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 synergy with AI. In traditional centralized architectures, AI computation and data resources are strictly controlled, facing numerous challenges such as computing power bottlenecks, privacy breaches, and algorithm opacity. Web3, based on distributed technology, injects new energy into AI development through shared computing power networks, open data markets, and privacy computing. At the same time, AI can also empower Web3 in many ways, such as optimizing smart contracts and anti-cheating algorithms, assisting in its ecosystem construction. Exploring the integration of Web3 and AI is crucial 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 a large amount of high-quality data in order 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 tech giants, creating data islands.
• Personal data privacy is at risk of leakage and abuse.

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

• The decentralized data collection platform allows users to sell idle network resources, providing AI companies with real, high-quality training data.
• Adopting a "label to earn" model, incentivizing global workers to participate in data annotation through token rewards, gathering expertise.
• The blockchain data trading platform provides a transparent trading environment for both data suppliers and demanders, promoting data innovation and sharing.

However, there are still issues in the real world data acquisition such as inconsistent quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data could become the future star of the Web3 data track. Based on generative AI technology and simulations, 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 Role of FHE in Web3

In the data-driven era, privacy protection has become a global focal point. However, 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 operations on encrypted data without the need for decryption, and the computation results are consistent with those 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 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 data leakage risks. FHEML reinforces data privacy and provides a secure computing framework for AI applications. FHEML complements ZKML, where 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. This not only limits the advancement of AI technology but also makes advanced AI models inaccessible to most researchers and developers. The global GPU utilization rate is below 40%, and factors such as chip shortages exacerbate the issue of computing power supply.

The decentralized AI computing power network aggregates idle GPU resources from around the world to provide an economically efficient computing power market for AI companies. Computing power demanders can publish computing tasks, and smart contracts will allocate the tasks to nodes that contribute computing power. The nodes execute the tasks and submit the results, and after verification, they receive rewards. This solution improves resource utilization efficiency and helps to address the computing power bottleneck issues in fields such as AI.

In addition to the general decentralized computing network, there are dedicated computing networks focused on AI training and inference. The decentralized computing network provides a fair and transparent computing power market, breaking monopolies, lowering application barriers, and improving computing power utilization 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.

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. In the Web3 domain, this is referred to as DePIN. Web3 emphasizes decentralization and user data sovereignty, and DePIN enhances user privacy protection and reduces the risk of data breaches by processing data locally. The Web3 native token economic mechanism can incentivize 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 10 billion USD.

IMO: New Paradigm for AI Model Release

The IMO concept tokenizes AI models. In the traditional model, AI model developers find it difficult to earn continuous revenue from the subsequent use of the models, and the lack of transparency regarding model performance and effectiveness limits market recognition and commercial potential.

IMO provides a new funding support and value-sharing method for open-source AI models. Investors can purchase IMO tokens to share in the profits generated by the model in the future. Some protocols use specific ERC standards, combining AI oracles and OPML technology to ensure the authenticity of AI models and the profit-sharing for token holders.

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. Currently, IMO is in the early trial phase, but 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 actions to achieve 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 to provide personalized solutions. Even without explicit instructions, AI agents can autonomously solve problems, enhance efficiency, and create new value.

Some open AI-native application platforms provide a comprehensive and user-friendly suite of creation tools, allowing users to configure robot functions, appearance, voice, and connect to external knowledge bases, aiming to build a fair and open AI content ecosystem. Utilizing generative AI technology, these platforms empower individuals to become super creators. By training specialized large language models, role-playing becomes more humanized; voice cloning technology can accelerate personalized interactions in AI products, significantly reducing voice synthesis costs. These customized AI Agents can currently be applied in various fields such as video chat, language learning, and image generation.

In the integration of Web3 and AI, the current focus is more on exploring the infrastructure layer, including acquiring high-quality data, protecting data privacy, on-chain model hosting, improving the efficient use of decentralized computing power, and verifying large language models, among other key issues. As these infrastructures gradually improve, the integration of Web3 and AI will give rise to a series of innovative business models and services.