Compute Futures
A Proposal for Standardized Markets in AI Compute
Executive Summary
Compute is becoming the most volatile, fast-depreciating input in modern industry. The current infrastructure imbalance is defined by a massive technological risk basis—the risk that a purchased GPU is made obsolete by a newer, more efficient chip—and a financial risk basis—the volatility of spot pricing and scarcity.
Top-tier GPUs age on 24–36 month cycles; every idle hour is value that never returns. Yet pricing remains frustratingly bilateral and opaque, forcing firms to over-commit capital or face crippling spot market instability.
This paper proposes a practical, low-friction path to financialize compute responsibly: standardize a performance-anchored unit (Effective Compute Hour, or ECH), publish a robust hub-based index, and list cash-settled futures. This approach echoes the critical step in energy and agricultural history where standardization unlocked massive capital pools.
The ECH path mirrors these mature commodity and power markets, emphatically moving beyond nominal “GPU hours” to emphasize performance, time, and location; to create a unified, transparent language for hedging, financing, and policy engagement.
Bottom Line
The market structure is sound if we transition pricing away from hardware inputs and toward throughput (e.g., tokens/sec on benchmark reference workloads) by time block and location. Futures must be cash-settled to an independently governed index, with clearly defined Quality Grades (e.g., SXM NVSwitch vs. PCIe Ethernet) to minimize basis risk and ensure fungibility of performance.
The Problem
The AI compute market faces critical inefficiencies that hinder both innovation and equitable access:
⚠️ Market Opacity
Lack of transparent pricing mechanisms and standardized units makes it difficult for consumers to compare offerings or plan budgets effectively.
💰 Access Barriers
Small researchers and organizations face prohibitive costs and uncertain availability, creating a two-tier system that favors well-capitalized incumbents.
📊 Investment Risk
Producers struggle to justify massive capital expenditures without forward visibility into demand and pricing, leading to boom-bust cycles.
🌍 Sustainability Concerns
No systematic mechanism exists to price in environmental or social values, resulting in suboptimal allocation of scarce computational resources.
From the Whitepaper
The current AI compute market operates without standardized units, transparent pricing mechanisms, or systematic ways to incorporate environmental and social values. This creates several critical inefficiencies:
For Consumers: Without standardized benchmarks, comparing offerings across providers is nearly impossible. A “GPU hour” from one vendor may deliver vastly different performance than from another, yet both claim similar specifications. This opacity makes budgeting difficult and creates information asymmetries that favor large, sophisticated buyers.
For Producers: Massive capital expenditures for AI infrastructure—often billions of dollars—must be made without forward visibility into demand or pricing. This leads to boom-bust cycles, with periods of chronic shortage followed by overcapacity and stranded assets.
For Society: There is no systematic mechanism to price in environmental externalities (energy consumption, water usage, carbon emissions) or to prioritize socially valuable workloads (academic research, healthcare AI, climate modeling) over purely commercial applications.
The Solution
A standardized compute futures market built on transparent benchmarks and flexible overlays
Core Innovation: The ECH Unit
We propose a standard unit of compute called the ECH (Effective Compute Hour), benchmarked against industry-standard MLPerf scores. This creates a universal language for pricing, trading, and allocating AI compute resources.
Standardization
Clear benchmarks and tiering (A/B/C) enable apples-to-apples comparisons across diverse hardware configurations
Financial Instruments
Futures, forwards, and options provide price discovery, risk management, and budget certainty for all market participants
Value Overlays
GreenCAC, OpenCAC, and sector-specific overlays embed social values directly into market mechanisms
Core Proposal
We propose establishing a standardized compute futures market built on three pillars:
1. The ECH (Effective Compute Hour) Standard Unit: A benchmarked unit of computational throughput that enables apples-to-apples comparisons across diverse hardware configurations. One ECH represents the computational capacity of a reference system (e.g., 8× NVIDIA H100 GPUs in a standard cluster configuration) for one hour, normalized by industry-standard MLPerf benchmarks.
2. Financial Instruments: Standard derivatives—futures, forwards, and options—adapted for compute markets. Cash-settled futures enable price discovery without physical delivery complexity. Forwards provide certainty for critical workloads. Options offer flexibility for variable or burst usage patterns.
3. Value-Added Overlays: Parallel markets that embed social and environmental values directly into pricing mechanisms. GreenCAC credits certify low-carbon compute. OpenCAC subsidizes research with open-source outputs. Sector-specific overlays (Health, Education) provide verified access for socially valuable applications.
Market Precedent: Oil Benchmarks
Commodity markets have long used standardized benchmarks with location-based pricing. The oil market's Brent-WTI basis spread demonstrates how delivery location affects pricing—a direct analog for compute node differentials.
