The US composite PMI (economic activity measure) rose to 58.4 fom 56 in August, the strongest expansion in private-sector activity since July 2021 and marking a fourth consecutive month of accelerating growth. The gains were driven by the service sector (including information services) with the steepest rise in output for over five years, while manufacturing also accelerated. New orders grew at the fastest pace since April 2022, while manufacturing hiring was the strongest since February 2021. [Chart]
Back in June, I commented that AI was just ‘one big trade for the US economy’. But now in September that appears to be an understatement. The AI build-out is on track to become the biggest economic bet in US history, dwarfing the investments made to fund other huge US infrastructure projects in the past, such as the railroads in the 19th century, the highway system in 20th century and the internet in the 21st century. [Chart]
Analysts estimate that capital spending at five of the so-called hyperscalers—Alphabet, Amazon.com, Meta Platforms, Microsoft and Oracle—will be $4.2 trillion in the four years ending in 2029, according to FactSet. Data-centre spending is greater than that for the canals, railroads and grid combined, projected to total $10.3 trillion from 2025 to 2032, according to new estimates by the Brookings Institution. That is a staggering average 3.6% of GDP a year. Never before has the US economy been so dependent on the build-out of a single industry. [Chart]
Up to July, $37 billion has been spent on private data-centres with most still not operating.
In contrast, US private construction spending on everything else—houses, apartment buildings, shopping centers and so on—was about $46 billion below year-earlier levels in the first seven months of this year.
AI investment has created 750,000 new jobs since 2023, according to LinkedIn estimates. And those jobs pay well: the median annual salary for AI-related job listings on LinkedIn is around $180,000, compared with $80,000 for all jobs.
Above all, the AI investment has led to huge gains in stock-market wealth. As of Q2 2026, US stock and mutual fund holdings came to $63 trillion, according to the Federal Reserve—nearly double the amount at the end of 2022. Most of this increase in financial wealth has gone to the already rich, as working people own little stocks or bonds. [Chart]
Foreign investors are piling into US assets. They now hold a record $39 trillion in US equities and bonds, up since 2022.. This is keeping the US dollar relatively strong and driving up stock prices. The wars in Ukraine and Iran encourage foreigners to shift their assets to the US to take advantage of the boom. [Chart]
At the same time, demand for equipment that goes into data centres like memory chips is driving up costs for tech products. Import prices on computers, peripherals (such as hard drives) and semiconductors were 20% higher in August than a year earlier. These high import prices are in turn putting upward pressure on the costs of consumer goods, such as iPhones and gaming consoles, and contributing to general inflation. [Chart]
But here is the problem. The gap between hyperscaler spending and cash flow is widening fast. Capital expenditures at Amazon, Meta, Microsoft, and Alphabet are projected to exceed $1 trillion in 2027 for the first time. At the same time, combined ‘free cash flow’ (ie money from profits in existing businesses) is projected to fall below $100 billion. A year ago, free cash flow was around $200 billion, while capex was $300 billion. Now, AI spending is accelerating at the same time as the cash available to fund it is disappearing. [Chart]
The bigger this gap becomes, the more the hyperscalers need to rely on debt and equity markets to finance their AI spend. [Chart]
The issue is that if AI spending fails to generate sufficient returns (profits), the stock market could take sharp turn downward as investors bail out. US stock market prices are massively overvalued relative to existing earnings. The trend ratio of stock market prices to earnings per share (called the CAPE ratio) is above the level just before the 2008 financial crash and nearly at the level just before the dot.com bust of 2000. [Chart]
Will profits come through? Research by Fathom Consulting shows that for the multitrillion-dollar AI boom to turn a profit, it would need the AI-related sales of the tech companies involved to rise by $600-800bn within the next two years. But the consulting firm Panmure Liberum calculated that current CAPEX and revenue forecasts through 2030 imply a negative internal rate of return on invested capital for Alphabet, Meta, Microsoft, and Oracle.
So either the hyperscalers significantly reduce their capital spending on AI to levels that generate a reasonable profit on capital already invested or by some miracle they deliver massive profitablity from a huge future increase in demand for AI products. If they cut spending, that would signal to investors that AI is not delivering and they would sell off accordingly. A crash would ensue. So they must keep spending more and more. [Chart]
At the same time, what companies can charge for AI computing costs (tokens) is falling fast. The LLM Token Expenditure Index, which tracks the market price companies pay for AI model output, has fallen to just $0.97, its lowest level since the index was created late last year and more than 50% below its summer peak. Token prices are collapsing as cheaper models, open-source Chinese competitors and falling training (inference) costs make AI usage increasingly cheap. That is eroding revenue growth for the AI labs, making it more difficult to meet the bills for AI infrastructure spend. [...]
A key question is whether AI is actually going to deliver a step-change in US labour productivity that could boost economic progress for a generation. The AI lab, Anthropic, wants to issue shares worth $100bn to the public in November (thus valuing the company at $2trn!). To build up its case, it published a report in which it claimed that if AI really takes off, US GDP could rise by 32% by 2030(!), that’s annual growth in GDP of up to 15% (against current US growth at 2.5% at best).
This is wild nonsense that assumes that AI works in boosting productivity growth as every company in the US adopts AI agents and tools to run their businesses, while sacking millions of workers who are no longer needed.
by Michael Roberts, The Next Recession | Read more:
Images: Financial Times; Commerce Dept.; uncredited
