AI is "overhauling" Web3: which sectors will undergo technological transformation?
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AI is "overhauling" Web3: which sectors will undergo technological transformation?
From the emergence of ChatGPT to the present, people's perception of AI has evolved from astonishment and concern to gradual acceptance.
Navigating Web3 tides with focused insightsIn the context of AI, the only certainty is uncertainty. People prefer certainty, but this uncertainty brought by AI, under the broader tide of technological advancement, is irreversible. Optimists believe that AI will bring unimaginable cost reductions and efficiency gains to the world. Pessimists argue that AI will profoundly reshape the rules of various industries, potentially leading to widespread job displacement.
Regardless, from the emergence of ChatGPT to today, public perception of AI has gradually shifted from shock and concern toward acceptance. It seems people now realize that whether welcomed or resisted, AI will undeniably penetrate every domain, disrupting industries through its mechanisms and potential.
Now, AI is entering Web3 and beginning to impact the entire industry.
Wang Yishi, former founder of OneKey, stated on Twitter that Web3 narratives have shifted from cryptocurrency to AI. Wang’s view is not isolated—many within the Web3 industry believe AI’s influence on Web3 is significant, especially in NFTs and GameFi. The emergence of AIGC (AI-Generated Content) signals a new paradigm in content creation.
From PGC (Professionally Generated Content) to UGC (User Generated Content), and now to AIGC, content creation is being handed over to algorithms.
Beyond AIGC’s impact on Web3 content, AI’s influence on Web3 is actually deeper than we might imagine.
AI Is "Restructuring" Web3
AI's "restructuring" of Web3 occurs in two ways: First, the emergence of AI technology has diverted capital attention away from Web3.
Before AI emerged, Web3 was once the darling of VCs and institutions, with industries rolling out various Web3-related concepts—like digital collectibles and the metaverse—as marketing hooks. But after AI arrived, this changed.
To institutional investors, AIGC appears at least more credible than Web3—something tangible rather than a speculative concept. Institutional interest is shifting, compounded by bear market conditions and regulatory pressures. According to Tuoluo Research Institute, in March this year, there were 86 global financing events in the Web3 sector totaling 5.676 billion yuan—a 47.98% drop year-on-year.
Capital is leaving Web3 and flowing into AI.
The second aspect of this "restructuring" is that AI's emergence is altering the mechanisms and logic within Web3. Web3 projects are increasingly incorporating AI components into their ecosystems. Some projects now feel they must include at least an AI concept or a GPT interface to remain competitive. We can view this as AI "restructuring" the Web3 world—or as Web3’s adaptive response to AI’s aggressive "invasion."
Thus emerged the AI + Web3 concept. In the convergence of AI and Web3, numerous products have surfaced, broadly falling into two categories: one involves integrating AI features into existing project directions. These products typically add AI tool integrations and emphasize AI’s empowering role in PR. For example, AIGOGE.
The other type of AI+Web3 integration focuses on cost reduction and efficiency enhancement—such as Pionex, which emphasizes AI-driven trading strategies; Getch, Cortex, and SingularityNET, focusing on AI-powered infrastructure; and Numerai, specializing in AI-based financial forecasting.
The proliferation of AI-infused Web3 products reflects market and investor enthusiasm. For instance, AIDOGE, launched on April 18, surged 218.50% within two days. Tokens like Fetch.ai (FET), SingularityNET (AGIX), and Ocean Protocol (OCEAN) rose 110%, 61.53%, and 66.67% respectively over 90 days.
While the secondary market for AI+Web3 concepts is heating up, the primary market looks even brighter. Since the beginning of this year, AI+Web3 projects have secured successive funding rounds. On March 29, Fetch.ai received a $40 million investment from SWF Labs.
Currently, the AI+Web3 concept appears poised to become a major trend. Below, veDAO Research Institute outlines key sectors where AI could transform Web3 for reference.
AI Empowering Different Web3 Sectors
AI-Based Trading Strategies
A typical approach to ChatGPT-powered liquidity mining strategies involves using the model to predict market movements and determine optimal timing for participation.
How AI enhances trading strategies:
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Data collection: Use APIs to gather liquidity mining data such as price pairs, volume, liquidity supply, and demand from exchanges.
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Data preprocessing: Clean, transform, and standardize collected data for analysis and modeling.
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Building ChatGPT models: Train ChatGPT on historical data to forecast current and future trends and returns in liquidity mining.
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Risk control: Based on ChatGPT predictions, implement risk management measures such as stop-loss/stop-gain settings and trade volume controls to protect investor interests.
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Strategy implementation: Develop trading strategies based on model outputs—selecting pairs, timing, and pricing.
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Trade execution: Automatically execute trades, allocating funds into mining operations to achieve expected returns.
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Monitoring and optimization: Regularly review performance and refine strategies to maintain strong returns and risk control.
AI-Based Sentiment Analysis Strategy
This strategy leverages ChatGPT’s natural language processing capabilities to analyze text data from news articles and social media posts to assess market sentiment. For example, if most texts reflect “positive” or “buy” sentiment, the strategy may trigger a buy signal—and vice versa.
