Problem Statement

GPU Compute Customer Challenges

High Costs: Hyperscaler solutions charge premium rates, making sustained AI development prohibitively expensive for many

Limited Availability: Quotas, waitlists, and vendor lock-in restrict access to needed resources

Lack of Flexibility: Rigid pricing and resource allocation models don't accommodate variable workload needs

Datacenter Provider Challenges

Capital Constraints: GPU infrastructure requires substantial upfront investment

Investment Illiquidity: Traditional GPU investments have limited options for partial ownership or transfer

Operational Complexity: Managing multiple clusters and payment streams is resource-intensive

Ecosystem-level Challenges

Centralization Risks: Concentration of resources among few players limits innovation

Quality and Reliability Concerns: Decentralized GPU provision raises consistency questions

Inefficient Resource Allocation: Current models create compute shortages for certain brokers while leaving capacity idle in others

Investor Challenges

Limited Investment Vehicles: Most investors can only seek exposure to AI opportunity via public market equities, with limited upside yields compared to direct infrastructure ownership

High Capital Requirements: Investment in AI infrastructure is typically limited to very large scale investments in entire AI datacenters, shutting out smaller investors and individual participants

Extreme Illiquidity: AI infrastructure investing is typically highly illiquid with few exit potentials beyond liquidation at end of life, locking capital for extended periods