traditional content delivery networks have largely been about edge storage, AI models benefit more from edge compute for inference operations. 

Where traditional content delivery minimized latency by storing popular content at the edge, AI inference operations benefit by conducting inference operations at the edge.

Content providers such as Netflix or Pandora deliver mostly pre-encoded, cacheable files (videos, audio) that can be pre-positioned at edges with high hit rates. 

In contrast, AI inference operations are:

• highly dynamic, as

• each prompt is unique

• limiting the value of caching

• so the value shifts more to distributed compute and intelligent routing.

But edge computing also helps with scale and demand spikes. CDNs also provide such features as:

• global load balancing

• auto-scaling across points of presence

• burst capacity without over-provisioning central clusters

• reduced data transfer (less backhaul to origin)

• efficient routing

• potential caching of reusable elements

• lower bandwidth and egress costs

• security 

Traditional CDNs provided value by moving content bits efficiently. 

For frontier AI models, the value comes from edge computation.

Lower latency is still the chief value, though.

Neoclouds and CLECs

For some of us who were active in the competitive local exchange carrier market around the time of the passage of The Telecommunications Act of 1996, neocloud providers such as CoreWeave, Nebius and many others seem to present a market opportunity that is temporary, if potentially lucrative in the short term. 

Though price arbitrage was the temporary CLEC opportunity, shortages of high-performance computing (graphics processing units and other accelerators) are the opportunity for neocloud providers.

A perhaps-lucrative but temporary market window seems to exist for neoclouds, as it once did for CLECs. 

By mandating that incumbent local exchange carriers unbundle their network elements and lease them at attractive wholesale rates to competitors at regulated rates, Congress effectively handed competitive local exchange carriers (CLECs) a business model: arbitrage the gap between the regulated wholesale price of network access and the retail price customers would pay.

But the discounts ultimately ended and the access market eventually shifted to broadband access on owned facilities. The wholesale model effectively collapsed for most CLECs. 

The generative artificial intelligence boom created excess demand for GPUs. 

So the neocloud model is structurally arbitrage: 

• As GPU supply is constrained, offer “GPU as a service”

• Sell access to that resource at a margin, reselling compute

• Build customer relationships before the incumbents close the gap.

CoreWeave, for example had a simple price pitch: 

• we have H100s

• we're GPU-native

• we'll get you capacity faster and cheaper than AWS or Azure. 

Dimension	CLECs (1996–2002)	Neoclouds (2022–?)
Enabling condition	Regulatory mandate opening ILEC networks	GPU supply shock creating hyperscaler rationing
Capital model	Debt-heavy buildout of switching infrastructure	Equity/debt-heavy GPU cluster acquisition
Competitive advantage	Access to regulated wholesale inputs	Early access to scarce NVIDIA allocations
Customer value prop	Cheaper/faster local access	Faster GPU availability, simpler pricing
Incumbent response	Network upgrade, litigation, lobbying	Massive capex, custom silicon, long-term NVIDIA contracts
Structural vulnerability	Unbundling obligations could be reversed	GPU scarcity is inherently temporary
Timeline pressure	~5 years before model collapsed	Likely 3–6 years before hyperscalers close gap

Of course, markets eventually will normalize:

• Nvidia has boosted production of H100s and is ramping B200/B300 series

• Hyperscalers have developed  custom silicon (Google's TPU v5, AWS's Trainium 2 and Inferentia, Microsoft's Maia, and Meta's MTIA)

• Hyperscaler capex is going to be hard to beat, long term

• The software stack advantage will benefit AWS, Google Cloud and Azure

• Customer lock-in dynamics favor hyperscalers.

Of course, history likely rhymes rather than repeating.

The neocloud endgame probably looks similar to the CLEC industry in many ways:

• Most will struggle as GPU spot prices normalize and hyperscaler capacity floods the market (2025–2027)

• A few might be acquired

• One or two may find durable niches

• But hyperscalers likely will dominate the enterprise AI compute market by 2028–2030.

The CLEC parallel is perhaps a reminder that cyclical scarcity is not long-term structural advantage. 

The neoclouds that survive will be those that use the current window not just to sell GPUs, but to build something (software, relationships, operational expertise or specialized capability) that persists after the scarcity evaporates. 

That will be hard to do. 

As investors, we might make some money on neocloud providers in the near term. But the CLEC experience might temper enthusiasm for some of us.