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

The rapid development of artificial intelligence and Web3 technology has attracted widespread attention globally. AI, as a technology that simulates and mimics human intelligence, has made significant breakthroughs in areas such as facial recognition, natural language processing, and machine learning. Web3, as an emerging network model, is changing people's perception and usage of the internet.

The market size of the AI industry reached 200 billion USD in 2023, with industry giants and excellent players such as OpenAI, Character.AI, and Midjourney emerging like mushrooms after rain. The market value of the Web3 industry reached 25 trillion USD, with projects like Bitcoin, Ethereum, and Solana emerging one after another. The combination of AI and Web3 has become the focus of attention for builders and VCs in both the East and the West.

This article will explore the current state of AI+Web3 development, analyze the situation of current projects, and discuss the limitations and challenges faced, providing references and insights for investors and practitioners.

The Ways AI Interacts with Web3

The development of AI and Web3 is like the two sides of a balance scale; AI brings an increase in productivity, while Web3 brings about a transformation in production relationships. We will first analyze the challenges and areas for improvement faced by the AI and Web3 industries, and then explore how they can help each other to solve these challenges.

The challenges faced by the AI industry

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

1. Computing Power: AI tasks require a large amount of computational resources for model training and inference. Acquiring and managing large-scale computing power is an expensive and complex challenge, especially for startups and individual developers.

2. Algorithm: Although deep learning algorithms have achieved great success, there are still some difficulties. Training deep neural networks requires a large amount of data and computational resources, and the interpretability and explainability of the models are insufficient. The robustness and generalization ability of the algorithms are also important issues.

3. Data: Acquiring high-quality and diverse data remains a challenge. Data in certain fields, such as healthcare data, is difficult to obtain. There are also issues with the quality, accuracy, and labeling of the data. 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 the unclear business models of AI projects, also need to be addressed.

The challenges faced by the Web3 industry

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

1. Data analysis and forecasting: AI technology can extract valuable information from vast amounts of data, enabling more accurate predictions and decision-making, which is of great significance for risk assessment, market forecasting, and asset management in the DeFi sector.

2. User Experience and Personalized Services: AI technology can provide personalized recommendations, customized services, and intelligent interactive experiences, enhancing user engagement and satisfaction.

3. Security and Privacy Protection: AI technology can be used to detect and defend against cyber attacks, identify abnormal behavior, and provide stronger security guarantees. At the same time, AI can also be applied to data privacy protection to safeguard users' personal information.

4. Smart Contract Auditing: AI technology can be used to automate contract auditing and vulnerability detection, enhancing the security and reliability of contracts.

Analysis of the Current Status of AI+Web3 Projects

AI+Web3 projects mainly focus on two aspects: leveraging blockchain technology to enhance AI project performance, and utilizing AI technology to serve the improvement of Web3 projects.

Web3 empowers AI

Decentralized Computing Power

With the rise of AI, the demand for GPUs has surged, leading to a supply shortage. To address this issue, some Web3 projects have started offering decentralized computing power services, such as Akash, Render, and Gensyn. These projects incentivize users to provide idle GPU computing power through tokens, offering computing support to AI clients.

The supply side mainly includes cloud service providers, cryptocurrency miners, and enterprises. Decentralized computing power projects can be roughly divided into two categories: those used for AI inference and those used for AI training. The former includes Render, Akash, Aethir, etc., while the latter includes io.net, Gensyn.

As a representative project, io.net currently has more than 500,000 GPUs, integrating the computing power of Render and Filecoin. Gensyn facilitates the allocation and rewards of machine learning tasks through smart contracts, achieving AI training.

Decentralized Algorithm Model

Decentralized algorithm model networks like Bittensor create a decentralized AI algorithm service market through a token incentive mechanism. This model has the potential to play an important role in the future development of AI.

Decentralized Data Collection

Some projects like PublicAI achieve decentralized data collection through token incentives. Users can contribute data or participate in data verification to receive token rewards. This approach fosters 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) enables the training and inference of machine learning models without disclosing the original data. Projects like BasedAI are exploring this field.

AI empowers Web3

Data Analysis and Prediction

Many Web3 projects are beginning to integrate AI services or develop their own AI to provide users with data analysis and forecasting services. For example, Pond predicts valuable alpha tokens using AI algorithms, while BullBear AI forecasts price trends based on historical data and market movements.

Numerai, as an investment competition platform, allows participants to predict the stock market based on AI and large language models. On-chain data analysis platforms like Arkham also combine AI to provide services.

Personalized Services

Web3 projects optimize user experience through AI integration. For example, Dune launched the Wand tool to write SQL queries using large language models. Platforms like Followin and IQ.wiki integrate ChatGPT for content summarization. Projects like NFPrompt use AI to reduce user creation costs.

AI Audit Smart Contract

AI also plays a role in smart contract auditing. For example, 0x0.ai provides an AI smart contract auditor that uses advanced algorithms to analyze smart contracts and identify potential vulnerabilities or issues.

Limitations and Challenges of AI+Web3 Projects

The real obstacles in decentralized computing power

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

2. Availability: Affected by the degree of supply and demand matching, which may lead to insufficient resources or an inability to meet user needs.

3. Technical Complexity: Users may need to understand knowledge such as distributed networks and smart contracts, which can have high usage costs.

4. Training Difficulty: Currently, decentralized computing power is mainly used for AI inference rather than AI training. The reason is that large model training requires a massive amount of data and high-speed communication bandwidth, which is difficult to achieve in a distributed environment.

The combination of AI and Web3 is relatively rough, not achieving 1+1>2.

1. Surface-level applications: Many projects simply leverage AI to enhance efficiency and conduct analysis, without demonstrating the native integration of AI with cryptocurrency and innovative solutions.

2. Marketing Orientation: Some Web3 teams only apply AI technology in limited areas, excessively promoting AI trends and lacking true innovation.

Token economics becomes a buffer for AI project narratives.

Many AI+Web3 projects use token economics as a means of financing and user participation, but whether token economics truly helps to address actual needs remains to be seen. Currently, most projects have not yet reached a practical stage and require more grounded teams with ideas to genuinely meet real-world demand scenarios.

Summary

The integration of AI and Web3 offers limitless possibilities for future technological innovation and economic development. AI technology can provide more efficient and intelligent application scenarios for Web3, such as data analysis, smart contract auditing, and personalized services. At the same time, the decentralization and programmability of Web3 also present new opportunities for the development of AI technology, such as decentralized computing power, algorithm sharing, and data collection.

Although AI+Web3 projects are still in their early stages and face numerous challenges, they also bring some advantages. For example, decentralized computing power and data collection can reduce dependence on centralized institutions, enhance transparency and auditability, and promote broader participation and innovation.

In the future, by combining the intelligent analysis and decision-making capabilities of AI with the decentralization and user autonomy of Web3, it is expected to build a smarter, more open, and more equitable economic and even social system. As research deepens and technology advances, we look forward to seeing more native and meaningful AI+Web3 solutions emerge in areas such as finance, decentralized autonomous organizations, prediction markets, and NFTs.