The Integration of AI and Blockchain: Exploring the Prospects and Challenges of Combining Web3 and Artificial Intelligence

In recent years, the rapid development of artificial intelligence (AI) and blockchain technology has made AI+Crypto an investment hotspot. The decentralized, highly transparent, and low energy consumption characteristics of blockchain compensate for the centralized and opaque issues of AI systems, and the combination of the two brings new opportunities to the industry.

Industry experts believe that the combined application of AI and Blockchain can be mainly divided into four categories: as application participants, application interfaces, application rules, and application objectives. The role of AI in Crypto should be considered more from the "application" perspective, including aspects such as optimizing computing power, algorithms, and data.

Research institutions categorize AI's involvement in Crypto technology into three layers: the base layer, execution layer, and application layer. For example, zkML technology combines zero-knowledge proof and Blockchain to provide a secure and verifiable solution for AI agent behavior. Furthermore, AI has shown great potential in data processing, automated dApp development, and on-chain transaction security at the execution layer. At the application layer, AI-driven trading bots, predictive analytics tools, and AMM liquidity management play important roles in the DeFi space.

This article will explore the investment direction in the AI+Crypto space, focusing on innovations and developments in the infrastructure and application layers, and analyzing the prospects and challenges of the combination of AI and Blockchain.

Key Directions in the AI Track

Blockchain stands in stark contrast to AI in terms of centralization, transparency, energy consumption, and monopolization. Industry experts categorize the applications that combine AI and blockchain into four major types:

1. AI as a participant in applications
2. AI as an application interface
3. Rules for AI as an Application
4. AI as the goal of the application

From the perspective of productivity and production relations, Crypto mainly provides production relations. It can be considered from three directions:

1. Optimize Computing Power: Provide decentralized and efficient computing resources, reduce single point of failure risk, and enhance overall computing efficiency.
2. Optimization Algorithm: Promote the open source, sharing, and innovation of algorithms or models.
3. Optimize Data: Achieve decentralized storage, contribution, utilization, and security management of data.

AI+Web3 projects can explore three directions: the foundational layer, the execution layer, and the application layer. The foundational layer includes model training, data, decentralized computing power, and hardware; the execution layer involves data processing, transmission, and technologies such as AI agents, zkML, and FHE; the application layer mainly focuses on fields like AI+DeFi, AI+GameFi, the Metaverse, AIGC, and Meme.

The following directions are worth paying special attention to:

1. zkML Direction

zkML technology provides a secure and verifiable solution for monitoring and constraining AI agent behavior by combining zero-knowledge proofs and Blockchain. It can prove that the AI has performed specific tasks while protecting privacy, pioneering new methods for using public models to validate private data or using public data to validate private models.

Typical projects include:

1. Modulus Labs: Offers a variety of ZKML applications, such as the on-chain trading bot RockyBot and the chess game Leela vs. the World.

2. Giza: A protocol for deploying AI models on the blockchain, utilizing the ONNX format and technologies such as Giza Transpiler.

3. Zkaptcha: Focused on solving the bot problem in Web3, providing CAPTCHA services for smart contracts.

2. Data Processing Direction

The breakthroughs of AI in the execution layer are mainly reflected in the following aspects:

1. AI and On-chain Data Analysis: Utilizing LLM large models and deep learning algorithms to mine data insights.

2. AI and Automated dApp Development: Provide automated development tools to help developers quickly write smart contracts and automatically correct errors.

3. AI and On-Chain Transaction Security: Deploy AI agents on the Blockchain to enhance the security and credibility of AI applications.

3. AI + DeFi Direction

The combination of AI and DeFi mainly includes the following directions:

1. AI-driven trading bot: Quickly and accurately executes trades, analyzes market data and price trends.

2. Predictive Analysis: Provides reliable forecasts of market trends and potential price movements.

3. AMM Liquidity Management: Smartly adjusting the liquidity range to optimize the efficiency and returns of automated market makers.

4. Liquidation Protection and Debt Position Management: Implement intelligent liquidation protection strategies by combining on-chain and off-chain data.

5. Complex DeFi structured product design: relying on financial AI model to design treasury mechanisms, increasing product intelligence and flexibility.

4. AI+GameFi Direction

The application of AI in GameFi projects is mainly reflected in:

1. Game strategy optimization: Adjusting game difficulty and strategies in real-time by learning player habits.

2. Game asset utilization management: Helping players manage and trade in-game virtual assets more effectively.

3. Enhance game interaction: Create smart responsive NPCs for more natural and smooth player interactions.

Investment Strategy Recommendations

• Short-term: Focus on the early application of AI in the Crypto field, such as conceptual AI applications and memes.

• Mid-term: Focus on the combination of AI Agent and Intent, as well as the integration with smart contracts.

• Long-term: Focus on the combination of AI and zkML technology, which may have a profound impact on the Crypto field.