The Integration of AI and Blockchain: Exploring the Prospects and Challenges of the Combination of 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, low-energy consumption, and anti-monopoly characteristics of blockchain complement AI systems, bringing us new opportunities.

Industry experts believe that the combined application of AI and Blockchain can be mainly divided into four categories: as application participants, interfaces, rules, and 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 classify the application of AI in Crypto into three layers: the foundational layer, the execution layer, and the application layer. Opportunities worth exploring exist at each level. For example, zkML technology combines zero-knowledge proofs and Blockchain, providing secure, verifiable, and transparent solutions for AI agent behavior. In addition, AI demonstrates great potential in data processing, automated dApp development, and on-chain transaction security at the execution level. In 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 in detail the investment directions of the AI+Crypto sector, focusing on innovations and developments at both the infrastructure and application levels, and analyzing the prospects and challenges of the integration of AI and Blockchain from the perspective of medium to long-term investment strategies.

Key Directions in the AI Track

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

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

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

1. Optimize computing power: Provide decentralized and efficient computing resources to reduce single point of failure risks and improve overall computing efficiency.
2. Optimization Algorithm: Promote the open source, sharing, and innovation of algorithms or models.
3. Optimize data: achieve decentralized storage, contribution, usage, and security management of data.

AI+Web3 projects can explore from three directions: the infrastructure layer, the execution layer, and the application layer.

• Foundation Layer: Includes model training, data, decentralized computing power, and hardware, with a focus on the integration of zk technology and ML technology.
• Execution Layer: Involves data processing, transmission, as well as model-level technologies such as AI agents, zkML, FHE, etc.
• Application Layer: Focuses mainly on AI+DeFi, AI+GameFi, Metaverse, AIGC, and Meme, as well as blockchain-level RAAS, Oracles, Co-processors, UBI, etc.

Among them, projects in the infrastructure layer and application layer are developing rapidly, such as Io.net in the computing power layer, Flock in the foundational model layer, ZeroGravity in blockchain infrastructure, Myshell for AI agents, and 0xScope in the application layer.

Key Exploration Directions

1. zkML direction

zkML technology provides a secure, verifiable, and transparent solution for monitoring and constraining AI agent behavior by combining zero-knowledge proofs and Blockchain. It can verify the execution of specific tasks by AI while protecting privacy, making smart contracts more flexible and adaptable to more application scenarios.

Typical projects include:

1. Modulus Labs: A diversified zkML project, building on-chain AI application examples.
2. Giza: A protocol that allows for the deployment of AI models on the blockchain, providing an alternative path for the development of on-chain AI.
3. Zkaptcha: Focused on robot issues 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: Utilize AI technology to deeply mine blockchain data and gain more insights.
2. AI and Automated dApp Development: Using AI 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.

Project Case: SeQure, a security platform that utilizes AI for real-time monitoring and analysis.

Three, AI + DeFi Direction

The combination of AI and DeFi is mainly reflected in the following aspects:

1. AI-driven trading bots: execute trades quickly and accurately, analyze market data.
2. Predictive Analysis: Provides reliable predictions of market trends and potential price movements.
3. AMM Liquidity Management: Smartly adjust the liquidity range to optimize AMM efficiency and returns.
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 models to design treasury mechanisms, increasing product intelligence and flexibility.

IV. AI + GameFi Direction

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

1. Game strategy optimization: Adjusting game difficulty and strategies by learning player habits.
2. Game Asset Utilization Management: Helping players effectively manage and trade virtual assets within the game.
3. Enhance game interaction: Create smart responsive NPCs to improve game immersion.

Investment Strategy Analysis

• Short-term: Focus on the areas where AI first lands in Crypto, 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 could ultimately have a profound impact on the Crypto space.

The AI Agent, as a subfield, is considered the AI area closest to large-scale applications. From a narrative perspective, the AI Agent can be compared to a sexy and attractive woman, while GPU cloud computing power resembles a stable and mature middle-aged entrepreneur, and the AI large model combined with the DA layer is like a disheveled scientist.