How does AI impact Web3?

In the context of AI, the only certainty is uncertainty. People like certainty, but this uncertainty brought about by AI is irreversible in the wave of technological development. Optimists believe that the emergence of AI will bring unimaginable help in reducing costs and increasing efficiency to the whole world. Pessimists believe that AI will have a profound impact on the rules of the current industry and therefore bring about a large number of job losses.

But no matter what, from the appearance of ChatGPT to now, people’s views on AI have gradually been accepted from surprise and concern. People seem to realize that whether welcoming or rejecting, AI will undoubtedly penetrate into various fields of people and bring subversion to various industries with its mechanism and potential.

Now, AI is beginning to enter Web3 and has an impact on the entire industry.

Wang Yishi, the former founder of OneKey, tweeted that the narrative of Web3 has shifted from cryptocurrency to AI. Wang’s view is not unique. Many people in the Web3 industry believe that AI has a huge impact on Web3, especially in the NFT and GameFi fields. The emergence of AIGC concept means that a new paradigm has been created in content creation. From PGC (Professionally Generated Content) to UGC (User Generated Content), and now to AIGC, the work of content creation is handed over to the program.

In addition to the impact of AIGC on Web3 content, in fact, the impact of AI on Web3 is more profound than we imagined.

AI is “rectifying” Web3

The “rectification” of Web3 by AI comes from two aspects: on the one hand, the emergence of AI technology has diverted capital’s attention from Web3.

Before the emergence of AI, Web3 was once a hot spot in the eyes of VCs and institutions. Various industries also launched various Web3 concepts (such as digital collections, metaverse, etc.) as gimmicks. However, this situation changed after the emergence of AI.

In the eyes of institutions, AIGC is at least more reliable than Web3, at least it is a practical thing, not a concept that needs to be anticipated. The interest of institutions is shifting, coupled with other reasons such as bear market and regulation. According to statistics from Gyro Research Institute, the global financing event in the Web3 field in March this year was 86, with an amount of 5.676 billion yuan, a year-on-year decrease of 47.98%.

Capital is leaving the Web3 field and moving into AI. On the other hand, the emergence of AI is changing the mechanisms and logic of the Web3 field. Web3 projects are beginning to focus on adding AI elements to their own ecosystems. Some projects are evolving to at least have a concept of AI or at least a GPT interface. We can view this phenomenon as AI’s “clean-up” of the Web3 world, or as the Web3 world’s self-defense mechanism based on AI’s strong “invasion”.

Thus, the concept of AI Web3 has emerged. In the process of integrating AI and Web3, many different products have emerged on the market, which can be roughly divided into two categories: one is to add AI elements based on the project’s own direction. These products often add some AI tool interfaces to their own products and emphasize the enabling and driving role of AI in external PR. Examples include AIGOGE.

Another type of combination of AI and Web3 is based on the idea of reducing costs and increasing efficiency, with a focus on AI and trading strategies like Pionex; AI and infrastructure construction like Getch, Cortex, and SingularityNET; and AI and financial forecasting like Numerai, and so on.

The emergence of Web3 products with different AI concepts reflects the market and capital’s favor for this type of product. For example, AIDOGE, which was launched on April 18, rose by 218.50% within two days. Tokens of projects like (FET), SingularityNET (AGIX), Ocean Protocol (Ocean) have grown by 110%, 61.53%, and 66.67%, respectively, in 90 days.

While the secondary market for the AI Web3 concept is hot, the primary market is even more exciting. Since the beginning of this year, AI Web3 concept products have received consecutive financing. On March 29th of this year, received a $40 million investment from SWF Labs.

At present, AI+Web3 concept seems to be a major trend in the future, so the veDAO research institute has organized different tracks for AI to bring changes to Web3 for reference.

AI Empowers Different Tracks of Web3

AI-based Trading Strategies

The general idea of liquidity mining strategies based on ChatGPT is to use the ChatGPT model to predict the market situation to determine whether to participate in liquidity mining and choose the best time.

