The Three Key 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.

Summary

1. Build an active smart agent-driven economy

Truth Terminal has confirmed the feasibility of AI agents operating on the blockchain. Experiments in this field are continually pushing the boundaries of agent operations on-chain, with immense potential and a broad design space. This has now become one of the most groundbreaking directions in the fields of encryption and AI, and this is just the beginning.

2. Enhance the capabilities of large language models in Solana development

Large language models perform exceptionally well in code writing and will further improve in the future. With these capabilities, developers' efficiency is expected to increase several times. Recently, establishing high-quality benchmarks to evaluate large language models' understanding and writing capabilities related to code will help understand their potential impact on the ecosystem. High-quality model fine-tuning schemes will be validated in benchmark tests.

3. Support open and decentralized AI technology stack

"An open and decentralized AI technology stack" includes the following key elements:

• Training data acquisition
• Training and inference computing capabilities
• Model Weight Sharing
• Model output verification capability

The importance of this open AI technology stack is reflected in:

• Accelerate model development innovation and experimentation
• Provide alternatives for users who do not trust centralized AI.

1. Create an Active Smart Agent-Driven Economy

When AI agents begin to participate in on-chain activities, a new world full of possibilities has already unfolded. Although it is currently impossible to accurately predict the future development of agents' on-chain behaviors, by observing the innovations that have already occurred, we can glimpse the vast potential of this design space:

• Some AI projects are developing new digital communities through Meme coins.
• Multiple platforms enable users to easily create and deploy smart agents and their associated tokens.
• AI fund managers trained on the personality traits of well-known encryption investors are emerging, creating a new ecosystem of AI funds and agent supporters.
• The gaming platform allows players to participate in the game by guiding agent actions, often resulting in unexpected innovative gameplay.

Future Development Direction

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. Specifically:

• Raise funds through a token fundraising platform
• Use the raised funds to pay for access to paid research materials and for the computational costs of compound simulations 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 artworks, with limitless possibilities for application scenarios.

Why does it make more sense for agents to conduct financial activities on the blockchain?

Encryption currencies have unique advantages in certain areas:

• Micropayment applications
• Speed Advantage: Instant settlement function helps agents achieve maximum capital efficiency
• Accessing the capital markets through DeFi: Agents can seamlessly mint assets, conduct trades, manage investments, perform lending operations, use leverage, etc.

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

Key Focus for Future Development

1. Risk Control Mechanism

   • Although the current model performs well, it is still far from perfect.
   • Cannot grant agents complete unrestricted freedom of action

2. Promote non-speculative use cases

   • Purchase tickets
   • Optimize the yield of stablecoin investment portfolio
   • Online food ordering

3. Development Progress Requirements

   • Must at least reach the prototype stage of the testnet
   • It is best to be running on the mainnet already.

2. Enhance the capabilities of large language models in development

Large language models have demonstrated powerful capabilities and are advancing rapidly. In their application areas, the field of code writing may see particularly steep progress curves, as this is a task that can be objectively assessed.

Today, although large language models are still not perfect in writing code and have obvious shortcomings, AI-native code editors have fundamentally changed software development. Considering the expected rapid rate of progress, these models are likely to completely transform software development.

However, there are currently several challenges that hinder large language models from achieving excellence in understanding specific development environments:

• Lack of high-quality original training data
• Insufficient build quantity
• Lack of high information value interactions
• Rapidly developing infrastructure may render old code obsolete.
• Lack of methods to assess the understanding level of the evaluation model

Key Focus for Future Development

• Help obtain better development data on the Internet
• More teams release verification builds
• More people in the ecosystem actively raise good questions and provide high-quality answers on communication platforms.
• Create high-quality benchmarks to assess the understanding level of large language models.
• Create fine-tuned models that perform well in benchmarks to accelerate developers' work efficiency.

The ultimate significant achievement will be: a brand new, high-quality, differentiated validation node client completely created by AI.

3. Support for 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 in specific application scenarios through fine-tuning.

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

1. Open-source models accelerate innovation iterations The rapid improvements and fine-tuning of open-source models by the open-source community demonstrate how the community can effectively supplement the work of large AI companies and push the boundaries of AI capabilities.

2. Provide choices for users who do not trust centralized AI. AI may be the most powerful tool in the arsenal of dictatorial or authoritarian regimes. Supporting an open-source AI tech stack can provide users with alternative options.

Multiple projects are already supporting the open AI technology stack:

• Data Collection
• Decentralized computing power
• Decentralized Training Framework

Key Focus of Future Development

I hope to build more products at all levels of the open-source AI technology stack:

• Decentralized Data Collection
• On-chain identity: Protocols that support wallet verification of human identity and protocols that verify AI API responses.
• Decentralized Training
• IP infrastructure: enabling AI to license (and pay for) the content it uses.