Three Major Development Directions of AI and Encryption Technology Integration

Currently, the intersection of AI and encryption technology is entering a rapid development stage. This article elaborates on the three key development directions of the AI + encryption integration.

1. Building an On-Chain Economy Driven by Smart Agents

The feasibility of intelligent agents operating on-chain has been validated. Experiments in this field continuously push the boundaries of agent operations on the chain, with immense potential and a wide design space. Currently, this has become one of the most groundbreaking and explosive directions in the fields of encryption and AI, and this is just the beginning.

In the future, intelligent agents can manage complex projects that require multi-party economic coordination. For example, in the field of scientific research, agents can be responsible for finding therapeutic compounds for specific diseases:

• Fundraising through token fundraising platforms
• Use the raised funds to pay for research data access fees and the computational costs for simulating compounds on a decentralized computing network.
• Recruit humans to perform experimental verification work through the bounty platform.

In addition to complex projects, agents can also perform simple tasks such as creating personal websites and producing artistic works, with limitless application possibilities.

Why do agents have an advantage in executing financial activities on the blockchain?

Cryptocurrency has unique advantages in certain fields:

• Micro Payment Applications
• Speed Advantage: The instant settlement feature helps agents achieve maximum capital efficiency.
• Entering the capital market through DeFi: agents can seamlessly mint assets, conduct transactions, invest and manage finances, engage in lending operations, and use leverage, etc.

From the perspective of technological development patterns, path dependence plays a key role. As more and more agents earn profits through encryption, encrypted connections are likely to become the core capability of agents.

key focus for future development

1. Risk control mechanism: Agents should not be given complete freedom of action without constraints.
2. Promote non-speculative use cases: such as purchasing tickets through encryption currencies, optimizing stablecoin investment portfolio returns, ordering food, etc.
3. Development progress requirements: At least reach the prototype stage of the testnet, preferably already running on the mainnet.

2. Enhance the ability of LLMs to write encryption code

Large language models have demonstrated excellent performance in code writing and are expected to improve further in the future. With these capabilities, the efficiency of encryption developers is expected to increase by 2 to 10 times. Recently, establishing high-quality benchmarks to evaluate LLMs' understanding and writing capabilities of encryption code will help understand the potential impact of LLMs on the encryption ecosystem.

However, there are still several challenges at present:

• Lack of high-quality raw training data
• Insufficient number of verification builds
• There is a lack of high-information-value interactions on the developer community platform.
• Encryption infrastructure is developing rapidly, leading to old code possibly being unsuitable for current needs.
• Lack of evaluation models to understand specific encryption projects.

Key Focus for Future Development

• Help to obtain better encryption-related data on the Internet
• Encourage more teams to publish verified builds
• Encourage more people in the ecosystem to actively ask and answer questions on the developer community platform.
• Create high-quality benchmarks to assess LLMs' understanding of encryption projects.
• Develop LLM fine-tuning models that perform well in benchmark tests to accelerate the work efficiency of encryption developers.

The final major achievement will be: a completely AI-generated, new, high-quality, differentiated validation node client.

3. Support Open and Decentralized AI Technology Stack

In the field of AI, the long-term balance of power between open-source and closed-source models remains unclear. The simplest expectation at present is to maintain the status quo—large tech companies drive cutting-edge developments, while open-source models quickly follow suit and gain unique advantages through fine-tuning in specific application scenarios.

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

1. Open-source models accelerate innovation and iteration: The rapid improvements and fine-tuning of open-source models by the open-source community demonstrate how the community effectively complements the work of large AI companies, pushing the boundaries of AI capabilities.
2. Provide choices for users who do not trust centralized AI: AI can become a powerful tool for authoritarian regimes. Supporting an open-source AI tech stack can offer alternatives for users.

key areas of future development

Hope to build more products at all levels of the open source AI technology stack:

• Decentralized Data Collection
• On-chain identity: Supports wallet protocols for human identity verification and protocols for verifying AI API responses, enabling users to confirm they are interacting with LLM.
• Decentralized Training
• IP infrastructure: enabling AI to license (and pay for) the content it uses

By supporting these open and decentralized AI technology stack elements, we can accelerate AI innovation and provide users with more choices and control.