The Integration and Collision of AI and Web3: Opportunities and Challenges Coexist

In recent years, the rapid development of artificial intelligence ( AI ) and Web3 technology has attracted widespread attention globally. AI has made significant breakthroughs in areas such as facial recognition, natural language processing, and machine learning, bringing tremendous changes and innovations to various industries. In 2023, the market size of the AI industry reached $200 billion, with companies like OpenAI, Character.AI, and Midjourney leading the AI boom.

Meanwhile, Web3, as an emerging network model, is changing our understanding and usage of the Internet. Based on decentralized blockchain technology, Web3 realizes data sharing and control, user autonomy, and the establishment of trust mechanisms through functions such as smart contracts, distributed storage, and decentralized identity verification. Currently, the market value of the Web3 industry has reached $25 trillion, with projects like Bitcoin, Ethereum, and Solana attracting more and more attention.

The combination of AI and Web3 has become a hot area of interest for developers and investors in both the East and the West. This article will explore the current development status of AI+Web3, analyze the situation of current projects, and discuss the challenges and opportunities faced.

Ways AI Interacts with Web3

The development of AI and Web3 is like the two sides of a balance; AI enhances productivity, while Web3 brings about a transformation in production relations. The combination of the two may spark new innovations.

The challenges faced by the AI industry

The core elements of the AI industry include computing power, algorithms, and data. In terms of computing power, AI tasks require a large amount of computational resources, and acquiring and managing large-scale computing power is an expensive and complex challenge. Regarding algorithms, while deep learning algorithms have been successful, there are still issues such as lack of interpretability and limited generalization ability. In terms of data, obtaining high-quality and diverse data remains difficult, and data privacy and security issues must also be considered. Additionally, the interpretability and transparency of AI models are also key concerns for the public.

Challenges Facing the Web3 Industry

The Web3 industry also faces many challenges, including insufficient data analysis capabilities, poor user experience, and security vulnerabilities in smart contracts. AI, as a tool for improving productivity, has great potential in these areas.

Analysis of the Current Status of AI+Web3 Projects

Web3 empowers AI

Decentralized Computing Power

With the development of AI, the demand for GPUs has surged, leading to a situation where supply cannot meet demand. Some Web3 projects have begun to attempt to provide decentralized computing power services, such as Akash, Render, and Gensyn. These projects incentivize users to provide idle GPU computing power through tokens, offering computing power support to AI clients.

Currently, most decentralized computing projects are primarily used for AI inference rather than training. This is because AI training requires a massive amount of data and high-speed communication bandwidth, making it more challenging to achieve. In contrast, AI inference has relatively lower demands for data and bandwidth, making it easier to implement.

Decentralized Algorithm Model

Some projects attempt to establish decentralized AI algorithm service markets, such as Bittensor. These projects link multiple AI models and select the most suitable model to answer questions based on user needs.

Decentralized Data Collection

To solve the problem of obtaining AI training data, some projects utilize Web3 technology for decentralized data collection. For example, PublicAI incentivizes users with tokens to contribute and verify data, providing a more diverse source of data for AI training.

ZK protects user privacy in AI

Zero-knowledge proof technology can help address privacy protection issues in AI. ZKML(Zero-Knowledge Machine Learning) allows for the training and inference of machine learning models without disclosing the original data.

AI empowers Web3

Data Analysis and Prediction

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, while BullBear AI predicts price trends based on historical data.

Personalized Services

Some Web3 platforms integrate AI to optimize user experience. For example, Dune launched the Wand tool, which uses large language models to write SQL queries; Followin integrates ChatGPT to summarize industry trends.

AI Audit Smart Contract

AI technology is applied in smart contract auditing, such as 0x0.ai providing AI smart contract auditors to help identify potential vulnerabilities and security risks.

Limitations and Challenges of AI+Web3 Projects

The real obstacles faced by decentralized computing power

Decentralized computing products may not perform as well in terms of performance, stability, and availability compared to centralized services. At the same time, the cost for users may be relatively high. Currently, decentralized computing is mainly used for AI inference and struggles to support large-scale AI training.

The combination of AI and Web3 is relatively rough.

Many projects only superficially use AI without achieving a deep integration of AI and cryptocurrency. Some teams are more focused on marketing the concept of AI, lacking true innovation.

Token economics serves as a buffer for AI project narratives.

Some AI projects choose to overlay Web3 narratives and token economics to address business model challenges. However, whether token economics truly helps to meet practical needs remains to be investigated.

Summary

The integration of AI and Web3 offers infinite possibilities for future technological innovation and economic development. AI can provide smarter application scenarios for Web3, while the decentralized nature of Web3 also brings new opportunities for AI development. Although it is still in the early stages and faces numerous challenges, the combination of the two also brings some advantages, such as reducing dependence on centralized institutions and increasing transparency. In the future, by combining the intelligent analytical capabilities of AI with the decentralized characteristics of Web3, it is expected to build a smarter, more open, and fair economic and social system.