Current AI API pricing in the market is static (e.g., pay-per-token). However, the real compute market experiences “peak-valley effects” and varying levels of urgency. Static pricing leads to wasted idle compute and fails to meet sudden high-concurrency or urgent task demands.
R&D Significance
Endows AI Agents with “economic rationality.” By introducing Nash Equilibrium, the algorithm enables Agents to dynamically bid like human traders based on market supply/demand and user intent, achieving optimal allocation of compute resources.
Use Cases
Dynamically adjusts an Agent’s quote and Service Level Agreement (SLA). Supports three game strategies: Surge Pricing for urgent tasks, Price War during idle times, and Reputation Staking for high-quality demands.
Achieved Results
Successfully automated a dynamic pricing mechanism featuring a “+20% surge for priority lanes” and a “-20% discount for budget users.” Notably, its proprietary “Gatekeeper” mechanism successfully rejected low-trust Agents from bidding on premium orders.