Author: Zhang Feng
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 merely a simple technological overlay, but a profound integration from underlying logic to application forms. From the initial role of AI as an "efficiency tool" to optimize Web3 development, to its gradual evolution into an "intelligent ecosystem" with autonomous evolution capabilities, it can be said that this represents a significant paradigm shift.
(1) Phase One: AI and Web3 as Mutual Infrastructure Optimizers
AI promotes the security of smart contracts. In the early stages of Web3 development, the security issues of smart contracts became a key bottleneck limiting their large-scale application. According to statistics from blockchain security company CertiK, losses due to security incidents reached nearly $2.5 billion in the first half of 2025. Traditional manual auditing methods are time-consuming and labor-intensive, heavily relying 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; identify potential logical flaws through pattern recognition; and generate interactive visualizations of smart contracts to assist developers in understanding complex contract relationships. For example, AI verification engines have provided formal verification services for some leading DeFi protocols, reducing audit time by over 60%. The emergence of such tools has significantly lowered the threshold and risk of Web3 development.
AI greatly enhances programming efficiency. With breakthroughs in code generation from large language models like GPT-4 and Claude, AI is becoming the "intelligent pair programmer" for Web3 developers. Developers can describe their requirements in natural language, and AI can generate corresponding smart contract frameworks, front-end interaction code, and even deployment scripts. This AI-assisted development model not only improves development efficiency but, more importantly, enables developers without a blockchain professional background to quickly enter the Web3 field, accelerating ecological innovation and iteration.
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 contract interaction.
Distributed computing enhances the efficiency of cloud computing infrastructure. At the same time, Web3 also provides AI with infrastructure options beyond traditional cloud computing. Centralized cloud computing models face issues such as single points of failure, data monopolies, and opaque pricing, 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 bidirectional empowerment constitutes the first phase of the AI+Web3 integration, but it is merely the starting point of the fusion.
For example, some decentralized computing markets allow users to rent idle GPU resources, providing distributed computing power for AI model training at costs 30-50% lower than traditional cloud services. Some data markets ensure data ownership and transaction transparency through blockchain technology, allowing data providers to participate in AI model training and receive corresponding benefits without disclosing original data.
(2) Phase Two: Verifiable and Value-Added AI Product Forms
The emergence of verifiable and value-added innovative product forms marks a new stage in the integration of AI and Web3. AI is no longer just an optimization tool but has become a core component of Web3 native applications, creating new interaction paradigms that are difficult to achieve in 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 "verifiable AI agent"—intelligent agents that can autonomously interact, make decisions, and execute tasks on the blockchain. Unlike traditional AI applications, on-chain AI agents have the following characteristics: First, verifiable behavior, meaning all interaction records and decision logic are stored on-chain and available for third-party audits; Second, economic autonomy, possessing a crypto wallet, allowing them to autonomously conduct transactions and contract interactions; Third, goal-driven, autonomously optimizing behavior based on preset goals or reinforcement learning strategies.
For example, some Autonomous Economic Agents (AEAs) can execute arbitrage strategies in decentralized exchanges, automatically adjusting parameters based on market conditions. The trading history, profit situation, and decision logic of these agents are completely transparent, forming "verifiable AI economic behavior."
The second form is the value feedback mechanism for data contributions. In traditional AI models, user-contributed training data is often used by platforms without compensation, with the created value monopolized by centralized companies. Web3 changes this model through token economics.
More refined data value-added products have begun to emerge, characterized by several aspects. First, personal data tokenization, allowing users to encapsulate their behavioral data and creative content in the form of NFTs or fungible tokens for sale in data markets; second, incentive models for federated learning, where devices participating in federated learning receive rewards based on data quality and contribution; third, crowdsourced model training, where AI companies issue tokens to raise training data and labeling work, with participants sharing future model profits.
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 form is the intelligent governance upgrade of DAOs. Decentralized Autonomous Organizations (DAOs), as the core organizational form of Web3, also benefit from the deep integration of AI. Traditional DAOs face issues such as low voting participation rates, varying proposal quality, and inefficient decision-making, which are being improved through AI tools. The emergence of AI governance tools enables DAOs to intelligently analyze proposals, with AI automatically assessing the feasibility, potential impact, and risks of proposals, providing decision-making references for members; predict voting behavior, predicting the probability of proposal approval based on members' historical behavior and preferences, optimizing governance strategies; automate execution, with AI agents automatically executing approved governance decisions, reducing manual operation delays.
Many AI governance assistants are now capable of automatically summarizing proposal content, identifying potential conflicts, and visualizing complex governance data, enabling DAO members to make more informed decisions.
(3) Phase Three: Formation of a Valuable Closed Loop Self-Evolving Ecosystem
As AI and Web3 further integrate deeply, a self-evolving ecosystem with a valuable closed loop is gradually taking shape. This intelligent value distribution not only improves incentive efficiency but, more importantly, allows ecological value to flow more fairly to true contributors, forming a healthier and more sustainable ecosystem.
One characteristic is the formation of a true data flywheel. When AI-driven DApps (decentralized applications) reach scale, a more profound transformation begins to occur: the ecosystem's self-evolving capability. The core mechanism here is the "data flywheel"—more users generate more data, which trains better AI models, attracting more users, creating a positive feedback loop.
