CoreWeave dismisses speculative analyst reports on GPU depreciation. Their metric for an asset's true value is the willingness of sophisticated buyers (hyperscalers, AI labs) to sign multi-year contracts for it. This real-world commitment is a more reliable indicator of long-term economic utility than any external model.

Countering the narrative of rapid hardware obsolescence, CoreWeave is still signing new contracts for NVIDIA's 2020-era A100 GPUs that extend to 2029. This proves the long-term useful life and economic viability of older chip generations for specific, optimized AI workloads, challenging the idea that only the latest hardware is valuable.

Despite the rapid pace of hardware innovation, the value of older NVIDIA GPUs like the H100 is holding strong. Cloud provider CoreWeave reports these chips are retaining 90-95% of their pricing power over a 5-6 year lifespan because compute demand far outstrips supply.

Contrary to typical hardware depreciation, GPUs like NVIDIA's H100 are becoming more valuable over time. This is because newer, more efficient AI models can generate significantly more output and value on the same hardware, tying the GPU's worth to its utility rather than its age.

The fear of chip depreciation is mitigated by repurposing older GPUs. While the latest models are crucial for high-speed training, older, less powerful chips are perfectly suitable and cost-effective for running inference, extending the hardware's useful life and long-term value.

Contrary to fears of rapid obsolescence, new domain-specific accelerators (DSAs) can be paired with older GPUs to handle specific tasks. This disaggregated approach extends the useful life of GPUs to 10-15 years, lowering financing costs for compute providers and invalidating bear cases.

Contrary to the belief that AI chips quickly become obsolete, CoreWeave's CEO argues their value holds, citing average five-year client contracts as proof. Older chips like the A100 have even appreciated in price as new use cases emerge, making rapid depreciation a myth.

Contrary to the belief that AI hardware becomes obsolete quickly, older GPUs like A100s will have a long depreciable life. As companies optimize costs, they'll use model routing to send simple queries to older, cheaper hardware, extending its utility for six to eight years.

Countering the narrative of rapid burnout, CoreWeave cites historical data showing a nearly 10-year service life for older NVIDIA GPUs (K80) in major clouds. Older chips remain valuable for less intensive tasks, creating a tiered system where new chips handle frontier models and older ones serve established workloads.