WTI (West Texas Intermediate) trades at Cushing, Oklahoma, while Brent trades in the North Sea. The spread between them reflects transportation costs, regional supply/demand, and infrastructure constraints—exactly the factors that would drive compute node basis spreads.
Market Precedent: Electricity LMP
Electricity markets pioneered Locational Marginal Pricing (LMP), where prices vary by node based on transmission constraints, losses, and local demand. This is the closest existing analog to what compute markets need.
CAISO (California ISO) data shows how prices fluctuate hourly and spatially. Northern California (NP15) often trades at different prices than Southern California (SP15) due to transmission congestion—just as compute prices should vary between East Coast, Central, and West Coast nodes.
Current GPU Market Landscape
Today's GPU cloud market lacks standardization. Prices for similar hardware vary dramatically across providers and regions, making it difficult for consumers to compare options or plan budgets effectively.
The visualizations below demonstrate three critical perspectives on GPU pricing: raw price ranges, normalized cost-efficiency, and regional basis spreads. Together, they reveal the need for standardized benchmarks like the ECH unit.
💡 Use the floating control panel on the right to switch between USD/GPU and $/ECH pricing views, and filter by instance size
Why a Compute Futures Market?
- A100 8-GPU: AWS $3.62/hr vs Azure $5.51/hr (34% premium)
- H100 8-GPU: AWS $8.71/hr vs Azure $14.63/hr (40% premium)
- Consumers locked into Azure cannot access AWS pricing
- ECH futures enable cross-provider hedging without migration
- Azure A100: US West $3.82/hr vs Brazil $8.38/hr (119% spread)
- Geographic arbitrage exceeds oil (5-10%) and electricity (10-30%) markets
- Location-based futures contracts enable geographic hedging
- Azure spot/low-priority: 74.5% discount for preemption risk
- No options contracts or insurance for spot interruption
- Futures + options enable preemption risk management
Market Overview: Current GPU Pricing
Distribution of GPU pricing across all providers, showing median, quartiles, and range for each hardware type. Illustrates overall price variability before examining individual factors.
Pricing Input Parameters: Individual Factor Analysis
GPU compute pricing is determined by multiple independent factors. Below, we isolate each parameter to understand its individual impact on cost:
🖥️ Parameter 1: Hardware Type Impact
How GPU architecture affects cost-efficiency. Older GPUs (V100) may offer better $/TFLOP for certain workloads, while newer GPUs (H100, A100) provide superior absolute performance but at premium pricing.
☁️ Parameter 2: Cloud Provider Impact
How provider choice affects pricing for identical hardware. Azure consistently commands 34-40% premiums over AWS, while GCP and CoreWeave offer competitive alternatives. Price ranges reveal provider lock-in costs.
🌍 Parameter 3: Geographic Region Impact
How datacenter location affects pricing (basis spreads). Similar to Brent-WTI oil differentials or electricity LMP nodes. Brazil commands 119% premium over US West for Azure A100, while US regions cluster within 10-15% of anchor prices.
AWS Regional Basis (A100)
Azure Regional Basis (A100)
Multi-Factor Synthesis: Combined Impact
While individual parameters show clear patterns, real-world pricing is determined by all factors simultaneously. The heatmap below synthesizes hardware type, geographic zone, and provider into a unified view, revealing that even when standardizing for these variables, significant pricing variance persists—demonstrating market fragmentation and the need for liquid futures contracts to enable efficient price discovery.
🔥 Multi-Dimensional Pricing Matrix
Hardware × Geographic Zone heatmap with normalized regions across providers. Each row represents a different GPU type (independent color scale per row), showing how mature hardware (V100, P100) exhibits consistent cross-region pricing, while newer hardware (H100, A100) shows high variance—signaling market immaturity and arbitrage opportunities.
Key Market Insights
Wide spreads between min/max prices within same GPU type reveal lack of price transparency and standardization
Cost-per-compute varies dramatically—older GPUs sometimes more cost-efficient for specific workloads
Geographic location creates significant basis spreads, similar to oil and electricity commodity markets
ECH normalization reveals true cost-efficiency, enabling apples-to-apples comparisons across hardware
Quantitative Market Analysis
Detailed statistical analysis revealing the magnitude of arbitrage opportunities and market inefficiencies. These tables provide the quantitative evidence supporting the need for a standardized compute futures market.
💡 Analysis Summary
Provider Arbitrage (34-40%): Consumers locked into Azure pay substantial premiums for identical hardware. Cross-provider hedging would unlock value.
Regional Basis (119%): Geographic pricing differences far exceed mature commodity markets. Location-based futures enable arbitrage.
