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

Web3, as a new paradigm of the decentralized, open, and transparent internet, has a natural opportunity for integration with AI. Under the traditional centralized architecture, AI computation and data resources are strictly controlled, facing numerous challenges such as computational power bottlenecks, privacy breaches, and algorithmic black boxes. In contrast, Web3, based on distributed technology, can inject new momentum into AI development through shared computational power 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 developing anti-cheating algorithms, aiding its ecosystem construction. Exploring the combination of Web3 and AI is crucial for building the next-generation internet infrastructure and unlocking the value of data and computational power.

Data-Driven: The Solid Foundation of AI and Web3

Data is the core driving force behind AI development, just like fuel is for an engine. AI models need to digest massive amounts of high-quality data to gain 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.

Traditional centralized AI data acquisition and utilization models have 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 is at risk of leakage and misuse.

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

• Users can sell idle network to AI companies to decentralize the collection of network data, which is cleaned and transformed to provide real, high-quality data for AI model training.
• Adopting the "label to earn" model, incentivizing global workers to participate in data annotation through tokens, gathering global expertise, and enhancing data analytics capabilities.
• The blockchain data trading platform provides a public and transparent trading environment for both data supply and demand, incentivizing data innovation and sharing.

However, there are also some issues with data acquisition in the real world, such as inconsistent data quality, difficulty in processing, 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 mimic the attributes of real data, serving as an effective supplement to real data and improving data utilization efficiency. In fields such as autonomous driving, financial market trading, and game development, synthetic data has already demonstrated its mature application potential.

Privacy Protection: The Role of FHE in Web3

In the data-driven era, privacy protection has become a global focus, as reflected by the introduction of regulations such as the EU's General Data Protection Regulation (GDPR), which underscores the strict safeguarding of personal privacy. However, this also presents challenges: some sensitive data cannot be fully utilized due to privacy risks, which undoubtedly limits the potential and reasoning capabilities of AI models.

FHE, or Fully Homomorphic Encryption, allows for computation to be performed directly on encrypted data without the need to decrypt it, and the computation results are consistent with those obtained from the same computation performed on plaintext data.

FHE provides solid protection for AI privacy computing, enabling GPU computing power to perform model training and inference tasks in an environment without touching the raw data. This brings huge advantages to AI companies. They can securely open API services while protecting their 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 strengthens data privacy and provides a secure computing framework for AI applications.

FHEML is a complement to ZKML; ZKML 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 that far exceeds 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. This 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, the global GPU utilization rate is below 40%, coupled with the slowdown in microprocessor performance improvements and the chip shortage caused by supply chain and geopolitical factors, all of which have exacerbated the computing power supply issue. AI practitioners are caught in a dilemma: either purchase hardware themselves or rent cloud resources, and they urgently need an on-demand, cost-effective computing service.

The decentralized AI computing power network aggregates idle GPU resources from around the world, providing AI companies with an economical and easily accessible computing power market. Demand-side users 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 results, receiving point rewards upon verification. This solution improves resource utilization efficiency and helps address the computing power bottleneck issues in fields like AI.

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

Decentralized computing power networks provide a fair and transparent computing power market, breaking monopolies, lowering application thresholds, and improving the efficiency of computing power utilization. In the web3 ecosystem, decentralized computing power 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 Empowering 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 critical fields such as autonomous driving.

In the Web3 field, we have a more familiar name---DePIN. Web3 emphasizes decentralization and user data sovereignty. DePIN can enhance user privacy protection and reduce the risk of data leakage by processing data locally; the native Token economic mechanism of Web3 can incentivize DePIN nodes to provide computing resources, constructing a sustainable ecosystem.

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

IMO: New Paradigm for AI Model Release

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

In the traditional model, due to the lack of a revenue-sharing mechanism, once an AI model is developed and brought to market, developers often find it difficult to obtain sustained revenue from the subsequent use of the model, especially when the model is integrated into other products and services. The original creators find it challenging to track usage, let alone derive 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, thus limiting the market recognition and commercial potential of the model.

IMO provides a new funding support and value-sharing method for open-source AI models, allowing investors to purchase IMO tokens to share in the subsequent profits generated by the models. A certain protocol uses two ERC standards, combining AI oracles and OPML technology to ensure 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 cryptocurrency market, and injects momentum into the sustainable development of AI technology. The IMO is currently still in the early experimental stage, 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 are capable of perceiving their environment, independent thinking, and taking 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.

A certain open AI-native application platform provides a comprehensive and easy-to-use suite of creation tools, supporting users to configure robot functions, appearance, voice, and connect to external knowledge bases, aiming to create a fair and open AI content ecosystem. By 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 interaction of 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, current efforts are more focused on exploring the infrastructure layer, such as how to obtain high-quality data, protect data privacy, host models on the chain, improve the efficient use of decentralized computing power, and validate 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.