OP Crypto Research Report: Unlimited reverie of the possibility of combining AI and Web3

As a Web3 practitioner who has been swept by the big wave of AI, after experiencing the information explosion in the two industries in recent months, I have sorted out some thoughts and research to share with Web3 practitioners:

AI and Web3, one breaks through our imagination of the upper limit of productivity, and the other reshapes our understanding of the economic model. As a cutting-edge technology that represents the future development direction, the combination of the two seems to be a natural combination, which can always inspire unlimited imagination, but when we turn our attention to reality, we find that there are very few projects that truly combine the two. few. The collision of the two tracks gave birth to a new narrative, but it also gave birth to a lot of bubbles and gimmicks, and many beautiful visions that complement each other in theory may not have real needs in reality; and those that can meet the actual needs Projects will also be difficult to implement due to cost or technical bottlenecks.

**I think that the idea of Web3 and AI ebb and flow is also proportional to the number of web3 projects that see AI content in the primary market and the number of AI projects that encounter unnecessary web3ization. **AI native entrepreneurs/project parties do not actually think about how to web3, such as data confirmation and on-chain, economic model, distribution of production relations, etc., because bottom-up projects in AI large-scale models have high demand for resources , The large amount of resources required makes AI very centralized from training to operation, and I am very cautious about the actual implementation feasibility of some Web3 project parties who so-called help AI improve production relations.

The Web3 market has encountered a lot of bottlenecks at both the macro policy level and the innovation level. Leaving aside the new regulatory pressure, from the innovation level, when AI improves productivity at a high speed and replaces human thinking ability, it attracts the vast majority of users, Builders and In the eyes of investors, Web3's industrial innovation dilemma is even more unconcealed. It has been a long time since Web3 has not had innovations at the level of AI. To be honest, most of the new projects that are getting some attention right now are minor changes to past technologies/products. For example, better pledge methods, multi-chain wallets with better user experience, meme coins with new gameplay, Dex with better liquidity on the new public chain, etc. These so-called "innovations" are helpful for introducing more users or areas. Does blockchain usage penetration really help, and is it what the industry really needs.

We need some new fields that can bring AI into Web3 and allow Web3 to go out, and the underlying nature of these blockchains, such as (1. Content creation rights confirmation, 2. Identity confirmation, 3. Financial system innovation, 4. . trustless termination, etc.) is about the future of the next paradigm shift for the entire industry. In line with the purpose of seeking an organic combination, this article starts from the adaptation and complementarity of the underlying technologies, comprehensively takes stock of the new fields generated by the combination of AI and Web3, and makes a comprehensive analysis of the actual needs, development bottlenecks, and prospects of each direction in these fields. A summary and analysis were made.

The picture above refers to the KK boss of Hash Global

TL;DR

• **AI and web3 have a conflict in the underlying logic. The large amount of resources required by the AI big model makes AI very centralized from training to operation, while the prospect of Web3 based on blockchain is decentralized and transparent. **This makes the combination of AI and web3 at the bottom layer very difficult. Whether its business logic is valid and whether the actual demand exists needs to be deliberated.
• But it is this contradictory logic at the bottom that makes AI and Web3 complement each other, not seeking to be the core of each other's narrative but a solution to each other's pain points, boosting their respective development. The two technologies will also bring many new narratives to each other, leaving a huge room for imagination. **The economic model design of Web3 can allow many AI project parties to increase the utilization rate of funds to boost project activity, and the benefits of the blockchain itself, such as reducing infrastructure costs and verifying identities, are in the data black box in artificial intelligence. Injecting democracy and transparency and providing incentives for data contribution can shed new light on product design for AI project teams. **
• **At the infrastructure layer, the decentralized mechanism of Web3 can solve the risks and problems of current AI from the bottom, such as privacy protection, data abuse, etc. **

• Provide a decentralized market for the essential elements of AI development, such as computing power and data, maximize the use of idle resources, optimize resource utilization and allocation, and promote the development and application of AI large models.
• The decentralization mechanism of Web3 enables AI to become more democratic from the bottom level. Through decentralized deployment, training and use of AI, users' data privacy can be better protected, and there is also the opportunity to share Data gets paid off.
• Blockchain can also be used to record and monitor the behavior of AI, thereby improving the security of AI and promoting the use of automated AI agents in various scenarios.

