Shares in this data center company are down 40% as investors lose faith in the AI story

edited by Swati Mestri, reviewed by Andrew Zinin

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Add to Preferred Sources Credit: ASX For two decades, data centers were relatively safe, lucrative real estate investments. They attracted investors because they served as digital infrastructure—the essential plumbing that keeps the internet running.

The growth of artificial intelligence (AI) and its increased demands on computing power have put data center builders in a race to build out sufficient supply.

But in the past two weeks, investors have started to have genuine concerns about the long-term viability of these businesses. On Wednesday, shares in NextDC, one of Australia's largest data center builders, collapsed by 40% after it said delays at its flagship Melbourne site meant its revenue would fall short of forecasts.

These delays came after the local community raised significant concerns about the impact of NextDC's M3 data center expansion. Electricity distributor Jemena paused the construction of the giant concrete power poles that had been snaking their way along Footscray streets after local councils raised concerns about the impact on residential neighborhoods.

These 22-meter (72-foot) power poles are crucial to NextDC's operations in Footscray. Data center operators make their profits from selling access to the massive amounts of power that AI chips need. Without that power, these facilities cannot remain viable.

NextDC is not alone in facing significant headwinds. Data center company Firmus abruptly canceled its multibillion-dollar listing on the Australian stock exchange due to a lack of investor demand.

Similarly, London-based data center developer Nscale is struggling to attract buyers for a stock market listing on Wall Street. Shares in CoreWeave, which claims to operate the world's most powerful AI supercomputer, dropped 15% in one day last week on concerns about its financial strength.

These jitters reflect the harsh reality of AI: While the hype is strong, the business models are coming under increasing scrutiny.

Hundreds of billions of dollars have been spent globally on training models and building the data centers to house the infrastructure that powers AI. The pipeline of investments is expected to reach some US$10 trillion (A$14.25 trillion) by 2032.

Until recently, much of the capital expenditure was funded internally by the AI companies or by raising money directly from investors; more recently, they have been borrowing vast sums in the corporate bond market.

And while those investing in AI are patient, they still expect to see significant financial returns.

Sticky inflation and rising interest rates complicate this further. Data center developers now face higher borrowing costs, plus higher construction costs. And rising interest rates can make other investments more attractive.

Analysts estimate that giants like OpenAI and Anthropic will need to increase revenues by 80% a year for the next six years to meet investors' revenue expectations. Yet indicators suggest that consumer and corporate spending on paid AI models is slowing down.

To make things worse, the hardware that AI depends upon is driving real investor concern. Historically, data centers could operate for decades with minimal maintenance, guaranteeing rents and returns.

But AI chips have a shelf life of only a few years. Each new generation requires upgrades to power delivery and cooling, trapping data centers on a treadmill of multibillion-dollar expenses to pay for these upgrades.

Even if AI revenues stabilize enough to support these expenses, there's no guarantee that data center companies will be able to access the chips they need to fill data centers and to support AI demand. Amazon recently admitted it is hoarding billions of AI chips, even though it doesn't have any capacity for installing them. This massive overinvestment is driven by anxieties around long-term supplies.

This gold rush for chips is why American chip manufacturer Nvidia is one of the few companies in the AI space to actually be making money. Nvidia also invests in the companies that buy its chips—its own customers—raising concerns about circular financing.

Beyond the balance sheet, investors are also concerned about the loss of social license for data centers amid local community protests. Community concerns around data centers are already high, driven by persistent noise (from energy transmission), visual impact, localized heating and environmental concerns.

And these concerns will only grow if data centers start to affect the cost of living by pushing up electricity or water prices.

Forecasts suggest that New South Wales data centers could consume up to 25% of Sydney's daily water demand within a decade.

In Victoria, transmission company AusNet is reviewing requests from data centers for more than 10 gigawatts of connections, which could exceed the state's entire supply capacity.

In both cases, this level of demand will inevitably affect consumer prices.

The AI age depends on the physical infrastructure of data centers. But the current path we are on demands ever-increasing capital from investors to fund a relentless upgrade cycle.

It demands huge volumes of power and water from local utilities. And it places a significant burden on the communities that host these data centers.

These factors mean the era of data centers being a safe stock market investment is over. And if investors become unwilling to fund these risks blindly, and communities become resistant to hosting these centers, this month's stock market jitters may turn into a steeper market sell-off.

To shore up investor confidence, it's important that investments in AI focus not just on building better models but also on building models that are more efficient—in terms of their costs, energy demands and spatial requirements.

And if they can be designed to support the electricity grid by adding, rather than displacing, renewable energy capacity, then communities may begin to feel the benefits of investments in AI.

Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile →

Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →

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