The Integration of AI and Web3: Current Status, Challenges, and Future

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

At the same time, Web3, as an emerging network model, is changing people's understanding and usage of the internet. Web3 is based on decentralized blockchain technology and achieves data sharing and user autonomy through features such as smart contracts, distributed storage, and decentralized identity verification. The market value of the Web3 industry has reached $25 trillion, with projects like Bitcoin, Ethereum, and Solana attracting significant attention.

The combination of AI and Web3 has become a key area of focus for developers and investors from both Eastern and Western backgrounds. This article will explore the current state of AI+Web3 development, its potential value, and the challenges it faces, providing references for related practitioners.

Interaction between AI and Web3

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, acquiring and managing large-scale computing resources is costly, posing challenges for startups and individual developers. Regarding algorithms, training deep learning models requires a substantial amount of data and computational resources, and there are also issues related to the interpretability and generalization ability of the models. In terms of data, obtaining high-quality and diverse data remains difficult, and data privacy and security are also important considerations. Furthermore, issues such as the interpretability and transparency of AI models and unclear business models are also hindering the development of the AI industry.

The challenges faced by the Web3 industry

The Web3 industry still has room for improvement in data analysis, user experience, and smart contract security. AI, as a tool for enhancing productivity, has significant potential in these areas. For example, AI can help analyze on-chain data, improve user interfaces, and audit smart contract code.

Analysis of the Current Status of AI+Web3 Projects

Web3 empowers AI

1. Decentralized Computing Power

Decentralized computing power projects such as Akash, Render, and Gensyn incentivize users to provide idle GPU computing power through tokens, supporting AI with computational resources. The supply side mainly includes cloud service providers, cryptocurrency miners, and enterprises with large amounts of GPUs. These projects are divided into two categories: those used for AI inference ( such as Render and Akash ), and those used for AI training ( such as io.net and Gensyn ).

2. Decentralized Algorithm Model

Decentralized algorithm model projects like Bittensor aim to establish a decentralized AI algorithm service market, connecting different AI models to provide services for users. This model has the potential to create a diverse AI ecosystem.

3. Decentralized Data Collection

Projects like PublicAI incentivize users to contribute and verify AI training data through tokens. Ocean collects user data through data tokenization, while projects like Hivemapper and Dimo are also collecting decentralized data in their respective fields.

4. Zero-Knowledge Proofs Protect User Privacy in AI

ZKML(Zero-Knowledge Machine Learning) technology allows for the training and inference of machine learning models without revealing the original data. Projects like BasedAI are exploring the combination of FHE and LLM to protect user privacy.

AI empowers Web3

1. Data Analysis and Prediction

Many Web3 projects are starting to integrate AI services to provide data analysis and predictions. For example, Pond uses AI graph algorithms to predict valuable tokens, BullBear AI predicts price trends based on historical data, and Numerai hosts AI investment competitions.

2. Personalized Services

Some Web3 platforms integrate AI to optimize user experience. For example, Dune's Wand tool helps users generate SQL queries using natural language, Followin and IQ.wiki use ChatGPT to summarize content, and NFPrompt assists users in generating NFTs with AI.

3. AI Audit Smart Contracts

Projects like 0x0.ai provide AI smart contract auditing tools that use machine learning technology to identify potential vulnerabilities in the code.

Limitations and Challenges of AI+Web3 Projects

1. The Real Obstacles Facing Decentralized Computing Power

Decentralized computing products may not perform as well as centralized products in terms of performance, stability, and ease of use. Currently, they are mainly limited to AI inference rather than training, as large model training requires extremely high bandwidth and stability. NVIDIA's advantages in single-card computing power and multi-card parallel ( NVLink ) make it difficult for decentralized computing to achieve large model training.

2. The combination of AI and Web3 is relatively rough.

Many projects only superficially use AI, lacking deep integration and innovation with cryptocurrency. Some projects exploit the concept of AI more at the marketing level, with limited actual innovation.

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

Some AI projects may choose to overlay Web3 narratives and token economics because they find it difficult to develop in Web2. The key is whether the token economics truly helps to address real needs, rather than being merely short-term speculation.

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, such as data analysis, smart contract auditing, and personalized services. Web3, on the other hand, provides new development opportunities for AI, such as decentralized computing power, algorithm sharing, and data collection.

Although AI + Web3 projects are still in their early stages and face many challenges, they also bring many advantages. Decentralized solutions can reduce reliance on centralized institutions, enhance transparency and auditability, and promote broader participation and innovation.

In the future, with advancements in technology and deeper research, we can expect to see a closer integration of AI and Web3, creating more meaningful native solutions in fields such as finance, governance, and prediction markets. By combining AI's intelligent analytical decision-making capabilities with Web3's decentralization and user autonomy, it is hoped to build a smarter, more open, and fair economic and social system.