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

Web3, as a new decentralized internet paradigm, has a natural opportunity for integration with AI. Under traditional centralized architectures, AI faces challenges such as computational power bottlenecks and privacy breaches. In contrast, Web3 is based on distributed technology, which can inject new momentum into AI through shared computing power networks and open data markets. At the same time, AI can empower the Web3 ecosystem. Exploring the combination of the two is significant for building the next generation of internet infrastructure and unlocking the value of data and computing power.

Data-Driven: The Cornerstone of AI and Web3

Data is the core driving force behind AI development. AI models require vast amounts of high-quality data to gain deep understanding and strong reasoning capabilities, and the quality of the data directly affects the model's performance.

The traditional centralized AI data model has the following issues:

• The cost of data acquisition is high, making it difficult for small and medium-sized enterprises to bear.
• Data resources are monopolized by giants, resulting in data islands.
• Personal data privacy is at risk of leakage.

Web3 offers a new decentralized data paradigm to address these pain points:

• Users can sell idle network resources to decentralize the collection of network data for AI training purposes.
• Adopt the "Annotation Earning" model to incentivize global workers to participate in data annotation.
• The blockchain data trading platform provides a public and transparent environment for data trading.

Nevertheless, the acquisition of real data still faces issues such as varying quality and processing difficulties. Synthetic data may become a highlight in the future, as it can simulate the properties of real data and has shown application potential in fields such as autonomous driving and financial trading.

Privacy Protection: The Important Role of FHE

In the data age, privacy protection has become a focal point. Some sensitive data cannot be fully utilized due to privacy risks, limiting the potential of AI models.

Fully Homomorphic Encryption ( FHE ) allows direct computation on encrypted data, obtaining the same results as plaintext calculations without the need for decryption. FHE provides protection for AI privacy computing, enabling GPUs to perform training and inference in an environment without touching the original data.

FHEML supports the encryption of data and models throughout the entire machine learning lifecycle, ensuring the security of sensitive information and preventing leakage risks. FHEML complements ZKML, which proves the correctness of machine learning execution, while the former emphasizes computing on encrypted data to maintain privacy.

Power Revolution: Decentralized AI Computing Network

The complexity of AI systems is rapidly increasing, leading to a surge in computing power demand. At the same time, the global GPU utilization rate is below 40%, compounded by factors such as chip shortages, creating severe issues in computing power supply. AI practitioners urgently need on-demand and efficient computing services.

The decentralized AI computing power network aggregates idle GPU resources from around the world, providing AI companies with an economically accessible computing power market. Demand-side parties publish tasks, and smart contracts allocate them to miner nodes for execution, with rewards given upon completion. This solution improves resource utilization efficiency and helps address the computing power bottleneck.

In addition to the general computing power network, there are dedicated platforms focused on AI training and inference. The decentralized computing power network provides a fair and transparent market, breaks monopolies, lowers thresholds, and improves efficiency, playing a key role in the web3 ecosystem.

DePIN: Web3 Empowers Edge AI

Edge AI enables computation to occur at the data source, achieving low-latency processing while protecting user privacy. In the Web3 space, this concept is referred to as DePIN. It enhances privacy protection through local processing, incentivizes nodes to provide resources via token economics, and builds a sustainable ecosystem.

Currently, DePIN is developing rapidly in a certain ecosystem, becoming one of the preferred platforms for projects. The platform's high TPS, low fees, and technological innovations provide strong support for DePIN projects. The market value of DePIN projects on the platform has exceeded 10 billion USD, with several well-known projects making significant progress.

IMO: New Paradigm for AI Model Release

The IMO concept was first introduced by a certain protocol, tokenizing AI models. In traditional models, developers find it difficult to profit from the subsequent use of the models, and there is also a lack of transparency regarding model performance.

IMO provides a new type of funding support and value-sharing method for open-source AI models. Investors purchase tokens to share in the model's earnings. A certain protocol uses specific standards combined with AI oracles and OPML technology to ensure the authenticity of the models and the sharing of profits.

IMO enhances transparency and trust, encourages open-source collaboration, adapts to the trends of the cryptocurrency market, and injects momentum into AI development. Currently in its early stages, but the potential value is worth looking forward to.

AI Agent: A New Era of Interactive Experience

AI Agents can perceive their environment, think independently, and take action to achieve goals. Supported by large language models, they understand natural language, plan decisions, and execute complex tasks. As virtual assistants, AI Agents learn user preferences, provide personalized solutions, autonomously solve problems, and improve efficiency.

A certain open AI native application platform provides comprehensive and easy-to-use creation tools, allowing users to configure robot functions, appearance, voice, etc., committed to creating a fair and open AI content ecosystem. The platform trains specialized large language models to make role-playing more humanized, and voice cloning technology accelerates personalized interaction with AI products. Its custom AI Agents can be applied in various fields such as video chatting, language learning, image generation, and more.

Currently, the integration of Web3 and AI is exploring more on the infrastructure layer, such as obtaining high-quality data, protecting privacy, on-chain model hosting, and improving the utilization of decentralized computing power. As the infrastructure improves, the integration of Web3 and AI will give rise to innovative business models and services.