The Integration of Web3 and AI: Exploring Six Key Areas of the Future Internet

Web3, as a decentralized, open, and transparent new paradigm of the internet, has a natural potential for integration with AI. Under traditional centralized architectures, AI computing and data resources are strictly limited, facing multiple challenges such as computing power bottlenecks, privacy leaks, and algorithm opacity. In contrast, Web3, based on distributed technology, can provide new momentum for AI development through shared computing power networks, open data markets, and privacy computing. At the same time, AI can also bring many benefits to Web3, such as optimizing smart contracts and developing anti-cheating algorithms, promoting its ecological construction. Therefore, exploring the combination of Web3 and AI is of great significance for building the next generation of internet infrastructure and unlocking the value of data and computing 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 digest vast amounts of high-quality data to achieve deep understanding and powerful 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 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 tech giants, creating data silos.
• Personal data privacy faces the risk of leakage and abuse

Web3 can address the pain points of traditional models with a new decentralized data paradigm:

• Users can sell idle network resources to AI companies, decentralizedly scraping web data, which, after cleaning and transformation, provides real, high-quality data for AI model training.
• Adopting a "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 suppliers and demanders, incentivizing data innovation and sharing.

However, there are still some issues with data acquisition in the real world, such as inconsistent data quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data may be a highlight in the future of the Web3 data field. Based on generative AI technology and simulation, synthetic data can mimic the attributes 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 Role of FHE in Web3

In the data-driven era, privacy protection has become a global focus, with regulations such as the European Union's General Data Protection Regulation (GDPR) reflecting 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 ability of AI models.

FHE (Fully Homomorphic Encryption) allows for direct computation on encrypted data without the need to decrypt it, and the computation results are consistent with those obtained from the same computation on plaintext data. FHE provides robust protection for AI privacy computing, enabling GPU computing power to perform model training and inference tasks in an environment that does not touch the original data. 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 lifecycle, ensuring the security of sensitive information and preventing the risk of data leakage. 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 immense computing power, equivalent to 355 years of training time on a single device. Such a shortage of computing power not only limits the advancement of AI technology but also makes advanced AI models out of reach for most researchers and developers.

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

The decentralized AI computing power network aggregates idle GPU resources from around the world to provide an economically accessible computing power market for AI companies. Demand-side participants can publish computing tasks on the network, and smart contracts assign tasks to miner nodes that contribute computing power. Miners execute the tasks and submit the results, receiving point rewards upon 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 specialized computing networks focused on AI training and inference. Decentralized computing networks provide a fair and transparent computing market, breaking monopolies, lowering application barriers, and improving computing resource utilization 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

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 key areas such as autonomous driving. In the Web3 space, we have a more familiar name—DePIN. Web3 emphasizes decentralization and user data sovereignty, and DePIN enhances user privacy protection and reduces the risk of data leakage by processing data locally; 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 a certain public chain ecosystem, becoming one of the preferred platforms for project deployment. The high TPS, low transaction fees, and technological innovations of this public chain provide strong support for DePIN projects. Currently, the market value of DePIN projects on this public chain has exceeded 10 billion US dollars, and several well-known projects have made significant progress.

IMO: New Paradigm for AI Model Release

The IMO concept tokenizes AI models. In traditional models, due to the lack of revenue-sharing mechanisms, AI model developers find it difficult to obtain ongoing 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, allowing investors to purchase IMO tokens and share the profits generated by the model in the future. A certain protocol uses a specific ERC standard, 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, adapts to trends in the crypto market, and injects momentum for the sustainable development of AI technology. The IMO is currently in its early trial 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 appropriate 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 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 to configure robot functions, appearance, voice, and connect to external knowledge bases, dedicated to building a fair and open AI content ecosystem. Utilizing generative AI technology, it empowers individuals to become super creators. The platform has trained a specialized large language model, making role-playing more human-like; its voice cloning technology can accelerate personalized interactions in AI products, reducing voice synthesis costs by 99%, with voice cloning achievable in just 1 minute. Customized AI Agents 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, including how to obtain high-quality data, protect data privacy, host models on-chain, efficiently utilize 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 birth to a series of innovative business models and services.