The Integration of AI and Web3: Opportunities and Challenges

1. Introduction

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 tremendous changes to various industries. In 2023, the market size of the AI industry reached $200 billion, with giants like OpenAI and Character.AI leading the trend.

At the same time, Web3, as an emerging network model, is changing people's understanding and usage of the internet. Web3 is based on blockchain technology and achieves data sharing and control through smart contracts, distributed storage, etc., giving users control over their data. Currently, the market value of the Web3 industry has reached 25 trillion, with projects like Bitcoin, Ethereum, and many others emerging continuously.

The combination of AI and Web3 has become a focal point of interest in both the East and the West. How to integrate the two is a question worth exploring. This article will discuss the current status, potential value, and impact of AI+Web3, providing references for investors and practitioners.

2. Interaction Methods Between AI and Web3

The development of AI and Web3 is like two sides of a balance, with AI enhancing productivity and Web3 transforming production relations. We will analyze the dilemmas and opportunities for improvement faced by both, and explore how they can address these issues together.

2.1 Challenges Facing the AI Industry

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

1. Computing Power: AI tasks require strong computing capabilities to process large amounts of data. In recent years, the development of hardware such as GPUs has driven the increase in computing power, with Nvidia holding a large market share.

2. Algorithm: The core of the AI system, including traditional machine learning and deep learning algorithms. The choice and design of algorithms are crucial to AI performance, and continuous innovation can improve accuracy and generalization ability.

3. Data: AI systems train models by learning patterns and rules from the data. Rich datasets help improve the accuracy and generalization ability of the models.

The main dilemmas faced by AI include:

• The cost of acquiring and managing computing power is high, posing challenges especially for startups and individual developers.

• Deep learning algorithms require a large amount of data and computational resources, and the interpretability of the models is insufficient.

• Difficulties in obtaining high-quality, diverse data; in some fields, data is sensitive and hard to acquire.

• The black box nature of AI models has raised public concern, as certain applications require explainable and traceable decision-making processes.

• Many AI projects have unclear business models, leaving entrepreneurs feeling confused.

2.2 Challenges Facing the Web3 Industry

The Web3 industry also has many problems that need to be solved:

• Data analysis capabilities need improvement
• The product user experience is poor
• The risk of vulnerabilities in smart contract code and hacker attacks is high.

AI, as a tool to enhance productivity, has great potential in these areas:

1. Data Analysis and Prediction: AI can extract valuable information from massive amounts of data, providing more accurate predictions and decision support for fields such as DeFi.

2. User Experience Optimization: AI can analyze user data, provide personalized recommendations and customized services, enhancing user experience.

3. Enhanced Security: AI can be used to detect network attacks, identify abnormal behavior, and provide stronger security guarantees.

4. Privacy Protection: AI can be applied to data encryption and privacy computing to protect users' personal information.

5. Smart Contract Audit: AI can achieve automated contract auditing and vulnerability detection, enhancing contract security.

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

AI+Web3 projects mainly approach from two directions: leveraging blockchain technology to enhance the performance of AI projects, and using AI technology to serve Web3 projects. Numerous projects like Io.net, Gensyn, Ritual, etc. are exploring this path.

3.1 Web3 Empowers AI

3.1.1 Decentralized Computing Power

The emergence of ChatGPT has sparked a wave of interest in AI, which has also led to a GPU shortage issue. To address this problem, some Web3 projects such as Akash, Render, and Gensyn are attempting to provide decentralized computing power services. These projects incentivize users to provide idle GPU computing power through tokens, offering support to AI clients.

The supply side mainly includes:

1. Cloud Service Providers: Large Cloud Service Providers and GPU Cloud Service Providers
2. Cryptocurrency Miners: Idle GPU Computing Power
3. Large Enterprises: Strategic Layout for Purchasing Idle GPUs

Decentralized computing projects are divided into two categories:

1. For AI inference: such as Render, Akash, Aethir, etc.
2. Used for AI training: such as io.net, Gensyn, etc.

These projects attract suppliers and users through token incentives, forming a virtuous cycle. The value of the tokens is aligned with the growth of participants, attracting more involvement.