• **AI at the application layer can help the development and popularization of Web3 applications. **

• First, as a productivity tool, AI can help Web3 applications to greatly increase the development speed, and as a knowledge engine, it can reduce the interaction and learning costs between users and dApps, and help more users enter Web3.
• AI can significantly reduce the technical threshold for developing dApps and issuing projects, making the project's competitiveness more focused on innovation and operations. It is in this direction that generative AI can bring new narratives to Web3 applications, For example, cutting-edge elements such as virtual humans and role AI are implanted in games, social and other ecology, and new gameplays are developed.

Infrastructure Layer

Token Incentive and Governance Mechanism: Decentralized Market Empowers AI Infrastructure

In the era of AI large models, all aspects of the infrastructure supporting the development of AI will become particularly important.

In the process of building and developing AI infrastructure, a key challenge is how to effectively motivate and coordinate participants so that they can jointly promote the development and operation of the system. Decentralized markets and token incentives provide a novel and powerful way to solve this problem. In such a market, tokens play an important role as a digital asset and value medium. Tokens can represent certain rights, functions or resources, and their transactions and transfers are carried out through smart contracts, realizing a safe, transparent and automated transaction process.

For AI infrastructure, token incentives can play multiple roles. First, tokens can be used as an incentive to reward and encourage those who contribute to the 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 realized the data flywheel effect through the chatbot creation workshop and data analysis. Users can customize the chatbot's voice, functions and knowledge base on the Myshell platform and interact with them. The data collected from these interactions are used to improve the performance of robots and personalized services, attract more users to use the platform, further increase data and value, and form a virtuous circle of growth.

By providing token rewards to participants, the economic model of Web3 can also attract more people to participate in the construction of AI infrastructure and promote resource sharing and cooperation. Tokens can be used to enable value flow and exchange in decentralized markets. Participants can use tokens to buy and sell resources, services, and algorithm models to achieve transactions and collaborations 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 respective needs and interests. **

Homomorphic encryption and federated learning: Integrating privacy protection into AI's underlying training

Efficient model training while ensuring personal privacy and data security has long been a challenge. In this regard, homomorphic encryption technology provides a powerful privacy protection method, which can be integrated into the underlying training of AI to ensure the security of sensitive data.

Homomorphic encryption is a special encryption technique that allows computations to be performed on data in an encrypted state without decryption. This means that model training and calculations can be performed on encrypted data 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 leaking sensitive data.

Here are some key steps and considerations when using homomorphic encryption for AI training:

1. **Data encryption: **Encrypt the data participating in AI training using a homomorphic encryption algorithm. This ensures data privacy and confidentiality during training.
2. **Encrypted computing: **Execute computing operations in an encrypted state, including model training, optimization, and inference. Homomorphic encryption makes these calculations possible without decrypting the data.
3. **Security parameter sharing: **All parties participating in the training need to share and exchange security parameters required for encryption calculations. These parameters are used to control the homomorphic encryption process and decryption results.
4. Encrypted result processing: After the encrypted calculation is completed, the result can be decrypted to obtain the final model weight or predicted output. Appropriate security measures need to be taken when decrypting the results to prevent data leakage or unauthorized access.

The integration of homomorphic encryption technology into privacy protection in the underlying training of AI has some advantages and potential application scenarios: a. Privacy Preservation: Homomorphic encryption makes it possible to train models on sensitive data without actually accessing or exposing that data. This helps maintain individual privacy and control of data owners. b. Data Collaboration: Multiple data owners can jointly participate in AI training without sharing their raw 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 compliance-compliant approach for AI training. This type of privacy can also be achieved through decentralized computing platforms. For example, Fluence is a decentralized computing platform that can run many programs including AI, aiming to realize the 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 backends on a permissionless peer-to-peer network.

zkML and AI inference on the chain: AI agent behavior monitoring and rights and responsibilities constraints

