This is not merely about burning cash on AI, but rather rewriting AI infrastructure from a "technology investment" to a "real estate + leasing" model. Companies like Microsoft and Meta are not just locking in the models themselves, but securing power, data center space, and rack positions available for the next decade, which is consistent with the logic of early cloud computing expansion where land, electricity, and grid connections were fiercely competed for.

In terms of capital pathways, giants first lock in capacity through leases, then funnel training and inference demands into these capacities; this means that returns on the asset side do not solely depend on AI user growth, but on whether utilization can cover long-term fixed payments. If demand slows, these "unoperational leases" could become a latent pressure similar to real estate vacancy rates.

Comparable historical cases include the expansion of communication data centers in the late 2000s, the cloud infrastructure race in the 2010s, and the pressure of locked rents in commercial real estate during high-interest rate cycles. The current closest structure is not the internet bubble, but a heavy capital infrastructure cycle: initially supported by growth narratives for valuation, later determined by cash flow and financing conditions for survival.

Essentially, this reflects capital concentration and industrial chain restructuring: when a few giants lock in future computing capacity in advance, midstream suppliers will be forced to build capacity, set prices, and schedule deliveries around these few clients. This occurs because AI computing power has shifted from "optional expansion" to "mandatory positioning"; those who sign contracts first will occupy the scarce supply points first.

ABAB News · Cognitive Laws

Future costs will start consuming cash flow today.
Those who lock in capacity first will secure pricing power first.
The hotter the technology narrative, the longer the debt terms.