The Integration of AI and Web3: Opportunities and Challenges

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 tremendous changes to 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 our understanding and usage of the Internet. Based on blockchain technology, Web3 achieves data sharing and user autonomy through smart contracts, distributed storage, and decentralized identity verification. Currently, the market value of the Web3 industry has reached $25 trillion, with projects such as Bitcoin, Ethereum, and Solana emerging one after another.

The combination of AI and Web3 has become a hot topic of interest for developers and investors in both the East and West. This article will explore the current status of AI+Web3, its potential value, and the challenges it faces, providing references for investors and practitioners.

Interaction Methods between AI and Web3

The development of AI and Web3 is like the two sides of a balance scale; AI enhances productivity, while Web3 transforms production relationships. What sparks might arise from their combination? Let's analyze the dilemmas and areas for improvement that each faces and discuss how they can mutually assist each other.

The challenges faced by the AI industry

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

1. Computing Power: AI tasks require large-scale computation and processing capabilities. In recent years, advancements in hardware technologies such as GPUs have greatly propelled the development of AI. However, acquiring and managing large-scale computing power remains an expensive and complex challenge, especially for startups and individual developers.

2. Algorithms: AI algorithms include traditional machine learning and deep learning algorithms. The choice and design of algorithms are crucial for the performance of AI systems. Continuously improving innovative algorithms can enhance system accuracy and generalization ability. However, training deep neural networks requires a large amount of data and computing resources, and there are still issues with model interpretability and robustness.

3. Data: A rich and diverse dataset is the foundation for training and optimizing AI models. However, obtaining high-quality data still faces challenges. In certain fields, data is difficult to obtain, and there are issues with data quality, accuracy, and labeling. At the same time, protecting data privacy and security is also an important consideration.

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

The Dilemmas Facing the Web3 Industry

The Web3 industry also faces many challenges, including data analysis, user experience, smart contract security, and more. AI, as a tool to enhance productivity, has great potential in these areas.

1. Data Analysis and Prediction: Web3 platforms require more efficient and intelligent data analysis and prediction capabilities, especially in areas like DeFi.

2. User Experience: The user experience of Web3 applications still needs improvement and requires more intelligent personalized services.

3. Security: Vulnerabilities in smart contract code and hacker attacks are major security issues faced by Web3.

4. Privacy Protection: Achieving data sharing and value creation while protecting user privacy is a significant challenge.

Analysis of the Current Status of AI+Web3 Projects

Currently, AI+Web3 projects mainly approach from two directions: leveraging blockchain technology to enhance AI project performance, and using AI technology to serve Web3 projects.

Web3 empowers AI

Decentralized Computing Power

With the explosion of AI, the demand for computing power such as GPUs has surged, leading to a pressing issue of supply shortages. Some Web3 projects are attempting to provide decentralized computing power services through token incentives, such as Akash, Render, Gensyn, and others.

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

Decentralized computing projects are mainly divided into two categories:

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

The former attracts users to provide computing power through token incentives, forming the demand side of the computing power network service. The latter, such as Gensyn, promotes the allocation and rewards of machine learning tasks through smart contracts.

Decentralized Algorithm Model

In addition to computing power, some projects are trying to build a decentralized AI algorithm service market. Bittensor, for example, connects several different AI models to provide answers based on the most appropriate model for the user's question.

In the Bittensor network, model providers ( and miners ) contribute machine learning models and receive token rewards. The network uses a unique consensus mechanism to ensure the best answers.

Decentralized Data Collection

For AI model training, a large supply of data is essential. However, most Web2 companies still claim user data as their own. Some Web3 projects achieve decentralized data collection through token incentives.

PublicAI allows users to contribute valuable content and verify data, and earn token rewards. This promotes a win-win relationship between data contributors and the AI industry development.

ZK protects user privacy in AI

Zero-knowledge proof technology can achieve information verification while protecting privacy. ZKML(Zero-Knowledge Machine Learning) allows machine learning model training and inference to be conducted without disclosing the original data through zero-knowledge proofs.

The field is still in its early stages, for example, BasedAI has proposed a decentralized method that integrates fully homomorphic encryption ( FHE ) with large language models ( LLM ) to protect user data privacy.

AI empowers Web3

Data Analysis and Prediction

Many Web3 projects have begun to integrate AI services for data analysis and prediction. For example, Pond predicts valuable tokens using AI algorithms; BullBear AI makes price predictions based on historical data and market trends; Numerai hosts investment competitions for AI predictions in the stock market, etc.

Personalized Service

Some Web3 projects use AI to optimize user experience. For example, Dune's Wand tool utilizes large language models to write SQL queries; the Web3 media platform Followin integrates ChatGPT to summarize industry trends; NFPrompt makes it easier for users to create NFTs through AI.

AI Audit Smart Contract

AI is also applied in smart contract auditing. For example, 0x0.ai provides an AI smart contract auditor, using machine learning technology to identify potential issues in the code. This helps to improve the security and reliability of contracts.

Limitations and Challenges of AI+Web3 Projects

The Real Obstacles Facing Decentralized Computing Power

1. Performance and Stability: Decentralized computing power relies on globally distributed nodes, which may experience latency and instability.

2. Availability: It may experience resource shortages or an inability to meet demand due to supply and demand matching.

3. Complexity: Users need to understand knowledge such as distributed networks and smart contracts, and the cost of use is relatively high.

4. Difficult to be used for AI training: Large model training requires extremely high bandwidth and stability, and currently decentralized computing power is difficult to meet these requirements.

The combination of AI and Web3 is relatively rough.

1. Surface Applications: Most projects simply use AI to improve efficiency, lacking deep integration and innovation.

2. Marketing Orientation: Some projects apply AI only in limited areas and excessively promote the concept of AI.

Token economics becomes a buffer.

Some AI projects struggle to develop in Web2 and instead overlay Web3 narratives and token economics. However, whether token economics truly helps address real needs still requires further validation.

Summary

The fusion of AI and Web3 provides infinite possibilities for technological innovation and economic development. AI can bring smarter application scenarios to Web3, while Web3 offers new development opportunities for AI. Although there are still many challenges at present, through continuous exploration and innovation, it is believed that a smarter, more open, and fair economic and social system can be constructed in the future.