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.

Unlike typical computer hardware that depreciates rapidly, H100 GPUs are trading above their launch price in secondary markets. This market anomaly, driven by the extreme and sustained compute shortage for AI, completely inverts traditional financial models for hardware assets.

Unlike oil, GPU compute is not a simple commodity. Its value is highly dependent on the specific software and workload being run, making it difficult to standardize and treat as a fungible asset. This presents a major obstacle to creating a liquid, tradable financial market for compute power.

Different financing vehicles focus on different layers of data center risk. Securitization primarily underwrites the long-term value of the physical building and tenant lease. The risk of rapid GPU obsolescence is largely ignored by these structures and is instead borne by private credit and equity investors who finance the hardware itself.

The staggering $100B+ guarantee from Nvidia is strictly for the "PowerShell" – the land, power, and physical data center building. This financing is completely separate from the even larger capital required to purchase the Nvidia GPUs that will fill it. This reveals a two-tiered financing challenge in AI infrastructure, requiring distinct capital stacks for the physical shell and the computational hardware within.

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.

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.

Lenders use conservative accounting depreciation for hardware. However, real-world rental income data shows GPUs' economic value (calculated via discounted cash flow) remains significantly higher for much longer, challenging assumptions of rapid obsolescence and unlocking new financing models.

The fundamental unit of AI compute has evolved from a silicon chip to a complete, rack-sized system. According to Nvidia's CTO, a single 'GPU' is now an integrated machine that requires a forklift to move, a crucial mindset shift for understanding modern AI infrastructure scale.