The Integration of Crypto and AI: From Infrastructure to Intelligent Economy

As the wave of artificial intelligence sweeps the globe, the world of cryptocurrency is also actively exploring paths for integration with AI. This process of fusion can be divided into several key stages.

Decentralized Computing Power: Initial Attempt

Initially, the combination of Crypto and AI mainly focused on the aggregation of decentralized computing resources. Characteristics of this stage include:

• Targeting the long-tail market, such as IO.net aggregating distributed GPU resources
• Emphasize flexibility, such as Gensyn activating idle GPUs through smart contracts.
• Explore new mechanisms, such as a certain network introducing model competition and subnet mechanisms.
• Combine DePIN, such as decentralized map networks and crowdsourced camera networks

Among them, a certain network modularizes AI service through an innovative subnet structure, with each subnet having its own independent community of miners and validators. Users can participate in the ecosystem and earn rewards in various ways.

However, this stage also exposes obvious limitations: the pure computing power market is highly competitive, the decentralization performance of the inference layer is insufficient, and there is a lack of application layer narratives in supply and demand matching. Crypto still remains in the role of underlying infrastructure in the AI world and has not truly connected to the user experience.

The Rise of AI Agents: Moving Towards the Application Layer

As the decentralized computing power market gradually stabilizes, the exploration of Crypto and AI has shifted from underlying resources to the application layer of intelligent agents. This transformation is marked by the rise of on-chain AI Agents, reigniting the market's expectations for the integration of Crypto and AI.

In the early stages, AI tokens mainly attracted attention through anthropomorphized and entertaining images. Subsequently, AI Agents began to possess initial interactive capabilities, performing simple tasks on social platforms. Soon, AI Agents penetrated more vertical application scenarios, such as on-chain finance, NFTs, data analysis, and other fields.

The real turning point was the emergence of the Agent framework and execution protocols. Some projects have developed modular frameworks that support personality modeling, task orchestration, and multi-agent collaboration, enabling on-chain agents to transition from isolated individuals to systematic operations. At the same time, the Agent economy began to sprout on-chain, with some projects establishing standards for autonomous token issuance, protocol collaboration, and social dissemination through AI Launchpad.

Collaboration and Standardization: The New Direction of MCP

As the market cools down, the combination of Crypto and AI is undergoing a profound reshuffle. Market sentiment is shifting from chasing narratives back to pursuing real product-market fit. In this context, MCP (Model Context Protocol), an open standard protocol designed for AI applications, has become the new catalyst most aligned with current demands.

MCP has designed a unified communication standard for AI applications, allowing any large language model to safely access external data sources and tools. The application ecosystem around MCP is rapidly emerging, with some projects already starting to use MCP to build new AI application infrastructure.

MCP opens up a new direction for the future of Crypto and AI:

• Multi-agent collaboration: Agents can be divided by function to complete complex tasks.
• On-chain transaction automation: Connecting various trading and risk control agents to improve security and efficiency.
• The Rise of InfoFi: Intelligent agents plan income paths based on user profiles, promoting new financial models.

Conclusion: The Long Evolution of the Agent Economy

The integration of Crypto and AI is a process of continuously deepening functionality and enhancing practicality. From the initial entertainment conversational agents to tool-based agents, and then to DeFAI intelligent agents, each advancement brings AI Agents closer to the demands of the real world.

In the future, the development of AI Agents will no longer rely on simple narratives, but must be based on real practicality. This path may be longer than any previous narrative cycle, but because it is supported by continuously accumulated practicality, the limits it can unlock will far exceed imagination.