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.

The industry typically depreciates GPUs over a standard six-year cycle, with fears that older chips become obsolete within three years. However, abstracting raw silicon into managed inference and fine-tuning services allows older hardware to serve cost-effective intelligence long-term. Even three years after release, Hopper GPUs command higher utilization rates than when brand new, demonstrating persistent economic value for non-frontier silicon.

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 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.

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.

Unlike typical IT assets that depreciate, AI-capable hardware inverts this trend. As more efficient and powerful models are released, the same physical machine can tackle increasingly complex problems, making its utility and value grow throughout its lifecycle.