OpenAI's strategy to lease rather than buy NVIDIA GPUs is presented as a shrewd financial move. Given the rapid pace of innovation, the future economic value of today's chips is uncertain. Leasing transfers the risk of holding depreciating or obsolete assets to the hardware provider, maintaining capital flexibility.

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.

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.

While renting GPUs works for smaller tasks, serious, large-scale model training requires owning a GPU cluster. This is because training needs a gigantic, co-located memory card with all the data directly accessible, a setup that cloud providers cannot easily or cheaply replicate for renters.

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.

As AI models achieve human-level capabilities in valuable roles like software engineering, they can generate significantly more revenue from the same hardware. This increased monetization potential will cause the rental price of GPUs to skyrocket, potentially by over 15x, to match the economic value they produce.

Counter to narratives about rapid depreciation, the market for used high-end GPUs is robust. Data from late 2023 showed a second-year H100 reselling for 85 cents on the dollar, and a third-year for 84 cents. This high residual value makes refurbished chips a viable and capital-efficient option for compute providers.

The rental prices for older NVIDIA GPUs, like the Hopper family and A100s, are increasing. This counterintuitive trend shows demand for AI compute is so far outstripping total supply that even previous-generation hardware is becoming more valuable, highlighting the severity of the GPU crunch.