The next stop for AI Agents: the transition from intelligent agents to economies

Author: Revc, Golden Finance

The combination of artificial intelligence (AI) and blockchain (Web3) is becoming an important trend, especially in the application of AI agents. AI agents achieve autonomous operation in the blockchain through perception, learning and execution of tasks, giving them the potential to gradually transform from tools of economic activities into independent economic entities. However, there is still controversy as to whether current AI agents 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, including productivity development, coordination of production relations, model training costs, incentive mechanisms, and explore how AI agents can move towards a broader AI economy.

1. Infrastructure limitations of Web3

1.1 Productivity and model training costs

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) attempt to use idle computing power to provide distributed support, but their efficiency and scale are still far lower than centralized platforms (such as AWS, Azure).

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

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

1.2 Coordination costs of production relations

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

- Complex incentive mechanism design: How to price the data and computing power contributed by users, how to distribute rewards, these issues are far from mature in Web3.

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

2. Advantages and potential of Web3 at the application layer

2.1 Application exploration of AI Agent

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

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

- MEME communication and community drive: AI Agent combines 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 to enhance its trust among users.

2.2 Economic Incentives and User Growth

Web3 lowers the user entry threshold through the tokenization model:

- Wealth effect: Token issuance attracts a lot 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 AI economy

3.1 Existing bubble: AI+encrypted MEME

Currently, many AI Agent-related projects are only at the stage of coin issuance and MEME dissemination, and their functions and actual landing 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 Agents still heavily rely on the model training infrastructure of Web2 and have 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)

- Evaluation mechanism for data sources and training, involving tokenized incentives and rewards for users

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

- Mechanism for establishing product forms and AI values

- Quantitative evaluation mechanism for the economy, involving dynamic adjustment mechanisms in operation and development

- Iterative governance mechanism based on market feedback

If AI Agent cannot obtain the support of these mechanisms, 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 $100 billion track. At present, the mechanism and product form of AI Agent are still in the 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 with application scenarios: Prioritize the development of application scenarios that are highly targeted and easy to promote (such as virtual assistants, automatic trading tools).

- Combine MEME economy to enhance the dissemination effect: Use MEME culture to promote project dissemination and community building.

- Gradually build infrastructure capabilities: Explore the feasibility of underlying facilities through distributed storage, decentralized annotation and computing power integration.

- Achieve economic independence and ecological autonomy: Give AI Agents autonomous decision-making and governance capabilities, so that they can gradually transition to an AI economy.

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

Web2's centralized AI platform is highly 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, and infrastructure construction still has bottlenecks

Currently, AI in the Web3 field is more suitable for focusing on application layer exploration 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, evolve a unique product form with the unique Web3 community atmosphere, gradually accumulate users and resources with the driving force of MEME, and explore 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 continuous drive of technological innovation and user needs.

Summary

Although the development of AI Agent in Web3 has achieved certain results, it still faces many challenges, especially in infrastructure construction, resource integration and model training costs. To achieve the successful transition of AI Agent to 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 the community.

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