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Forrester has published research arguing that an AI bubble is not about to burst, even though investment is running ahead of measurable value.
The research estimates AI must lift US GDP by 5% by 2035 for current infrastructure investment to break even. Published estimates of AI-driven productivity gains average 2%, leaving roughly USD $1.3 trillion in additional value to come from benefits beyond productivity.
The analysis is based on a model tracking infrastructure supply, enterprise demand and the financial health of the companies funding the AI build-out. It found the largest builders remain financially healthy, but newer filings show some expansion is beginning to outrun operating cash in parts of Big Tech.
That shift has led to greater use of borrowing and share issuance to fund spending, signalling rising pressure rather than imminent financial distress.
Funding pressure
Among the findings, Amazon, Alphabet and Meta each spent more than the operating cash they generated in the last quarter. Microsoft continued to fund spending from operating cash, but also raised USD $25 billion in bonds.
Forrester said financial risk is moving closer to the centre of the market, even though most AI infrastructure funding still comes from large technology groups with strong balance sheets. It added that middle-tier suppliers, including AI-native startups, software vendors, neoclouds and model labs, can absorb capacity and make demand appear stronger for a time.
That dynamic could extend the infrastructure build-out even if end demand from enterprises remains uncertain. The research also argues that oversupply alone will not determine whether a correction takes place.
Usage and value
The report points to a widening gap between AI consumption and realised value. Google's monthly token processing rose from about 9.7 trillion in May 2024 to more than 3.2 quadrillion in May 2026, while the model puts AI's productivity uplift to US GDP at less than half a percentage point so far.
Forrester said that divergence shows why rapid growth in AI usage should not be treated as proof of sustainable demand. In its view, the key unknown is whether enterprises can find durable value in AI beyond straightforward productivity gains.
The largest AI infrastructure players still score within the model's healthy range, but the margin has narrowed. Forrester's financial health score for the nine companies in its model fell from 98 to 82 over the past year.
The emerging concern, according to the research, is growing reliance on outside capital at the edge of the market. That makes supplier financial health a more important factor for enterprise buyers weighing long-term AI strategies.
Brian Hopkins, Vice President, Emerging Technology and Principal Analyst at Forrester, set out the firm's assessment of the market and the risks it is watching.
"Fears that an AI bubble is about to burst are premature. 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. 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. Leaders shouldn't pull back. Instead, they should keep investing where AI delivers value they can demonstrate, watch the financial health of the companies building AI computing closely, and be ready for corrections, large or small," Hopkins said.
The findings come amid broader debate over whether heavy spending on data centres, chips and networking can be justified by near-term returns. Forrester concludes the market does not yet point to a sudden break, but the economics of the build-out are becoming more demanding as funding needs spread beyond internal cash flow.
Its model suggests the strongest signal to watch is not simply how much computing capacity is being added, but whether enterprise customers can turn rising AI use into lasting economic gains. Until that happens at sufficient scale, pressure on the companies financing the expansion is likely to remain a central issue.