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

Web3, as a new internet paradigm that is decentralized, open, and transparent, has a natural opportunity for integration with AI. Under the traditional centralized architecture, AI computing and data resources are subject to strict control, facing numerous challenges such as computing power bottlenecks, privacy leaks, and algorithmic black boxes. 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 developing anti-cheating algorithms, supporting 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: A 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 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.

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 silos.
• Personal data privacy is at risk of leakage and abuse.

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

• Users can sell idle networks to AI companies, decentralized data scraping networks to provide real, high-quality data for AI model training.
• Adopting the "label to earn" model, incentivizing global workers with 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 sides, incentivizing data innovation and sharing.

However, there are also some issues with data acquisition in the real world, such as inconsistent data quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data could 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 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, and regulations such as the European Union's General Data Protection Regulation (GDPR) reflect a strict guardianship of personal privacy. However, this also poses 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 for computation operations directly on encrypted data without the need to decrypt the data, and the computation results are consistent with the results 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 enormous advantages to AI companies, enabling them to securely open API services while protecting trade secrets.

FHEML supports encryption processing 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, 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 supply of existing computational resources. For example, the training of a certain well-known AI model requires tremendous computing 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 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 exacerbate the issue of computing power supply. AI practitioners are caught in a dilemma: either purchase hardware themselves or lease cloud resources, and they urgently need a scalable and cost-effective computing service model.

A decentralized AI computing power network aggregates idle GPU resources from around the world, providing an economically accessible computing power market for AI companies. Computing power demanders 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 the general decentralized computing networks, there are platforms focused on AI training and dedicated computing networks focused on AI inference.

Decentralized computing networks provide a fair and transparent computing power market, breaking monopolies, lowering application thresholds, and improving computing power utilization efficiency. In the web3 ecosystem, decentralized computing 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 Empowers Edge AI

Imagine that your mobile phone, smart watch, and even smart devices in your home all have the capability to run AI—this is the charm of Edge AI. It enables computing 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 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, 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 innovation of this public chain provide strong support for DePIN projects. Currently, the market value of DePIN projects on this public chain exceeds 10 billion USD, and some well-known projects have made significant progress.

IMO: AI Model Releases New Paradigm

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

In the traditional model, due to the absence of a revenue-sharing mechanism, once an AI model is developed and launched in the market, developers often find it difficult to derive continuous income from the subsequent use of the model, especially when the model is integrated into other products and services, making it hard for the original creators to track usage, let alone obtain revenue from it. Furthermore, the performance and effectiveness of AI models often lack transparency, making it challenging for potential investors and users to assess their true value, thereby limiting the market recognition and commercial potential of the models.

IMO provides a new way of funding support and value sharing 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 two ERC standards, combining AI oracles and OPML technology to ensure the authenticity of 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 for the sustainable development of AI technology. The IMO is currently still in the early trial stage, but with the increase in market acceptance and the expansion of participation, its innovation and potential value are worth looking forward to.

AI Agent: A New Era of Interactive Experience

AI Agents can perceive the environment, think independently, and take corresponding actions to achieve established 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 AI-native 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, dedicated to building 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 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, how to host models on-chain, how to improve the efficient use of decentralized computing power, and how to 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.