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