Author: Revc, Golden Finance

The combination of artificial intelligence (AI) and blockchain (Web3) It is becoming an important trend, especially in the application of AI agents. AI agents realize autonomous operations in the blockchain by sensing, learning and executing tasks, giving them the potential to gradually transform from a tool for economic activities into an independent economic entity. However, it is still controversial whether the current AI Agent should focus on AI development at the application layer rather than the infrastructure layer.

This article will analyze the potential and current limitations of the combination of Web3 and AI from multiple perspectives such as productivity development, coordination of production relations, model training costs, incentive mechanisms, etc., and Explore how AI Agents can move toward a broader AI economy.

1. Infrastructure limitations of Web3

1.1 Productivity and model training cost

AI model Training is highly dependent on computing resources (computing power) and high-quality data, and the decentralized nature of Web3 makes resource integration difficult.

- Computing power limitations: Decentralized computing power platforms (such as DePIN) try to use idle computing power to provide distributed support, but their efficiency and scale are still far low On centralized platforms (such as AWS, Azure).

- Data cost and quality: The data on the chain is not enough to support large-scale AI training, and decentralized data annotation and coordination efficiency is lower than that of traditional centralized platforms.

- Hardware dependence: The production capacity of leading hardware suppliers such as NVIDIA is almost completely monopolized by OpenAI, XAI and other companies, making it difficult for Web3 infrastructure to enter this track.

1.2 Coordination costs of production relations

The core of a decentralized system lies in fairness and Transparency, but complex coordination mechanisms often increase decision-making costs.

- The design of the incentive mechanism is complex: how to price the data and computing power contributed by users, and how to distribute rewards. These issues are far from mature in Web3.

- Low coordination efficiency: compared toCentralized enterprises and Web3 organizations are slow to respond and inefficient due to their decentralization, making it difficult to adapt to rapidly changing AI needs.

2. The advantages and potential of Web3 in the application layer

2.1 Application exploration of AI Agent

AI Agent has clearer use cases and profit models in the application layer of Web3:

-Personalized scenarios: AI Agent can realize customized applications through Web3 technology, such as Decentralized finance (DeFi) assistant, on-chain game interaction, etc.

- MEME communication and community drive: AI Agent is combined with MEME economy to enhance community participation and project influence through creative narrative and social interaction.

- Autonomy and transparency: Web3 gives AI Agent digital identity and autonomous asset management capabilities, enhancing its trust among users.

2.2 Economic incentives and user growth

Web3 reduces user entry through the tokenization model Threshold:

- Wealth effect: Token issuance attracts a large amount of speculative funds and user participation.

- User participation and co-construction: users are not only consumers, but also token holders and community participants. This model increases user stickiness.

3. Challenges and transition paths for AI Agent to move towards an AI economy

3.1 Existing bubble: AI+encryption MEME

Currently, many AI Agent-related projects are only in the currency issuance and MEME dissemination stages, and their functions and practical implementation capabilities are limited.

-Lack of revolutionary features: Many AI Agents cannot go beyond simple interaction or content generation and fail to solve user pain points.

- Shortage of data and models: AI Agent still relies heavily on Web2's model training infrastructure and has not formed an independent ecosystem.

AI Agent needs at least a clear iteration path, including:

- Diversity of data model selection (currently relying on Web2 Infrastructure)

- Data sources and training evaluation mechanisms, involving user tokenized incentives and rewards

- Dynamic adjustment mechanism of rewards based on market changes (income)

- The establishment mechanism of product form and AI values ​​​​

- The quantitative evaluation mechanism of the economy, involving operation and development, etc. Dynamic adjustment mechanism of direction

- Iterative governance mechanism based on market feedback

If AI Agent cannot obtain the support of these mechanisms, and the popularity brought by the bull market and MEME may not be sustainable. The rapid growth of the market requires refined operations to consolidate the foundation of the hundreds of billions of dollars. Currently, the mechanism and product form of AI Agent are still in the process of development. It is still in its early stages, but some professional AI startups, such as UBC and the updated ELIZA, have begun to promote the upgrade of the track.

3.2 Transition path: from lightweight applications to infrastructure

AI Agent can start with lightweight applications of Web3 and gradually expand to more complex economic activities:

- Drive user growth through application scenarios: Prioritize the development of highly targeted and easy-to-promote application scenarios (such as virtual assistants, automatic trading tools)

- Combined with the MEME economy to enhance the communication effect: use MEME culture to promote project communication and community building

- Gradually build infrastructure capabilities: through distributed storage and decentralized annotation. Integrate with computing power to explore the feasibility of underlying facilities

-Achieve economic independence and ecological autonomy: empower AI Agents with autonomous decision-making and governance.capabilities to gradually transition to an AI economy.

4. Comparison between AI Agent and Web2: Advantages and Disadvantages

Web2’s centralized AI platform is efficient in resource integration, market response and technology research and development, while Web3's decentralized AI platform emphasizes user data autonomy and diversified innovation.

5.AI Agent is currently suitable for the application layer, but there are still bottlenecks in infrastructure construction

Currently, AI in the Web3 field is more suitable to focus on the exploration of the application layer rather than infrastructure construction. The decentralized nature of Web3 gives AI Agent greater autonomy and economic participation, but it is inferior to the centralized platform of Web2 in terms of resource integration, efficiency and coordination.

If AI Agent wants to move towards a more comprehensive AI economy, it needs to start with lightweight applications. The unique Web3 community atmosphere will evolve into a unique product form. , combining MEME driving force to gradually accumulate users and resources, while exploring the feasibility and efficiency improvement of decentralized infrastructure. The combination of Web3 and AI is still in its early stages, and its future development will rely on the continued drive of technological innovation and user needs.

Summary

Although the development of AI Agent in Web3 has initially achieved certain results, it still faces many challenges, especially in infrastructure construction, resource integration and model training. cost, etc. To realize the successful transition of AI Agent into an AI economy, the industry must gradually improve the decentralized infrastructure, optimize the incentive mechanism, and clearly communicate its iteration path to ensure the recognition and support of the market and community.

The combination of AI and Web3 has huge potential. In the future, AI Agent is expected to become a core component of the Web3 ecosystem, promoting the vigorous development and growth of decentralized economies.