The Integration of AI and Web3: Building the Next Generation of Internet Infrastructure

As a decentralized, open, and transparent new paradigm of the internet, Web3 has a natural opportunity for integration with AI. Under the traditional centralized architecture, AI computation and data resources are subject to strict control, facing challenges such as computational power bottlenecks, privacy breaches, and algorithmic black boxes. In contrast, Web3, based on distributed technology, injects new vitality into AI development through shared computing networks, open data markets, and privacy computing. At the same time, AI can also provide substantial support for Web3, such as smart contract optimization and anti-cheating algorithms, promoting its ecosystem construction. Therefore, exploring the combination of Web3 and AI is of great significance for building next-generation internet infrastructure and unlocking the value of data and computational power.

Data-Driven: The Solid Foundation of AI and Web3

Data is the core driving force behind the development of AI. AI models need to process a large amount of high-quality data to gain deep understanding and strong reasoning abilities. Data not only provides the training foundation for machine learning models but also determines the accuracy and reliability of the models.

The traditional centralized AI data acquisition and utilization model has the following main issues:

• The cost of data acquisition is high, making it difficult for small and medium-sized enterprises to bear.
• Data resources are monopolized by large technology companies, creating data silos.
• Personal data privacy is at risk of leakage and abuse.

Web3 addresses the pain points of traditional models through a new decentralized data paradigm:

• Users can sell idle network resources to AI companies to crawl web data in a decentralized manner, providing real and high-quality data for AI model training after cleaning and transformation.
• Adopting the "label to earn" model, incentivizing global workers to participate in data annotation through tokens, gathering global expertise, and enhancing data analysis capabilities.
• The blockchain data trading platform provides a transparent trading environment for both data supply and demand sides, promoting data innovation and sharing.

However, there are still some issues with acquiring real-world data, such as inconsistent data quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data may become the future star in the Web3 data field. Based on generative AI technology and simulation, synthetic data can mimic the properties of real data and serve as an effective supplement to improve data utilization efficiency. In fields such as autonomous driving, financial market trading, and game development, synthetic data has already demonstrated mature application potential.

Privacy Protection: The Application of FHE in Web3

In the data-driven era, privacy protection has become a global focus. The introduction of relevant regulations reflects a strict protection of personal privacy. However, this also brings challenges: some sensitive data cannot be fully utilized due to privacy risks, limiting the potential and reasoning capabilities of AI models.

Fully Homomorphic Encryption (FHE) allows for computing operations directly on encrypted data without the need to decrypt it, and the computation results are consistent with those obtained from plaintext data. FHE provides solid protection for AI privacy computing, enabling GPU computing power to perform model training and inference tasks in an environment where the original data is not accessed. This brings significant advantages to AI companies, allowing them to securely open API services while protecting trade secrets.

FHEML supports encrypted processing of data and models throughout the entire machine learning cycle, ensuring the security of sensitive information and preventing the risk of data leakage. FHEML enhances data privacy and provides a secure computing framework for AI applications. FHEML complements ZKML, which proves the correct execution of machine learning, while FHEML emphasizes computing on encrypted data to maintain data privacy.

Power Revolution: AI Computing in Decentralized Networks

The current AI system's computational complexity doubles every three months, leading to a surge in computing power demand that far exceeds the existing supply of computing resources. For example, training a well-known AI model requires immense computing power, equivalent to 355 years of training time on a single device. The shortage of computing power not only limits the advancement of AI technology but also makes it difficult for most researchers and developers to access advanced AI models.

At the same time, the global GPU utilization rate is below 40%, coupled with a slowdown in microprocessor performance improvements and chip shortages caused by supply chain and geopolitical factors, which exacerbates the computing power supply problem. AI practitioners are facing a dilemma: either purchase hardware themselves or lease cloud resources, and they urgently need on-demand, cost-effective computing services.

Some decentralized AI computing power networks aggregate idle GPU resources globally to provide an economically accessible computing power market for AI companies. Demand-side users can post computational tasks on the network, and smart contracts allocate tasks to nodes that contribute computing power. The nodes execute the tasks and submit results, receiving rewards upon verification. This solution improves resource utilization efficiency and helps address the computing power bottleneck issues in fields like AI.

In addition to the general decentralized computing network, there are also dedicated computing networks focused on AI training and inference. The decentralized computing network provides a fair and transparent computing power market, breaking monopolies, lowering application thresholds, and improving computing power utilization efficiency. In the Web3 ecosystem, decentralized computing networks will play a key role in attracting more innovative applications to join and jointly promote the development and application of AI technology.

DePIN: Web3 Empowers Edge AI

Edge AI enables computation to occur at the source of data generation, achieving low latency and real-time processing while protecting user privacy. Edge AI technology has been applied in critical areas such as autonomous driving.

In the Web3 domain, DePIN (Decentralized Physical Infrastructure Network) shares similarities with Edge AI. Web3 emphasizes decentralization and user data sovereignty, while DePIN enhances user privacy protection and reduces the risk of data breaches through local data processing. The native token economic mechanism of Web3 can incentivize DePIN nodes to provide computing resources, building a sustainable ecosystem.

Currently, DePIN is developing rapidly in certain public chain ecosystems, becoming one of the preferred platforms for project deployment. High TPS, low transaction fees, and technological innovations provide strong support for DePIN projects. Some well-known DePIN projects have made significant progress.

IMO: New Paradigm for AI Model Release

The IMO (Initial Model Offering) concept tokenizes AI models. In the traditional model, AI model developers find it difficult to obtain continuous revenue from the subsequent use of the models, especially when the models are integrated into other products and services. Furthermore, the performance and effectiveness of AI models often lack transparency, making it difficult for potential investors and users to assess their true value, which limits the market recognition and commercial potential of the models.

IMO provides a new funding support and value-sharing method for open-source AI models. Investors can purchase IMO tokens to share in the profits generated by the models in the future. Some protocols use specific technical standards, combining AI oracles and OPML technology to ensure the authenticity of AI models and that token holders can share in the profits.

The IMO model enhances transparency and trust, encourages open-source collaboration, and adapts to the trends of the crypto market, injecting momentum into the sustainable development of AI technology. The IMO is currently in the early trial stage, but as market acceptance increases and the scope of participation expands, its innovation and potential value are worth looking forward to.

AI Agent: A New Era of Interactive Experience

AI Agent can perceive the environment, think independently, and take corresponding actions to achieve set goals. Supported by large language models, AI Agents can not only understand natural language but also plan decisions and execute complex tasks. They can serve as virtual assistants, learning user preferences through interaction and providing personalized solutions. Even without explicit instructions, AI Agents can autonomously solve problems, improve efficiency, and create new value.

Some AI-native application platforms provide a comprehensive and easy-to-use set of creation tools, allowing users to configure robot functions, appearance, voice, and connect to external knowledge bases, aiming to build a fair and open AI content ecosystem. Utilizing generative AI technology, they empower individuals to become super creators. These platforms may train specialized large language models to make role-playing more human-like; voice cloning technology can accelerate personalized interactions in AI products, significantly reducing the cost of voice synthesis. Customized AI Agents using these platforms can currently be applied in various fields such as video chat, language learning, and image generation.

In the integration of Web3 and AI, there is currently more focus on exploring the infrastructure layer, such as obtaining high-quality data, protecting data privacy, hosting models on-chain, improving the efficient use of decentralized computing power, and verifying large language models. As these infrastructures gradually improve, the integration of Web3 and AI is expected to give rise to a series of innovative business models and services.