Three Key Development Directions for the Integration of AI and Encryption Technology

Recently, the combination of AI and encryption technology is experiencing a rapid development period. This article explores three important directions of this integration.

1. Building an Active Smart Agent Economic Ecosystem

The feasibility of smart agents operating on the chain has been verified. This field is continuously breaking innovation boundaries, showcasing tremendous potential and vast design space. It has become one of the most groundbreaking directions in AI and encryption, and this is just the beginning.

In the future, intelligent agents are expected to manage complex multi-party collaborative projects. For example, in the field of scientific research, agents could be responsible for finding treatment solutions for specific diseases:

• Raise funds through a token fundraising platform
• Use funds to pay for access to research materials and simulation costs for decentralized computing networks.
• Recruit humans to conduct experimental verification through the bounty platform.

In addition to complex projects, agents can also perform simple tasks such as building personal websites and creating artworks, with unlimited application scenarios.

The advantages of using encryption currency systems in intelligent agents include:

• Convenience of micro payments
• Instant settlement, improving capital efficiency
• Seamlessly conduct various financial activities through DeFi

As more and more agents generate profits through encryption, encrypted connections are likely to become a core capability for agents.

2. Enhance the capability of AI to write Solana code

Large language models have already shown excellent performance in code writing, and will further improve in the future. With these capabilities, the efficiency of Solana developers is expected to increase by 2-10 times.

However, there are still some challenges at present:

• Lack of high-quality original training data
• Insufficient build quantity
• There is a lack of high-value interaction at the technical forum.
• Solana infrastructure iterates quickly, and old code may not be applicable.
• Lack of assessment models for understanding the degree of Solana.

Future development directions include:

• Improve the quality of online Solana-related data
• Encourage more teams to release verified builds.
• Provide high-quality Q&A on technical forums
• Create a benchmark test to evaluate AI's understanding of Solana.
• Develop AI models that perform excellently in benchmark tests.

The long-term goal is to achieve a high-quality Solana validator client completely created by AI.

3. Support for Open and Decentralized AI Technology Stack

The long-term development landscape of open-source and closed-source AI models remains unclear. Currently, it is expected that large technology companies will drive cutting-edge development, while open-source models will quickly follow and gain advantages in specific scenarios.

The importance of supporting an open AI technology stack is reflected in:

1. Accelerate innovation iteration: The open source community can effectively supplement the work of large AI companies and push the boundaries of AI capabilities.

2. Providing choices for users: In the face of the risk that AI may be used as a control tool, the open-source AI technology stack offers alternatives.

Multiple projects within the Solana ecosystem are already supporting the open AI technology stack, including data collection, decentralized computing power, and decentralized training frameworks.

In the future, it is expected to build more products at various levels of the open-source AI technology stack, such as decentralized data collection, on-chain identity verification, decentralized training, and IP infrastructure.