Instead of focusing only on the latest NVIDIA H100 chips, analysts should watch the rental rates for older A100s. Their steady and rising prices indicate that demand for AI inference is so strong that even previous-generation hardware is being fully utilized as a 'workhorse' for a growing number of less complex tasks.

While focus is on massive supercomputers for training next-gen models, the real supply chain constraint will be 'inference' chips—the GPUs needed to run models for billions of users. As adoption goes mainstream, demand for everyday AI use will far outstrip the supply of available hardware.

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.

The dominant use of AI compute is moving from training massive models to running inference tasks. This shift fundamentally alters the market, enabling broader enterprise adoption via cheaper, open models and changing the demand profile for compute hardware beyond the absolute cutting edge.

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 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.

In a striking economic anomaly, the cost to rent older NVIDIA H100 AI chips is increasing, not decreasing. This is because the growth in AI's usefulness is outstripping the tripling annual supply of compute. It signals that the value being generated by AI models is growing faster than our ability to manufacture the hardware to run them.

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.

Contrary to the "iPhone model" of tech obsolescence, older GPUs like the Nvidia A100 are seeing their rental rates rise. This is driven by explosive demand for AI inference tasks, which don't always require the latest hardware, proving the chips' long-term economic viability and value.