**In the context of the rapid development and widespread application of artificial intelligence (AI) technology, it becomes even more important to ensure that AI systems behave in accordance with ethical and legal requirements. **AI systems are often viewed as agent entities capable of performing tasks and making decisions that can have profound effects on humans and society. Therefore, monitoring the behavior of AI agents and constraining their rights and responsibilities has become a key issue in protecting public interests and personal rights. As an innovative method, zkML provides a safe, verifiable and transparent solution for monitoring AI agent behavior and constraining 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 key data or sensitive information will not be leaked during the operation of the AI system. By applying zero-knowledge proofs during computation, the project can prove to regulators or stakeholders that its AI performed a specific task without revealing actual data or internal models. This approach protects personal privacy and commercial confidentiality, while providing a means of auditing and verifying the behavior of AI agents. A decentralized monitoring and constraint framework established by zkML can monitor and review the decision-making process and behavior 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 the AI system, a series of rules and conditions can be set to limit the scope of behavior of the AI agent and ensure its compliance with ethical guidelines and legal regulations. This power-responsibility constraint mechanism makes the AI system a reliable tool that can create value for human society without abusing power or causing harm to human interests. This technology provides an important foundation for building sustainable, ethical and responsible AI systems.

execution layer

Improving production efficiency, an accelerator for Web3 development

In the development of Web3, artificial intelligence (AI) plays an important role, combining with various fields to improve productivity and create better user experience. Here are a few key areas where AI and Web3 combine:

1. AI and on-chain data collection and analysis

AI technology plays an important role in on-chain data collection and analysis. As a distributed database, the blockchain records a large number of transactions and information. By leveraging AI technology, data on the blockchain can be better understood and utilized. For example, Web3 Analytics is an AI-based analytics platform that utilizes machine learning and data mining algorithms to collect, process and analyze on-chain data. It can help users gain insights into on-chain transactions, market trends, and user behavior patterns, thereby providing users with more accurate data analysis and decision support. A similar platform is MinMax AI, which provides AI-based on-chain data analysis tools to help users discover potential market opportunities and trends. 2. AI and automated dApp development

The application of AI technology in automating the dApp development process is also very important. Smart contract and dApp development usually requires writing a lot of code, and tedious testing and deployment work. By combining AI with smart contracts and dApp development tools, a more efficient and intelligent dApp development process can be achieved. AI can help automate code generation, verification and testing of smart contracts, and deployment and maintenance of dApps. This saves time and resources and increases the efficiency and accuracy of the development process. For example, some AI-assisted development tools use natural language processing and machine learning techniques to help developers write smart contracts faster and automatically detect and fix potential errors. 3. AI and on-chain transaction security

In the Web3 world, on-chain transaction security is paramount. Due to the openness and transparency of the blockchain, malicious attacks, fraud and data leakage are all risks. 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, fraud and data leakage, and provides real-time monitoring and alarm mechanisms to ensure the security and stability of transactions on the chain. Similar security tools include AI-powered Sentinel.

Optimize resource allocation, a navigator for the Web3 world

Optimizing resource allocation is a key challenge in a Web3 world. With the combination of blockchain technology and artificial intelligence, we can use AI as a navigator to achieve more efficient resource allocation and utilization. Here are a few areas where AI is navigating the Web3 world:

1. **AI and chain activity optimization: **Activities on the blockchain include transactions, contract execution, and data storage. Through the intelligent analysis and prediction capabilities of AI, we can better optimize on-chain activities and improve overall efficiency and performance. AI can help identify transaction patterns, detect unusual activity, and provide real-time recommendations to optimize resource allocation for blockchain networks through data analysis and model training.
2. **AI and on-chain advertising mechanism: **In the Web3 world, advertising is also a type of resource. AI can play a key role in the on-chain advertising mechanism, helping advertisers target audiences more accurately and provide personalized advertising content. By analyzing the data and behavior patterns of users on the chain, AI can achieve more accurate advertisement placement, improve the click-through rate and conversion rate of advertisements, thereby optimizing the allocation and utilization of resources.
3. **AI and DAO Governance: ** Decentralized Autonomous Organization (DAO) is a new type of organization in the Web3 world. AI can be used as an important tool for DAO governance, assisting decision-making, voting mechanism and community governance. AI can help DAO members better understand the needs and opinions of the community through data analysis and prediction, and provide decision support. Through the participation of AI, DAO can operate more efficiently, optimize resource allocation, and promote the development and growth of the community.

Application Layer

Reduce entry barriers, a booster for Web3 popularization

• Friendly user interface embedded with AI

For example, Fuzzland, a Web3 audit platform, uses AI to help code auditors check code vulnerabilities and provide natural language explanations to assist audit expertise. Fuzzland also uses AI to provide natural language explanations of formal specifications and contract codes, as well as some sample code to help developers understand potential problems in the code. By combining AI technology with audit expertise, Fuzzland makes it easier for developers in the Web3 industry to understand and explain code, improving audit efficiency and accuracy.

