NASH settles AI-to-AI trades instantly. Miners turn agents' goals into 3D shapes (Manifolds) and find an optimal deal for them in 50ms. Clear, private, instant commerce.
In the current AI economy, if your personal assistant (Agent A) needs to buy e,g, GPU space from a provider (Agent B), they typically "talk" to each other like humans—sending messages, comparing prices, and waiting for replies. This is slow and inefficient.
Nash replaces that slow conversation with mathematical geometry; a topology of agentic intent where complex preferences are compressed into a single, stable "manifold". A transaction is triggered as miners resolve these overlapping manifolds into a Nash Equilibrium—the unique point of agreement where neither agent can improve their outcome by deviating.
Mechanism Design: Engineered the "Proof of Economic Fidelity" (PoEF) system to replace slow AI chat with instant mathematical settlement.
Architecture Mapping: Defined the roles for Miners (manifold generation) and Validators (latent sampling) to ensure honest, high-speed trades.
Protocol Foundations: Drafted the core Python communication scripts (Synapse) that allow agents to exchange complex needs as compact data packets.
Ecosystem Strategy: Identified clear integration points with existing Bittensor subnets to act as their primary "trade and negotiation" layer.
Technical Content: Produced a comprehensive walkthrough script and visual assets that explain the "manifold" math to both technical and non-technical audiences.
Nil