<div id="readability-page-1"><div><div><p><span>Author: Zhang Feng</span></p><p><span><span>Artificial intelligence (AI) is reshaping productivity with its powerful learning and generative capabilities, while Web3 is reconstructing trust and value transfer mechanisms through blockchain and decentralized protocols. The combination of the two is not a simple technological overlay, but a deep integration from underlying logic to application forms. From AI initially serving as an &#34;efficiency tool&#34; to optimize Web3 development, to its current role in fostering an &#34;intelligent ecosystem&#34; with autonomous evolution capabilities, this can be described as a profound paradigm shift.</span></span></p><p><span><span><img src="https://imageproxy.pbkrs.com/https://img.jinse.com.cn/7426609_image3.png?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" width="600" height="364" original-src="https://imageproxy.pbkrs.com/https://img.jinse.com.cn/7426609_image3.png"/></span></span></p><h2><span><span>(I) First Phase: AI and Web3 as Infrastructure Optimizers for Each Other</span></span></h2><p><strong><span><span>AI</span></span></strong><strong><span><span>Promoting Smart Contract Security. In the early stages of Web3 development, the security of smart contracts became a key bottleneck restricting their large-scale application. According to blockchain security company CertiK, nearly $2.5 billion in losses were incurred due to security incidents in the first half of 2025 alone. Traditional manual auditing methods are time-consuming, labor-intensive, and highly dependent on the experience of auditing experts. The intervention of AI technology has changed this situation. Deep learning-based code analysis tools can automatically detect common vulnerability patterns such as reentrancy attacks and integer overflows; discover potential logical flaws through pattern recognition; and generate interactive visualizations of smart contracts to help developers understand complex contract relationships. For example, AI verification engines have provided formal verification services for some leading DeFi protocols, reducing audit time by more than 60%. The emergence of such tools has significantly lowered the threshold and risks of Web3 development. AI greatly improves programming efficiency. With breakthroughs in code generation from large language models such as GPT-4 and Claude, AI is becoming an &#34;intelligent pair programmer&#34; for Web3 developers. Developers can describe their requirements using natural language, and AI can then generate corresponding smart contract frameworks, front-end interactive code, and even deployment scripts. This AI-assisted development model not only improves development efficiency, but more importantly, it enables developers without a blockchain background to quickly enter the Web3 field, accelerating the innovation and iteration of the ecosystem. For example, some decentralized application platforms have launched AI development kits that can automatically generate smart contracts in specific languages ​​based on developers' intentions; provide contract optimization suggestions to reduce gas consumption; and generate React components and API interfaces for interaction with contracts. Distributed computing power improves the efficiency of cloud computing infrastructure. Meanwhile, Web3 also provides AI with infrastructure options beyond traditional cloud computing. Centralized cloud computing models suffer from single points of failure, data monopolies, and price opacity, while blockchain-based distributed computing networks offer new solutions. AI optimizes the development and application of Web3, while Web3 provides decentralized infrastructure for AI. This two-way empowerment constitutes the first stage of AI+Web3 integration, but this is only the starting point of integration. For example, some decentralized computing power markets allow users to rent out idle GPU resources to provide distributed computing power for AI model training, reducing costs by 30-50% compared to traditional cloud services. Meanwhile, some data markets use blockchain technology to ensure data ownership and transaction transparency, enabling data providers to participate in AI model training without disclosing the original data and to obtain corresponding benefits. (II) Second Stage: Verifiable and Valuable AI Product Forms The emergence of verifiable and valuable innovative product forms marks a new stage in the integration of AI and Web3. AI is no longer merely an optimization tool, but has become a core component of native Web3 applications, creating new interaction paradigms that are difficult to achieve with the traditional internet. The first form is the rise of on-chain AI agents. With the improvement of infrastructure, the combination of AI and Web3 has begun to give rise to entirely new product forms. The most representative is the &#34;verifiable AI agent&#34;—an intelligent agent capable of autonomously interacting, making decisions, and executing tasks on the blockchain. Unlike traditional AI applications, on-chain AI agents have the following characteristics. First, the behavior is verifiable, meaning all interaction records and decision-making logic are stored on-chain and can be audited by third parties. Second, there is economic autonomy, meaning they possess encrypted wallets, enabling them to autonomously conduct transactions and contract interactions. Third, it is goal-driven, meaning they autonomously optimize their behavior based on preset goals or reinforcement learning strategies. For example, some Autonomous Economic Agents (AEAs) are already able to execute arbitrage strategies on decentralized exchanges, automatically adjusting parameters according to market conditions. The transaction history, profitability, and decision-making logic of these agents are completely transparent, forming &#34;verifiable AI economic behavior.