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.
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The current AI spending spree by tech giants is historically reminiscent of the railroad and fiber-optic bubbles. These eras saw massive, redundant capital investment based on technological promise, which ultimately led to a crash when it became clear customers weren't willing to pay for the resulting products.
During the dot-com crash, application-layer companies like Pets.com went to zero, while infrastructure providers like Intel and Cisco survived. The lesson for AI investors is to focus on the underlying "picks and shovels"—compute, chips, and data centers—rather than consumer-facing apps that may become obsolete.
Before AI delivers long-term deflationary productivity, it requires a massive, inflationary build-out of physical infrastructure. This makes sectors like utilities, pipelines, and energy infrastructure a timely hedge against inflation and a diversifier away from concentrated tech bets.
The debate over AI chip depreciation highlights a flaw in traditional accounting. GAAP was designed for physical assets with predictable lifecycles, not for digital infrastructure like GPUs whose value creation is dynamic. This mismatch leads to accusations of financial manipulation where firms are simply following outdated rules.
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.
Unlike the dot-com bubble's finite need for fiber optic cables, the demand for AI is infinite because it's about solving an endless stream of problems. This suggests the current infrastructure spending cycle is fundamentally different and more sustainable than previous tech booms.
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.
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.
As AI gets exponentially smarter, it will solve major problems in power, chip efficiency, and labor, driving down costs across the economy. This extreme efficiency creates a powerful deflationary force, which is a greater long-term macroeconomic risk than the current AI investment bubble popping.
The AI infrastructure boom is a potential house of cards. A single dollar of end-user revenue paid to a company like OpenAI can become $8 of "seeming revenue" as it cascades through the value chain to Microsoft, CoreWeave, and NVIDIA, supporting an unsustainable $100 of equity market value.
