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 the dot-com bubble's speculative fiber build-out which resulted in unused "dark fiber," today's AI infrastructure boom sees immediate utilization of every GPU. This signals that the massive investment is driven by tangible, present demand for AI computation, not future speculation.

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.

Major AI labs plan and purchase GPUs on multi-year timelines. This means NVIDIA's current stellar earnings reports reflect long-term capital commitments, not necessarily current consumer usage, potentially masking a slowdown in services like ChatGPT.

The head of AI at Hudson River Trading describes an incredibly competitive market for GPU capacity. Providers offer newly available leases that require a commitment to multi-year contracts for thousands of GPUs by the end of the day. This high-stakes, high-speed environment means buyers cannot be picky about location or terms.

Contrary to the historical boom-bust cycle of memory chips, Micron's new long-term, high-margin contracts show that major AI players are locking in supply for years. This suggests the AI buildout is causing a sustained, structural shift in hardware demand, not just a temporary, cyclical spike.

A major shift in behavior among top AI labs is their move from three-year to five-year take-or-pay contracts for GPU infrastructure. They are locking in capacity at massive scale for longer durations, signaling extreme confidence in sustained, long-term demand for compute.

The trend of some firms seeking cheaper AI options isn't a sign of a bubble bursting but rather healthy market maturation. The most expensive, powerful AI models are being concentrated among firms with the resources and expertise to generate the highest returns—an efficient allocation of scarce compute resources.

Contrary to expectations of easing supply, the GPU shortage has intensified since 2023. With clearer AI business models, mega-customers like OpenAI and Anthropic are spending even more aggressively, creating a fierce bidding war that pushes startups out.