The role of AI in trading strategies:

  • Data collection: Use APIs to obtain data required for liquidity mining from exchanges, such as trading pair prices, trading volumes, liquidity provision amounts, and attraction volumes.

  • Data preprocessing: Clean, transform, and standardize collected data for subsequent analysis and modeling.

  • Build ChatGPT model: Use trained ChatGPT models to analyze historical data and predict current and future liquidity mining trends and returns.

  • Risk control: Based on ChatGPT’s predictive results, formulate risk control strategies, such as setting stop-loss and take-profit conditions, controlling trading volume, etc., to protect investors’ interests.

  • Implement trading strategies: Develop trading strategies based on ChatGPT model’s predictive results, such as selecting trading pairs, determining trading timing, setting trading prices, etc.

  • Trading execution: Execute trades according to trading strategies, and AI systems automatically invest funds in liquidity mining and gain expected returns.

  • Monitoring and optimization: Regularly monitor trading results and model performance, optimize and adjust strategies to maintain good investment returns and risk control effects.

AI-based Sentiment Analysis Strategy

This strategy is based on ChatGPT’s natural language processing ability, which analyzes text data such as news reports and social media posts to perform sentiment analysis on the market. When the sentiment tendency in most of the text is “positive” or “buy”, the trading strategy may choose to buy; conversely, it may choose to sell.

The implementation of this strategy requires collecting text data related to the market and cleaning, analyzing, and modeling the data. Supervised learning algorithms can be used for modeling sentiment analysis models, using labeled training data for training to predict the sentiment tendency of text. Trading strategies can be adjusted based on the model’s predictive results combined with market trends and other factors.

AI-based Trading Strategy Analysis

The strategy is based on ChatGPT’s ability to understand text descriptions of trading strategies, analyze and evaluate trading strategies. For example, analyzing the backtesting results and historical returns of trading strategies to evaluate their effectiveness and reliability, and formulating trading strategies based on this. Machine learning algorithms can be used for the analysis and evaluation of trading strategies to predict the rate of return and risk of the strategy through model training and optimization. The formulation of trading strategies can be adjusted according to the model’s predicted results and other factors such as trial production trends.

AI-based Asset Portfolio Management

The ChatGPT-based asset portfolio management tool uses natural language processing technology to help users better manage asset portfolios, optimize asset allocation and risk control, and provide more accurate predictions and recommendations for investment decision-making programs. It can achieve:

Automated asset analysis and token selection: Using ChatGPT’s natural language processing capabilities to analyze and evaluate the fundamentals, market conditions, macroeconomic factors, etc. of various assets to automatically select suitable investment targets and reduce the risk of incorrect decisions.

Asset portfolio optimization: Provide asset portfolio optimization suggestions to users by predicting market trends and risks through ChatGPT, achieving risk diversification and maximizing returns.

Automated trading execution: Automatically execute buy and sell trades based on ChatGPT’s trading decision-making model to achieve real-time asset adjustment and optimization while reducing the risk of human intervention.

AI-based Simulation Trading Tool (AI Demo Account)

The AI-based simulation cryptocurrency trading tool is a virtual trading platform that simulates the real cryptocurrency market environment based on AI algorithms and provides virtual funds for users to simulate trading. Users can learn about cryptocurrency trading on the platform, develop trading strategies, and conduct simulated trading without the risk of real trading, allowing more users to experience AI functions and advance their investment skills.

Feasible Direction of DEX+AI:

Decision-making assistance: Analyzing and mining trading data, providing more accurate and comprehensive market analysis and predictions, and helping traders make wiser investment decisions.

  • Optimizing Asset Portfolio Management: AI technology can provide users with more personalized and efficient asset portfolio management services by analyzing information such as investment preferences, risk tolerance, and historical trading data.

