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.

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.

AI software is improving so rapidly that older hardware, like a three-year-old NVIDIA inference chip, is now more profitable than it was when new. This phenomenon, where software advancements outpace hardware depreciation, is unprecedented and makes existing infrastructure increasingly valuable.

The current AI moment is unique because demand outstrips supply so dramatically that even previous-generation chips and models remain valuable. They are perfectly suited for running smaller models for simpler, high-volume applications like voice transcription, creating a broad-based boom across the entire hardware and model stack.

While focus is often on the latest chips, the rental rate of the older A100 GPU serves as a crucial economic indicator. Strong rates for this hardware signal that broad, foundational demand for AI inference is healthy, independent of the demand for cutting-edge training from frontier models.

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.

Andreessen highlights a unique economic phenomenon: the pace of AI software improvement outstrips hardware depreciation. This means a three-year-old NVIDIA inference chip can generate more revenue today than when it was new, a complete reversal of typical tech hardware value cycles.

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.