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

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, natural language processing, and machine learning, bringing transformation and innovation to various industries. At the same time, Web3, based on decentralized blockchain technology, is changing people's perceptions and usage of the internet through features like smart contracts and distributed storage.

This article will delve into the current development status of AI + Web3, analyze the potential value and impact of their integration, and discuss the challenges currently faced. We will first introduce the basic concepts of AI and Web3, and then explore their interrelationship. Next, we will analyze the current state of AI + Web3 projects and discuss the limitations and challenges they face in depth. It is hoped that this will provide valuable references for relevant practitioners and investors.

Ways AI Interacts with 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 kind of sparks can their combination create? Let's first analyze the difficulties and areas for improvement that each faces, and then discuss how they can help each other address these challenges.

The challenges faced by the AI industry

The core of the AI industry is inseparable from the three key elements of computing power, algorithms, and data:

1. Computing Power: Refers to the ability to perform large-scale calculations and processing. AI tasks require handling massive amounts of data and complex calculations; high-intensity computing power can accelerate model training and inference, improving the performance of AI systems. In recent years, developments in hardware technology such as GPUs have greatly propelled AI advancements.

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

3. Data: It is the foundation for training and optimizing models. A vast and diverse amount of data can help AI systems learn more accurate models and better understand and solve real-world problems.

The main challenges currently faced by the AI industry:

• The cost of acquiring and managing computing power is high, especially for startups and individual developers.
• Deep learning algorithms require a large amount of data and computational resources, and the interpretability and generalization ability of the models still need improvement.
• High-quality data acquisition is difficult, and data privacy and security issues are prominent.
• The black box nature of AI models raises public concerns about interpretability and transparency.
• Many AI startup projects have unclear business models.

The challenges faced by the Web3 industry

The Web3 industry also has many problems that need to be solved, mainly reflected in:

• Insufficient data analysis capabilities
• Poor user experience
• There is a risk of vulnerabilities in the smart contract code
• Frequent hacker attacks

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

• Utilizing AI algorithms for intelligent data analysis and mining to enhance risk assessment and decision-making capabilities in areas such as DeFi.
• Optimize user experience through AI, providing personalized services
• Use AI technology to detect network attacks and enhance security.
• Utilize AI for automated audits of smart contracts to enhance contract security.

Analysis of the Current Status of AI+Web3 Projects

AI+Web3 projects mainly approach from two directions: leveraging blockchain technology to enhance AI project performance, and utilizing AI technology to serve Web3 projects. Currently, a number of exploratory projects have emerged, such as Io.net, Gensyn, Ritual, etc. We will analyze the current status and development situation from different sub-tracks.

Web3 empowers AI

Decentralized Computing Power

With the explosion of AI, the demand for computing power such as GPUs has surged. Taking ChatGPT as an example, it is reported that it requires 30,000 NVIDIA A100 GPUs to operate. This has led to a division between "GPU rich" and "GPU poor," with a few companies monopolizing a large amount of high-end GPU resources.

To address the issue of computing power shortage, some Web3 projects have begun to explore decentralized computing power services, such as Akash, Render, and Gensyn. These projects attract users to provide idle GPU computing power through a token incentive mechanism, creating a network of computing power supply.

The supply side mainly includes:

• Cloud service providers ( such as AWS, Azure, etc. )
• Cryptocurrency miners ( such as Ethereum after the transition to PoS with idle GPUs )
• Large enterprises like Tesla, Meta, etc.

Currently 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.

Such projects create a supply-demand cycle through token incentives, achieving a cold start. As the scale expands, it can bring more value to both supply and demand sides.

(# Decentralized Algorithm Model

In addition to computing power, algorithm models can also be decentralized. Taking Bittensor as an example, it has created a decentralized AI algorithm service market that connects multiple different AI models. When users ask questions, the system selects the most suitable model to answer.

Compared to a single large model like ChatGPT, this decentralized algorithm network is more like a school with multiple experts, which has great potential in the long run.

)# Decentralized Data Collection

For AI model training, a large amount of high-quality data is crucial. However, most Web2 platforms currently prohibit data collection for AI training or unilaterally sell user data to AI companies.

