A new category of "NeoCloud" or "AI-native cloud" is rising, focusing specifically on AI training and inference. Unlike general-purpose clouds like AWS, these platforms are GPU-first, catering to massive AI workloads and addressing the GPU scarcity and different workload patterns found in hyperscalers.

A simple average of GPU prices is useless because 'two H100s' can have different CPUs, RAM, and locations. A valid index requires ingesting thousands of daily prices and normalizing them against a base case, using a model that identifies key price-driving factors. This is crucial for creating a reliable hedging instrument.

Don Wilson says a neocloud selling to less creditworthy customers faces a higher cost of capital. Once GPU futures exist, it can buy chips, sell a strip of futures, and still sell on-demand or short contracts with collateral. He calls this a giant basis trade that markets should reward with cheaper funding.

Hyperscalers like AWS charge 2-3 times more for identical GPUs compared to smaller clouds. This premium isn't for the chip itself but for a bundled product including a legacy of software, compliance, safety, and long-standing enterprise relationships that create customer stickiness.

A liquid futures market for GPU compute would create price transparency, threatening the business models of hyperscale cloud providers. These giants benefit from opaque, bundled pricing and controlling supply. They will naturally resist the standardization and transparency that an open futures market would bring.

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.

A new category of cloud providers, "NeoClouds," are built specifically for high-performance GPU workloads. Unlike traditional clouds like AWS, which were retrofitted from a CPU-centric architecture, NeoClouds offer superior performance for AI tasks by design and through direct collaboration with hardware vendors like NVIDIA.

Unlike physical commodities like oil, AI compute lacks strong regional price differences. For non-real-time tasks like model training, the physical location of the GPU and its associated latency are negligible. This allows users to source compute globally, driving prices toward a single international benchmark.

The CME is creating futures contracts based on H100 and B200 GPU rental price indices. This marks the financialization of compute, allowing providers to hedge revenue and enterprises to lock in future costs, with a long-term vision for physical delivery of compute capacity.