The Fusion of Web3 and Artificial Intelligence: Building a New Generation Internet Ecosystem

Web3, as a decentralized, open, and transparent emerging internet model, has a natural synergy with artificial intelligence. Under the traditional centralized architecture, the computing and data resources of AI are subject to strict limitations, facing challenges such as insufficient computing power, privacy risks, and opaque algorithms. In contrast, Web3, based on distributed technology, can provide new momentum for AI development through shared computing networks, open data markets, and privacy computing, among other means. At the same time, AI can also bring numerous enhancements to Web3, such as optimizing smart contracts and improving anti-cheating algorithms, thereby promoting its ecological development. Therefore, exploring the integration of Web3 and AI is of great significance for building the next-generation internet infrastructure and unleashing the value of data and computing power.

Data-Driven: The Cornerstone of AI and Web3

Data is the core element driving the development of AI. AI models need to digest vast amounts of high-quality data to gain deep insights 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.

The traditional centralized AI data acquisition and usage 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 tech companies, creating data islands.
• Personal data privacy is at risk of leakage and abuse.

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

• Through a distributed network, users can sell idle network resources to AI companies, decentrally collecting network data, which is cleaned and transformed to provide real, high-quality data for AI model training.
• Adopt a "labeling for profit" model, incentivizing global workers to participate in data labeling through tokens, gathering global expertise, and enhancing data analysis capabilities.
• The blockchain data trading platform provides a public and transparent trading environment for both data suppliers and demanders, incentivizing data innovation and sharing.

However, the acquisition of real-world data still faces issues such as varying quality, difficulties in processing, and insufficient diversity and representativeness. Synthetic data may be an important direction for the Web3 data track in the future. Based on generative AI technology and simulation, synthetic data can mimic the attributes of real data, serving as an effective supplement to improve data usage 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 Fully Homomorphic Encryption in Web3

In the data-driven era, privacy protection has become a global focus. The introduction of regulations such as the EU GDPR 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 computations to be performed directly on encrypted data without the need to decrypt it, and the results of the computations are consistent with those obtained by performing the same computations on plaintext data. FHE provides strong protection for AI privacy computing, enabling GPU computing power to perform model training and inference in an environment without accessing the original data. This offers significant advantages to AI companies, allowing them to safely 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. In this way, FHEML strengthens 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 computational complexity of current AI systems doubles every three months, leading to a surge in demand for computing power, far exceeding the supply of existing computing resources. For example, training a large language model requires enormous computing power, equivalent to 355 years of training time on a single device. This shortage of computing power not only restricts advances in AI technology but also makes advanced AI models inaccessible to most researchers and developers.

At the same time, the global GPU utilization rate is below 40%, coupled with the slowdown in microprocessor performance improvements, and supply chain and geopolitical factors leading to chip shortages, which exacerbates the issue of computing power supply. AI practitioners face a dilemma: either purchase hardware themselves or rent cloud resources, and they urgently need a demand-driven, cost-effective computing service.

The decentralized AI computing power network aggregates idle GPU resources globally, providing AI companies with an economically accessible computing power market. Demand-side users can publish computing tasks on the network, and smart contracts allocate tasks to nodes that contribute computing power. Nodes execute tasks and submit results, and after verification, they receive rewards. This solution improves resource utilization efficiency and helps to address the computing power bottleneck issues in fields like AI.

In addition to the general decentralized computing network, there are dedicated computing platforms focused on AI training and inference. These 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 applications to join and jointly promote the development and application of AI technology.

Edge AI: Web3 Empowering Edge Computing

Imagine that your mobile phone, smart watch, and even smart devices in your home all have the capability to run AI—this is the charm of edge AI. It 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 space, we have a more familiar name - Decentralized Physical Infrastructure Network ( DePIN ). Web3 emphasizes decentralization and user data sovereignty, while DePIN enhances user privacy protection and reduces the risk of data breaches by processing data locally; the native token economic mechanism of Web3 incentivizes DePIN nodes to provide computing resources, building a sustainable ecosystem.

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

Initial Model Issuance: A New Paradigm for AI Model Release

The concept of the initial model issuance ( IMO ) was first proposed by a certain protocol, which aims to tokenize AI models. In traditional models, due to the lack of revenue-sharing mechanisms, AI model developers often find it difficult to obtain continuous revenue from subsequent use of the models, especially when the models are integrated into other products and services. Moreover, the performance and effectiveness of AI models often lack transparency, making it difficult for potential investors and users to assess their true value, thereby limiting 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 subsequent profits generated by the models. By combining specific technical standards, AI oracles, and on-chain machine learning technology, it ensures the authenticity of the AI models 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 still in the early trial stage, but as market acceptance increases and participation expands, its innovation and potential value are worth looking forward to.

AI Agents: A New Era of Interactive Experience

AI agents can perceive their 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 user-friendly set of creative tools, allowing users to configure robot functions, appearance, voice, and connect to external knowledge bases, committed to building a fair and open AI content ecosystem. Utilizing generative AI technology, these platforms empower individuals to become super creators. These platforms have trained specialized large language models to make role-playing more human-like; voice cloning technology can accelerate personalized interaction in AI products and significantly reduce the cost of voice synthesis. AI agents customized using these platforms can currently be applied in various fields such as video chatting, language learning, and image generation.

In the integration of Web3 and AI, current exploration is more focused on the infrastructure layer, including key issues such as how to obtain high-quality data, protect data privacy, host models on-chain, efficiently utilize decentralized computing power, and verify large language models. As these infrastructures gradually improve, we have reason to believe that the integration of Web3 and AI will give birth to a series of innovative business models and services.