Spot Markets (74.5% discount): Price sensitivity exists but lacks hedging instruments. Options contracts would enable preemption risk management.
Conclusion: These inefficiencies demonstrate a fragmented, immature market ready for standardization. ECH futures would enable hedging for consumers, forward sales for producers, and arbitrage for market makers—driving efficiency through price discovery.
Market Architecture
The compute futures market is built on a layered architecture, flowing from standardized units through financial instruments to value-added overlays. Explore the interactive diagram below:
The ECH Unit
ECH = Effective Compute Hour
A standardized Effective Compute Hour (ECH), defined as performance-normalized throughput over a time block, serves the role of the bushel or barrel in commodity markets.
Formal Definition
One ECH equals delivering a minimum verified throughput on an agreed reference workload during a one-hour block at a defined hub, with specified cluster/fabric characteristics.
Where ThroughputConfig is the measured performance of the specific configuration and ThroughputBase is the reference baseline (e.g., 8× H100 SXM with NVSwitch).
Benchmarked
MLPerf training and inference scores provide objective performance metrics across diverse hardware
Tiered
Tier A (premium), Tier B (standard), Tier C (budget) classifications match different performance needs
Scalable
Standard cluster sizes (Train-8, Train-64, Train-512) support everything from research to frontier models
Quality Grades
Quality is defined by Grades to minimize quality basis risk. Each contract specifies the grade, with different grades trading at basis (premium/discount) to the anchor.
| Quality Grade | Description (Quality Ladder) | Example Hardware/Fabric |
|---|---|---|
| Grade A | High-density training cluster, emphasizing low-latency fabric | ≥8 H100/B-series SXM, NVSwitch/IB ≥3.2 Tb/s aggregate |
| Grade B | Cost-optimized training/large-scale inference cluster | ≥8 H100/B-series PCIe, IB/Ethernet with defined minima |
Each other region/provider/hardware class then trades as a basis (premium/discount) to the anchor.
ECH Technical Specification
The ECH standard addresses a fundamental challenge in AI compute markets: how to compare the computational capacity of vastly different hardware configurations. Current approaches rely on vendor-specific metrics (FLOPS, memory bandwidth, interconnect topology) that are difficult to translate across platforms.
Benchmarking Methodology: We leverage MLPerf—the industry-standard benchmarking suite developed by MLCommons—to normalize performance across hardware. MLPerf provides objective scores for both training (large language models, computer vision, recommendation systems) and inference (real-time serving, batch processing) workloads.
Tiering System: To accommodate diverse use cases, ECH units are classified into grades: Grade A represents cutting-edge performance (latest GPUs, NVLink fabric, premium interconnects), Grade B offers reliable mainstream performance at lower cost, enabling different use cases and price points while maintaining standardized performance benchmarks.
Cluster Configurations: Standard sizes (Train-8, Train-64, Train-512) reflect common scaling patterns in distributed training, enabling standardized contracts without excessive fragmentation.
Financial Instruments
Standard financial instruments adapted for the compute market enable price discovery, risk management, and capital efficiency:
Futures (Cash-Settled)
Standardized contracts for future delivery of ECH units at predetermined prices. Cash-settled to avoid physical delivery complexity.
Forwards (Physical Delivery)
Customized contracts for actual compute resource delivery. Provides certainty for critical workloads.
Options (Priority & Burst)
Right (but not obligation) to access compute at specific prices. Ideal for variable or burst workloads.
Ancillary Services
Service Parameters
Contracts can be customized along multiple dimensions to match specific workload requirements:
Performance / Latency
Reliability / Preemptibility
Hardware Specifications
Geographic Location
Efficiency, Energy & Societal Utility
The ECH market framework provides the transparency necessary to incentivize energy efficiency and steer compute usage toward high-utility workloads.
TP$·W: The North Star Metric
Our North-star metric is Tokens per Dollar per Watt (TP$·W), the practical synthesis of cost, energy, and capability. Optimizing this aligns provider incentives (lower opex/capex), buyer incentives (lower $/token), and public goals (lower energy intensity per useful output).
As Microsoft CEO Satya Nadella has stated, the future of AI infrastructure is optimizing for TP$·W, anchoring this market focus in industry leadership and current supply constraints.
TPW (Tokens per Watt)
Measures energy efficiency
TP$ (Tokens per Dollar)
Measures cost efficiency
TP$·W (Unified Metric)
The ultimate efficiency frontier
⚠️ Jevons Paradox and Market Design
Efficiency lowers the unit cost of compute, which, rather than reducing overall consumption, often unlocks massive new demand that can increase total aggregate energy consumption as delineated in the well-documented Jevons Paradox.
