The Integration of Web3 and AI: Building a New Generation of Internet Infrastructure

Web3, as a decentralized, open, and transparent new paradigm of the internet, has a natural opportunity for integration with AI. Under traditional centralized architectures, AI computing and data resources are strictly limited and face numerous challenges such as computational bottlenecks, privacy leakage, and algorithmic black boxes. Web3, based on distributed technology, can provide new impetus for AI development through shared computing networks, open data markets, and privacy computing. At the same time, AI can also empower Web3 in various ways, such as optimizing smart contracts and anti-cheating algorithms, promoting its ecological development. Therefore, exploring the combination of Web3 and AI is of great significance for building the next-generation internet infrastructure and unlocking the value of data and computing.

Data-Driven: The Cornerstone of AI and Web3

Data is the core driving force behind the development of AI. AI models need to digest 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.

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 tech companies, creating data silos.
• Personal data privacy is at risk of leakage and abuse.

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

• Users can sell idle network resources to AI companies to crawl web data in a decentralized manner, which is cleaned and transformed to provide real, high-quality data for AI model training.
• Adopting the "Earn by Annotation" 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 and open trading environment for both data supply and demand sides, encouraging data innovation and sharing.

Nevertheless, 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 the highlight of the Web3 data domain in the future. Based on generative AI technology and simulation, synthetic data can mimic 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, with regulations such as the EU General Data Protection Regulation 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 capabilities of AI models.

Fully Homomorphic Encryption ( FHE ) allows for direct computation on encrypted data without the need to decrypt it, and the results of the computation are consistent with the results 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 that does not access the original data. This brings significant advantages to AI companies, allowing them to securely open API services while protecting trade secrets.

FHEML supports 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 computing framework for AI applications.

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

Computing 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 that far exceeds the current supply of computational resources. For example, training a large language 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 unattainable for 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 chip shortages caused by supply chain and geopolitical factors, which exacerbate the computing power supply issue. AI practitioners face a dilemma: either purchase hardware themselves or rent 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. Computing power demanders can publish computing tasks on the network, and smart contracts will assign tasks to nodes that contribute computing power. The nodes execute the tasks and submit results, and after verification, they receive rewards. 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 network, there are dedicated computing platforms focused on AI training and inference. The decentralized computing network provides a fair and transparent market, breaks monopolies, lowers application barriers, and improves computing power utilization efficiency. In the Web3 ecosystem, the decentralized computing network 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

Imagine that your smartphone, smart watch, and even smart devices at home have the capability to run AI - this is the charm of edge AI. It allows computing 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 field, the name we are more familiar with is DePIN. Web3 emphasizes decentralization and user data sovereignty, while 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.

DePIN is currently 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 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 revenue-sharing mechanisms, AI model developers find it difficult to obtain continuous income from the subsequent use of the models, especially when the models are integrated into other products and services. The original creators struggle to track usage and even more to obtain revenue. 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 a specific ERC standard, 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 cryptocurrency market, and injects momentum into the sustainable development of AI technology. The IMO is currently in the 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 to provide personalized solutions. Even without explicit instructions, AI Agents can autonomously solve problems, enhance efficiency, and create new value.

A certain open native AI application platform provides a comprehensive and user-friendly set of creative tools, supporting users in configuring robot functions, appearance, voice, and connecting to external knowledge bases, aiming to create 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 to make role-playing more human-like; voice cloning technology can accelerate personalized interactions for AI products, reducing voice synthesis costs by 99%, with voice cloning achievable in just 1 minute. The AI Agent customized using this platform can currently be applied in various fields such as video chatting, language learning, and image generation.

The current exploration of the integration of Web3 and AI is more focused on the infrastructure layer, addressing key issues 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. 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.