As a Web3 practitioner swept up in the AI wave, after experiencing the information explosion of the two industries in recent months, I have put together some thoughts and research for my fellow Web3 practitioners:
AI and Web3, one breaks through our imagination of productivity limits, and the other reshapes our understanding of economic models. As the frontier technology representing the future development direction, the combination of the two seems to be a natural fit, always inspiring unlimited imagination, but when we turn our attention to reality, we find that there are few projects that truly combine the two organically. The collision of the two tracks has given birth to a new narrative, but it has also spawned many bubbles and gimmicks. Many beautiful visions that complement each other in theory may not have real demand in reality, while projects that can benchmark real demand may be difficult to implement due to cost or technological bottlenecks.
I believe that the idea of Web3 and AI growing at each other’s expense is also proportional to the number of web3 projects with AI content seen in the primary market and the number of AI projects that are not necessary to be web3ized. AI-native entrepreneurs/project parties do not actually think about how to Web3ize, such as putting data rights on the chain, economic models, and production relationship allocation, because the high resource requirements of AI from training to operation make AI highly centralized, and the practical feasibility of so-called Web3 project parties helping AI improve production relationships is something I remain very cautious about.
The Web3 market has encountered significant bottlenecks both at the macro-policy level and the innovation level. New regulatory pressures aside, from an innovation perspective, when AI attracts the attention of the vast majority of users, builders, and investors by rapidly improving productivity and replacing human thinking ability, the innovation dilemma of the Web3 industry is even more difficult to conceal. Web3 has not had the level of innovation like AI for quite some time now. Frankly speaking, most of the new projects that have attracted attention lately are minor improvements to past technologies/products. For example, better staking methods, multi-chain wallets with better user experiences, meme coins with new gameplay, DEX with better liquidity on new public chains, etc. Are these so-called “innovations” really helpful in introducing more users or increasing the penetration rate of blockchain, and are they really what this industry needs?
We need some new areas that can bring AI into Web3 and allow Web3 to expand, and the actual use of these underlying blockchain natures, such as (1. Content creation confirmation, 2. Identity confirmation, 3. Financial system innovation, 4. Trustless end, etc.) are related to the future of the next paradigm shift of the entire industry. With the aim of seeking organic integration, this article starts from the adaptation and complementarity of underlying technology and comprehensively lists the new areas produced by the combination of AI and Web3, and summarizes and analyzes the actual needs, development bottlenecks, and prospects of each direction in these areas.
The above picture is based on the boss of Hash Global’s KK.
TL;DR
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There is a conflict between AI and Web3 at the underlying logic. The large amount of resources required by AI’s large models makes AI highly centralized from training to operation, while Web3 based on blockchain construction gives priority to decentralization and public transparency. This makes it very difficult for AI and Web3 to combine at the underlying level, and whether their business logic is valid and whether there is actual demand remains to be explored.
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However, it is precisely this contradictory logic at the underlying level that enables AI and Web3 to complement each other, not to become each other’s narrative core, but to become a solution to each other’s pain points, promoting their respective development. The two technologies will also bring many new narratives to each other, leaving a huge space for imagination. Web3’s economic model design can allow many AI project parties to improve the utilization of funds, promote project promotion and activation, while the benefits of blockchain, such as lowering infrastructure costs, verifying identity, injecting democracy and transparency into the data black box of artificial intelligence, and providing data contribution incentives, can provide new ideas for the product design of AI project teams.
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At the infrastructure level, Web3’s decentralized mechanism can solve the risks and problems of AI at the bottom, such as privacy protection, data abuse, etc.
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Provide a decentralized market for the necessary elements of AI development, such as computing power and data, maximize the use of idle resources, optimize resource utilization and configuration, and promote the development and application of AI large models.
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Web3’s decentralized mechanism allows AI to become more democratic from the most basic aspects. Through decentralized deployment, training, and use of AI, user data privacy can be better protected, while also having the opportunity to share data and obtain rewards.
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Blockchain can also be used to record and monitor AI behavior, thereby improving AI security and promoting the use of automated AI agents in various scenarios.
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At the application layer, AI can help the development and popularization of Web3 applications.
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Firstly, AI can help Web3 applications greatly improve development speed as a productivity tool, and as a knowledge engine, it can reduce the interaction and learning costs between users and dApps, helping more users enter Web3.
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AI can significantly reduce the technical threshold for developing dApps and launching projects, making project competitiveness more focused on innovation and operation. It is in this direction that generative AI can bring new narratives to Web3 applications, such as embedding virtual people, character AI and other cutting-edge elements in the game and social ecology, and developing new gameplay.
Infrastructure Layer
Token Incentives and Governance Mechanisms: Empowering AI Infrastructure with Decentralized Markets
In the era of AI large models, every aspect of AI infrastructure will become particularly important.