Some Web3 projects are beginning to achieve decentralized data collection through token incentive methods. For example, PublicAI allows users to tag valuable content on social platforms and receive token rewards, or participate in data validation. This promotes a win-win situation between data contributors and the AI industry.

ZK Protects User Privacy in AI

Zero-knowledge proof ### ZK ### technology enables information verification while protecting privacy, helping to resolve the contradiction between data privacy and sharing in AI.

ZKML(Zero-Knowledge Machine Learning) allows for model training and inference without disclosing the original data. This is of great significance in sensitive data fields such as healthcare and finance.

The field is currently in its early stages, such as BasedAI proposing to combine fully homomorphic encryption ( FHE ) with large language models ( LLM ) to protect user data privacy.

( AI empowers Web3

)# Data Analysis and Prediction

Many Web3 projects are beginning to integrate AI services to provide data analysis and predictions. For example:

• Pond: Using AI graph algorithms to predict valuable tokens
• BullBear AI: Predict price trends based on historical data
• Numerai: AI investment competition platform
• Arkham: An on-chain data analysis platform that combines AI

![Newbie Science Popularization丨In-depth Analysis: What Kind of Sparks Can AI and Web3 Generate?]###https://img-cdn.gateio.im/webp-social/moments-de2f6c381547c3d62e1f40e50f67e32d.webp###

(# Personalized Services

The application of AI in fields such as search recommendations is also applicable to Web3 projects:

• Dune: New Wand tool launched, utilizing large language models to write SQL queries
• Followin, IQ.wiki: Integrate ChatGPT summary content
• Kaito: Web3 Search Engine Based on LLM
• NFPrompt: Using AI to Lower the Barriers for NFT Creation

)# AI Audit Smart Contract

AI can audit smart contract code more efficiently and accurately, identifying potential vulnerabilities. For example, 0x0.ai offers a machine learning-based smart contract auditing tool that can flag potential issues in the code.

Limitations and Challenges of AI+Web3 Projects

The real obstacles faced by decentralized computing power

Decentralized computing projects are innovative, but they also face some challenges:

1. Performance and Stability: Distributed nodes may experience latency and instability.
2. Resource matching: Ensuring a balance between supply and demand is difficult.
3. Usage Threshold: More complex compared to centralized services

Currently, most decentralized computing projects can only be used for AI inference and are difficult to perform large model training. The reason is:

1. Large model training requires extremely high computing power and bandwidth.
2. Interruptions during training can cause huge losses.
3. Multi-card parallel communication ### such as NVLink ### has strict limitations on physical distance.

Therefore, decentralized computing power is currently more suitable for scenarios with lower computing power demands, such as AI inference or training small models.

( The combination of AI and Web3 is still rough.

Most AI-powered Web3 projects currently remain at the surface application level:

1. There is no essential difference between AI applications of Web2 projects.
2. Some projects only use AI concepts at a marketing level, lacking true innovation.

This reflects that there has not yet been a deep integration between AI and cryptocurrency, and further exploration of native and meaningful solutions is still needed.

) Token economics serves as a buffer for the narrative of AI projects.

Due to the uncertainty of the AI business model, some projects choose to overlay Web3 narratives and token economics to attract users. However, whether token economics truly helps address the real needs of AI projects remains to be seen.

I hope that in the future there will be more projects that not only use tokens as promotional tools, but truly meet the needs of actual scenarios.

![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###

Summary and Outlook

The integration of AI and Web3 offers limitless possibilities for future technological innovation and economic development. AI can bring more intelligent and efficient application scenarios to Web3, such as investment decision support, smart contract auditing, and personalized services. Web3, in turn, provides a decentralized computing power, data, and algorithm sharing platform for AI, which is expected to alleviate the bottlenecks in AI development.

Although AI + Web3 projects are still in the early stages and face many challenges, their advantages are also quite obvious: reducing dependence on centralized institutions, increasing transparency and auditability, and promoting broader participation and innovation. In the future, it will be necessary to continuously weigh the pros and cons in practice and take appropriate measures to overcome challenges.

It is believed that by combining the intelligent analysis and decision-making capabilities of AI with the decentralized characteristics of Web3, it is possible to build a more intelligent, open, and fair economic and even social system in the future. The deep integration of AI and Web3 still requires time, but its development prospects are promising.