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

Web3, as a decentralized, open, and transparent new paradigm of the internet, has a natural synergy with AI. Under the traditional centralized architecture, AI computation and data resources are strictly controlled, facing numerous challenges such as computational bottlenecks, privacy breaches, and algorithm opacity. In contrast, Web3, based on distributed technology, can inject new momentum into AI development through shared computational networks, open data markets, and privacy-preserving computation. At the same time, AI can empower Web3 in many ways, such as optimizing smart contracts and developing anti-cheating algorithms, aiding its ecosystem construction. Therefore, exploring the integration of Web3 and AI is crucial for building the next generation of 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 the development of AI, just as fuel is to an engine. AI models need to digest a large amount of high-quality data in order to gain deep understanding and strong reasoning abilities. Data not only provides the training foundation for machine learning models but also determines the accuracy and reliability of the models.

In traditional centralized AI data acquisition and utilization models, there are several 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 abuse.

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

• Users can sell idle networks to AI companies, which will decentralizedly scrape network data, clean and transform it, providing real and high-quality data for AI model training.
• Adopting the "label to earn" model, incentivizing global workers through tokens to participate in data annotation, gathering global expertise, and enhancing data analysis capabilities.
• The blockchain data trading platform provides a public and transparent trading environment for both data supply and demand parties, incentivizing innovation and sharing of data.

Nevertheless, there are still some issues with real-world data acquisition, such as varying data quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data may be the future star of the Web3 data track. 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 of attention. The introduction of regulations such as the EU's General Data Protection Regulation (GDPR) reflects a strict commitment to personal privacy. However, this also brings 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 directly on encrypted data without the need to decrypt it, and the computation results are consistent with those obtained from performing the same calculations 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 that does not touch the original data. This brings significant advantages to AI companies. They can securely open API services while protecting trade secrets.

FHEML supports encryption processing of data and models throughout the entire machine learning cycle, 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, 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 computational 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. This shortage of computing power not only limits the advancement of AI technology but also makes these 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 shortages caused by supply chain and geopolitical factors, which have exacerbated the computing power supply issue. AI practitioners find themselves in a dilemma: either purchase hardware 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 a cost-effective and easily accessible computing power market. Demand-side users can publish computing tasks on the network, and smart contracts assign the tasks to miner nodes that contribute computing power. Miners execute the tasks and submit results, receiving points as rewards after verification. This solution improves resource utilization efficiency and helps address the computing power bottleneck issues in fields such as AI.

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

The decentralized computing network provides a fair and transparent computing power market, breaking monopolies, lowering application thresholds, and improving the efficiency of computing power utilization. In the web3 ecosystem, the decentralized computing 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 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 already 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 breaches 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 within a certain 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 some 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 continuous revenue from the subsequent use of the model. This is especially true when the model is integrated into other products and services, making it hard for the original creators to track usage, let alone derive any revenue from it. Additionally, 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 models in the future. A certain protocol uses a specific ERC standard, combined with 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 still in its early trial phase, but as market acceptance increases and the scope of participation expands, its innovativeness 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 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 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 open AI-native application platform provides a comprehensive and easy-to-use set of creative tools, supporting users to configure robot functions, appearances, voices, and connect 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 dedicated large language model to make role-playing more human-like; voice cloning technology can accelerate personalized interaction in 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, there is currently more exploration of the infrastructure layer, including how to obtain high-quality data, protect data privacy, host models on the chain, efficiently utilize 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.