Implementation requires gathering relevant textual market data, cleaning it, and building analytical models. Supervised learning algorithms can be used to train sentiment classification models on labeled datasets to predict emotional tone. Trading decisions can then be adjusted based on these predictions and broader market trends.
AI-Based Trading Strategy Analysis
This approach uses ChatGPT’s ability to understand textual descriptions of trading strategies to evaluate their effectiveness. For instance, analyzing backtesting results and historical returns to assess reliability. Machine learning models can be trained to predict a strategy’s return and risk profile. Final strategy design can incorporate these forecasts along with market conditions.
AI-Based Portfolio Management
ChatGPT-powered portfolio management tools use NLP to help users manage assets more effectively, optimize allocations, and improve risk control, offering more accurate predictions and recommendations for investment decisions. Capabilities include:
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Automated asset analysis and coin selection: Using ChatGPT’s NLP capabilities to assess fundamentals, market conditions, and macroeconomic factors to automatically identify promising investments and reduce decision errors.
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Portfolio optimization: Predicting market trends and risks with ChatGPT to offer diversification and maximization advice.
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Automated trade execution: Execute buy/sell orders automatically based on AI-driven decisions, enabling real-time adjustments while minimizing human error.
AI-Powered Demo Trading Tools (AI Demo Account)
An AI-based simulated crypto trading tool is a virtual platform that uses AI algorithms to mimic real market environments and provides virtual funds for practice trading. Users can learn crypto trading, develop strategies, and simulate trades without financial risk—allowing more users to enhance their investment skills while experiencing AI functionalities.
Feasible Directions for DEX + AI:
Decision Support: Analyze and mine trading data to deliver more accurate and comprehensive market insights and forecasts, helping traders make smarter investment choices.
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Optimized portfolio management: AI analyzes user preferences, risk tolerance, and trading history to provide personalized, efficient portfolio services.
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Enhanced user experience: Through smart customer service, intelligent recommendations, and Q&A systems, AI delivers faster, smarter, and more responsive trading experiences, boosting satisfaction and loyalty.
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Investment information aggregation: AI gathers sentiment,舆情, and risk intelligence.
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Price prediction: Leveraging big data and machine learning, AI analyzes market data to forecast cryptocurrency price movements and guide better decisions.
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Trading decisions: AI-powered automated systems execute trades based on predefined rules and strategies, reducing human bias.
AI Security:
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Fraud analysis: AI monitors network traffic to detect and prevent cyberattacks and fraudulent activities, enhancing DEX security and trustworthiness.
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Smart contract auditing: AI improves smart contract coding and deployment, enhancing code quality and reliability; also helps monitor and prevent malicious behavior, reducing risks and vulnerabilities.
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Credit analysis: Using big data and machine learning, AI evaluates credit risk by analyzing customers’ credit history, financial status, social networks, and behavioral data to predict default likelihood.
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Scam detection: Natural language processing and image recognition help analyze transaction records and behaviors to identify potential fraud.
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Transaction monitoring: Real-time data analysis detects anomalous trading patterns.
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Risk management: A ChatGPT-powered risk management system uses NLP to analyze financial data and live market news, generating risk forecasts and alerts to help investors manage exposure.
Improving Trade Speed and Efficiency: Optimizing trading workflows via AI (e.g., optimal routing) reduces congestion, lowers costs, and accelerates settlement times.
Solving Key DEX Challenges:
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Liquidity shortage: DEXs typically have lower trading volumes than CEXs, resulting in poor liquidity and price slippage. AI can enhance trading bot intelligence, improving efficiency, profitability, volume, and liquidity.
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Security issues: Due to decentralization, DEXs face risks such as asset theft and smart contract vulnerabilities. AI strengthens risk control with intelligent threat detection and prevention.
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Poor user experience: DEX interfaces are often less polished than CEXs. AI enables personalized services, intelligent CRM, and recommendation systems to elevate UX.
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High transaction costs: Compared to low-fee CEXs, DEXs currently suffer from high gas fees. AI-optimized trading strategies can reduce costs and risks while increasing profitability.
Conclusion
Overall, AI is far more than just a new technology—it represents a new concept, a new frontier, capable of iteratively transforming, even disrupting, society’s foundational operating logic. The same applies to Web3. The relationship between AI and Web3 will extend beyond mere conceptual fusion or simple integration of AI tools into individual projects. Instead, AI will penetrate deep into Web3’s core logic, infusing meaning into every action within Web3, making it more efficient and intelligent.
Just as tools of production relate to relations of production in philosophy, the two cannot be separated. The nature of production tools defines productivity, and productivity shapes the emergence and spread of corresponding production relations. If blockchain-based Web3 represents an evolved mode of production relations, then AI is undoubtedly the most advanced tool of production of our era. Therefore, we have good reason to believe that the emergence, adoption, and integration of AI as a production tool will play a decisive role in the future popularization and advancement of Web3.
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