Introduction to ChainOpera

ChainOpera is a decentralized AI platform designed to integrate AI and blockchain technologies, aiming to break the monopoly of Web2 companies over AI resources and data. It seeks to establish an open AI ecosystem that promotes the collaborative training of models and AI Agents. Leveraging the underlying Layer 1 protocol optimizes AI training efficiency, scalability, and security while recording and verifying each participant’s contributions, providing a fair incentive mechanism. The platform utilizes a Federated AI architecture, a decentralized machine learning model that allows multiple third-party entities to train models collaboratively without directly sharing their data. This fully integrates resources from data providers, AI models, computing power, and cloud service providers, reducing the risk of privacy breaches and ensuring users have full control over their personal data and models. Through this approach, ChainOpera’s Federated AI platform demonstrates several key advantages:

• Co-Training
  Participants can engage in model training without directly providing data, sharing the final model outcomes.

• Co-Serving
  The platform supports the real-time deployment of generative AI, allowing users to quickly apply models to real-world scenarios, such as natural language processing, image generation, and more.

• Data Privacy and Security
  Participants’ data is processed locally and encrypted, protecting individual data privacy and security.

• Open AI Application Market
  The built-in market allows anyone to list their own developed AI applications, promoting diversity and prosperity within the ecosystem.

Through the collaboration between the Layer 1 infrastructure and Federated AI system, ChainOpera is not only an AI Agents launch platform but also integrates AI development resources using blockchain technology. It rewards participants for their contributions, creating a fair AI ecosystem.

Funding and Team Background

ChainOpera’s co-founders, Salman Avestimehr and Aiden He, both have profound knowledge and industry experience in the AI field. Avestimehr is the Dean of the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Southern California (USC), as well as the Director of USC-Amazon Secure and Trusted Machine Learning. He is also a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in AI and decentralized computing. Aiden He is an expert in machine learning and AI applications, with rich R&D experience accumulated at companies like Meta, Google, AWS, and Tencent. He has also been deeply involved in several Web3 projects. Before ChainOpera, Avestimehr and He co-founded two AI companies, TensorOpera and FedML, providing AI Agent and GenAI model services to enterprises and developers.

Additionally, other team members come from top global institutions, including the University of California, Berkeley, Stanford University, the University of Southern California, MIT, Tsinghua University, Google, Amazon, Tencent, Meta, and Apple. The team comprises talents from across Europe, America, and Asia, bringing rich experience in AI and Web3 application development and operations.

On December 25, 2024, ChainOpera announced the completion of a $3.5 million seed round, bringing the total funding amount to $17 million. Participating institutions include Finallity Capital, Road Capital, IDG Capital, Amber Group, ABCDE Capital, and more. Well-known angel investors, such as David Tse (co-founder of Babylon), Sreeram Kannan (founder and CEO of EigenLayer), and Jeff Ren (early investor in AI and Web3), also participated in this funding round.

Operating Mechanism

ChainOpera’s operational structure (Source: ChainOpera)

ChainOpera’s ecosystem revolves around a Federated AI platform, integrating data from data sources, AI Agents, and AI Chains, and distributing tasks. The following will introduce several core components of its ecosystem:

AI Terminal

The operational process of ChainOpera is as follows: First, ChainOpera has developed an application on mobile devices as an AI Terminal. Users can download this app to interact with the AI Agents in the ChainOpera ecosystem and freely trade tokens. During the interaction, users provide personal data to participate in the training of LLM and GenAI, earning rewards through the “Type to Earn” model.

ChainOpera’s AI Terminal (Source: ChainOpera)

In addition to interacting with individual AI Agents, users can create their own AI Agent “LinkedIn,” where multiple AI Agents can converse and interact with each other, integrating the functions of different AI Agents to work for the user.

Federated AI Platform

Architecture of the Federated AI Platform (Source: ChainOpera)

The Federated AI Platform is a core component of the ChainOpera ecosystem, supported by its self-developed Federated AI OS (Operating System). It is a decentralized machine learning platform that accepts real-time data from users and application scenarios through data sources such as AI Terminal, AI Agents, and AI Agents LinkedIn. The platform uses this data for distributed model training across multiple nodes, optimizing model performance while protecting user data privacy.

The system provides the CoAI SDK, which allows anyone to easily create, deploy, and manage their own AI Agent on the platform. The SDK supports not only ChainOpera’s self-developed technology framework but also integrates mainstream AI Agent architectures available on the market. It offers diverse model services, enabling developers to design tailored solutions based on different application scenarios, including:

• TesorOpera
  An efficient framework optimized for generative AI training and inference, developed specifically by ChainOpera.

