The Integration of AI and Web3: Opportunities and Challenges

The rapid development of artificial intelligence ( AI ) and Web3 technology is leading a technological revolution. AI has made significant breakthroughs in areas such as facial recognition and natural language processing, with a market size reaching $200 billion in 2023. At the same time, blockchain-based Web3 is transforming the internet landscape, empowering users with control over their data, and its market value has reached $25 trillion. The combination of AI and Web3 has become a highly regarded innovative direction.

This article will explore the current state of AI+Web3 development, its potential value, and the challenges it faces. We will analyze the situation of current projects, delve into the limitations that exist, and provide references for relevant practitioners.

Ways AI Interacts with Web3

The development of AI and Web3 is like the two ends of a balance beam. AI enhances productivity, while Web3 transforms production relationships. What sparks might arise from their combination? Let's first analyze the dilemmas and areas for improvement that each faces, and then discuss how they can complement each other.

The dilemmas faced by the AI industry

The core elements of AI include computing power, algorithms, and data:

1. Computing Power: AI requires massive computational capacity to process data and train models. In recent years, the development of hardware such as GPUs has greatly advanced AI progress. However, acquiring and managing large-scale computing power still faces challenges in terms of cost and complexity.

2. Algorithm: AI algorithms are the core of the system, including traditional machine learning and deep learning algorithms. The choice and design of algorithms are crucial for AI performance. Continuous improvement of algorithms can enhance accuracy and generalization ability.

3. Data: Large-scale, high-quality data is the foundation for training AI models. Diverse and rich datasets help improve model performance. However, obtaining data in certain domains can be challenging.

In addition, AI also faces issues such as interpretability and transparency. The business models of many AI projects are not clear enough.

The dilemmas faced by the Web3 industry

Web3 also faces numerous challenges, including:

• Insufficient data analysis capabilities
• Poor user experience
• Smart contract security risks
• Hacker attack risk

AI, as a productivity tool, has a lot of room for development in these areas.

Analysis of the Current Status of AI+Web3 Projects

Web3 empowers AI

Decentralized Computing Power

With the surge in AI demand, resources such as GPUs are in short supply. Web3 projects provide decentralized computing power through token incentives, such as Akash, Render, and Gensyn. These projects connect idle computing power globally to support AI.

Decentralized computing power is mainly used for AI inference, rather than training. This is because training large models requires a large amount of data and high bandwidth, with strict requirements on the physical distance between computing nodes, making decentralized computing power difficult to meet. However, for lightweight tasks such as inference, decentralized computing power still has great potential.

Decentralized Algorithm Model

Some projects are attempting to build decentralized AI algorithm service markets. For instance, Bittensor attracts model contributors through token incentives, providing users with a diverse range of AI capabilities. This model may have great potential in the future AI landscape.

Decentralized Data Collection

Data is the key resource for AI. Some projects like PublicAI incentivize users to contribute data through tokens, providing richer data sources for AI training. This helps to break the data monopoly of large platforms and promotes the open development of AI.

ZK protects user privacy in AI

Zero-knowledge proof technology can achieve data verification while protecting privacy. ZKML(Zero-Knowledge Machine Learning) allows for model training and inference without disclosing the original data. This provides new ideas for addressing privacy issues in the field of AI.

AI empowers Web3

Data Analysis and Forecasting

Many Web3 projects are beginning to integrate AI services to provide data analysis and predictions. For example, Pond uses AI algorithms to predict valuable tokens, and BullBear AI helps users forecast price trends. Platforms like Numerai encourage participants to use AI to predict financial markets.

Personalized Services

AI can optimize the user experience of Web3 projects. For example, Dune's Wand tool utilizes large language models to generate SQL queries, lowering the user threshold. Some content platforms also integrate AI to summarize and recommend content.

AI Auditing Smart Contract

AI can efficiently identify vulnerabilities in smart contracts. For example, 0x0.ai offers AI smart contract auditing services, which help enhance the security of the Web3 ecosystem.

Limitations and Challenges of AI+Web3 Projects

The Real Obstacles to Decentralized Computing Power

Compared to centralized services, decentralized computing power faces challenges such as performance, stability, and ease of use. Especially in the training of large models, decentralized solutions are difficult to implement due to the strict requirements for multi-card parallelism and communication bandwidth.

The combination of AI and Web3 is not deep enough.

Currently, many projects only superficially use AI and do not demonstrate a deep integration with Web3. Some teams emphasize the concept of AI more for marketing purposes, lacking substantial innovation.

Tokenomics becomes a buffer agent

Some AI projects leverage Web3 narratives and token economics to attract users and investors. However, whether token economics truly helps address the practical needs of AI projects still requires further verification.

Summary

The integration of AI and Web3 provides vast prospects for technological innovation and economic development. AI can bring intelligent capabilities to Web3, while Web3 offers decentralized infrastructure and incentive mechanisms for AI. Although it is still in the early stages and faces numerous challenges, exploration in this field will surely drive technological progress and social change. In the future, we can expect to see more native innovations deeply integrating AI and Web3, building a smarter, more open, and fair economic and social system.