&#34; The second form is the value feedback mechanism for data contributions. In traditional AI models, user-contributed training data is often used free of charge by the platform, and the value created is exclusively appropriated by centralized companies. Web3 changes this model through token economics. More refined data-value products have begun to emerge, with the following key characteristics. First, personal data tokenization: users can encapsulate their behavioral data and creative content in the form of NFTs or fungible tokens and sell them on the data marketplace. Second, incentivized federated learning models: devices participating in federated learning are rewarded based on data quality and contribution. Third, model training crowdsourcing: AI companies raise funds for training data and annotation work by issuing tokens, with participants sharing the future revenue of the model. Some emerging projects have built decentralized machine learning networks where participants earn token rewards by contributing computing resources or training data. This model rebalances the relationship between AI value creation and distribution, transforming users from passive data providers into co-builders and beneficiaries of the ecosystem. The third type is the intelligent governance upgrade of DAOs. Decentralized Autonomous Organizations (DAOs), as a core organizational form of Web3, also benefit from the deep integration of AI. Problems faced by traditional DAOs, such as low voting participation rates, inconsistent proposal quality, and low decision-making efficiency, are being improved through AI tools. The emergence of AI governance tools enables DAOs to: **Intelligent Proposal Analysis:** AI automatically analyzes the feasibility, potential impact, and risks of proposals, providing decision-making references for members; **Voting Behavior Prediction:** Based on members' historical behavior and preferences, it predicts the probability of proposals passing, optimizing governance strategies; and **Automated Execution:** Through AI agents, it automatically executes approved governance decisions, reducing delays caused by manual operations. Many AI governance assistants are now capable of automatically summarizing proposals, identifying potential conflicts, and visualizing complex governance data, enabling DAO members to make more informed decisions. (III) Third Stage: Forming a Self-Evolving Ecosystem with a Valuable Closed Loop As AI and Web3 further integrate, a self-evolving ecosystem with a value closed loop is gradually emerging. This intelligent value allocation not only improves incentive efficiency but, more importantly, enables ecosystem value to flow more fairly to genuine contributors, forming a healthier and more sustainable ecosystem. One characteristic is the formation of a true data flywheel. As AI-driven DApps (decentralized applications) reach a certain scale, a more profound transformation begins to occur: the self-evolving capability of the ecosystem. The core mechanism here is the &#34;data flywheel&#34;—more users generate more data, data trains better AI models, and better models attract more users, creating a positive feedback loop. Unlike the data flywheel of the traditional Internet, the data flywheel in the Web3 environment has unique advantages: First, data sovereignty belongs to the user: users control their own data and can selectively authorize specific applications. Second, value circulates within the ecosystem: data contributors, model trainers, and application developers share the benefits of ecosystem growth. Third, it is anti-monopolistic. **Key Feature Two:** Open-source models and decentralized storage prevent a single entity from controlling critical data. Taking decentralized social graph protocols as an example: users' social behaviors across different DApps form composable graph data. This data can be used to train recommendation algorithms, resulting in more accurate social recommendations and attracting more users. Users retain ownership of their data and can choose to use it for personalized services in other applications, maximizing its value. **Feature Two:** The formation of autonomous economic systems. Building upon the data flywheel, the integration of AI and Web3 is fostering truly autonomous economic systems. These systems can autonomously adjust parameters based on external conditions and internal states, achieving continuous ecosystem optimization. For example, AI-driven decentralized market makers (AMMs) can automatically adjust fee curves based on market depth and liquidity needs; predict market volatility and adjust reserve ratios in advance; identify and defend against manipulation attacks, and maintain system stability. These systems no longer rely on manual parameter adjustments but continuously optimize strategies through reinforcement learning, forming an adaptive financial market infrastructure. The third characteristic is the formation of a value capture mechanism. In traditional internet platforms, most of the value created by network effects is captured by the platform company, with users and developers receiving only a very small portion. Web3 changed this distribution model through token economics, and the addition of AI makes value distribution more intelligent and fair. The intelligent value capture mechanism includes: dynamic reward distribution, which dynamically adjusts token rewards based on users' actual contributions to the ecosystem (data quality, activity, network effect, etc.); predictive incentives, which use AI to predict which behaviors or contributions will bring long-term ecosystem value and provide incentives in advance; and an anti-manipulation mechanism, which uses anomaly detection algorithms to