• FedML
  A powerful federated learning toolkit for distributed model training.

• ScaleLLM
  Supports the training and operation of large-scale language models (LLM), catering to the growing demands of the generative AI field.

• Edge-Cloud Hybrid Service
  Combines the advantages of edge computing and cloud computing to achieve efficient resource allocation and low-latency AI application deployment.

In addition, the CoAI SDK integrates cryptocurrency features, allowing developers to issue tokens when deploying AI Agents. These tokens represent user engagement levels with different AI Agents and serve as a reward mechanism, encouraging more users to interact with AI Agents and contribute data, thus promoting model training and optimization. The built-in AI Agent marketplace on the platform provides users with a channel to trade tokens. General users can find and interact with suitable AI Agents, while developers can receive rewards from their created AI Agents, establishing a mutually beneficial economic system.

ChainOpera AI Chain

ChainOpera AI Chain is the underlying Layer 1 protocol supporting the Federated AI Platform, developed independently by the ChainOpera team. It utilizes the PoI (Proof of Intelligence) consensus mechanism to fairly verify and record the computational resources or data contributed by each participant in a decentralized manner, issuing token rewards based on contribution value. Additionally, the ChainOpera AI Chain integrates the Federated AI OS to optimize inference efficiency, scalability, and security for AI models, aiming to overcome the performance limitations of traditional blockchains and provide a low-latency, high-throughput environment for various AI applications.

ChainOpera AI Chain will be progressively deployed in three phases:

• Phase One

  • Deployment on a local testnet
  • CoAI deployed in the form of smart contracts

• Phase Two

  • Deployment on high-performance public blockchains such as Solana, Base, or Ethereum

• Phase Three

  • Official launch of AI Chain with Proof of Intelligence consensus replacing smart contracts
  • Integration of the Federated AI OS into the AI Chain

Current Ecosystem

ChainOpera’s Decentralized AI Platform (Source: ChainOpera)

Currently, only a limited set of features is live on the ChainOpera platform. Users can deploy some AI models on its decentralized AI platform, interact with these models, or have them generate photos and videos. The first testing phase has just concluded, and the platform is preparing to enter the second phase, which involves launching the AI Terminal. This will allow users to start providing personal data as a source for model training. Although it has not been fully released yet, interested users can register now to join the waiting list.

Comparison with Virtuals Protocol

Overall, ChainOpera shares many similarities with the highly popular Virtuals Protocol that emerged recently. Both platforms are low-barrier AI Agent launch platforms that enable users without technical backgrounds to create their own AI Agents and issue tokens. They also support diverse AI models, allowing developers to construct a wide range of AI applications rather than being limited to a single function. However, there are several differences between ChainOpera and Virtuals Protocol regarding market positioning, AI Agent creation methods, and tokenomics, which will be examined below.

Market Positioning

In terms of market positioning, ChainOpera distinguishes itself from Virtuals Protocol by doing more than simplifying AI Agent creation through the CoAI SDK. ChainOpera integrates all AI-related resources, including enabling users to participate in model training, pooling distributed computing power, and building a high-performance Layer 1 to enhance AI operations. Its goal is to democratize AI development, making it accessible and beneficial for everyone rather than being monopolized by large corporations. ChainOpera envisions an AI landscape where AI development—from model training to creating AI applications—is a collaborative effort. Users can contribute data, train AI models, and freely create various AI Agents, transforming AI technology into a more equitable and transparent market.

G.A.M.E. Workflow (Source: Virtuals Protocol)

In contrast, Virtuals Protocol has not developed its own blockchain but instead builds its protocol on Base, focusing on optimizing a user-friendly AI Agent creation process. For users interested in launching AI Agents, Virtuals Protocol offers a modular development framework called G.A.M.E. (Generative Autonomous Multimodel Entities), composed of five core components:

• Perception Subsystem
  Defines how the AI Agent receives and processes environmental inputs, supporting multimodal processing of text, voice, and images.

• Strategic Planning Engine
  Designs the decision-making logic of the AI Agent.

• Dialogue Processing Module
  Develops natural language processing capabilities, enabling context understanding and appropriate response generation.

• Long-Term Memory Processor
  Stores historical data and datasets, retrieving past experiences, reflections, dynamic personalities, worldviews, and working memory to enhance decision-making abilities.

• On-chain Wallet Operator
  Integrates an on-chain wallet to set asset management and reward distribution transaction rules.

Developers can select predefined modules to customize functionality, adjusting response speed, content generation levels, personality traits, behavior patterns, and tone. By combining different modules, AI Agents can exhibit diverse, complex capabilities and unique personal characteristics, continuously refined through user feedback.

