AI: just one big trade
Goldman Sachs, the mega investment bank, reckons that AI is just “one big trade on the US economy”. And the AI investment bubble is getting even larger. In the last week, the AI model maker, Anthropic, announced that it was issuing shares to potential investors in what is called in stock market jargon, an Initial Public Offering (IPO). Anthropic was following Elon Musk’s Space X planned IPO of a humungous $1.8trn. This would value SpaceX in the market at 92 times its annual revenue!
Alphabet, Google’s parent, also plans to raise $85bn in equity funding — its first stock offering in more than two decades. Together, these three giant IPOs could command a combined valuation of around $4trn. That’s one-third of all the value of US IPOs since 1980 (inflation-adjusted)! And yet SpaceX, OpenAI and Anthropic are all currently loss-making and the commercial potential of AI models and, in the case of Space X in going to Mars, remains unknown.
AI is one big trade for the US stock market investors and one big bet on the US economy. That’s because the amount of capital investment being made by the companies called the ‘hyperscalers’ into AI models, data centres and other AI equipment is staggering. As a share of US GDP, it is now set to far surpass the 19th-century railroad build-out.
Back in December 1996, then Federal Reserve chair Alan Greenspan characterised the boom in technology, media and telecom stocks as showing signs of “irrational exuberance”. Almost 30 years later, we can say the same about the AI boom with bells on. This investment boom is already much larger than the dot.com internet investment of the late 1990s ever was. In 2025, US businesses invested almost $1.5trn in IT equipment and software. At the peak of the dot.com bubble, it was $466bn, or $829bn when adjusted for inflation. The hyperscalers Microsoft, Alphabet, Amazon, Meta and Oracle plan to invest hundreds of billions in the next five years in data centres to provide the computing power to run these AI models. Capital investments are expected to rise by 20 per cent a year, a growth rate never seen before in this industry.
As I showed in my last post, US corporate profits have risen significantly. But according to Brian Green in a recent post, around 80% of the increase in US non-financial corporate profits came from Nvidia and hyperscalers. The stock market is increasingly concentrated in a handful of AI‑linked stocks, which now account for roughly 40 per cent of the S&P 500’s market capitalisation, according to Bank of America data. Headline profitability is being flattered by a small slice of the economy earning extraordinary returns from the scramble to build AI capacity. The risk, then, is that the economy, the profit cycle and the stock market “are all leaning on the same narrow pillar. If the expected returns on AI infrastructure and platforms are questioned, the fallout may not stop at a few richly valued technology stocks.”
As I have pointed out in previous posts, up to now the massive investment in AI has been mostly funded by the profits already being made by the hyperscalers. But given the impossibility of finding enough additional revenues to self-finance their capex plans, hyperscalers and their hardware providers are increasingly using external financing to fund them.
The first game is ‘circular financing’ ie by cross-investments between Microsoft, OpenAI, and others. In essence, a cash-rich hyperscaler like Microsoft buys hardware from Nvidia, AMD and other suppliers. Nvidia then uses that revenue to buy a multi-billion-dollar stake in OpenAI. OpenAI then uses this cash to secure compute in Microsoft data centres. Microsoft itself also invests in OpenAI and enters into a mutual revenue share where some of OpenAI’s revenues flow to Microsoft and vice versa as the two companies use each other’s products. Assuming that Microsoft spends $100bn to order hardware for data centres, Nvidia, AMD and other suppliers can recognise this $100bn as revenues. They then use that cash to invest in OpenAI (for example), which then uses this money to book data centre capacity with Microsoft. Microsoft recognises this OpenAI investment as revenue, thus effectively turning its $100bn expense into billions of revenue!
Even this is no longer enough, and increasingly, hyperscalers have started to resort to borrowing to raise the cash for investment. The US tech giants are issuing debt all over the world. Google/Alphabet is leading the charge.
So first, they invested with their own funds; then in each other; then they borrowed from the banks and so-called private credit funds; and now they are putting the risk of success or failure on investors in the stock market. If all this investment fails to deliver the expected returns, it will hit the financial sector and the wider economy big time.
But don’t worry, say the AI companies and hyperscalers, revenues are expected to grow 15 per cent annually. If we make the heroic assumption that there are no costs, then this additional revenue is the profit these companies are expected to make from their additional investments in AI data centres. Yet, even under these extremely optimistic assumptions, the implied return on investment is highly negative for all except Amazon.If the hyperscalers need to generate, say, a 10 per cent return on investment, they would have to find an additional $2-5tn in revenue a year. That’s a tall order for a group of companies that currently generates revenues of just $1.5tn per year. The other option is that the planned investment in data centres, computer chips and other areas never materialises — maybe as equity investors turn more cautious on the sector, or if debt funding for data centres becomes harder to get. A JP Morgan analysis found that more than 60% of data centre capacity planned for completion in 2027 isn’t yet under construction, and another 7% is delayed. What will happen if these companies announce cutbacks on some of their investment plans?
