How will AI Agents reshape the future of Web3?

Shaw, the founder of Eliza Labs, shared deep insights on the development of AI Agents in the Web3 field in a recent interview. Here are the main points from the interview:

Discussion on the Practicality of AI Agents

Shaw believes that the three most valuable types of Agents currently are:

1. Agent that allows users to make money
2. Agent who can recommend products to suitable clients
3. Agent that can save time for users

He is particularly optimistic about the Agent application in the DeFi space, believing that there are numerous arbitrage opportunities in DeFi, but many people do not know how to operate. The Agent can automatically identify arbitrage opportunities and execute trades, transferring the profits back to the user's wallet.

Shaw does not recommend using large language models directly for trading decisions; rather, LLMs should be used to convert unstructured data into structured data, such as extracting valuable trading signals from social media.

Advantages of the Eliza Framework

Shaw believes that the main advantages of the Eliza framework include:

1. Based on JavaScript/TypeScript, the developer ecosystem is vast.
2. Provide end-to-end solutions, ready to use.
3. Built-in rich plugins and functions, such as speech-to-text, PDF processing, etc.
4. Abstracted the underlying complexity, allowing developers to focus on product logic.

Non-developers can quickly create basic Agents through simple configuration. Shaw hopes that in the future, Agents will be able to autonomously write new plugins, creating a positive feedback loop.

The Vision of Open Source AGI

Shaw believes that AGI is more like a form of the internet, composed of a large number of specialized agents that collaborate to form superintelligence. Open-source and distributed development will accelerate the realization of AGI, rather than relying on a single company.

Cryptocurrency plays an important role in this vision:

1. Provide a crowdfunding mechanism, no longer relying on traditional venture capital.
2. Can accurately capture user emotional needs
3. Open-source auditability improves capital allocation efficiency
4. Agents can trade tokens directly with each other.

Token Economic Design

Shaw believes that the current Launchpad model is just the initial version, and in the future, more token value capture mechanisms will be explored.

1. Investment in technological projects that can expand ecological value
2. Buy back tokens through revenue and reinvest them.
3. Increase long-term yield pressure
4. Build an investment platform that allows the community to verify and invest in quality projects.

The goal is to create a positive cycle: the growth of token value drives ecological development, and ecological development in turn enhances token value.

Cross-Chain Development Strategy

Shaw believes that all mainstream public chains should be covered, focusing on user needs. Currently, Solana is chosen mainly because users and ecological projects are on this chain, but more public chains will be supported in the future.

Future Development of AI Agents

Shaw expects that a "high-performance Agent" capable of truly creating value will emerge soon. The Agent applications in the DeFi space have not been fully developed, and there may be significant breakthroughs in the coming month.

He also foresaw the emergence of an Agent authentication and rating system, similar to the trust scores of DEX, to enhance users' trust in Agents. Open source will become an important trust mechanism.

Overall, Shaw holds an optimistic attitude towards the application prospects of AI Agents in the Web3 field, believing that it will reshape the industry landscape and bring new user experiences and business models.