A key challenge in building and developing AI infrastructure is how to effectively incentivize and coordinate participants to work together to drive the development and operation of the system. Decentralized markets and token-incentive mechanisms provide a novel and powerful way to address this issue. In such a market, tokens play an important role as a digital asset and a medium of value. Tokens can represent specific rights, functions, or resources, and their transactions and transfers are conducted through smart contracts, achieving secure, transparent, and automated transaction processes.
For AI infrastructure, token-incentive mechanisms can play multiple roles. Firstly, tokens can serve as a means of incentive for rewarding and encouraging participants who contribute to AI infrastructure. These contributions can include providing computing resources, datasets, algorithm models, computing power, and more. For example, the recently popular AI voice chatbot creation platform, MyShell, has achieved a data flywheel effect through chatbot creation workshops and data analysis. Users can customize the voice, functions, and knowledge base of chatbots on the MyShell platform and interact with them. The data collected from these interactions is used to improve the performance and personalized services of the robots, attracting more users to use the platform, further increasing data and value, and forming a virtuous cycle of growth.
By providing token rewards to participants, the Web3 economic model can also attract more people to participate in the construction of AI infrastructure, promote resource sharing and cooperation. Tokens can be used to realize the flow and exchange of value in decentralized markets. Participants can buy and sell resources, services, and algorithm models, etc. through token transactions, realizing trading and collaboration in the market. This mechanism of value flow can provide a more flexible and efficient way for the development of AI infrastructure, enabling participants to better meet their needs and interests.
Homomorphic Encryption and Federated Learning: Incorporating Privacy Protection into the Bottom of AI Training
Ensuring effective model training while maintaining personal privacy and data security has long been a challenge. In this regard, homomorphic encryption technology provides a powerful privacy protection method that can integrate privacy protection into the underlying training of AI, ensuring the security of sensitive data.
Homomorphic encryption is a special encryption technology that allows data to be computed in an encrypted state without decryption. This means that encrypted data can be used for model training and calculation without exposing the content of the original data. By applying homomorphic encryption to the underlying training process of AI, privacy protection can be achieved without revealing sensitive data.
When using homomorphic encryption for AI training, the following are some key steps and considerations:
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Data encryption: Encrypt the data involved in AI training using homomorphic encryption algorithms. This ensures the privacy and confidentiality of the data during the training process.
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Encryption calculation: Perform calculation operations, including model training, optimization, and inference, in an encrypted state. Homomorphic encryption technology makes these calculations possible without decrypting the data.
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Secure parameter sharing: The parties involved in the training need to share and exchange the secure parameters required for encryption computation. These parameters are used to control the homomorphic encryption process and decrypt the results.
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Encryption result processing: After completing the encryption calculation, the results can be decrypted to obtain the final model weights or prediction outputs. When decrypting the results, appropriate security measures need to be taken to prevent data leakage or unauthorized access.
Integrating privacy protection into the underlying training of AI using homomorphic encryption technology has some advantages and potential applications: a. Privacy protection: Homomorphic encryption makes it possible to conduct model training on sensitive data without actually accessing or exposing this data. This helps maintain individual privacy and data owner control. b. Data collaboration: Multiple data owners can participate in AI training together without sharing their original data. Homomorphic encryption technology makes this data collaboration possible, promoting opportunities for cooperation and sharing. c. Legal compliance: For sensitive data subject to legal and regulatory restrictions (such as medical records or financial data), homomorphic encryption provides a compliant method for AI training. This privacy can also be achieved through a decentralized computing platform. For example, Fluence is a decentralized computing platform that can run many programs including AI, aimed at achieving freedom of digital innovation through peer-to-peer applications. It provides an open Web3 protocol, framework, and tools for developing and hosting applications, interfaces, and back-ends on permissionless peer-to-peer networks.
zkML and On-Chain AI Inference: Monitoring AI Agent Behavior and Enforcing Responsibility
In the rapid development and widespread application of artificial intelligence (AI) technology, it is particularly important to ensure that the behavior of AI systems complies with ethical and legal requirements. AI systems are typically regarded as agent entities that can perform tasks and make decisions, and these decisions may have far-reaching implications for humans and society. Therefore, monitoring AI agent behavior and constraining their rights and responsibilities become key issues to safeguard public interests and individual rights. As an innovative approach, zkML provides a secure, verifiable and transparent solution for monitoring AI agent behavior and constraining their rights and responsibilities. By combining zero-knowledge proof and blockchain technology, zkML ensures the compliance and credibility of AI systems while protecting privacy.
Taking Modulus Labs as an example, the project uses zkML technology to ensure that critical data or sensitive information is not leaked during the operation of AI systems. By applying zero-knowledge proof in the calculation process, the project can prove to regulators or stakeholders that its AI has performed specific tasks without disclosing actual data or internal models. This approach protects personal privacy and business secrets while providing means for auditing and verifying AI agent behavior. The decentralized monitoring and constraint framework established by zkML can monitor and review the decision-making process and behavioral path of AI agents in real time.