This comparison clearly shows that Virtuals Protocol focuses on simplifying AI Agent creation and management, while ChainOpera offers an integrated AI service. In addition to AI Agent creation, ChainOpera promotes user participation in model pre-training, aiming to build a broader AI ecosystem.

AI Agent Creation Process

ChainOpera is still in its early development phase, so it has yet to demonstrate how AI Agents will be created on its platform. However, insights can be drawn by referencing the process used by Virtuals Protocol to infer ChainOpera’s potential implementation strategies and challenges.

In Virtuals Protocol, deploying a new AI Agent requires spending a certain amount of $VIRTUAL tokens. Users must name the AI Agent, set an avatar, specify a token name, and provide a description outlining its features and personality. Once this setup is complete, and the AI Agent’s token market cap reaches $42,000 through sufficient purchases, the AI Agent officially goes live as a fully autonomous entity with its own dedicated X account. Early token investments contribute $35,000 toward establishing a permanent liquidity pool on Uniswap.

ChainOpera will likely draw inspiration from Virtuals Protocol but differs fundamentally by operating on its own Layer 1 blockchain. Developing a Layer 1 chain, particularly regarding infrastructure, requires time to mature and stabilize. Therefore, ChainOpera’s initial priority is to ensure its Layer 1 protocol can handle sufficient transaction throughput reliably.

Additionally, unlike Uniswap, which has established significant liquidity and a robust protocol structure over the years, ChainOpera may face liquidity challenges for AI Agent tokens. Beyond liquidity, gaps in DeFi infrastructure, including oracles, cross-chain bridges, stablecoins, lending protocols, and trade aggregators, could hinder user trading experiences and limit external capital flow. Notably, ChainOpera’s testnet deployment on Ethereum and Solana indicates potential support for EVM or SVM in its Layer 1 chain. A practical short-term solution to address liquidity issues would be integrating liquidity pools on established platforms like Uniswap or Raydium to leverage existing protocols.

Despite these challenges, ChainOpera’s broader vision extends beyond merely being an AI Agent launch platform. It aims to construct a comprehensive on-chain AI ecosystem. Users can contribute data through AI Terminal to participate in model training, become nodes providing computational power to enhance AI performance, or deploy additional AI applications on its Layer 1. Although the AI Agent launch platform may struggle to match Virtuals Protocol’s success during the cold-start phase, ChainOpera’s expansive vision holds greater long-term potential.

Tokenomics

Unlike most protocols where tokens primarily serve governance purposes, both ChainOpera and Virtuals Protocol assign more practical utility to their tokens. This design allows token holders to capture intrinsic value from the protocol, enabling the token price to grow alongside the protocol’s development and success.

$VIRTUAL Token Allocation (Source: Virtuals Protocol Whitepaper)

First, regarding Virtuals Protocol, its native token $VIRTUAL has a total supply of 1 billion. Of this, 60% is already in circulation, 5% is allocated for liquidity, and the remaining 35% is reserved for ecosystem development, managed by the Virtuals DAO multisig wallet with an annual release cap of 10% over the next three years. $VIRTUAL serves numerous use cases within its ecosystem:

• AI Agent creation fees
• Liquidity pool trading pairs
• LP staking
• Routing token

In summary, every transaction within the Virtuals Protocol ecosystem requires $VIRTUAL as the medium of exchange. From the creation of AI Agents to the purchase of AI Agent tokens, $VIRTUAL is necessary. The Uniswap pools only support pairing with $VIRTUAL, locking more circulating tokens in liquidity pools. Additionally, LP tokens can be staked to earn rewards and gain voting tokens that can be delegated to validators. As a routing token, $VIRTUAL facilitates swaps between different AI Agent tokens by first converting them into $VIRTUAL. Thus, $VIRTUAL functions as the central pillar of Virtuals Protocol’s operations.

As for ChainOpera, while its tokenomics and specific use cases have yet to be announced, its token utility will likely extend beyond the AI Agent platform. It will serve as a transaction fee on its Layer 1 chain and reward AI resource providers, driving demand and capturing protocol value.

Conclusion

Whether the current wave of AI Agents is merely another fleeting bubble remains a topic of debate. However, it is undeniable that AI Agents bring significant automation capabilities to users, enabling them to handle vast workloads. Developers can accelerate protocol deployment and product iterations, traders can optimize strategies, and investors can gain in-depth market insights, lowering the barrier for Web2 users to enter the Web3 space. ChainOpera positions itself at the forefront of this trend, recognizing the potential of AI Agents while building a robust infrastructure for AI development. This foundation aims to drive large-scale adoption of AI applications within Web3, shaping the future of AI integration in decentralized ecosystems.