3.1.2 Decentralized Algorithm Model

The decentralized algorithm model network is an AI algorithm service marketplace that connects different AI models. When users ask questions, the marketplace selects the most suitable model to respond.

Compared to single models like ChatGPT, decentralized algorithm networks like Bittensor have greater potential. They allow multiple models to collaborate and provide optimal solutions for different problems.

3.1.3 Decentralized Data Collection

Data is crucial for training AI models. However, Web2 platforms often prohibit the collection of data for AI training or sell user data without sharing profits.

Some Web3 projects achieve decentralized data collection through token incentives. For example, PublicAI allows users to contribute and verify data to earn token rewards. Other projects like Ocean and Hivemapper are also exploring similar models.

3.1.4 ZK Protection of User Privacy in AI

Zero-knowledge proof technology can resolve the conflict between privacy protection and data sharing. ZKML allows for model training and inference without disclosing the original data.

Projects like BasedAI are exploring the integration of FHE with LLM to protect user data privacy. This opens up new possibilities for AI applications in sensitive fields such as healthcare and finance.

3.2 AI empowers Web3

The support of AI for the Web3 industry is mainly reflected in:

3.2.1 Data Analysis and Forecasting

Many Web3 projects integrate AI services to provide users with data analysis and predictions. For example, Pond uses AI algorithms to predict valuable tokens, BullBear AI predicts price trends. Numerai hosts AI prediction competitions for the stock market, and Arkham provides on-chain data analysis.

3.2.2 Personalized Services

Web3 projects optimize user experience by integrating AI. For example, Dune's Wand tool uses large language models to write SQL queries, Followin and IQ.wiki integrate ChatGPT to summarize content, and NFPrompt uses AI to simplify NFT creation.

3.2.3 AI Audit Smart Contract

AI can efficiently and accurately identify vulnerabilities in smart contract code. Projects like 0x0.ai provide AI smart contract auditing tools that use machine learning to identify potential issues.

In addition, there are projects such as PAAL that help create personalized AI Bots and Hera that provides AI-driven multi-chain DEX aggregators, all of which support the development of Web3 from a tool perspective.

4. Limitations and Challenges of AI+Web3 Projects

4.1 The Real Obstacles Facing Decentralized Computing Power

Decentralized computing projects face the following challenges:

1. Performance and Stability: Distributed nodes may experience latency and instability.

2. Resource matching: Imbalance between supply and demand may lead to resource shortages or inability to meet demand.

3. Complexity of Use: Users need to understand knowledge such as distributed networks and smart contracts.

4. Difficult to use for AI training: Large model training requires a huge amount of data and bandwidth, and decentralized computing power is hard to meet the requirements.

5. Nvidia's advantages are hard to surpass: the CUDA software ecosystem and NVLink multi-card communication are key.

Decentralized computing power is currently mainly suitable for AI inference and small model training, and it is difficult to achieve large model training.

The combination of AI and Web3 is relatively rough.

Currently, AI+Web3 projects face the following issues:

1. Surface Applications: Most projects only use AI to improve efficiency in a simple way, lacking deep integration.

2. Marketing Hype: Some projects only apply AI in limited areas, over-promoting the concept.

3. Insufficient Innovation: Lack of innovative solutions that integrate AI with native cryptocurrency.

4.3 Token economics becomes a buffer for AI project narratives

Many AI + Web3 projects use token economics as a means to attract users and raise funds, but they may not truly address actual needs. Projects need to more solidly meet real-world scenarios, rather than just creating short-term hype.

V. Conclusion

The integration of AI and Web3 offers limitless possibilities for technological innovation and economic development. AI can provide Web3 with intelligent analysis, predictions, and personalized services, enhancing user experience and security. Web3, in turn, offers AI a decentralized computing power, data, and algorithm-sharing platform.

Although it is still in the early stages and faces many challenges, the combination of AI and Web3 also brings numerous advantages. Decentralized computing power and data collection can reduce dependence on centralized institutions, improving transparency and innovation. In the future, through the deep integration of AI's intelligent decision-making and Web3's decentralized characteristics, it is expected to build a smarter, more open, and fair economic and social system.