
COAI
ChainOpera AI
$10.95
14.10%
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COAI | BEP20 | BNB | 0x0A8D6C86e1bcE73fE4D0bD531e1a567306836EA5 | 2025-09-22 |
소개 ChainOpera AI
COAI is the utility token of ChainOpera’s decentralised AI stack spanning an agent super-app, developer platform, model/GPU layer and an AI-native protocol. The system uses Proof-of-Intelligence to verify and account for contributions across compute, models, data and agents, with privacy-preserving training and public attribution. COAI is used for service access, resource coordination, contribution accounting and governance, and sits within a roadmap toward an AI-focused Layer-1 chain.
ChainOpera AI (COAI) is the native utility token of ChainOpera, a decentralised AI stack combining:
- AI Terminal & Agent Social Network for end-user access and agent distribution with on-chain attribution.
- Agent Developer Platform with templates, SDKs and multi-agent frameworks.
- Model & GPU Platform for decentralised training/serving and federated learning.
- CoAI protocol layer providing verifiable coordination and accountability, with a stated path toward a native Layer-1 chain.
The network architecture centres on Proof-of-Intelligence (PoI), a consensus and coordination framework that measures “intelligence work” across roles (agents, models, GPUs, data and annotation), preserves privacy via federated approaches, and publishes contribution assessments and reputation on-chain.
COAI functions as a utility token to:
- access premium features and developer tooling in the AI Terminal and Agent Social Network.
- register and coordinate resources (compute, models, data) for discovery and usage.
- record contribution credits and reputation tied to verifiable work.
- participate in governance focused on technical standards and software roadmaps.
It also supports mechanisms described for service-quality assurance (e.g., provider staking) and aligns access with measured contributions under PoI.
ChainOpera was co-founded by Prof. Salman Avestimehr and Dr. Aiden (Chaoyang) He. Avestimehr is a professor at USC and directs the USC-Amazon Centre on Trustworthy AI. He has industry experience at major technology firms and co-created the FedML framework.