DSHR's Blog: Portents Of Doom

Tuesday, July 14, 2026

Portents Of Doom

Elon Musk is the world champion of totally implausible projections, and Kim Khan reported on a personal best in SpaceX sees total addressable market rivaling size of the U.S. economy:

The $28.5T forecast compares to U.S. Q1 2026 nominal GDP of nearly $32T, with the estimate for the market of AI enterprise applications of $22.7T about 70% of total U.S. economic output.

Sam Altman and Dario Amodei just aren't this good, but their projections of their Total Available Market (TAM) are still turning out to be vastly optimistic. In AI's Affordability Crisis I showed evidence that the AI platforms could no longer afford the massive subsidies they were using to artifically inflate demand for their product, and that reducing the subsidies had made their enterprise customers reconsider their enthusiasm for deploying them. This is leading to investors belatedly realizing that AI platforms' projections of their TAM and thus their valuations are totally implausible.

This re-calibration is just one of the many signs that the AI bubble is about to deflate. Below the fold I present a necessarily incomplete list of them, which I will try to update as more appear.

Source<br>The AI bubble isn't just an equity bubble, as Torsten Slok explains in AI Is Penetrating Every Corner of Financial Markets:

AI now accounts for nearly half of all IG issuance, 87% of VC funding and a growing share of HY, underscoring how deeply the AI investment cycle has penetrated every corner of finance.

Bryce Elder's This is nuts upon nuts. When’s the crash? compares this bubble with its two biggest predecessors:

Here’s the title page of this month’s Panmure Liberum market update from strategists Joachim Klement and Francisca Reis. Our emphasis in bold below:

In 1929, the cyclically-adjusted P/E-ratio (CAPE) of the S&P 500 reached 32.6x according to Prof. Robert Shiller’s data. This was 1.8 standard deviations above trend at the time. In 2000, the CAPE reached 44.2x, or 3.3 standard deviations above trend – a clear sign of a bubble. However, as our chart below shows, earnings in both instances were within normal range, less than one standard deviation above trend.

Today, the CAPE is at 41.0x, or 2.9 standard deviations above trend. Once again, we are clearly in bubble territory for stock market valuations. However, unlike in previous bubbles, we are having extremely high CAPE at a time when earnings themselves are 1.8 standard deviations above trend. In other words, we are in a valuation bubble at a time when earnings are in a bubble themselves.

If we correct for the earnings bubble, the current CAPE would be 67.6x or 4.6 standard deviations above trend, a bubble that surpasses anything ever seen in US history by an extreme margin. If valuations followed a normal distribution (which they don’t, so don’t take this literally), this would happen in 0.00019% of months or once every 43,432 years.

But Panmure Liberum is underestimating how bad things are. Baolian Wang's The $69 Billion Mirage: How an Accounting Rule Inflated S&P 500's Q1 Earnings by 12% explains:

A massive chunk of this quarter’s blockbuster "growth" didn’t come from selling more software, shipping more microchips, or delivering more packages. Instead, it came from an accounting rule that forced massive, illiquid "paper gains" onto the income statements of tech giants.

In Q1 2026 alone, just three companies—Alphabet, Amazon, and Nvidia—reported a staggering $69.2 billion in non-operating windfall under their Other Income and Expenses (OI&E) lines. When you run the macro numbers, this single accounting phenomenon artificially inflated the entire S&P 500’s quarterly earnings by about 12%.

That 12% takes the factor from 67.6 to 75.7. Wang notes that correcting for the 12%, "the Q1 2026 earnings growth rate will not be very different from the 5-year average of 16%." In other words, the bubble is feeding upon itself — increased stock prices causes increased earnings causes increased stock price ... But suppose, for example, that OpenAI were to suffer a down round. Then Alphabet, Amazon, Nvidia and others who included paper gains in the "other income" on the way up would have to include paper losses in their income on the way down, amplifying the crash. OpenAI's last round valued the company around $750B and they were planning an IPO for at least $1T, but had to postpone it.

Back in March Jared James Grogan published The End of the Foundation Model Era: Open-Weight Models, Sovereign AI, and Inference as Infrastructure setting out the forces changing the structure of the AI market:

The foundation model era — roughly 2020 to 2025 — is over. The forces that defined it have inverted. Open source models have reached frontier performance while inference costs approach zero, exposing what was always structurally true: pre-training large language models at scale is not a durable competitive moat. The US government's...