1.Introduction

In the current era of Web3 and AI acceleration integration, emerging projects continue to emerge, and market enthusiasm continues to rise. BAD AI, with its unique design and technology, is making a name for itself in this field. This report delves into BAD AI, starting from the broader context of Web3 and AI integration, providing a detailed analysis of BAD AI’s core technology, application scenarios, market potential, economic model, and future development trends, presenting a comprehensive overview for readers.

2. Industry Background

2.1 Web3 and AI Integration Trends

In recent years, the Web3 space has seen a surge in enthusiasm for AI-driven intelligent agents and meme coins based on machine learning models. Such projects have a total market value of $10.4 billion, covering areas such as generative art, transaction analysis, and satirical social media interactions, reflecting the increasing attention to AI in Web3. Tokens like Zerebro, Aixbt, and GOAT have shown significant market value growth, demonstrating strong development momentum. However, this market is highly volatile, with project popularity fluctuating, similar to the hype cycles of meme coins and AI narratives.

2.2 Current Development Status of AI Intelligent Agent Market

The AI intelligent agent market is booming, with various types of projects emerging. For example, GOAT, as a memetic currency, has seen a rapid increase in market value driven by the community; ACT, rumored to be supported by a16z, saw a significant increase in market value shortly after listing on Binance; ai16z ecosystem tokens, multiple AI intelligent agent issuance platforms, and AI intelligent agent infrastructure projects are all playing important roles in their respective fields.

3. BAD AI Project Overview

3.1 Project Positioning and Innovative Concept

BAD AI aims to reshape the landscape of AI intelligence, establish a new benchmark for the AI ecosystem with the help of ChainGraph technology, multi-faceted and persistent multimodal intelligent agents, and integration with social platforms. The project focuses on the extraordinary modular design of intelligent agent functions, addressing the sustainability of token economy and business models for AI intelligent agent platforms, and leveraging the power of the open-source community to build a collaborative ecosystem.

3.2 Core Technology - ChainGraph

ChainGraph is the core technology of BAD AI, written in Golang. It provides high flexibility for designing intelligent agent data pipelines and decision trees, with persistent context (deep memory) and time decay settings to prioritize the most relevant and up-to-date data. This technology can be used not only to build individual intelligent agents but also to define interaction rules among multiple intelligent agents, forming a collaborative intelligent agent environment. In addition, the special nodes of ChainGraph can seamlessly integrate with platforms such as Telegram and Twitter, enhancing the project’s accessibility.

4. Application Scenarios of BAD AI

4.1 Intelligent Agent Collaboration (Agent Swarms)

Multiple AI agents can collaborate to complete tasks or solve complex problems. They learn from each other, divide work, and improve efficiency. For example, one agent provides market updates, and another agent executes predefined operations based on this information. This collaboration can facilitate the exchange of currency and contextual value, forming a complete ecosystem.

4.2 Interaction with dApps

Users specify the quantity and type of tokens to purchase, the AI agent can recommend the optimal platform, and can also directly generate an iframe for decentralized applications in the chat interface, simplifying the user operation process. In DeFi protocols, the agent can dynamically adjust parameters such as interest rates and collateral to achieve autonomous trading.

4.3 Market Trends Analysis & Trading

AI intelligent agents integrate decentralized exchange data to execute trades based on predefined or user-customized strategies. Users can fine-tune strategies to achieve optimal trading performance. The ability of intelligent agents to quickly access data and execute trades gives them an advantage in market analysis and trading.

4.4 Agent Influencer

AI intelligent agents can comment on Web3-related news, market trends, and protocol development. By integrating with Telegram and X (formerly Twitter), the intelligent agent can post threads on X, tag key opinion leaders, participate in discussions in the Telegram community, and can also be equipped with a 3D avatar to enhance community interaction.

4.5 Venture Capital Intelligent Agent (VC Agent)

Specially designed AI intelligent agents can act as venture capitalists, evaluating the token economics and product fundamentals of projects, and providing unique insights. By integrating more data, such as token economic comparisons of successful and failed projects, strategic visions, and product roadmaps, the analytical capabilities of the intelligent agents are further enhanced.

5. The architecture and user experience of BAD AI

5.1 Architecture Overview

The architecture of BAD Protocol mainly includes the backend, UI, and integration with other platforms. The backend is responsible for handling core business logic, such as request routing, database access, and coordination of intelligent agent operations; the UI provides users with an intuitive operating interface; integration with external platforms expands the project’s functionality and usage scenarios.

