The relentless pace of new AI models, which perform best on the latest hardware, drastically shortens the effective lifespan of GPUs. This changes the traditional 6-year depreciation model and complicates the financial calculus for building data centers versus renting cloud capacity.

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.

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.

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.

The massive investment in data centers isn't just a bet on today's models. As AI becomes more efficient, smaller yet powerful models will be deployed on older hardware. This extends the serviceable life and economic return of current infrastructure, ensuring today's data centers will still generate value years from now.

While the industry standard is a six-year depreciation for data center hardware, analyst Dylan Patel warns this is risky for GPUs. Rapid annual performance gains from new models could render older chips economically useless long before they physically fail.

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.

The useful life of an AI chip isn't a fixed period. It ends only when a new generation offers such a significant performance and efficiency boost that it becomes more economical to replace fully paid-off, older hardware. Slower generational improvements mean longer depreciation cycles.