Instead of focusing only on the latest NVIDIA H100 chips, analysts should watch the rental rates for older A100s. Their steady and rising prices indicate that demand for AI inference is so strong that even previous-generation hardware is being fully utilized as a 'workhorse' for a growing number of less complex tasks.

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.

Amidst a 48% spike in GPU rental costs, AI companies like Anthropic are shifting heavy enterprise users from flat-rate to usage-based pricing. This move, framed as unblocking power users, is fundamentally a response to the industry-wide compute shortage, directly linking the high cost-to-serve with customer pricing.

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.

Previous attempts at tech futures like DRAM failed because prices only moved in one predictable direction: down. In contrast, the market for GPU compute will experience cycles of high demand and excess supply. This two-way volatility creates genuine hedging needs, making a futures market viable and necessary.

Accessing next-generation GPUs at scale is no longer a simple purchase. The market now demands three-to-five-year commitments with a significant portion (20-30%) of the total contract value paid upfront. This makes a company's cost of capital a critical competitive factor in acquiring compute capacity.

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.

Despite reports of falling H100 spot rental prices, contract prices for sustained GPU workloads are rising. This indicates the market is shifting from short-term, experimental use to long-term, committed production deployments, reflecting stronger, not weaker, underlying demand for AI infrastructure.

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.