5.2 User Functionality and Experience

1. Chat and AI Agent Market: Users can recruit AI agents to enter custom chat rooms for collaboration, information sharing, and task execution.
2. Collaboration Capability: Admin users can invite friends to join the chat, create collaborative spaces, and facilitate the joint resolution of issues between humans and AI entities.
3. Maintainer function: Maintainers can access and adjust the context of the intelligent agent through the chat room, carry out in-depth optimization, and store operation records in the persistent database.
4. Custom Parameters: Admin users can adjust the parameters of the chatbot for specific chat rooms to enhance the adaptability of the chatbot.
5. Shared Context: Users can upload files in various formats to create a shared context database, which intelligent agents can use to achieve their goals.
6. Persistent Vector Database: Stores intelligent agent datasets, ensuring data persistence and high performance, providing support for intelligent agent learning and decision-making.

5.3 Wallet Login and Interaction

Supports login with multiple wallets, such as those supported by Metamask and WalletConnect, securely stores user private keys, and only uses them during transactions. User on-chain data can be utilized by intelligent agents to enhance their understanding and responsiveness to user needs.

5.4 Intelligent Agent Modeling and Interaction

1. The multiple attributes of intelligent agents: Intelligent agents are not only tools, but also tradable assets. They can import and export contexts between different chats and intelligent agents, enabling collaboration and evolution.
2. Intelligent Agent Modeling Customization: Users can upload, adjust, and optimize the context, personality, and tools of intelligent agents through the chat interface, supporting various data formats. Custom tools can also be created to meet personalized needs.
3. Multi-agent Collaboration: Multiple intelligent agents can collaborate and compete in the same chat room to achieve common goals. The personalities, tools, and contexts of each intelligent agent blend together, forming an evolving environment.

6. Token Economics of BAD AI

6.1 Token Basic Information (2025-2-21)

BAD AI’s token is $BADAI, issued on the BSC chain with a total supply of 1 billion tokens.

1. Market Cap: 1358.00 million USDT
2. Circulation: 5.60 billion BADAI
3. Total supply: 10.00 billion BADAI

6.2 Token Allocation Mechanism

Token distribution covers multiple aspects:

1. The SFLOKI ecosystem airdrops account for 35%, with 27% airdropped to FLOKI holders, 4% airdropped to $TOKEN holders, and 4% airdropped to Floki trading robot users;
2. SCAT ecosystem airdrop accounts for 2%;
3. The financing round conducted through TokenFi accounts for 10%, targeting STOKEN pledgers;
4. Private placements of key advisors and supporters account for 2.8%;
5. Strategic sales account for 2% separately (two parts);
6. Community growth and incentives account for 8%;
7. Liquidity accounts for 20%;
8. Market makers account for 5%;
9. The foundation accounts for 6.5%;
10. Treasury accounts for 6.7%.

6.3 Token Utility and Economic Model

On the demand side: Users can use the staked $BADAI tokens to initiate advanced intelligent agents, receive platform income distribution, participate in platform governance, attract the attention of the risk investment intelligent agent Peppenberg, and participate in joint investment. These mechanisms encourage users to hold tokens for the long term and participate in ecosystem development.

Supply side: Users interacting with intelligent agents deployed on BAD can receive $BADAI inflation rewards, with some of the inflation used to incentivize on-chain liquidity. In addition, a portion of the token supply and funding for high-quality project launches will enter the treasury, benefiting stakers through a buyback mechanism, while deployment fees for non-premium intelligent agents also constitute protocol revenue entering the treasury.

6.4 Market Performance of BADAI Token

Currently, the BADAI token has been listed on the Gate.io spot trading zone. Click to trade:https://www.gate.io/trade/BADAI_USDT

Conclusion

BAD AI, as an innovative project integrating Web3 and AI, has a certain level of competitiveness and development potential in the market due to its unique technology, rich application scenarios, and reasonable economic model. However, the risks faced by the project in terms of technology, market, regulations, and economic models cannot be ignored. In the future development, BAD AI needs to continue to innovate, optimize products, and address risks in order to stand out in the fierce market competition, achieve its goal of reshaping the AI intelligence landscape, and bring true value to users and the industry.