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.

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.

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.

Skeptics argue data centers face severe depreciation because rapidly obsolete computing chips lose value like perishable goods. However, rental prices for standard Nvidia GPUs have held strong or risen over multi-year periods. Because newer software and models extract vastly more commercial value per generated token, the economic productivity of underlying computing hardware continues to climb.

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.

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.

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.

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.

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.