The Integration of Crypto and AI: From Computing Power Resources to Intelligent Agent Economy

As the wave of artificial intelligence sweeps across the globe, the cryptocurrency field is also exploring the possibilities of combining with AI. This integration initially focused on the consolidation of decentralized Computing Power resources, aiming to coordinate globally idle GPU and CPU resources through blockchain technology, achieve supply-demand matching, reduce costs, and provide contributors with a transparent and fair incentive mechanism.

Decentralized Computing Power: An Initial Exploration of the Fusion of AI and Blockchain

Early explorations mainly targeted long-tail market demands, emphasizing flexibility and low barriers to entry. Some projects like IO.net aggregate distributed GPU resources, lowering the barriers for lightweight inference and model fine-tuning. Gensyn incentivizes individual users to participate in training through smart contracts, activating idle GPUs.

Bittensor introduces an innovative subnet structure that modularizes AI services. Users can participate in the ecosystem and earn rewards in various ways, forming a unique economic model. However, attempts at this stage have also exposed limitations, such as the pure Computing Power market falling into price wars and issues with insufficient performance.

The Rise of AI Agents: Moving Towards the Application Layer

As the decentralized Computing Power market stabilizes, the combination of Crypto and AI begins to develop towards the application layer. The emergence of on-chain AI Agents marks this transition. In the early stages, AI tokens attracted attention with entertaining images. Subsequently, AI started to perform simple tasks on social platforms, gradually evolving towards more specialized application scenarios.

Real breakthroughs come from the emergence of the Agent framework and execution protocols. Modular frameworks such as Eliza, GAME, and Rig support personality modeling, task orchestration, and multi-agent collaboration, promoting the shift of AI on-chain from point applications to systematic operations.

Some projects like Virtuals Protocol and Eliza are building an AI-native economic system. Virtuals Protocol enables autonomous interaction, collaboration, and trading among agents through the Agent Commerce Protocol (ACP). Eliza, on the other hand, has restructured the plugin system with ElizaOS V2 and launched the AUTOFUN platform to lower the barrier for creating AI Tokens.

Standardization and Collaboration: The Rise of MCP

As the market cools down, the AI Agent field is undergoing a deep reshuffle. Against this backdrop, the Model Context Protocol (MCP) has emerged as an open standard protocol for AI applications, pointing to a new direction for future development.

MCP standardizes the communication methods between large language models and external data and tools, greatly simplifying the development process of AI applications. The application ecosystem around MCP is rapidly developing, such as the DARK project in the Solana ecosystem and the SKYAI project on the BNB chain.

MCP has opened new pathways for the fusion of Crypto and AI, including multi-agent collaboration, on-chain transaction automation, and the rise of information finance (InfoFi). These developments are expected to promote the evolution of the agent economy to a higher level.

The Evolution Path of Smart Economy

The integration of Crypto and AI has undergone a development process from simple entertainment dialogue agents to tool-based agents, and then to trading execution agents and the DeFAI abstraction layer. In the future, collective intelligence and multi-agent collaboration will become important development directions.

This evolution process continues to bring AI Agents closer to real-world demands. Future developments will place greater emphasis on practicality, which may take a longer time, but is also expected to open up broader application spaces.

Development Stages and Future Prospects of AI Agents

AI Agent has gone through several key stages of development:

1. Decentralized Computing Power: Aggregating GPU resources through blockchain to provide computing power support for AI. Representative projects include Bittensor, IO.net, etc.

2. Meme Agent: An early AI token that attracts attention with an entertaining image, such as Truth Terminal.

3. Lightweight Interaction Agent: An AI that performs simple tasks on social platforms.

4. Vertical Application Agent: Focused on specific scenarios such as on-chain finance, NFTs, etc. For example, aixbt.

5. Agent Framework: A framework that supports modular development has emerged, such as Eliza, GAME, Rig, etc.

6. Agent Economy: Build an AI-native economic system. Such as Virtuals Protocol, Eliza, etc.

7. Standardized Protocols: Open standards like MCP promote multi-agent collaboration.

Future Development Direction

1. Stronger practicality: AI Agent needs to solve real problems, not just hype concepts.

2. Multi-Agent Collaboration: Achieve collaborative work between different agents through standardized protocols.

3. Deep integration with the real world: AI Agents will be more involved in decision-making and operations in the real world.

4. New Economic Models: New financial models such as InfoFi based on information flow may emerge.

5. Security and Privacy: With the widespread use of AI Agents, how to protect user data security will become a key issue.

6. Governance Innovation: Exploring new governance models involving collaboration between humans and AI.

Overall, AI Agents are gradually moving from the proof-of-concept stage to practical implementation, and will play an important role in improving efficiency and innovating business models in the future. However, they also face challenges in technology, ethics, regulation, and other areas, requiring joint efforts from all sectors of society to explore solutions.