The sustainability of the AI infrastructure boom is debated. One view is that GPUs depreciate rapidly in five years, making current spending speculative. The counterargument is that older chips will have a long, valuable life serving less complex models, akin to mainframes, making them a more durable capital investment.

The massive investment in data centers isn't just a bet on today's models. As AI becomes more efficient, smaller yet powerful models will be deployed on older hardware. This extends the serviceable life and economic return of current infrastructure, ensuring today's data centers will still generate value years from now.

While the industry standard is a six-year depreciation for data center hardware, analyst Dylan Patel warns this is risky for GPUs. Rapid annual performance gains from new models could render older chips economically useless long before they physically fail.

Hyperscalers are extending depreciation schedules for AI hardware. While this may look like "cooking the books" to inflate earnings, it's justified by the reality that even 7-8 year old TPUs and GPUs are still running at 100% utilization for less complex AI tasks, making them valuable for longer and validating the accounting change.

NVIDIA’s business model relies on planned obsolescence. Its AI chips become obsolete every 2-3 years as new versions are released, forcing Big Tech customers into a constant, multi-billion dollar upgrade cycle for what are effectively "perishable" assets.

Hyperscalers face a strategic challenge: building massive data centers with current chips (e.g., H100) risks rapid depreciation as far more efficient chips (e.g., GB200) are imminent. This creates a 'pause' as they balance fulfilling current demand against future-proofing their costly infrastructure.

Some tech companies have doubled the depreciable life of their AI hardware (e.g., from 3 to 6 years) for accounting purposes. This inflates reported earnings, but it contradicts the economic reality that rapid innovation is shortening the chips' actual useful life, creating a significant red flag for earnings quality.

While the current AI phase is all about capital spending, a future catalyst for a downturn will emerge when the depreciation and amortization schedules for this hardware kick in. Unlike long-lasting infrastructure like railroads, short-term tech assets will create a significant financial drag in a few years.

Unlike the railroad or fiber optic booms which created assets with multi-decade utility, today's AI infrastructure investment is in chips with a short useful life. Because they become obsolete quickly due to efficiency gains, they're more like perishable goods ('bananas') than permanent infrastructure, changing the long-term value calculation of this capex cycle.