Big Tech is on track to deploy nearly $700 billion into AI infrastructure during 2025, continuing an expansion cycle that began in earnest eighteen months ago and shows no technical signs of plateau. The figure aggregates capital expenditure across the hyperscaler cohort—Meta, Microsoft, Google, Amazon—plus ancillary hardware partners in compute, networking, and power distribution. What remains absent is clarity on when this buildout reaches saturation, or whether marginal returns compress before physical limits arrive.

The spend breaks into three layers: data center construction and retrofit, GPU procurement at scale, and the less-visible but equally capital-intensive power infrastructure required to run 100,000-plus H100 or B200 equivalent clusters. Microsoft disclosed $80 billion in planned capex for fiscal 2025 during its January earnings call. Meta signaled a range between $60 billion and $65 billion for the calendar year. Google's parent Alphabet has guided toward $75 billion, while Amazon's AWS capex is tracking near $100 billion when including both AI and legacy cloud. These are not exploratory budgets. They represent committed capital with multi-year depreciation schedules and contracted supplier relationships already in motion.

What separates this cycle from prior infrastructure waves is the absence of a demand ceiling or a technical forcing function that signals completion. In the 2000s, fiber buildout ended when overprovisioning met reality. In the 2010s, mobile tower densification slowed once LTE coverage reached population thresholds. Here, the endpoint is undefined because the application layer—what enterprises and consumers will ultimately pay for at scale—remains speculative. Foundation models are improving, but commercial monetization outside of chatbot subscriptions and API resale has yet to justify the capex multiple. Allocators are effectively underwriting infrastructure ahead of proof of demand elasticity.

The second-order effect is capital market distortion. Nvidia's forward order book extends into 2026, with lead times on advanced nodes still running six to nine months. TSMC's Arizona and Taiwan fabs are pre-sold. Power utilities in Northern Virginia, West Texas, and parts of the Pacific Northwest are negotiating multi-gigawatt contracts with hyperscalers, locking in offtake agreements that resemble sovereign-level commitments. Equity analysts are pricing these companies on revenue growth assumptions that require AI workloads to replace declining legacy cloud margins within 24 to 36 months. If that substitution fails to materialize, or if utilization rates on deployed infrastructure fall below 60%, the writedowns will be structural, not cyclical.

Allocators should track three indicators over the next two quarters. First, GPU utilization disclosures—Microsoft and Meta have both hinted at publishing internal metrics on compute efficiency, which would provide the first real-time view into whether capacity is being absorbed or banked. Second, power purchase agreement filings in key markets; a slowdown in new contracts would signal confidence erosion before it appears in earnings guidance. Third, the trajectory of Nvidia's data center revenue growth rate; any deceleration below 30% quarter-over-quarter would force a recalibration across the ecosystem. The buildout continues because no single player can afford to fall behind in a winner-take-most market structure, but that game theory holds only as long as the applications justify the iron.

The capex wave is real, committed, and already flowing through supply chains. The question is not whether Big Tech will spend $700 billion this year—it will—but whether the other side of the ledger, the revenue that funds the next cycle, arrives in time.