  • Improving User Experience: AI technology can provide users with more intelligent, fast, and user-friendly trading service experience through intelligent customer service, intelligent recommendation, intelligent Q&A, and other methods, improving user satisfaction and loyalty.

  • Investment Information Collection: AI can help provide public opinion, sentiment, and risk information.

  • Price Prediction: AI can use big data and machine learning technologies to analyze market data to predict the trend of cryptocurrency prices, helping users make wiser investment decisions.

  • Trading Decisions: Artificial intelligence can use automated trading systems to execute trading decisions, such as trading based on preset rules and strategies, thus reducing the impact of human factors on trading.

AI Security:

  • Fraud Analysis: AI technology can monitor and analyze network traffic through artificial intelligence to recognize and prevent network attacks and fraudulent behavior, improving the security and credibility of DEX.

  • Contract Audit: AI technology can help optimize the writing and deployment of smart contracts, improving the quality and reliability of their code; it can also help monitor and prevent malicious behavior, reducing the risks and vulnerabilities of DEX.

  • Credit Analysis: Using big data and machine learning technologies, artificial intelligence can analyze multi-dimensional information such as customer credit history, financial status, social network, and behavior data to evaluate customer credit risk levels. Artificial intelligence can use big data and machine learning algorithms to analyze customer credit history, financial status, and other relevant data to predict customer default risk.

  • Fraud Detection: Artificial intelligence can use natural language processing and image recognition technologies to analyze customer transaction records and other behavior data to detect potential fraud.

  • Trading Monitoring: Artificial intelligence can use real-time data analysis technology to monitor trading activities to identify potential abnormal trading behavior.

  • Risk Management: The ChatGPT-based risk management system is a system that uses natural language processing technology to analyze and evaluate financial market risks. By analyzing financial data and real-time market news, it can generate predictions and warnings about market risks to help investors better manage risks.

Improve transaction speed and efficiency: AI technology can optimize the transaction process (such as selecting the best route), reducing transaction congestion and costs, and speeding up transaction completion time.

Address the major issues facing DEX:

  • Insufficient liquidity: Compared to CEX, DEX has smaller trading volume, leading to insufficient liquidity and easily influenced transaction prices by market fluctuations. AI technology can increase the intelligence of trading robots, thereby improving transaction efficiency and profitability, increasing trading volume and liquidity.

  • Security issues: Due to its decentralized nature, DEX has security risks during the transaction process, such as asset theft, contract vulnerabilities, etc. AI technology can improve risk control ability, achieve intelligent risk control and security monitoring, and prevent risk events from happening.

  • Poor user experience: Compared to CEX, DEX has a relatively simple user interface and poor user experience. AI technology can improve the personalized service capabilities for users, achieve intelligent customer relations and recommendation systems, and enhance the user experience.

  • High transaction costs: Compared to CEX’s low-cost handling fees, DEX’s transaction costs are relatively high due to miner fees and other reasons. AI technology can optimize the trading strategies of trading robots, reduce transaction costs and risks, and increase profitability.


Overall, the emergence of AI is not just a new technology, but a new concept and a new field. It will bring a series of iterations or even subversions to the underlying operating logic of the entire society. The same is true for the Web3 world. The relationship between AI and Web3 will not be limited to the fusion of concepts, or the simple addition of AI tools to a certain project. It will directly penetrate into the underlying logic of Web3, so that all behaviors in Web3 are endowed with the meaning of AI existence, making Web3 more efficient and smarter.

Just like the philosophy of production tools and production relations. The two cannot be viewed independently. The type of production tools determines the productivity, and the productivity provides the necessary conditions for the emergence and popularization of corresponding production relations. If Web3 based on blockchain represents updated production relations, then AI is undoubtedly the most advanced production tool of this era. Therefore, we have reason to believe that the emergence, popularization, and integration of AI technology as a production tool will inevitably play a decisive role in the popularization and promotion of the Web3 concept that follows.

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