Will the AI heroes, OpenAI and Anthropic deliver the returns that the hyperscalers and their investors hope and expect? Corporate CEOs are optimistic. Over the last three years, since OpenAI launched ChatGPT, they claim that cumulative productivity gains have been in the order of 0.3% to 1% per year. For the next three years, they estimate productivity gains to accelerate to 1.4%, with executives in the US and UK far more optimistic than in Germany and Australia.
These productivity gains, they reckon, will be achieved by shedding labour. Business leaders expect headcount in their firms to drop by about 0.7% in the next three years, again with executives in the US and the UK expecting far more pronounced drops in employment than executives in Germany and Australia. In the last three years, the same executives saw no employment impact from AI. So this is all expectation. Moreover, the Business Trends and Outlook Survey of the US Census Bureau shows that companies with 50 employees or more show no more growth in AI use since Q2 2025. Businesses are still unsure how to use AI effectively and are increasingly worried about the drawbacks of AI when they use it.
Those drawbacks include ‘hallucinations’ (ie fictions made up by the AI model), which are inherent in LLMs. One study found that for a training set of 32,000 words, the average hallucination rate in LLMs was 6.8%. When that was expanded to 128,000 words, the average hallucination rate rose to 10%. That’s a lot of correction time and monitoring for human workers.
Another problem is that because LLMs are designed to be good at everything, they are not very good at any one thing compared to specialised apps. One report on using AI in software development found an explosive impact at the start, with coders creating or editing almost 300 per cent more files, but that boost was halved to 150 per cent by the time companies got the number of pieces of work submitted for review, and that in turn shrunk five-fold to a roughly 30 per cent uplift at the point of full software releases.
Moreover, when researchers looked at whether AI-assisted increases in software production have led to increased usage by clients, they found little evidence. The marked increase in mobile app releases over the past year has not been accompanied by any increase in downloads — most of the new apps fail to capture even a modest audience.
Meanwhile, OpenAI has burnt through some $6bn, rising to $17bn in 2026. By 2028, inference (training) costs alone are expected to grow to $121bn and losses are projected to be $85bn. Anthropic’s cash burn is much smaller, but was still $3bn in 2025. Unless the companies that build LLMs can find large amounts of new revenue in the next couple of years, the losses will increase exponentially, especially given the fact that current price charged per ‘token’ is not the true cost of compute. If AI companies were to charge the cost price per token, the losses may decline, but demand for LLMs may decline even more.
Despite this, the hype around AI remains so big that essentially all private investments in the US are now in tech hardware and software. Over the last three years, the average annual growth in IT equipment investments has been 11% and 8% in software. Meanwhile, investments in all other parts of the US economy put together have declined by 1.6% per year.
The US economy today really is two economies in one. There is the tech economy and then there is everything else. Over the last four quarters to the end of Q1 2026, 93% of US GDP growth is due to tech investment alone (although much of the purchases are imports and not produced domestically).
This is a bubble waiting to burst. In the aftermath of the TMT bubble, private fixed investment dropped more than 12.7% between 2000 and the end of 2002 as a recession took hold in the US. In the initial year after the TMT bubble burst, tech investments dropped 12%, while fixed investments in general dropped 7.6%.
Gita Gopinath, former chief economist at the IMF, has calculated that an AI stock market crash equivalent to that which ended the dot-com boom, would erase some $20tn in American household wealth and another $15tn abroad, enough to strangle consumer spending and induce a global recession. This is also the view of the IMF. The IMF fears that AI firms could fail to deliver earnings commensurate with their lofty valuations. The collapse of previous investment booms knocked about 1 pp on average of US real GDP growth. Even a moderate correction in AI stock valuations would reduce global growth by 0.4%. “ Combined with lower-than-expected total factor productivity gains, and a more significant correction in equity markets, global output losses could increase further, concentrated in tech-heavy regions such as the United States and Asia.” Another study found that even a very mild drop in tech investment of just 3% would cut US real GDP growth by 1%, or half the current rate. The impact would be greater in Europe.
None of this is to conclude that AI will not at some point deliver with higher profitability for the companies involved and higher productivity for the US economy as a whole. But that will not happen before there is a bursting of the investment bubble – as there was in the railway mania of the 1870s and in dot.com bubble of the late 1990s. As other studies have shown, it will take a decade or more for AI to become a generalised technology that delivers.
For working people, AI poses a different problem. For capital and the mega media companies, the aim is to make AI a profitable technology, but that can only be done by shedding labour and by stopping any attempt to regulate its applications and use. If AI is to succeed for capital, it will only be at the expense of most working people and their families.
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