Day-ahead scheduling. Real-time balancing. Network-level resource pooling. Market infrastructure for AI compute.
Current compute allocation relies on long-term bilateral contracts and static provisioning. This structure creates persistent inefficiencies in utilization, price discovery, and capacity planning.
Inference workloads spike. Training jobs batch. Operators overprovision for peak demand. Result: billions in stranded capacity sitting idle.
Independent datacenters are forced to accept wholesale offtakes to secure stable cashflows. Compressed margins across newer players.
Long-term bilateral contracts dominate. Prices are negotiated privately, not discovered in open markets. No mechanism exists to determine real-time compute value.
Demand swings wildly. Prices don't adjust. No forward market, no real-time balancing, no way to hedge exposure.
Two-settlement design. Primary and secondary clearing. Network-level resource pooling. The structure that made electricity markets efficient—applied to AI compute.
Schedule predictable workloads 24 hours in advance. Lock in capacity commitments. Clear at auction-determined prices that reflect expected scarcity.
Handle deviations as they occur. Dispatch flexible capacity to meet realized demand. Prices form continuously based on marginal cost of serving load.
Go beyond just FLOPS. A standardized unit of compute that allows comparisons between heterogeneous hardware and give fundementals to price discovery. Performance factors via MLCommons benchmarks translate raw capacity into comparable, tradeable units.
Every participant benefits from transparent pricing and efficient allocation.
AI Labs • Enterprises • Researchers
Hyperscalers • Neo-Clouds • Colos
Trading Firms • Market Makers
When workloads are pooled across the network, individual spikes become statistical noise. The law of large numbers absorbs volatility at scale.
We're looking for partners across the compute ecosystem—data centers, cloud providers, AI labs, and infrastructure investors. Reach out to explore how we can work together.
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