The AI bubble can wait, but your exit plan can't

Infrastructure spending outruns measurable returns, with supplier finances key to judging when trouble arrives

The AI bubble isn't about to burst, Forrester reckons, although businesses should prepare for a correction that could upend their suppliers, contracts, and prices.

Spending on infrastructure has raced ahead of measurable returns, the analyst says, yet the financial strength of the companies funding the build-out is what keep Forrester from predicting an imminent crash.

"Fears that an AI bubble is about to burst are premature," said Forrester emerging technology veep Brian Hopkins.

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"About nine-tenths of the money building today's AI computing infrastructure comes from large, healthy technology companies paying out of their own cash flow. The risk is concentrated in a few places: a small number of builders under real financial strain, and circular financing deals with the AI labs."

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Conveniently, Forrester has developed an AI Bubble Model to help technology leaders monitor the risks and prepare.

Its latest report, "An AI Bubble Burst Is Not Imminent," notes what many in the IT industry, including The Register, have been saying for a while: that the hyperscale operators are committing a record share of revenue into capital expenditure to bring online more infrastructure to support AI development and use.

At the same time, the boost in productivity doesn't justify this supplier capacity build-out. Forrester calculates that the infrastructure investments break even only if AI raises US GDP by 5 percent by 2035. Published estimates suggest productivity improvements could provide about two percentage points of that uplift, leaving roughly $1.3 trillion to come from other sources of value.

A recent report from management consultants Bain & Company came to similar conclusions, estimating the AI industry needs to generate $6 trillion in annual revenue by 2031 to keep funding all the infrastructure it needs, and this will require new uses beyond simply enhancing employee productivity.

Forrester also claims that the forces making AI abundant also make parts of the market unstable, and says that enterprise users should take heed of how prices collapsed as major providers like Anthropic slashed token costs in response to competition from open source models.

If demand disappoints as new capacity comes online, suppliers could cut investment, merge, change prices, or discontinue products. Customers could find themselves rebuilding AI plans around suppliers and services that no longer exist.

Forrester advises businesses to track whether their AI projects deliver measurable value and limit their exposure to vulnerable suppliers – with its help, of course.

Companies can judge how things are going by looking at the gap between suppliers' contracted business and revenue actually recognized, along with how they fund their build-outs. If a vendor leans more on outside investment and customer prepayments, as Oracle and CoreWeave have, this is a red flag to cut back dependencies on them, Forrester says.

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Enterprises should also stress-test AI plans against scenarios such as a key vendor announcing end of support in 12 months, and an AI model supplier defaulting on payments to its compute vendor and cutting back service-level agreements for enterprise workloads. Companies should know in advance which AI use cases to drop, what data and workloads need to be moved, and at what cost.

But Hopkins sounded a further note of caution: "Oversupply on its own won't decide whether a correction comes. Demand will, and that's the biggest unknown, because it depends on enterprises finding lasting value in AI beyond simple productivity gains."

Organizations shouldn't pull back, he insists. They should keep investing where AI delivers value that can be demonstrated, closely monitor the financial health of the companies building AI computing, and be ready for corrections.

Recently, the International Monetary Fund started ringing alarm bells that financial markets are increasingly exposed to an AI-driven correction that could lead to widespread economic disruption.

Earlier this year, the Bank for International Settlements also raised concerns that the current situation with the AI industry bore the hallmarks of historical economic bubbles and presented a risk to global economic stability. ®