Unlike traditional internet data flywheels, the data flywheel in the Web3 environment has unique advantages:
First, data sovereignty belongs to users: Users control their data and can selectively authorize it to specific applications.
Second, value circulates within the ecosystem: Data contributors, model trainers, and application developers share the dividends of ecological growth.
Third, anti-monopoly: Open-source models and decentralized storage prevent a single entity from controlling key data.
Taking decentralized social graph protocols as an example: Users' social behaviors across different DApps form combinable graph data, which can be used to train recommendation algorithms. The improved algorithms provide more accurate social recommendations, attracting more users to join. Users always retain ownership of their data and can choose to use it for personalized services in other applications, maximizing data value.
The second characteristic is the formation of autonomous economic systems. Based on the data flywheel, the integration of AI and Web3 is giving rise to truly autonomous economic systems. These systems can autonomously adjust parameters based on external conditions and internal states, achieving continuous optimization of the ecosystem.
For example, AI-driven decentralized automated market makers (AMMs) can automatically adjust fee curves based on market depth and liquidity demand; predict market fluctuations and adjust reserve ratios in advance; identify and defend against manipulation attacks, maintaining system stability.
Such systems no longer rely on manual parameter adjustments but continuously optimize strategies through reinforcement learning, forming financial market infrastructure with adaptive capabilities.
The third characteristic is the formation of value capture mechanisms. In traditional internet platforms, most of the value created by network effects is captured by platform companies, leaving users and developers with only a small portion. Web3 changes this distribution model through token economics, and the addition of AI makes value distribution smarter and fairer.
Intelligent value capture mechanisms include dynamic reward distribution, which dynamically adjusts token rewards based on users' real contributions to the ecosystem (data quality, activity, network effects, etc.); predictive incentives, where AI predicts which behaviors or contributions will bring long-term ecological value and provides incentives in advance; anti-manipulation mechanisms, which use anomaly detection algorithms to identify behaviors such as wash trading and witch attacks, ensuring fairness in reward distribution.
(4) Future Vision: A Symbiotic and Integrated Intelligent Digital Society
The rise of new digital organizations. The deep integration of AI and Web3 will give birth to entirely new organizational forms—highly autonomous, adaptive, and value-driven digital entities. These organizations may have the following characteristics: human-machine hybrid governance, where human members and AI agents participate in decision-making, each leveraging their comparative advantages; dynamic organizational structure, automatically forming and adjusting workgroups based on task needs, breaking fixed departmental boundaries; transparent value flow, where all contributions and distributions are automatically executed through smart contracts, reducing trust costs.
Such organizations will be more flexible and adaptive than traditional companies, and more intelligent and efficient than traditional DAOs, representing a new direction for organizational forms in the digital age.
Redefining human-machine relationships. The integration of AI and Web3 will redefine the relationship between humans and machines, where humans are no longer the sole controllers of technology but form a symbiotic relationship with AI agents. The two are collaborative rather than substitutive, with AI handling repetitive calculations and pattern recognition while humans focus on creative decision-making and ethical judgments; mutually enhancing rather than diminishing, with AI tools enhancing individual capabilities, enabling everyone to participate in complex value creation; and value-sharing rather than exploitation, with the value created jointly by humans and AI being fairly distributed through transparent mechanisms. This new type of human-machine relationship will drive society towards a more inclusive, efficient, and sustainable direction.
Deep challenges of technological integration. Despite the broad prospects of AI+Web3 integration, it still faces numerous challenges, including scalability issues, where on-chain AI computation requires substantial resources, conflicting with blockchain scalability; the balance between privacy and transparency, as AI training requires data while blockchain pursues transparency, creating inherent tension; and regulatory uncertainty, with issues such as the legal status of autonomous AI agents and the liability of smart contracts still unclear.
Addressing these challenges requires the coordinated advancement of technological innovation and institutional design. Privacy protection technologies such as zero-knowledge proofs and secure multi-party computation are expected to enable AI model training while protecting data privacy; layer two scaling solutions and modular blockchain architectures can improve on-chain computing efficiency; and DAO-driven community governance can establish ethical frameworks and oversight mechanisms for AI systems.
(5) The Path to Advancement: Evolution from Tools to Partners
The integration of AI and Web3 will undergo an evolution from "efficiency tools" to "autonomous ecosystems," progressing from surface-level to deeper levels. From the initial role of AI as an efficiency tool to optimize Web3 development, to becoming a core component of Web3 native applications, and ultimately giving rise to an autonomous ecosystem with self-evolving capabilities, this path reflects the intrinsic logic of technological integration: from solving specific problems to creating new possibilities, and finally forming a new paradigm.
This transformation is not only a technological advancement but also an innovation in the ways 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, fair, and intelligent digital society. In this society, technology is no longer a tool for a few to monopolize profits but rather the infrastructure for shared prosperity; innovation is no longer the patent of centralized organizations but an emergent property of distributed networks.
The integration of AI and Web3 is not merely a simple overlay 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 advancing towards a more open, intelligent, and mutually prosperous digital future.
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