The Deep Integration of AI and Web3: Shaping the Future Internet Landscape

Web3, as a decentralized, open, and transparent new internet paradigm, has a natural opportunity for integration with AI. Under traditional centralized architectures, AI computing and data resources are strictly controlled, facing challenges such as computing power bottlenecks, privacy leaks, and algorithm opacity. Web3, based on distributed technology, injects new momentum 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, aiding its ecological construction. Exploring the combination 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 massive amounts 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 problems:

• 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 misuse.

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

• Users can sell idle network resources and decentralize the capture of network data to provide real, high-quality data for AI model training.
• Adopt the "Annotation for Earnings" 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 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 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 real data and improving data usage efficiency. In fields such as autonomous driving, financial market trading, and game development, synthetic data has shown mature application potential.

Privacy Protection: The Role of FHE in Web3

In the data-driven era, privacy protection has become a global focus. The introduction of regulations such as the General Data Protection Regulation (GDPR) by the European Union reflects a strict safeguarding of personal privacy. However, this has also brought challenges: some sensitive data cannot be fully utilized due to privacy risks, limiting the potential and reasoning capabilities of AI models.

FHE, or Fully Homomorphic Encryption, allows computation directly on encrypted data without the need to decrypt the data, and the computation results are consistent with the results of performing the same computation on plaintext data.

FHE provides solid protection for AI privacy computing, allowing GPU computing power to perform model training and inference tasks in an environment without touching the original data. This brings significant advantages to AI companies. They can safely 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 breaches. In this way, FHEML reinforces 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 Computation 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 computational resources. For example, the training of a large language model by a well-known AI company requires immense computational 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 these 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 the chip shortages caused by supply chain and geopolitical factors, this has made the computing power supply issue even more severe. AI practitioners find themselves in a dilemma: either purchase hardware themselves or rent cloud resources, and they urgently need a demand-driven, cost-effective computing service model.

A decentralized AI computing power network aggregates idle GPU resources from around the world to provide an economical and easily accessible computing power market for AI companies. The demand side for computing power can post computational tasks on the network, and smart contracts allocate tasks to miner nodes that contribute computing power. Miners execute the tasks and submit results, and upon verification, they receive points as 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 networks, there are platforms focused on AI training and dedicated computing networks specialized for AI inference.

The decentralized computing power network provides a fair and transparent computing power market, breaking monopolies, lowering application barriers, and improving the utilization efficiency of computing power. In the web3 ecosystem, the decentralized computing power network will play a key role in attracting more innovative dapps to join, jointly promoting the development and application of AI technology.

DePIN: Web3 Empowers Edge AI

Imagine that your smartphone, smart watch, and even smart devices at home all have the capability to run AI—this is the charm of Edge AI. It allows computation to occur at the source of data generation, achieving low latency and real-time processing while protecting user privacy. Edge AI technology has already been applied in critical areas such as autonomous driving.

In the Web3 field, we have a more familiar name – DePIN. Web3 emphasizes decentralization and the sovereignty of user data. 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 the ecosystem of a well-known public chain, 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. At present, the market value of DePIN projects on this public chain exceeds $10 billion, 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, which tokenizes 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 continuous revenue from the subsequent use of the model, especially when the model is integrated into other products and services. The original creators find it hard 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, which limits 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 and share in the profits generated by the model in the future. A certain protocol uses a specific ERC standard, combined with AI oracle 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 still 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 serve as virtual assistants, learning user preferences through interaction and providing personalized solutions. Even without explicit instructions, AI Agents can autonomously solve problems, enhance 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 to make role-playing more humanized; voice cloning technology can accelerate the personalized interaction of AI products, reducing voice synthesis costs by 99%, with voice cloning achievable 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, the current focus is more on exploring the infrastructure layer, such as how to obtain high-quality data, protect data privacy, host models on-chain, improve the efficient use of 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.