This decentralized monitoring mechanism ensures transparency and traceability, enabling violations or improper decisions to be discovered and corrected in a timely manner. zkML also provides a mechanism for constraining the rights and responsibilities of AI agent behavior. By combining smart contracts with the operation and decision-making process of AI systems, a series of rules and conditions can be set to limit the scope of AI agent behavior and ensure compliance with ethical standards and legal regulations. This system of rights and responsibilities ensures that AI systems become a reliable tool that can create value for human society without abusing power or harming human interests. This technology lays an important foundation for building sustainable, ethical and responsible artificial intelligence systems.
Execution Layer
Improving production efficiency, accelerator of Web3 development
In the development process of Web3, artificial intelligence (AI) plays an important role, combining with various fields to improve production efficiency and create better user experience. Here are a few key areas where AI is combined with Web3:
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AI and On-Chain Data Collection and Analysis
AI plays an important role in on-chain data collection and analysis. As a distributed database, blockchain records a large amount of transactions and information. By utilizing AI technology, it is possible to better understand and utilize the data on the blockchain. For example, Web3 Analytics is an AI-based analytics platform that uses machine learning and data mining algorithms to collect, process, and analyze on-chain data. It can help users gain insight into on-chain transactions, market trends, and user behavior patterns, providing users with more accurate data analysis and decision-making support. Similar platforms include MinMax AI, which provides AI-based on-chain data analysis tools to help users discover potential market opportunities and trends.
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AI and Automated dApp Development
AI technology is also very important in the process of automated dApp development. Smart contract and dApp development typically require writing a large amount of code and performing tedious testing and deployment work. By combining AI with smart contract and dApp development tools, it is possible to achieve a more efficient and intelligent dApp development process. AI can help automate code generation, smart contract verification and testing, as well as dApp deployment and maintenance. This can save time and resources, and improve the efficiency and accuracy of the development process. For example, some AI-assisted development tools use natural language processing and machine learning technologies to help developers write smart contracts faster and automatically detect and fix potential errors.
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AI and On-Chain Transaction Security
In the Web3 world, on-chain transaction security is crucial. Due to the openness and transparency of blockchain, there are risks of malicious attacks, fraudulent behavior, and data leaks. AI technology can be used to enhance the security and privacy protection of on-chain transactions. For example, the Web3 security platform SeQure uses AI to detect and prevent malicious attacks, fraudulent behavior, and data leaks, and provides real-time monitoring and alert mechanisms to ensure the security and stability of on-chain transactions. Similar security tools include AI-powered Sentinel.
Optimizing Resource Allocation, the Navigator of the Web3 World
In the Web3 world, optimizing resource allocation is a key challenge. With the combination of blockchain technology and artificial intelligence, we can use AI as a navigator to achieve more effective resource allocation and utilization. Here are several areas in which AI serves as a navigator in the Web3 world:
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AI and on-chain activity optimization: Activities on the blockchain include transactions, contract execution, and data storage, among others. Through the intelligent analysis and predictive capabilities of AI, we can better optimize on-chain activities and improve overall efficiency and performance. AI can help identify transaction patterns, detect abnormal activity, and provide real-time recommendations to optimize resource allocation on the blockchain network through data analysis and model training.
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AI and on-chain advertising mechanisms: In the Web3 world, advertising is also a resource. AI can play a key role in on-chain advertising mechanisms, helping advertisers more accurately target their audiences and provide personalized ad content. By analyzing on-chain user data and behavioral patterns, AI can achieve more accurate ad placements, improve click-through rates and conversion rates, and optimize resource allocation and utilization.
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AI and DAO governance: Decentralized Autonomous Organizations (DAOs) are a new type of organization in the Web3 world. AI can be an important tool for DAO governance, assisting with decision-making, voting mechanisms, and community governance. By analyzing data and making predictions, AI can help DAO members better understand community needs and opinions, and provide decision-making support. With AI’s participation, DAOs can operate more efficiently, optimize resource allocation, and promote community development and growth.
Application Layer
Lowering the barriers to entry, a booster for Web3 popularization
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User-friendly interface embedded with AI
For example, the Web3 audit platform Fuzzland uses AI to help code auditors check for code vulnerabilities and provide natural language explanations to assist with audit expertise. Fuzzland also uses AI to provide natural language explanations of formal specifications and contract code, as well as some sample code to help developers understand potential issues in the code. By combining AI technology with audit expertise, Fuzzland makes it easier for Web3 industry developers to understand and explain code, improve audit efficiency and accuracy.
