AI-carus?
AI is flying high. Will it fly too close to the sun and crash like Icarus in Greek myth? Physical constraints and ROI are the sunrays in this story. The likely downswing in prices and investment could be challenging but need not end in meltdown.
Is AI a bubble? The extraordinary valuations and scale of investment raise the question. Missing, though, is the speculative behaviour of buying an asset now with the intent of resale for profit later.
It is natural to want to look for parallels in history. Neither the late 1990s/early 2000s ‘dot com’ nor the 19th century railway booms quite match the current episode. Better analogies may be found in other price cycles stemming from investment booms, spurred by structural shifts. In the rush to build for the new technology, construction costs and end-product prices rise before eventually correcting. Time-to-build and resource constraints can create price cycles even where classic speculative behaviour is absent.
Non-financial goals around geopolitics or a rush to build a god-like intelligence could blind investors to commercial reality and encourage investments that do not pay off. To avoid a mad flap that takes us, Icarus-like, too close to the sun, choices must be based on cost and ROI.
The exceptional valuations on AI-adjacent firms are matched only by the extraordinary scale of capital expenditure on data centres, both overseas and in Australia. The use of leverage and sometimes opaque financing structures twitch the antennae of those of us who have been through financial boom–bust cycles before. Naturally, people question the return on investment and whether this is a “bubble”. But is it?
It helps to define terms. The usual definition of a bubble is a deviation of asset prices from “fundamental” value, but this is only as good as your model of fundamentals. The distinctive feature in episodes labelled as bubbles is speculative behaviour: investors buying an asset mainly or solely because they expect its price will rise so they can resell at a profit. (In technical terms, the current price of the asset depends on expectations of future price growth). In the extreme, the market is in “Greater Fool” territory, where investors admit that they do not think the revenue warrants the price, but believe they will find a Greater Fool to buy it later.
Property assets are one of the main areas where “bubbly” boom–bust cycles can occur, because investors can use more leverage than for other assets. The asset also does not become obsolete quickly and much of the value is in the non-depreciating land or location component.
Data centres are a kind of property, and leverage is clearly being used to finance their construction. Even so, the speculative behaviour that typically characterises property bubbles is missing: nobody is making these investments in expectation of reselling data centres to make a quick buck. It is all about the expected value of the computing services these data centres will provide. Currently the “cost of compute” seems to be on the rise as the firms building the models try to shift their customer bases away from free and flat-rate subscriptions onto higher-cost pay-by-use plans. Yet everyone knows that AI can only achieve the desired returns by expanding usage, which means it needs to be cheaper than the human labour it seeks to replace. Even if investment decisions are based on expected returns on services provided by that capital, the realism of those return expectations can and should be scrutinised.
It is natural to want to look for parallels in history.
The seemingly obvious one is the ‘dot com’ boom of the late 1990s/early 2000s. But while there are some similarities to that episode, there are also important differences. Back then, the tech firms building the new technology were not so financially intertwined with the telco firms buying the spectrum, laying the fibre – and borrowing the money.
There are also similarities to the railway boom of the 19th century, except that location is not as inherent to a data centre. You can train an LLM anywhere you can access the electricity, water and construction inputs. A railway, by contrast, goes from somewhere to somewhere else, and the returns to duplicating the route decline rapidly. Put more technically, data centres do not have the same network effects or location-based natural monopoly characteristics.
Better analogies to the current AI/data centre boom can be found in other price cycles. Many boom–bust cycles start from something real and substantial. A new technology might instigate an equities boom. Opening up a new region might instigate a land boom there. The value underlying the boom remains even after the period of over-exuberance ends. We did not regret creating the Internet, or building that fibre network, or opening up regions through better transport.
What we did sometimes do, though, is pay too much for what we got. If everyone is rushing to build the infrastructure, hoping not to be the Slower Builder who ends up losing money, pressure on construction resources rises, and so do construction prices. We have seen similar cost surges during the mining boom and more recently in the cycle of infrastructure projects in the eastern seaboard states. (This is likely one reason why suppliers of construction materials were so quick to pass increased fuel and transport costs into their prices.)
Along the way, the cost of the services also rise, precisely because of the lags involved in expanding the infrastructure. There is an analogy here in things like shipping costs, which show boom–bust price cycles even though there has obviously never been a bubble in shipping services. In shipping, office property and now data centres, demand might increase but supply is fixed in the short term because it takes time to build that infrastructure. The price of the service therefore rises in the upswing phase.
This upswing phase can also spill over into related sectors, as seen this time in the broader tech sector. Export prices for Korean semiconductors have exploded in the past 6–9 months, up more than 150% over the year. Pass-through to US import prices is more muted, given lags and the usual pricing to market, but tech inflation has increased. As well as the much-publicised price increases for iPads and Macs, the CPI components covering computers and other tech goods have picked up recently in Australia and other advanced economies, to inflation rates close to the peak of the pandemic. This is a departure from their usual deflationary pulse and shows that the current inflationary environment is about more than the US–Iran conflict.
The high costs of the upswing phase nonetheless contain the seeds of their reversal. They discourage use of the underlying service, so adoption is slower than its boosters assume. They also encourage further innovations to economise on the expensive input; routing algorithms to choose the most cost-effective AI model are one example. Over time, as data centres are completed, the supply of computing power expands and its price falls. Investments that only made sense when the service’s price was high will become distressed.
What worries me about the current boom is how far financial considerations seem to sit in the background. Capitalism has its drawbacks, but at least raw commercial incentives limit people’s appetite to overpay for things. When all the focus is instead on geopolitical rivalries or being the first to build god-like intelligence, commercial discipline is lost. A potentially beneficial innovation wave instead risks becoming an Icarus-like mad flap that takes us too close to the sun. That is the perfect environment for a price cycle, including in the tech sector more broadly.
The end result need not be a meltdown. That depends on the debt: how much there is relative to revenues after the price downswing, who borrowed it and who lent it. The role of deep-pocketed tech firms eases some concerns. Some borrowers are substantial firms with other revenues. In addition, other tech firms who have not leveraged themselves to the data centre build-out have big enough cash piles to pick up the pieces (at discounted prices) should some projects come unstuck. Regulators will be most concerned about whether the lenders who lent to the hyperscalers and AI firms in turn funded themselves from parts of the financial system that amplify shocks through their leverage and connection to the regulated banking system.
The answer is not to regulate AI out of existence or stymie its further development – a point the federal government has clearly heeded in the PM’s latest announcements. The advice I’d give now is the same as I gave a decade ago, in a different context: in the face of structural change giving rise to a boom, go in with your eyes open. Don’t get ahead of yourself. And have something to catch you when you fall.
Humans have managed to transcend Greek mythology and master the art of flying without melting. Yet the message of Icarus’s story endures: there are dangers when hubris goes unchecked. To avoid getting ahead of yourself, it helps to let commercial considerations shape decisions, especially around borrowing. Far better that than basing decisions on imagined scenarios that do not survive contact with real-world economics.
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Australia and NZ Weekly 13 July 2026
Australia and NZ Weekly 13 July 2026
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