Hubic.ai
  • Hubic AI
  • EMBARK UPON
    • Introduction
      • Proof-of-Inference (PoI)
      • Proof-of-Weights (PoWg)
      • Why Hubic?
      • Main Actors and Their Roles
      • Architecture Overview
      • Use Case Examples
      • Hubic AI Hub – Model Registry
      • RWA Integration
    • Registry & System Architecture
      • Sovereign AI Agents (On-chain AI Logic Executors)
      • Liquid Strategy Engine (LSE)
      • Proof-of-Weights (PoW2)
      • Governance System
      • Hubic Intelligence Hub (Expanded)
      • Visual System Map
    • Economic Model
      • HUB Token Utility
      • Economic Actors & Reward Mechanics
      • Token Flow Diagram
      • Long-Term Sustainability
      • Optional Enterprise Layer
      • Security & Reputation Systems
      • Summary Table
      • Future Expansion Points
      • Final Notes
    • Program Flow Overview
      • Model Registration (One-Time)
      • Inference Request (User Job)
      • Execution Phase (Off-Chain)
      • Verification Phase
      • Rewards & Settlement
      • Optional Extensions
      • Key Takeaways
    • Real-World Use Case Example
      • Introduction
      • Problem Statement
      • System Actors
      • End-to-End Flow: DAO Delegation Automation
      • Benefits to DAO Operations
      • Extensions & Advanced Use
  • Hubic Economic Engine
    • Tokenomics
    • Roadmap
  • Links
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  1. EMBARK UPON
  2. Real-World Use Case Example

Introduction

PreviousReal-World Use Case ExampleNextProblem Statement

Last updated 6 days ago

Validator delegation strategies within Ethereum DAOs are often based on informal heuristics, periodic governance votes, or centralized dashboards. This leads to inefficiencies, delayed reactions to performance fluctuations, and missed staking rewards.

Hubic enables DAOs to replace manual delegation with autonomous AI agents that:

  • Operate on zk-verified logic,

  • Are provably correct,

  • Can be tokenized as Real World Assets (RWAs).

🎯 Scenario Overview

A DAO managing a large ETH treasury wants to maximize staking yield while minimizing exposure to downtime or slashing. Instead of:

  • Manually evaluating validator stats,

  • Proposing reallocation votes,

  • Or outsourcing to centralized bots,

the DAO uses a zk-model registered on Hubic to automate this logic in a fully transparent, programmable, and revenue-generating way.

💼 Real-World Impact:

  • Faster reallocation based on verifiable data

  • AI agent can be tokenized → DAO earns revenue from model usage

  • All logic is on-chain, transparent, and composable with DeFi or RWA flows

This use case shows how verifiable AI agents can power DAO governance, optimize treasury returns, and serve as monetizable infrastructure via RWA tokenization.