The Integration of AI and Web3: Opportunities and Challenges Coexist

In recent years, the rapid development of artificial intelligence ( AI ) and Web3 technology has attracted widespread global attention. AI has made significant breakthroughs in areas such as facial recognition, natural language processing, and machine learning, bringing transformation and innovation to various industries. At the same time, Web3, as an emerging network model, is changing our perception and usage of the internet. This article will explore the current status of the integration of AI and Web3, its potential value, and the challenges it faces.

1. Current Development Status of AI and Web3

The market size of the AI industry reached $200 billion in 2023, giving rise to giants like OpenAI, Character.AI, and Midjourney. The market capitalization of the Web3 industry reached $25 trillion, with projects like Bitcoin, Ethereum, and Solana emerging one after another. The combination of AI and Web3 has become a hot area of interest in both the East and the West.

2. Interaction Methods between AI and Web3

The challenges faced by the AI industry 2.1

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

1. Regarding computing power: Acquiring and managing large-scale computing power is expensive and complex, posing challenges for startups and individual developers.

2. Algorithm aspect: Deep learning requires a large amount of data and computational resources, and there are still issues with model interpretability and generalization ability.

3. Data aspect: Obtaining high-quality and diverse data is difficult, and data quality and privacy protection are also challenges.

In addition, issues such as the interpretability and transparency of AI models, as well as unclear business models, urgently need to be addressed.

The Challenges Facing the Web3 Industry

Web3 has room for improvement in data analysis, user experience, and smart contract security. AI, as a tool for enhancing productivity, has potential application prospects in these areas.

III. Analysis of the Current Status of AI + Web3 Projects

3.1 Web3 Empowers AI

3.1.1 Decentralized Computing Power

With the surge in AI demand, GPU supply cannot keep up. Some Web3 projects are attempting to offer decentralized computing power services, such as Akash, Render, Gensyn, etc. These projects incentivize users with tokens to provide idle GPU computing power, supporting AI clients with computing resources.

The supply side mainly includes cloud service providers, cryptocurrency miners, and large enterprises. The projects are divided into two categories: those for AI inference and those for AI training.

3.1.2 Decentralized Algorithm Model

Some projects like Bittensor attempt to build a decentralized AI algorithm service market, linking different AI models to provide users with more diverse options.

3.1.3 Decentralized Data Collection

Projects like PublicAI achieve decentralized data collection through token incentives, providing data support for AI training.

3.1.4 ZK Protecting User Privacy in AI

Projects like BasedAI use zero-knowledge proof technology to protect user privacy in AI applications.

3.2 AI empowers Web3

3.2.1 Data Analysis and Prediction

Many Web3 projects integrate AI services to provide data analysis and predictions, such as Pond, BullBear AI, etc.

3.2.2 Personalized Services

Platforms like Dune and Followin integrate AI to provide personalized content recommendations and search services.

3.2.3 AI Audit Smart Contracts

Projects like 0x0.ai use AI technology to audit smart contract code, enhancing security.

4. Limitations and Challenges of AI + Web3 Projects

4.1 The Real Obstacles Faced by Decentralized Computing Power

Decentralized computing power has disadvantages in terms of performance, stability, availability, and complexity. Currently, it is mainly limited to AI inference rather than training, the reason being:

1. AI training requires a huge amount of data and bandwidth, making it difficult to implement.
2. Large model training requires high stability, and the cost of interruptions is high.
3. The parallel connection of multiple cards is limited by physical distance, making it difficult to form a cluster with distributed computing power.

The combination of AI and Web3 is relatively rough.

Many projects only superficially use AI and have not achieved a deep integration with cryptocurrency. Some teams only leverage the concept of AI at the marketing level, lacking innovation.

4.3 Token economics serves as a buffer for AI project narratives.

Some projects aim to address financing difficulties, overlaying Web3 narratives and token economics. However, it remains to be seen whether token economics truly helps AI projects meet actual needs.

5. Conclusion

The integration of AI and Web3 offers new possibilities for technological innovation and economic development. AI can provide intelligent application scenarios for Web3, while Web3 offers new opportunities for the development of AI. Although it is still in the early stages and faces many challenges, there is hope for building a smarter, more open, and fair economic and social system in the future through the combination of AI's intelligent analysis and Web3's decentralized characteristics.