New AI models are designed to perform well on available, dominant hardware like NVIDIA's GPUs. This creates a self-reinforcing cycle where the incumbent hardware dictates which model architectures succeed, making it difficult for superior but incompatible chip designs to gain traction.

The primary bear case for specialized neoclouds like CoreWeave isn't just competition from AWS or Google. A more fundamental risk is a breakthrough in GPU efficiency that commoditizes deployment, diminishing the value of the neoclouds' core competency in complex, optimized racking and setup.

The competitive landscape for AI chips is not a crowded field but a battle between two primary forces: NVIDIA’s integrated system (hardware, software, networking) and Google's TPU. Other players like AMD and Broadcom are effectively a combined secondary challenger offering an open alternative.

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.

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.

NVIDIA's annual product cadence serves as a powerful competitive moat. By providing a multi-year roadmap, it forces the supply chain (HBM, CoWoS) to commit capacity far in advance, effectively locking out smaller rivals and ensuring supply for its largest customers' massive build-outs.

Unlike railroads or telecom, where infrastructure lasts for decades, the core of AI infrastructure—semiconductor chips—becomes obsolete every 3-4 years. This creates a cycle of massive, recurring capital expenditure to maintain data centers, fundamentally changing the long-term ROI calculation for the AI arms race.

While powerful, Google's TPUs were designed solely for its own data centers. This creates significant adoption friction for external customers, as the hardware is non-standard—from wider racks that may not fit through doors to a verticalized liquid cooling supply chain—demanding extensive facility redesigns.