The Integration of AI and Web3: Current Analysis and Future Prospects

1. Introduction

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 enormous transformations and innovations to various industries. As an emerging internet model, Web3 is changing our understanding and usage of the internet.

The AI industry reached a market size of $200 billion in 2023, with giants like OpenAI, Character.AI, and Midjourney rapidly emerging. The market value of the Web3 industry reached $25 trillion, with projects like Bitcoin, Ethereum, and Solana continuously emerging. The combination of AI and Web3 has become a focal area of interest for builders and VCs from both the East and the West.

This article will explore the current state, potential value, and impact of AI+Web3. We will analyze the current status of projects, the limitations and challenges they face, and provide insights for investors and practitioners.

2. The Interaction Between AI and Web3

The Dilemmas Facing the AI Industry 2.1

The core elements of the AI industry are computing power, algorithms, and data.

1. Computing Power: AI tasks require a large amount of computing resources, and acquiring and managing large-scale computing power is costly. This is especially difficult for startups and individual developers.

2. Algorithm: Although deep learning algorithms have achieved great success, there are still issues. Training requires a large amount of data and computational resources, the interpretability of the models is insufficient, and their robustness and generalization capabilities need to be improved.

3. Data: It is difficult to obtain high-quality and diverse data. In certain areas, data is hard to acquire, and data quality and labeling are also issues. Protecting data privacy and security is also an important consideration.

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

2.2 The Challenges Facing the Web3 Industry

The Web3 industry still has room for improvement in areas such as data analysis, user experience, and smart contract security. AI, as a tool to increase productivity, has great potential in these fields.

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

3.1 Web3 empowers AI

3.1.1 Decentralized Computing Power

With the explosion of AI demand, GPUs are in short supply. Some Web3 projects are attempting to provide decentralized computing power services through token incentives, such as Akash, Render, and Gensyn.

These types of projects incentivize users to provide idle GPU computing power through tokens, offering computing support for AI clients. The supply side mainly includes cloud service providers, cryptocurrency miners, and large enterprises.

The project is mainly divided into two categories: one is for AI inference ( such as Render, Akash ), and the other is for AI training ( such as io.net, Gensyn ). The core difference lies in the different computational power requirements.

As a representative project, io.net currently has over 500,000 GPUs, integrating Render and Filecoin computing power, with the ecosystem continuously developing.

3.1.2 Decentralized Algorithm Model

Taking Bittensor as an example, the supply side of the algorithm model contributes machine learning models to the network and receives token rewards. The network uses a consensus mechanism to ensure the best answers. The token TAO is used to incentivize miners to contribute algorithm models and for users to pay usage fees.

3.1.3 Decentralized Data Collection

Decentralized data collection achieved through token incentives. For example, PublicAI allows users to collect AI data on social media and receive token rewards. Ocean collects user data services for AI through data tokenization.

3.1.4 ZK protection of user privacy in AI

Zero-knowledge proof technology can achieve information verification while protecting privacy. ZKML allows for model training and inference without revealing the original data. BasedAI proposes integrating FHE with LLM to protect data privacy.

3.2 AI Empowering Web3

3.2.1 Data Analysis and Forecasting

Many Web3 projects integrate AI services to provide data analysis and predictions. For example, Pond uses AI algorithms to predict valuable tokens, BullBear AI forecasts price trends, and Numerai hosts AI prediction competitions for the stock market.

3.2.2 Personalized Services

Web3 projects integrate AI to optimize user experience. For example, Dune launched the Wand tool to generate SQL queries using AI, Followin and IQ.wiki integrated ChatGPT to summarize content, and NFPrompt uses AI to generate NFTs to reduce creation costs.

3.2.3 AI Audit Smart Contracts

AI can more efficiently and accurately identify vulnerabilities in smart contract code. For example, 0x0.ai provides an AI smart contract auditor that uses machine learning to identify potential issues.

4. Limitations and Challenges of AI + Web3 Projects

4.1 The Real Obstacles Facing Decentralized Computing Power

1. Performance and stability may not be as good as centralized computing power products.
2. Availability is affected by the degree of supply and demand matching.
3. High technical complexity, high user entry barriers
4. Currently mainly limited to AI inference, making large-scale AI training difficult.

Cause Analysis:

• AI training requires a huge amount of data and bandwidth, which is difficult to meet in a decentralized manner.
• Large model training requires high stability, and the cost of interruptions is high.
• The advantages of NVIDIA NVLink multi-card communication are obvious, and decentralization is difficult to achieve.

The combination of AI and Web3 is relatively rough.

1. Most projects only superficially use AI and have not achieved deep integration.
2. Many teams only utilize AI concepts at the marketing level, lacking innovation.

4.3 Token economics becomes a buffer for the narrative of AI projects

1. Some projects leverage Web3 narratives and token economics to promote user participation.
2. Whether token economics truly addresses the actual needs of AI projects is debatable.
3. Most projects have not yet reached the practical stage.

5. Conclusion

The integration of AI and Web3 offers limitless 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 the projects are still in the early stages and face numerous challenges, they also bring advantages such as decentralization and transparency.

In the future, the combination of AI and Web3 is expected to build a more intelligent, open, and fair economic and social system. The key lies in in-depth research and innovation to achieve a close integration of AI and cryptocurrencies, creating native and meaningful solutions in various fields.