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Intelligent contract interpretation embedded with AI
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AI-embedded smart contract writing
In the development of Web3, reducing barriers to entry is key to achieving mass adoption. To achieve this goal, AI-integrated technologies have played an important role in providing user-friendly interfaces, interpreting smart contracts, and writing smart contracts. AI-embedded user interfaces provide a more intuitive and convenient user experience for users of the Web3 platform. Traditional blockchain technology typically requires users to learn complex commands and syntax to interact and execute operations. However, by applying AI technology to user interface design, natural language processing, graphical interfaces, and other functions can be achieved, allowing users to easily perform various operations on the Web3 platform without having to delve deeply into technical details. AI also provides users with a better understanding and interpretation of smart contracts. By applying AI technology, automatic parsing and visualization of smart contracts can be achieved, clearly presenting the logic flow and conditional expressions in smart contracts to users, enhancing their understanding and trust of smart contracts.
Enriched plot gameplay, the creative library of the Web3 world
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AI and generative NFTs
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AI automated trading agent
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Role AI and game NPC
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AI and metaverse scene automatic rendering
The rise of generative AI has brought new possibilities to the creative industry, bringing more diverse and innovative experiences to the Web3 world, allowing users to participate in rich plots and gameplay. In the past NFT bull market, AI injected infinite creativity into generative NFTs. Generative NFTs (Non-Fungible Tokens) are algorithm and data-based artworks or digital assets that can generate unique and diversified artworks and characters through AI technology. These generative NFTs can become characters, props, or scene elements in games, virtual worlds, or metaverses, providing users with a rich choice and personalized experience. In the DeFi craze, AI automated trading agents have also brought convenience and efficiency to the economic transaction process in the creative library. In the Web3 world, users can obtain profits by owning, trading, or participating in digital assets in the creative library. AI automated trading agents use intelligent algorithms and machine learning technology to automate asset trading, helping users obtain the best trading opportunities and maximize profits. AIGC also brings new gameplay and creativity to content platforms and UGC communities. For example, Yodayo is an AI art platform for virtual anchors and anime fans to share and create more of what they love. By integrating the AIGC engine, Yodayo makes it easier for users to create and interact on content creation platforms, allowing most users who are usually “silent” on traditional platforms to become creators and up-and-coming hosts, transforming from content consumers to content creators, establishing closer ties with the community, and making contributions.
The combination of role-playing AI and game NPC brings a more realistic and interactive experience to the game plot in the creative library. By applying AI technology to game characters and non-player characters (NPC), they can be given intelligent behavior, autonomous decision-making, and emotional expression abilities. This makes the game plot more diverse, and players can interact with characters with realistic artificial intelligence to explore the game world and solve various challenges together. The combination of AI and metaverse scene automatic rendering creates a more realistic and vivid environment for the virtual world in the game. For example, Inward AI systematically analyzes the player’s behavior and preferences, based on their previous interactions, allowing key characters in the game to provide unique tasks or information, shaping personalized storylines for each player. The real-time combat AI provided by rctAI can make every battle lifelike. The characters who play against the player can continuously learn from the player’s combat strategy, improve their skills, and adjust their strategies, making the battle more unpredictable and exciting. The integration of these AI technologies creates dynamic and interactive narratives, realistic and challenging battle scenes, and makes the game world more immersive and attractive.
Conclusion
As Web3 practitioners swept by the AI wave, after experiencing the explosion of information in two industries in recent months, we have had deeper thinking about the combination of AI and Web3. Although the two have conflicts in their underlying logic, AI’s centralized characteristics and Web3’s decentralized principles seem difficult to reconcile, but it is precisely this contradictory logic that makes AI and Web3 complement each other and become solutions to each other’s pain points, promoting each other’s development. Web3’s decentralized mechanism can fundamentally solve the privacy protection and data abuse problems faced by AI, and the application of Web3 and blockchain technology can also monitor and record the behavior of AI, improve the security of AI, and promote the promotion and application of automated AI agents in various fields. Although the combination of AI and Web3 at the underlying level is difficult, it can create many new possibilities and narratives at the application level: AI can become an important aid to Web3 applications, greatly improving the development speed of Web3 applications, reducing the interaction and learning costs between users and dApps, and helping more users enter the Web3 world. At the same time, while reducing the technical threshold for dApp development and project issuance, AI can also bring more gameplay and improve competitiveness to the project in innovation and operation, such as embedding virtual people and character AI in game and social ecology, which will bring new narratives and experiences to Web3 applications and further promote the development and promotion of the Web3 industry.
Despite the challenges and limitations facing the combination of AI and Web3, we believe that only the organic combination of the two can support the narrative and ideals of the next generation of the Internet. We look forward to seeing more innovative projects that can bring AI into Web3 and push Web3 into broader fields, and we hope that the development of these two cutting-edge technologies can continue to help each other break through technical bottlenecks, overcome cost limitations, and jointly create a smarter and more open future.
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