• Interpretation of smart contracts embedded with AI
• Smart contract writing embedded with AI

In the development of Web3, lowering the barriers to entry is the key to popularization. To achieve this, technologies embedded with artificial intelligence (AI) play an important role in providing user-friendly interfaces, smart contract interpretation, and smart contract writing. The friendly user interface embedded with AI provides a more intuitive and convenient operation experience for users using the Web3 platform. Traditional blockchain technology usually requires users to learn complex commands and syntax in order to interact and perform operations. However, by applying AI technology to user interface design, functions such as natural language processing and graphical interface can be realized, so that users can easily use the Web3 platform to perform various operations without in-depth understanding of technical details. AI also provides users with the ability to better understand and interpret smart contracts. Through the application of AI technology, the automatic analysis and visual display of smart contracts can be realized, and the logical flow and condition expression in smart contracts can be clearly presented to users, so as to improve users' understanding and trust in smart contracts.

Rich plot gameplay, creative library of Web3 world

• AI and Generative NFT
• AI automatic trading agent
• Role AI and game NPC
• 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 has injected infinite creativity into the generative NFT. Generative NFT (Non-Fungible Token) is a kind of artwork or digital assets based on algorithms and data. Various unique and diverse artworks and characters can be generated through AI technology. These generative NFTs can become characters, props or scene elements in games, virtual worlds or metaverses, providing users with rich choices and personalized experiences. In the upsurge of DeFi, AI automatic trading agent also brings convenience and efficiency to the economic transaction process in the creative library. In the Web3 world, users can earn benefits by owning, trading or participating in digital assets in the creative library. AI automatic trading agents use intelligent algorithms and machine learning technology to automate asset transactions, helping users obtain the best trading opportunities and maximize returns. AIGC also brings new gameplay and ideas to the content platform and UGC community. For example, Yodayo is an AI art platform for virtual hosts and anime fans to share and create more content they love. By connecting to the AIGC engine, Yodayo makes the creation and interaction of users on the content creation platform easier and easier to operate, so that most users who are usually "silent" on traditional platforms can also become creators and up masters. Consumers become content creators, connecting more closely with and contributing to the community.

The combination of character 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 (NPCs), they can be endowed with intelligent behavior, autonomous decision-making and emotional expression. This makes the game plot more rich and varied, 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 the automatic rendering of metaverse scenes creates a more realistic and vivid environment for the virtual world in the game. For example, Inward AI will systematically analyze the behavior and preferences of players, and based on their previous interactions, let the key characters in the game provide unique tasks or information, and create a personalized storyline for each player. The real-time combat AI provided by rctAI can make every battle come alive. The characters fighting against the player can learn from the player's combat strategy, improve their skills and adjust their strategy, making the battle full of more uncertainties and changes. more exciting. The integration of these AI technologies creates a dynamic and interactive narrative, realistic and challenging combat scenes, making the game world more immersive and attractive.

Conclusion

As Web3 practitioners swept by the big AI wave, after experiencing the information explosion in the two industries in recent months, we have more in-depth thinking on the combination of AI and Web3. Although there are conflicts in the underlying logic between the two, the centralization of AI and the principle of decentralization of Web3 seem difficult to reconcile, but it is this contradictory logic that enables AI and Web3 to complement each other and become solutions to each other's pain points. promote each other's development. **The decentralization mechanism of Web3 can fundamentally solve the problems of privacy protection and data abuse 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 bottom layer is difficult, it can create many new possibilities and narratives at the application level: **AI can become an important booster for Web3 applications, greatly improving the development speed of Web3 applications and reducing the need for users and The interaction and learning costs of dApp help more users enter the Web3 world. At the same time, while AI lowers the technical threshold for dApp development and project distribution, it can also bring more ways to play and enhance competitiveness in terms of innovation and operation of the project, such as embedding virtual people and role AI in games and social ecology Novel elements will bring a new narrative and experience to Web3 applications, and further promote the development and promotion of the Web3 industry. **

Although the combination of AI and Web3 faces some challenges and limitations, we believe that only the organic combination of the two can support the narrative and ideal of the next generation Internet. We look forward to seeing the emergence of more innovative projects that can bring AI into Web3 and push Web3 to a wider field. We also hope that the development of these two cutting-edge technologies can continue to help each other break through technical bottlenecks, overcome cost constraints, and jointly Create a smarter and more open future.