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

Web3, as a decentralized, open, and transparent new paradigm of the internet, has a natural opportunity for integration with AI. Traditional centralized architectures face challenges such as computational power bottlenecks, privacy breaches, and opaque algorithms, while Web3, based on distributed technology, can inject new momentum into AI through shared computing networks, open data markets, and privacy computing. At the same time, AI can empower Web3 in various ways, such as optimizing smart contracts and developing anti-cheat algorithms. Exploring the combination of the two is of great significance for building the next generation of internet infrastructure and unlocking the value of data and computational power.

Data-Driven: The Foundation of AI and Web3

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

Traditional centralized AI data acquisition and utilization models have several major 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 tech companies, creating data islands.
• Personal data privacy is at risk of leakage and misuse.

The decentralized data paradigm of Web3 can address these pain points:

• Users can sell idle network resources to AI companies to decentralizedly crawl network data, which is cleaned and transformed to provide real, high-quality data for AI model training.
• Adopting a "label to earn" model, incentivizing global workers through tokens to participate in data labeling, gathering global expertise, and enhancing data analysis capabilities.
• The blockchain data trading platform provides a publicly transparent trading environment for both data suppliers and demanders, incentivizing data innovation and sharing.

Nevertheless, there are still some issues with real-world data acquisition, such as varying data quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data may become the star of the Web3 data track in the future. Based on generative AI technology and simulation, synthetic data can simulate the properties of real data, serving 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 shown mature application potential.

Privacy Protection: The Application of FHE in Web3

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

FHE stands for Fully Homomorphic Encryption, which allows computation directly on encrypted data without the need to decrypt it, and the computation results are consistent with the results of the same computation performed on plaintext data.

FHE provides solid protection for AI privacy computing, allowing GPU computing power to perform model training and inference tasks in an environment without accessing the original data. This brings significant advantages to AI companies, enabling them to safely open API services while protecting trade secrets.

FHEML supports the encryption of data and models throughout the entire machine learning lifecycle, ensuring the security of sensitive information and preventing the risk of data leakage. In this way, FHEML enhances data privacy and provides a secure computational framework for AI applications.

FHEML is a supplement to ZKML, where ZKML proves the correct execution of machine learning, while FHEML emphasizes performing computations on encrypted data to maintain data privacy.

Power Revolution: AI Computing in Decentralized Networks

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

At the same time, global GPU utilization is below 40%, coupled with a slowdown in microprocessor performance improvements and chip shortages caused by supply chain and geopolitical factors, making the computing power supply issue even more severe. AI practitioners are in a dilemma: either purchase hardware themselves or rent cloud resources, and they urgently need a demand-based, cost-effective computing service model.

Some decentralized AI computing power networks aggregate idle GPU resources from around the world to provide AI companies with an economical and user-friendly computing power market. Demand-side users can publish computing tasks on the network, and smart contracts will allocate tasks to miner nodes that contribute computing power. Miners execute the tasks and submit results, receiving rewards after verification. This solution improves resource utilization efficiency and helps address the computing power bottleneck issues in fields such as AI.

In addition to the general decentralized computing networks, there are platforms focused on AI training and dedicated computing networks for AI inference.

Decentralized computing networks provide a fair and transparent computing market, breaking monopolies, lowering application barriers, and improving computing efficiency. In the web3 ecosystem, decentralized computing networks will play a key role in attracting more innovative dapps to join and jointly promote the development and application of AI technology.

DePIN: Web3 Empowers Edge AI

Imagine that your smartphone, smartwatch, and even smart devices at home have the ability to run AI—that's the charm of Edge AI. It allows 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 space, we have a more familiar name – DePIN. Web3 emphasizes decentralization and user data sovereignty, while DePIN enhances user privacy protection and reduces the risk of data leaks through local data processing; the native Token economic mechanism of Web3 incentivizes DePIN nodes to provide computing resources, building a sustainable ecosystem.

Currently, DePIN is developing rapidly in a certain public chain ecosystem, becoming one of the preferred platforms for project deployment. The high TPS, low transaction costs, and technological innovations of this public chain provide strong support for DePIN projects. Currently, the market value of DePIN projects on this public chain exceeds $10 billion, and some well-known projects have made significant progress.

IMO: New Paradigm for AI Model Release

The IMO concept was first proposed by a certain protocol to tokenize AI models.

In traditional models, due to the lack of a revenue-sharing mechanism, AI model developers often find it difficult to obtain continuous income from the subsequent use of their models, especially when the models are integrated into other products and services. The original creators find it hard to track usage, let alone gain revenue from it. Moreover, 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, allowing investors to purchase IMO tokens and share in the profits generated by the model in the future. A certain protocol uses two ERC standards, combining AI oracles and OPML technology to ensure the authenticity of the AI model and that token holders can share in the profits.

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

AI Agent: A New Era of Interactive Experience

AI Agents can perceive their environment, think independently, and take corresponding actions to achieve established goals. Supported by large language models, AI Agents can not only understand natural language but also plan decisions and execute complex tasks. They can act 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.

A certain AI-native application platform provides a comprehensive and user-friendly set of creation tools, supporting users in configuring robot functionalities, appearances, voices, and connecting to external knowledge bases, committed to building a fair and open AI content ecosystem. By leveraging generative AI technology, it empowers individuals to become super creators. The platform has trained a specialized large language model to make role-playing more human-like; voice cloning technology can accelerate personalized interactions with AI products, reducing voice synthesis costs by 99%, and voice cloning can be achieved in just 1 minute. The customized AI Agent from this platform can currently be applied in various fields such as video chatting, language learning, and image generation.

In the integration of Web3 and AI, the current focus is more on exploring the infrastructure layer, such as how to obtain high-quality data, protect data privacy, host models on the blockchain, improve the efficient use of decentralized computing power, and verify large language models, among other key issues. As these infrastructures gradually improve, we have reason to believe that the integration of Web3 and AI will give rise to a series of innovative business models and services.