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**Hugh** · Today, 07:48

https://graceblakeley.substack.com/p...rcular-economy

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Imagine if a car manufacturer invested in a taxi firm, which used the money to buy only that car manufacturer’s vehicles. The carmaker could claim rising sales, the taxi firm could say it was growing, and investors might conclude the transport sector was booming. Everyone would start to throw money at these firms for fear of missing out on the boom, creating a self-fulfilling cycle of rising valuations. But there’s no one at the end of the chain buying the product – taxi rides. Instead, much of the activity is being financed by the carmaker’s own money. Unless real customers start paying lots of money for taxi rides, the flow of money between these companies will eventually reach an abrupt halt. But why would anyone pay so much money for a taxi ride when, thanks to the investment boom, there are so many options available?

This kind of loop is known as circular financing: when a company’s investment or lending ends up returning as revenue through an interconnected web of partners or subsidiaries. There’s nothing illegal about it – and it’s not like investors can’t see what’s happening. But it is yet more evidence that the hype surrounding AI is driving a huge bubble.

Inflating the Bubble

Financial markets’ enthusiasm for AI has already reached astonishing heights. AI companies have accounted for 80 per cent of the gains in US stocks so far in 2025. Nvidia, already the world’s most valuable chipmaker, is trading at valuations that assume it will be able to keep growing at the same rate for decades. Oracle’s stock soared on the promise of cloud contracts that depend heavily on OpenAI’s still-unproven business model. And OpenAI is pumping hundreds of billions of dollars into deals that assume the bubble will continue forever, even as Sam Altman says it won’t.

The last time we saw anything like this level of circularity in the tech sector was the dot-com bubble. The FT explains:

“Back then, an enterprise software company might have paid to advertise with a new internet media company, in return for the media company buying its software. That artificial arrangement would have created the illusion of stronger demand for both companies’ services…

In the closest parallel to today’s AI infrastructure boom, telecom equipment companies such as Lucent and Nortel advanced money to customers in the 1990s to buy equipment, only to face write-offs when a wave of bankruptcies hit the industry.”

The big difference this time is scale. The amounts being pumped into AI infrastructure dwarf the amounts that were spent laying down fibre optic cables. All in all, according to the same article, OpenAI has put itself at the centre of a web of deals worth at least $1trn. And that’s just OpenAI. Google, Amazon, and Anthropic are part of their own web of circular deals too.

Another difference is debt levels, which were much higher among the telecoms companies at the peak of the boom than they are among the infrastructure firms now. But the direction of travel is heading very much in the wrong direction. Elon Musk is trying to raise over $12bn in debt for xAI, and Oracle has also been drawing much more heavily on bond markets to raise money. As I wrote last week, many other data centre builders are also relying on backroom deals with ‘shadow banks’ to raise money.

https://www.theguardian.com/technolo...orkers-fallout

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Employers are navigating a flood of errors and low-quality work that are sapping employee time, raising privacy and security risks and pushing up costs. Now, some businesses that started off fast and furious on AI are pumping the brakes, implementing restrictions where experimentation was once encouraged, bringing back experienced workers and upping training efforts.

C-suite leaders say when it comes to AI, there’s a sense that “we’re driving the truck and building it at the same time, and that’s a problem,” said Johnny C Taylor Jr, president and CEO of the Society for Human Resource Management. “If we could all rewind … we would have introduced [this] in a more thoughtful, slow way.”

Despite billions of dollars that firms around the globe have invested in AI over the past few years, major returns remain to be seen: 90% of executives say AI efforts have yet to translate to productivity gains at their companies, according to an Atlanta Federal Reserve working paper. And an Ernst & Young survey published last month found that 36% of senior AI executives at companies with at least $1bn in annual revenue reported an AI incident that led to issues including data loss, financial damage, brand damage or operational disruptions.

Firms from Microsoft to McKinsey are limiting usage for employees, as the flood of new tools and rapacious consumption weighs on bottom lines. Others, such as Palantir, Booz Allen Hamilton and Nvidia, are restricting employee use over growing concerns about the security of their organizations’ data with AI companies, Reuters reported.