AI: the biggest economic bet in US history
The US economy continues to expand faster than other G7 economies, but the driver is the humungous investment in AI models, data centres and all the AI-related chips and technology.
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.
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 19thcentury, the highway system in 20th century and the internet in the 21st century.
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.
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.
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.
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.
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.
The bigger this gap becomes, the more the hyperscalers need to rely on debt and equity markets to finance their AI spend.
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.
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.
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.
The AI labs (OpenAi and Anthropic) continue to claim they will soon make big profits and so the hyperscalers will eventually get their share of the booty. But much of these claims are based on dubious profit estimates. AI-related investment gains increasingly flatter earnings, with so-called “other income” (contracts with other AI firms) rising to 54% of pretax income.
Indeed, the AI companies are keeping their heads above water only through what is called ‘circular financing’ where one firm lends funds to another and the latter then claims it has made a profit. Sona Asset Management have mapped the AI universe and catalogued the interconnections between major players. Everybody is depending on everybody else to deliver.
Sona also found that AI firms’ revenue is almost completely tied to the capex decisions of one or two other AI players. This is a systemic bust in the making.
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.
Historically, automation has historically proceeded at roughly 2% of tasks per year for two centuries, without ever pushing growth much above 2%. Past so-called ‘general-purpose technologies’ took decades to diffuse even after the technology itself worked. For example, electrification took around 40 years to show up in factory productivity. Similarly, Comin and Mestieri’s study of technology adoption across countries documents average adoption lags of around 45 years, and still 7-18 years for more recent technologies.
AI adoption appears to be much faster than that of the PC or the internet, but adoption is only the first step: measured productivity typically first falls while firms make the necessary complementary investments, the “productivity J-curve” of Brynjolfsson, Rock and Syverson. Even the most bullish insiders are noticing this, for example Sam Altman who recently conceded: “I think I was wrong about a few things, but one of them, in terms of the speed, one of them is the economy just has so much inertia. […] we’ve all been too ambitious on timelines […] Society and the economy will adapt more slowly.”
And remember most work is physical. An AI capability explosion is first and foremost an explosion in cognitive capabilities. But only around a third of the economy consists of work that can be done on a computer (Epoch AI’s remote-work piece). The rest of GDP is produced in mines, on construction sites, in kitchens, hospitals and care homes. Automating two thirds of all tasks by 2035 therefore requires robots that do a large share of physical work. These robots would need to be designed, manufactured, installed and maintained by the billions within a decade. While progress is certainly happening, robotics development is slower than software development and robots that can do a wide range of physical tasks at close to the cost of a worker are still not on the horizon.
So far there is scant evidence of economy-wide productivity gains; in fact, total factor productivity (a measure of productivity from new technologies) is falling below trend.
It’s true that AI adoption by companies is picking up, at least among service companies. In just two years, AI usage has risen from 25% to 61% among service firms and from 16% to 51% among manufacturers. But only 17% of employees in services and 7% in manufacturing are actually using AI regularly in their work.
As the Federal Bank of New York admitted: “The next stage will be much more important as, for the moment, AI mainly helps people write, summarize, code or analyze faster. Tomorrow, agents and specialized tools could handle entire workflows. At that moment, the impact on productivity and employment could really move to another level. Usage is exploding while corporate spending remains relatively limited but the price of intelligence keeps falling. Very bullish for AI diffusion and productivity and much less obviously bullish for the monetization of every player in the ecosystem.” In other words, productivity growth may eventually rise but at the expense of profitability as AI prices for usage fall: a classic contradiction for capitalism.
And there remain faultlines in the very nature of AI Large Language Models. Two years ago Oxford and Cambridge Researchers proved that every LLM trained only on AI-generated content develops an irreversible disorder. Each new model learns only from what the one before it wrote. By generation nine, the new model had forgotten what it was talking about. Ask it about medieval church towers and it answers with a list of jackrabbits. They called it ‘model collapse’. Every time someone posts machine output, the next model gets a little more average.
The warning signs of the coming crash keep multiplying. An upward trend in interest rates amid falling bond prices could be the crisis-triggering factor. Or it could be a failed IPO (Anthropic, OpenAI), or surging competition from so-called ‘open weight’ LLM models from China and elsewhere driving down profits. The biggest bet in US economic history is a very risky one.
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