identify behaviors such as fraudulent transactions and Sybil attacks to ensure the fairness of reward distribution. (IV) Future Vision: A Symbiotic and Harmonious Intelligent Digital Society The Rise of New Digital Organizations. The deep integration of AI and Web3 will give rise to entirely new organizational forms—highly autonomous, adaptive, and value-driven digital entities. These organizations may possess the following characteristics: **Hybrid human-machine governance:** Human members and AI agents jointly participate in decision-making, each leveraging their comparative advantages; **Dynamic organizational structure:** Working groups are automatically formed and adjusted based on task requirements, breaking down fixed departmental boundaries; **Transparent value flow:** All contributions and allocations are automatically executed through smart contracts, reducing trust costs. These organizations will be more flexible and adaptable than traditional companies, and more intelligent and efficient than traditional DAOs, representing a new direction for organizational forms in the digital age. Redefining the Human-Machine Relationship. The convergence of AI and Web3 will redefine the relationship between humans and machines. Humans will no longer be the sole controllers of technology, but will form a symbiotic relationship with AI agents. The two will collaborate rather than replace each other; AI will handle repetitive calculations and pattern recognition, while humans will focus on creative decision-making and ethical judgment. They will mutually reinforce each other rather than weaken each other; AI tools will enhance individual capabilities, enabling everyone to participate in complex value creation. The value created jointly by humans and AI will be shared rather than exploited, and the value created will be fairly distributed through transparent mechanisms. This new human-machine relationship will drive society towards a more inclusive, efficient, and sustainable direction. The profound challenges of technological convergence. Despite the promising future of the AI+Web3 convergence, numerous challenges remain, including: scalability issues (on-chain AI computing requires significant resources, contradicting the scalability requirements of blockchain); the balance between privacy and transparency (AI training needs data, while blockchain prioritizes transparency, creating an inherent tension); and regulatory uncertainty (the legal status of autonomous AI agents and the attribution of responsibility for smart contracts remain unclear). Solving these challenges requires the coordinated advancement of technological innovation and institutional design. Privacy-preserving technologies such as zero-knowledge proofs and secure multi-party computation are expected to enable AI model training while protecting data privacy; layer-2 scaling solutions and modular blockchain architectures can improve on-chain computing efficiency; and DAO-driven community governance can establish an ethical framework and oversight mechanism for AI systems. (V) The Path to Advancement: From Tool to Partner The integration of AI and Web3 will undergo an evolutionary process from superficial to profound, from &#34;efficiency tool&#34; to &#34;autonomous ecosystem.&#34; From its initial role as an efficiency tool for optimizing Web3 development, to becoming a core component of native Web3 applications, and ultimately fostering a self-evolving ecosystem, this path reflects the inherent logic of technological convergence: from solving specific problems to creating new possibilities, and finally forming a new paradigm. This transformation is not merely technological progress, but also a revolution in the way value is created and distributed. When the capabilities of AI are deeply integrated with the value transfer mechanisms of Web3, we can expect to build a more open, equitable, and intelligent digital society. In this society, technology is no longer a tool for a few to monopolize profits, but rather an infrastructure for everyone to share prosperity; innovation is no longer the patent of centralized organizations, but an emergent attribute of distributed networks. The integration of AI and Web3 is not merely a simple superposition of two technological fields, but a paradigm revolution in the digital world. On this path, challenges and opportunities coexist, but the direction is clear: steadily moving towards a more open, intelligent, and prosperous digital future.</span></span></strong></p></div></div></div>

<p>The integration of AI and Web3 is transforming productivity and trust mechanisms. Initially, AI served as an efficiency tool for Web3, enhancing smart contract security and programming efficiency. AI tools have reduced audit times significantly and enabled developers without blockchain backgrounds to innovate. The evolution has led to verifiable AI agents capable of autonomous decision-making on the blockchain, creating new economic behaviors. Additionally, Web3 allows for a value feedback mechanism for data contributions, ensuring that users benefit from their data in AI models, marking a significant shift in the ecosystem.</p>

- Artificial intelligence (AI) is significantly enhancing productivity and the development of Web3 through deep integration of their functionalities.  
- This integration leads to a redefined digital ecosystem where AI optimizes Web3 and vice versa, resulting in new product forms, autonomous economic systems, and a value capture mechanism benefiting participants fairly.  
- Future challenges include scalability, privacy concerns, and regulatory uncertainties, necessitating advancements in technology and governance to achieve a harmonious digital society.

From Tool Plugins to Intelligent Entities: The Evolution and Ecosystem Revolution of AI+Web3 Products