Web3, as a decentralized, open, and transparent internet paradigm, has a natural synergy with AI. In traditional centralized architectures, AI computing and data resources are tightly controlled, and there are many challenges such as computational bottlenecks, privacy leaks, and algorithmic black boxes.
Web3, based on distributed technology, injects new energy into AI development through shared computing networks, open data markets, privacy computing, and other means. At the same time, AI can also bring many capabilities to Web3, such as optimizing smart contracts and anti-cheating algorithms, which help in its ecosystem construction. Therefore, exploring the combination of Web3 and AI is crucial for building the next-generation 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 AI development, just as fuel is to an engine. AI models require a large amount of high-quality data to gain deep understanding and powerful reasoning abilities. Data not only provides the training foundation for machine learning models but also determines the accuracy and reliability of the models.
In the traditional centralized AI data acquisition and utilization model, there are several main problems:
– High cost of data acquisition, which is difficult for small and medium-sized enterprises to afford.
– Data resources monopolized by tech giants, forming data silos.
– Risks of personal data leakage and abuse.
Web3 can solve the pain points of traditional models through a new decentralized data paradigm.
– Grass allows users to sell idle network capacity to AI companies, decentralizing the collection of network data. After cleaning and transformation, it provides real and high-quality data for AI model training.
– Public AI adopts a “label to earn” model, incentivizing global workers to participate in data labeling, aggregating global expertise and enhancing data analysis capabilities.
– Blockchain data trading platforms such as Ocean Protocol and Streamr provide an open and transparent trading environment for data supply and demand, incentivizing data innovation and sharing.
However, real-world data acquisition still faces some challenges, such as inconsistent data quality, processing difficulties, and lack of diversity and representativeness. Synthetic data may be the star of the Web3 data track in the future. Based on generative AI technology and simulation, synthetic data can simulate the attributes of real data and serve as an effective complement to real data, 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 era of data-driven AI, privacy protection has become a global focus. Regulations such as the General Data Protection Regulation (GDPR) in the European Union reflect strict protection of 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 abilities of AI models. Fully Homomorphic Encryption (FHE) allows direct computation on encrypted data without decryption, and the computation results are consistent with those obtained from plaintext data.
FHE provides robust protection for AI privacy computing, enabling GPU computing to perform model training and inference tasks in an environment that does not touch the original data. This brings great 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 cycle, ensuring the security of sensitive information and preventing data leakage risks. In this way, FHEML strengthens data privacy and provides a secure computing framework for AI applications. FHEML is a complement to ZKML, which proves the correct execution of machine learning, while FHEML emphasizes the computation on encrypted data to maintain data privacy.
Computational revolution: AI computing in decentralized networks
The computational complexity of current AI systems doubles every three months, leading to a surge in computing demand that exceeds the supply of existing computing resources. For example, training OpenAI’s GPT-3 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 inaccessible to most researchers and developers.
At the same time, the global utilization rate of GPUs is less than 40%, coupled with the slowdown in the performance improvement of microprocessors and chip shortages caused by supply chain and geopolitical factors, which have made the problem of computing power supply more severe.
AI practitioners are caught in a dilemma: either purchasing hardware themselves or leasing cloud resources. They urgently need an on-demand and cost-effective computing service. IO.net is a decentralized AI computing network based on Solana, which aggregates idle GPU resources worldwide and provides an economical and accessible computing market for AI companies. Computational demanders can publish computing tasks on the network, and smart contracts will assign tasks to contributing miner nodes. Miners execute tasks and submit results, receiving rewards after verification.
IO.net’s solution improves resource utilization efficiency and helps solve the bottleneck of computing power in AI and other fields. In addition to general decentralized computing networks, there are also specialized platforms focusing on AI training, such as Gensyn and Flock.io, as well as dedicated computing networks focusing on AI inference, such as Ritual and Fetch.ai. Decentralized computing networks provide a fair and transparent computing market, breaking monopolies, lowering application barriers, and improving computing utilization efficiency.
In the Web3 ecosystem, decentralized computing networks will play a key role, attracting more innovative dapps to join and jointly promote the development and application of AI technology.
DePIN: Empowering Edge AI in Web3
Imagine that your phone, smartwatch, and even smart devices in your home all have the ability to run AI—that’s the charm of Edge AI. It enables computation to happen at the source of data generation, achieving low latency, real-time processing, and 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, and DePIN enhances user privacy protection by processing data locally, reducing the risk of data leakage. Web3’s native token economy mechanism incentivizes DePIN nodes to provide computing resources, creating a sustainable ecosystem. Currently, DePIN is rapidly developing in the Solana ecosystem and has become one of the preferred public chain platforms for project deployment.
Solana’s high TPS, low transaction fees, and technological innovations provide strong support for the DePIN project. Currently, the DePIN project on Solana has a market value of over $10 billion, and well-known projects such as Render Network and Helium Network have made significant progress.
IMO: A new paradigm for AI model deployment
The concept of IMO was first proposed by the Ora protocol, tokenizing AI models. In the traditional model, due to the lack of profit-sharing mechanisms, developers often find it difficult to sustainably benefit from the subsequent use of AI models, especially when the models are integrated into other products and services, making it difficult for the original creators to track usage and derive profits.
Moreover, the performance and effectiveness of AI models often lack transparency, making it difficult for potential investors and users to evaluate their true value, limiting market recognition and commercial potential. IMO provides a new way of funding support and value sharing for open-source AI models, where investors can purchase IMO tokens and share the subsequent revenue generated by the models.
Ora Protocol uses two ERC standards, ERC-7641 and ERC-7007, combined with Onchain AI Oracle and OPML technology to ensure the authenticity of AI models and enable token holders to share revenue. The IMO model enhances transparency and trust, encourages open collaboration, adapts to the trends of the crypto market, and injects momentum into the sustainable development of AI technology.
IMO is still in the early stages of experimentation, but with the increasing market acceptance and expanding 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 predetermined goals. With the support of 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. Without explicit instructions, AI Agents can autonomously solve problems, improve efficiency, and create new value.
Myshell is an open AI-native application platform that provides a comprehensive and user-friendly set of creative 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 and empower individuals to become super creators using generative AI technology.
Myshell has trained specialized large language models to make role-playing more human-like. Voice cloning technology can accelerate personalized interaction with AI products, reducing the cost of voice synthesis by 99% and achieving voice cloning in just one minute. Using customized AI Agents, Myshell can currently be applied in various fields such as video chatting, language learning, and image generation.
In the fusion of Web3 and AI, the current focus is more on the exploration of the infrastructure layer, such as how to obtain high-quality data, protect data privacy, host models on the chain, improve the efficient utilization of decentralized computing power, and verify large language models. With the gradual improvement of these infrastructures, we have reason to believe that the convergence of Web3 and AI will give birth to a series of innovative business models and services.
