The massive capital investment in AI infrastructure is predicated on the belief that more compute will always lead to better models (scaling laws). If this relationship breaks, the glut of data center capacity will have no ROI, triggering a severe recession in the tech and semiconductor sectors.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

A primary risk for major AI infrastructure investments is not just competition, but rapidly falling inference costs. As models become efficient enough to run on cheaper hardware, the economic justification for massive, multi-billion dollar investments in complex, high-end GPU clusters could be undermined, stranding capital.

The massive capital expenditure in AI infrastructure is analogous to the fiber optic cable buildout during the dot-com bubble. While eventually beneficial to the economy, it may create about a decade of excess, dormant infrastructure before traffic and use cases catch up, posing a risk to equity valuations.

The massive capital rush into AI infrastructure mirrors past tech cycles where excess capacity was built, leading to unprofitable projects. While large tech firms can absorb losses, the standalone projects and their supplier ecosystems (power, materials) are at risk if anticipated demand doesn't materialize.

The current AI investment boom is focused on massive infrastructure build-outs. A counterintuitive threat to this trade is not that AI fails, but that it becomes more compute-efficient. This would reduce infrastructure demand, deflating the hardware bubble even as AI proves economically valuable.

The common goal of increasing AI model efficiency could have a paradoxical outcome. If AI performance becomes radically cheaper ("too cheap to meter"), it could devalue the massive investments in compute and data center infrastructure, creating a financial crisis for the very companies that enabled the boom.

While power supply is a current data center bottleneck, a more significant long-term risk is technological disruption. Chip innovations promising 10-1000x more power efficiency could make today's massive, power-centric data center investments obsolete or oversized before they are fully utilized.

Large-cap tech's massive spending and debt accumulation to win the AI race is analogous to past commodity supercycles, like gold mining in the early 2010s. This type of over-investment in infrastructure often leads to poor returns and can trigger a prolonged bear market for the sector.