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

The rapid development of artificial intelligence ( AI ) and Web3 technology is leading a technological revolution. AI has made significant breakthroughs in areas such as facial recognition and natural language processing, with the AI industry market size reaching $200 billion in 2023. Meanwhile, Web3, based on blockchain, is reshaping the internet through decentralized technology, with the current industry market value reaching $25 trillion. The combination of AI and Web3 has become a hot topic of interest in the tech communities of both the East and West.

This article will delve into the current development status, potential value, and challenges faced by AI+Web3, providing insights for relevant practitioners and investors.

The 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 and poses challenges for startups. Regarding algorithms, training deep learning models requires vast amounts of data and computing resources, and the interpretability and robustness of the models need to be improved. In terms of data, obtaining high-quality and diverse data remains difficult, and data privacy and security issues cannot be ignored. Furthermore, the black-box nature of AI models raises public concerns about interpretability and transparency.

The challenges faced by 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 enhancing productivity, has great potential in these areas. AI can improve the data analysis and forecasting capabilities of Web3 platforms, optimize user experience, provide personalized services, and enhance security and privacy protection.

Analysis of the Current State of AI+Web3 Projects

Web3 empowers AI

Decentralized Computing Power

With the surge in AI demand, the shortage of GPUs has become a bottleneck in the industry. Some Web3 projects are attempting to provide computing power services in a decentralized manner, such as Akash, Render, and Gensyn. These projects incentivize users to contribute idle computing power through tokens, supporting AI clients. The supply side mainly includes cloud service providers, cryptocurrency miners, and enterprises with a large number of GPUs.

Decentralized computing projects are mainly divided into two categories: those used for AI inference like Render, Akash( and those used for AI training like io.net, Gensyn). AI inference has lower computing power requirements and is easier to decentralize; AI training, on the other hand, has higher requirements for computing power and bandwidth, making it more difficult to achieve.

(# Decentralized Algorithm Model

Some projects attempt to establish decentralized AI algorithm service markets, such as Bittensor. This model connects multiple AI models and selects the most suitable model to provide services based on user needs. Compared to a single large model, this approach potentially offers greater diversity and flexibility.

)# Decentralized Data Collection

Data is key for AI training, but currently most Web2 platforms prohibit the collection of data for AI training. Some Web3 projects achieve decentralized data collection through token incentives, such as PublicAI, which allows users to contribute and verify AI training data and receive token rewards.

Privacy Protection

Zero-knowledge proof technology provides new ideas for privacy protection in AI. ZKML###Zero-Knowledge Machine Learning### allows for model training and inference without revealing the original data. Projects like BasedAI are exploring the combination of FHE and LLM to achieve AI functionality while protecting privacy.

( AI empowers Web3

)# Data Analysis and Forecasting

Many Web3 projects are beginning to integrate AI services for data analysis and forecasting. For example, Pond uses AI graph algorithms to predict valuable tokens, and BullBear AI predicts price trends based on historical data. Numerai hosts AI prediction competitions for the stock market, where participants can earn token rewards.

![Newcomer Science Popularization丨In-depth Analysis: What Kind of Sparks Can AI and Web3 Ignite?]###https://img-cdn.gateio.im/webp-social/moments-8bda459009fffde5316e2118f4a0e9fa.webp###

(# Personalized Services

AI is being used to optimize the user experience of Web3 projects. For example, Dune has integrated AI-assisted SQL query functions, Followin and IQ.wiki utilize AI to summarize blockchain-related content, and NFPrompt helps users generate NFTs through AI.

)# AI Audit Smart Contract

AI is also applied in smart contract auditing. For example, 0x0.ai provides AI smart contract auditing tools that utilize machine learning to identify potential vulnerabilities in the code.

![Newcomer Science Popularization丨In-depth Analysis: What Kind of Sparks Can AI and Web3 Create?]###https://img-cdn.gateio.im/webp-social/moments-48fe2f2dc021b1b25d8d17f3a503cd7c.webp###

Limitations and Challenges of AI+Web3 Projects

( The Real Obstacles to Decentralized Computing Power

Decentralized computing power faces challenges such as performance, stability, and availability. Compared to centralized services, the performance and stability of decentralized computing power may be inferior. Moreover, decentralized computing power is currently mainly suitable for AI inference and struggles to meet the demands of large model training. This is mainly limited by:

1. Large model training requires extremely high bandwidth and stability.
2. NVIDIA NVLink technology limits the physical distance between graphics cards.
3. Distributed computing power is difficult to form an effective computing power cluster.

Therefore, the application scenarios of decentralized computing power may be more suitable for AI inference, training of small to medium-sized models, and edge computing.

) The combination of AI and Web3 is not deep enough.

Currently, many AI + Web3 projects are merely superficial combinations and have not achieved true deep integration. The application of AI often remains at the level of efficiency improvement, lacking native integration with cryptocurrency. Some projects even use the concept of AI purely for marketing purposes, lacking substantial innovation.

Tokenomics becomes a buffer.

Faced with difficulties in business models, some AI projects are turning to Web3 for support in token economics. However, whether token economics truly helps to address the practical needs of AI projects or merely serves as a short-term hype tactic is worth pondering.

![Newbie Science Popularization丨In-depth Analysis: What Kind of Sparks Can AI and Web3 Create?]###https://img-cdn.gateio.im/webp-social/moments-324da84c0f2e8d100ca49ed2f72c7cac.webp###

Summary

The integration of AI and Web3 provides infinite possibilities for technological innovation and economic development. AI can bring intelligent analysis and decision-making capabilities to Web3, while Web3 provides decentralized infrastructure and new incentive mechanisms for AI. Although the integration is still in its early stages and faces many challenges, in the long run, this combination is expected to build a more intelligent, open, and fair economic and social system.

In the future, we look forward to seeing more innovative projects that deeply integrate AI and Web3, truly leveraging the synergistic advantages of both to create real value for users and the industry. At the same time, we need to cautiously view the current trend, addressing practical needs while pursuing innovation, and promoting the healthy development of technology and applications.