In compute, this means that as TP$·W improves, the marginal cost of training new, ever-larger foundation models drops, leading to a surge in total tokens consumed. To address this inevitable surge, markets must be designed to channel the throughput toward socially valuable ends and cleaner energy sources.
Mitigations via Market Design
We propose market-based solutions, primarily through optional carbon-aware hubs/tenors and peak/off-peak blocks. By aligning contract delivery periods to regional clean-energy windows (e.g., high solar/wind output), we reward clean supply without mandating technology choices. This creates a powerful financial incentive for demand response and helps internalize the carbon externality.
Regulatory Alignment
Voluntary disclosures and structure should align with FERC/RTO constructs. This is necessary because the fundamental nature of compute hinges on locational, time-sensitive, and non-storable parameters which parallels the physics and finance of electricity grid management.
Index Governance
Adoption of IOSCO-style benchmark governance is non-negotiable for the Compute Index. This framework ensures transparency, reliability, and non-manipulability, which are preconditions for widespread financial and regulatory adoption of the ECH as a primary economic indicator.
Value-Added Overlays
Overlay markets enable value-aligned compute allocation without sacrificing market efficiency:
GreenCAC
Compute hours certified for low carbon footprint and sustainable water usage. Premium pricing reflects environmental value.
OpenCAC
Subsidized compute for research that commits to open-source outputs and reproducibility. Accelerates scientific progress.
Sector-Specific CAC
Health, education, and public sector allocations managed through registrar verification to ensure equitable access.
Market Design Philosophy
The overlay system represents a key innovation in market design: embedding social and environmental values directly into pricing mechanisms without distorting core price signals.
GreenCAC operates as a premium market where providers voluntarily certify compliance with environmental standards (renewable energy usage above threshold, PUE below benchmark, water consumption limits). Third-party auditors verify claims, and premium pricing creates market incentives for sustainable infrastructure investment. This approach avoids regulatory mandates while channeling capital toward environmentally responsible capacity.
OpenCAC addresses the public goods problem in AI research. Academic institutions and researchers often generate socially valuable outputs (open-source models, published methodologies, reproducible experiments) but lack resources to compete with well-capitalized commercial entities. OpenCAC provides subsidized compute conditional on open-source commitments, funded through a mix of government grants, philanthropic capital, and cross-subsidies from commercial users.
Sector-specific overlays (HealthCAC, EduCAC) use registrar-based verification to ensure that discounted compute reaches intended beneficiaries. This model draws on established patterns in software licensing (educational discounts) while adding cryptographic verification to prevent arbitrage.
Stakeholder Benefits
Producers
- ▸Hedge-to-Build: Forward contracts enable capital investment with demand certainty
- ▸Utilization: Improved capacity planning reduces idle infrastructure
- ▸Price Discovery: Transparent market signals guide capacity expansion
Consumers
- ▸Budget Certainty: Fixed-price contracts eliminate cost uncertainty
- ▸Access: Standardized units lower barriers for smaller organizations
- ▸Flexibility: Options and spot markets support diverse workload patterns
Society
- ▸Price Signals: Efficient allocation of scarce computational resources
- ▸Equitable Access: Overlay markets prioritize social benefit
- ▸Sustainability: GreenCAC incentivizes environmental responsibility
Implementation Challenges
While the vision is compelling, several technical and market challenges must be addressed:
Hardware Heterogeneity
GPU architectures, memory configurations, and interconnects vary significantly. Normalizing performance requires sophisticated benchmarking and continuous recalibration.
Verification Complexity
Ensuring providers actually deliver contracted performance levels requires trusted attestation mechanisms without excessive overhead.
Liquidity Fragmentation
Too many contract variants (different tiers, locations, parameters) could split liquidity and reduce market efficiency.
Regulatory Ambiguity
Compute futures may face commodity regulations, securities laws, or entirely novel regulatory frameworks depending on jurisdiction.
Equity vs Efficiency
Market mechanisms naturally favor those with capital. Overlay systems must be carefully designed to promote access without distorting price signals.
Path Forward
The AI compute market is at an inflection point. As models grow larger and compute becomes the critical bottleneck for AI progress, we need market infrastructure that matches the sophistication of the technology itself.
A standardized futures market for AI compute can deliver price discovery, risk management, and equitable access—all while embedding sustainability and social values directly into market mechanisms.
Next Steps
- Benchmark Validation: Work with MLCommons to refine ECH standardization and tier definitions
- Pilot Exchange: Launch limited OTC market with select producers and consumers
- Overlay Prototypes: Demonstrate GreenCAC and OpenCAC with philanthropic partners
- Regulatory Engagement: Proactively work with CFTC, SEC, and international bodies
- Ecosystem Building: Recruit market makers, clearinghouses, and risk management providers
For more information, contact: